A full life cycle operation and maintenance system of a membrane separation water treatment device

Abstract

This invention relates to the field of water treatment equipment condition monitoring technology, and in particular to a full life-cycle operation and maintenance system for membrane separation water treatment equipment. The system acquires influent environmental data to generate scene labels and configures tolerance coefficients. A baseline array is constructed based on the scene labels and the initial transmembrane pressure difference. Real-time operating data is matched to determine the target scene label. The difference between the real-time pressure difference and the initial pressure difference is calculated to output deviation characteristics. When a threshold is exceeded, a cleaning command is issued, and an attenuation index is calculated based on the pressure difference after cleaning to update the baseline array. The permeate flux within adjacent cleaning intervals is accumulated to generate a periodic load. The attenuation index is normalized using the periodic load and tolerance coefficient to output the aging equivalent. Each aging equivalent is accumulated over time to generate a cumulative attenuation value. When the cumulative attenuation value exceeds a preset critical value, an equipment replacement command is triggered. This improves the long-term accuracy of membrane module condition monitoring and effectively prevents equipment from exceeding its service life.

Description

Technical Field

[0001] This invention relates to the field of water treatment equipment condition monitoring technology, and in particular to a full life cycle operation and maintenance system for membrane separation water treatment equipment. Background Technology

[0002] With the widespread application of membrane separation technology in water treatment, industrial wastewater reuse, and high-quality separation processes, the membrane module, as a core functional unit, directly affects the system's effluent performance and operational stability. Therefore, establishing reasonable state characterization and evaluation methods based on the performance changes of the membrane module during operation has become one of the important technical directions for improving the level of refined system management.

[0003] During the long-term operation of membrane separation water treatment systems, transmembrane pressure difference is usually used as an important indicator to measure the degree of fouling and performance degradation of membrane modules. Most of the relevant assessment methods are based on fixed initial reference values ​​to compare and analyze operating data. However, due to the differences in water quality conditions, operating loads and process scenarios in actual applications, the operating conditions fluctuate significantly between different operating stages, making it difficult for a single reference benchmark to fully reflect the true aging state of membrane modules in complex environments.

[0004] Existing technologies typically do not segment operating scenarios and lack a unified correction mechanism for differences in permeate loads, resulting in insufficient comparability of attenuation results calculated under different operating conditions. In addition, some methods do not dynamically correct for baseline drift caused by irreversible fouling, making it easy for evaluation results to deviate during long-term operation and affecting the stable judgment of membrane module status. Summary of the Invention

[0005] To overcome the above shortcomings, this invention provides a full life-cycle operation and maintenance system for membrane separation water treatment equipment, which aims to improve the problems of existing technologies that generally do not segment the operating scenarios and lack a unified correction mechanism for differences in product water load.

[0006] This invention provides the following technical solution: a full life-cycle operation and maintenance system for membrane separation water treatment equipment, comprising: The tag generation module obtains the inlet water temperature, inlet water turbidity and product water flux range, converts them into viscosity and resistance ranges using preset exponential and preset logarithmic functions, divides the product water flux levels according to preset flux division thresholds and configures tolerance coefficients, and outputs scene tags. The baseline archiving module obtains the initial transmembrane pressure difference of the target based on each of the scene labels, and constructs a baseline array by mapping the scene labels with the initial transmembrane pressure difference. The sensing and mapping module acquires real-time influent temperature, real-time influent turbidity, real-time permeate flux, and real-time transmembrane pressure difference. It calculates real-time viscosity and resistance values ​​using the preset exponential and logarithmic functions, respectively, and matches them to the corresponding real-time viscosity and resistance ranges. It also matches the corresponding gear according to the preset flux threshold. The deviation comparison module matches the target scene label in the baseline array based on the corresponding gear level, calculates the difference between the initial transmembrane pressure difference of the target scene label and the real-time transmembrane pressure difference, and outputs the deviation feature. The attenuation maintenance module issues a timestamped cleaning command when the deviation feature is greater than a preset deviation alarm threshold, obtains the transmembrane pressure difference after cleaning, calculates the difference between the transmembrane pressure difference after cleaning and the initial transmembrane pressure difference of the target scene label, outputs the attenuation index, and updates the baseline array. The load assessment module extracts the time interval between adjacent cleaning command timestamps, accumulates the real-time water production flux within the time interval to generate a periodic load, and divides the decay index by the product of the periodic load and the tolerance coefficient of the target scene label to output the aging equivalent.

[0007] Preferably, in the tag generation module, the output scene tags specifically include the following steps: The historical operation database was analyzed to extract temperature boundary values, turbidity boundary values, and flux boundary values, and influent temperature range, influent turbidity range, and product water flux range were constructed. The inlet water temperature range is used as an independent variable and input into the preset exponential function to calculate and output the viscosity range. The influent turbidity range is used as an independent variable and input into the preset logarithmic function to calculate the output resistance range. Extract a preset flux node array as the preset flux division threshold, divide the water production flux interval according to the preset flux division threshold, and output multiple independent flux intervals as water production flux levels; Extract the center value of the flux for each of the water production flux levels, and calculate the ratio of the center value of the flux to the preset rated flux to generate a tolerance coefficient; By splicing together the viscosity range, the resistance range, and the permeable flow rate level configured with the tolerance coefficient, a scene label is generated.

[0008] Preferably, the calculation of the output viscosity range and the calculation of the output resistance range specifically includes the following steps: Extract the preset temperature reference constant and turbidity reference constant; The deviation ratio between the inlet water temperature range and the temperature reference constant is calculated to generate a temperature independent variable range. The temperature independent variable range is then input into the preset exponential function with the natural constant as the base to output the viscosity range. The deviation ratio between the influent turbidity range and the turbidity reference constant is calculated to generate a turbidity independent variable range. The turbidity independent variable range is then input into the preset logarithmic function with a preset base constant to output the resistance range.

[0009] Preferably, in the baseline archiving module, the construction of the baseline array specifically includes the following steps: Historical water production logs within the initial preset time period after the extraction membrane separation water treatment equipment is put into operation for the first time; Cluster the historical water production logs according to each scenario label, and filter the target differential pressure data under each scenario label whose fluctuation variance is less than a preset variance threshold. Calculate the arithmetic mean of the target pressure difference data and output the initial transmembrane pressure difference; Using the scene label as index coordinates and the initial transmembrane pressure difference as node values, a multidimensional mapping matrix is ​​constructed to output the baseline array.

[0010] Preferably, in the perception mapping module, matching the corresponding gear position specifically includes the following steps: The sensor array collects real-time influent temperature, real-time influent turbidity, real-time permeate flux, and real-time transmembrane pressure difference for the current operating cycle. The real-time inlet water temperature is used as an independent variable to input the preset exponential function to calculate the real-time viscosity value. The real-time viscosity value is compared with each viscosity range to match the corresponding real-time viscosity range. The real-time influent turbidity is used as an independent variable to input the preset logarithmic function to calculate the real-time resistance value. The real-time resistance value is compared with each resistance interval to match the corresponding real-time resistance interval. Extract the preset flux node array as the preset flux division threshold, compare the real-time water production flux with the preset flux division threshold, and determine the output level of the flux interval to which the real-time water production flux belongs.

[0011] Preferably, in the deviation comparison module, the output deviation feature specifically includes the following steps: A real-time feature vector is generated by splicing the real-time viscosity range, the real-time resistance range, and the corresponding gear position. The scene label that matches the real-time feature vector in the baseline array is used as the target scene label; The initial transmembrane pressure difference corresponding to the target scene label is retrieved from the baseline array as the target initial transmembrane pressure difference; The deviation characteristic is output by performing a subtraction operation between the real-time transmembrane pressure difference and the target initial transmembrane pressure difference.

[0012] Preferably, in the attenuation maintenance module, updating the baseline array specifically includes the following steps: Extract the pollution tolerance threshold as the preset deviation alarm threshold, compare the deviation feature with the preset deviation alarm threshold, and generate a cleaning instruction with a timestamp when the deviation feature is greater than the preset deviation alarm threshold; During the preset recovery period after the timestamped cleaning command is completed, the differential pressure data of the rerun is collected and the average value is calculated and output as the transmembrane differential pressure after cleaning. The attenuation index is output by subtracting the target initial transmembrane pressure difference from the post-cleaning transmembrane pressure difference. Replace the initial transmembrane pressure difference corresponding to the target scene label in the baseline array with the transmembrane pressure difference after cleaning, and output the updated baseline array.

[0013] Preferably, in the load assessment module, the output aging equivalent specifically includes the following steps: Parse the historical instruction log to extract the first timestamp of the previous cleaning instruction and the second timestamp of the current cleaning instruction, and calculate the difference between the first timestamp and the second timestamp to generate the time interval; Retrieve multiple real-time water production fluxes within the time interval, perform discrete integral summation on all real-time water production fluxes within the time interval, and output the periodic load. Extract the tolerance coefficient configured within the target scene label, and calculate the product of the periodic load and the tolerance coefficient to output the equivalent load denominator; Perform the division operation of the attenuation exponent by the denominator of the equivalent load and output the normalized ratio as the aging equivalent.

[0014] Preferably, the output cycle load specifically includes the following steps: The real-time water production fluxes within the time interval are sorted according to the time series, and the time difference between two adjacent real-time water production fluxes is extracted as the integration step size. Compare the real-time water production flux with a preset abnormal fluctuation threshold, and remove invalid flux data that exceeds the abnormal fluctuation threshold; Based on the integration step size, the trapezoidal integration algorithm is used to perform surface accumulation calculation on the remaining real-time water production flux after removing invalid flux data, and the cycle load is output.

[0015] Preferably, the system further includes a decommissioning determination module, used to accumulate each aging equivalent over time to generate a cumulative decay value, and trigger a device replacement command when the cumulative decay value exceeds a preset threshold value, specifically including the following steps: Establish a lifecycle data table, and store the aging equivalent of each output in the lifecycle data table according to the generation time order; Extract all the aging equivalents from the lifecycle data table, sum them up, and output the cumulative decay value. Extract the preset end-of-life data as a preset threshold value, and compare the cumulative decay value with the preset threshold value; When the cumulative attenuation value exceeds the preset threshold, a device replacement instruction containing the device identification code is generated and sent to the operation and maintenance terminal.

[0016] The present invention has the following beneficial effects: 1. In this invention, by introducing target scenario labels to match exclusive parameters and combining them with the actual water production load, the decay index is normalized. At the same time, the irreversible pollution results are solidified into a dynamically updated health baseline, thereby reducing the evaluation bias caused by variable operating conditions and cross-cycle operation, and enabling the membrane module status monitoring to maintain good objectivity and continuous accuracy in complex environments.

[0017] 2. In this invention, by constructing a life cycle data table and accumulating the aging equivalent of each cycle in chronological order, the continuous quantification of irreversible damage to the membrane module throughout its entire life cycle is realized. Based on this, combined with the comparison mechanism between cumulative damage and life limit, replacement instructions can be triggered in a timely manner, thereby reducing the water quality risk caused by the equipment operating beyond its service life.

[0018] 3. In this invention, by removing abnormal jumps in real-time water flux data and performing trapezoidal integral calculations in conjunction with time step reconstruction, the impact of sensor anomalies or pulse interference on load accumulation results is effectively reduced, thereby improving the stability and reliability of the calculation process for key evaluation parameters. Attached Figure Description

[0019] Figure 1 This is an architecture diagram of a full life-cycle operation and maintenance system for membrane separation water treatment equipment proposed in this invention. Detailed Implementation

[0020] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] This invention provides a full lifecycle operation and maintenance system for membrane separation water treatment equipment, such as... Figure 1 As shown, it includes: The tag generation module obtains the inlet water temperature, inlet water turbidity and product water flux range, converts them into viscosity and resistance ranges using preset exponential and preset logarithmic functions, divides the product water flux levels according to preset flux division thresholds and configures tolerance coefficients, and outputs scene tags. Furthermore, in the tag generation module, outputting scene tags specifically includes the following steps: The historical operation database was analyzed to extract temperature boundary values, turbidity boundary values, and flux boundary values, and influent temperature range, influent turbidity range, and product water flux range were constructed. The inlet water temperature range is used as the independent variable to input a preset exponential function, and the output viscosity range is calculated. The influent turbidity range is used as the independent variable to input a preset logarithmic function to calculate the output resistance range. Extract the preset flux node array as the preset flux division threshold, divide the water production flux interval according to the preset flux division threshold, and output multiple independent flux intervals as water production flux levels; Extract the center value of the flux for each water production flux level, and calculate the ratio of the center value of the flux to the preset rated flux to generate the tolerance coefficient; By combining viscosity ranges, resistance ranges, and permeate flux levels with tolerance coefficients, scene tags are generated.

[0022] Furthermore, the calculation of the output viscosity range and the calculation of the output resistance range specifically include the following steps: Extract the preset temperature reference constant and turbidity reference constant; The deviation ratio between the inlet water temperature range and the temperature reference constant is calculated to generate the temperature independent variable range. The temperature independent variable range is then input into a preset exponential function with the natural constant as the base to output the viscosity range. The deviation ratio between the influent turbidity range and the turbidity reference constant is calculated to generate the turbidity independent variable range. The turbidity independent variable range is then input into a preset logarithmic function with a preset base constant to output the resistance range.

[0023] Specifically, the offline historical operation database of the membrane separation equipment is first analyzed to extract the influent temperature boundary value, influent turbidity boundary value, and product water flux boundary value for the equipment in previous complete operating cycles. Based on these extreme value data, influent temperature range, influent turbidity range, and product water flux range covering the actual operating conditions of the equipment are constructed, thereby establishing effective data boundaries for subsequent labeling processing.

[0024] To transform environmentally influenced physical parameters into state variables reflecting membrane fouling potential, the system extracts preset temperature and turbidity reference constants. For the influent temperature, the system inputs the boundary values ​​of the influent temperature range as independent variables into a preset exponential function with a base of the natural constant. The viscosity range is generated by calculating the deviation ratio between the influent temperature and the temperature reference constant. The formula for the preset exponential function is as follows: In the formula This represents the calculated viscosity boundary value. Indicates the temperature sensitivity coefficient. This represents the boundary value of the influent temperature range that is being substituted. This represents the preset temperature reference constant. The constant represents the natural constant. The temperature sensitivity coefficient is generated by fitting the nonlinear mapping relationship between influent temperature fluctuations and transmembrane pressure changes in the historical operation database, and is set to a negative value to conform to the objective fluid physics law that water viscosity decreases with increasing temperature.

[0025] Similarly, the system inputs the boundary values ​​of the influent turbidity range as independent variables into a preset logarithmic function with a preset base constant. It then calculates the corresponding results using the deviation ratio between the influent turbidity and the turbidity reference constant, generating the resistance range. The formula for the preset logarithmic function is as follows: In the formula This represents the calculated boundary value of the resistance. Indicates the preset base constant. Indicates the turbidity sensitivity coefficient. This represents the boundary value of the influent turbidity range. This represents the preset turbidity reference constant. The preset baseline constant and turbidity sensitivity coefficient are determined by fitting a dataset of water quality turbidity jumps and membrane fouling resistance growth trends from a historical operating database. The preset baseline constant is preferably a constant value between 2 and 10. Through the above calculations, the system transforms nonlinear environmental parameters into characteristic intervals associated with the evolution of transmembrane pressure difference.

[0026] After completing the feature interval transformation, the system extracts a pre-defined tiered water production allocation strategy based on historical equipment operation experience, and uses this to generate a preset flux node array as a preset flux division threshold. The system then uses this preset flux division threshold to segment the constructed water production flux interval, outputting multiple independent and contiguous flux intervals as water production flux levels. Subsequently, the system uses a formula... Calculate the flux center value for each water production flux level, where... Indicates the central value of flux. This represents the upper flux threshold of the quantum interval. This represents the lower limit flux threshold of the quantum interval. Further, the system utilizes the formula... Calculate and generate a tolerance coefficient specific to each water production flux level, where... This represents the tolerance coefficient. This indicates the preset rated flux of the membrane separation water treatment equipment.

[0027] Finally, the system combines and concatenates the obtained viscosity range, resistance range, and permeable flux levels configured with corresponding tolerance coefficients in a multi-dimensional feature vector format to generate structured scene labels. Specifically, these scene labels are represented in the form of one-dimensional arrays, and their data organization structure is as follows: In the formula This indicates the generated scene labels. and These represent the lower and upper boundary values ​​of the viscosity range, respectively. and These represent the lower and upper boundary values ​​of the resistance zone, respectively. and These represent the lower and upper flux thresholds for each water production flux level, respectively. This represents the tolerance coefficient. This feature vector directly serves as a unique numerical index in the subsequent baseline array for matching different operating conditions. By structuring the continuously fluctuating environment and operating parameters into a clear numerical label matrix, the interference of fuzzy variables on model calculations is eliminated, significantly improving the data retrieval speed and matching accuracy of subsequent transmembrane pressure difference comparison calculations.

[0028] The baseline archiving module obtains the initial transmembrane pressure difference of the target based on each scene label, and constructs a baseline array by mapping the scene label with the initial transmembrane pressure difference. Furthermore, in the baseline archiving module, constructing the baseline array specifically includes the following steps: Historical water production logs within the initial preset time period after the extraction membrane separation water treatment equipment is put into operation for the first time; Cluster the historical water production logs according to each scenario label, and filter the target differential pressure data under each scenario label whose fluctuation variance is less than the preset variance threshold; Calculate the arithmetic mean of the target pressure difference data and output the initial transmembrane pressure difference; Using scene labels as index coordinates and initial transmembrane pressure difference as node values, a multidimensional mapping matrix is ​​constructed to output the baseline array.

[0029] Specifically, to establish benchmark reference data for the membrane separation water treatment equipment under pure conditions, the system extracts historical permeate logs within the initial preset time period after the equipment's first commissioning. These historical permeate logs fully record the real-time influent temperature, real-time influent turbidity, real-time permeate flux, and actual transmembrane pressure differential during the equipment's stable break-in period with timestamps. The system substitutes the read real-time influent temperature and real-time influent turbidity into the aforementioned preset exponential and logarithmic functions, respectively, to calculate the corresponding real-time viscosity and real-time resistance values ​​at the current moment. Subsequently, the system iterates through all scene labels generated in the previous stage, where the multidimensional feature intervals of each scene label are constructed as mutually exclusive partitioned spaces at the system's underlying layer, with no overlapping regions. When the real-time viscosity, real-time resistance, and real-time permeate flux simultaneously meet the following conditions... , , When three conditions are met, the system categorizes and clusters the actual transmembrane pressure difference at that moment under the corresponding scene label, where... This represents the real-time viscosity value at the current moment. This represents the real-time resistance value at the current moment. This represents the real-time water production flux at the current moment. and These represent the lower and upper boundary values ​​of the viscosity range within the scene label, respectively. and These represent the lower and upper boundary values ​​of the resistance zone, respectively. and These represent the lower limit flux threshold and the upper limit flux threshold for each water production flux level.

[0030] To eliminate transient abnormal pressure fluctuations caused by hydraulic impact in the pipeline network or noise at the sensor's underlying layer, the system performs continuity and stability screening on the clustered data under each scenario label. The system sorts the actual transmembrane pressure difference under each scenario label according to the time series and introduces a sliding data window of preset length to calculate the fluctuation variance of the data within the window. The sliding data window is constructed point-by-point from the starting position according to the time series, and the system only performs fluctuation variance calculation on windows containing the complete preset number of data points. The fluctuation variance is calculated using the overall variance form, with the specific formula as follows: The mean within the window In the formula This represents the variance of the current sliding data window. This indicates the amount of data contained within the preset sliding data window. Represents the first in the data window The actual transmembrane pressure difference, This indicates the data window. The system calculates the arithmetic mean of the actual transmembrane pressure differentials. It compares the calculated variance with a preset variance threshold set by the equipment manufacturer. Using a sliding data window as the basic filtering unit, all transmembrane pressure differential data within that window are marked as qualified only if the variance of that window is less than the preset variance threshold. The system performs deduplication on transmembrane pressure differential data repeatedly covered by multiple qualified windows, retaining only a unique record. The deduplicated set of actual transmembrane pressure differentials is then extracted as the target pressure differential data.

[0031] After cleaning the highly volatile data, the system performs overall averaging on all target differential pressure data retained under each scenario label to extract the most representative benchmark pressure characteristics. The calculation formula is as follows: In the formula This represents the initial transmembrane pressure difference calculated from the output. This indicates the total number of target differential pressure data points filtered under the current scene label. Indicates the first Target pressure differential data.

[0032] The final system generates a one-dimensional array of scene labels in the previous processing stage: Using the index coordinates in the multidimensional space, the initial transmembrane pressure difference calculated above is used as the index coordinates. As the unique node value corresponding to that specific coordinate, in the formula This represents a one-dimensional array of scene labels. This represents the tolerance coefficient corresponding to the aforementioned configuration. to All of these are the corresponding interval boundary values ​​mentioned above. The system iterates through all scene labels with valid clustering data and repeats the above calculation and assignment operations to construct a multi-dimensional mapping matrix covering all actual operating boundary conditions, and outputs it as a baseline array stored in the system storage space.

[0033] This established a precise initial health differential pressure reference benchmark for membrane separation equipment under various operating conditions, effectively eliminating differential pressure interference caused by normal fluctuations in environmental parameters, and providing a reliable basis for comparison in subsequent judgment of the actual membrane fouling level.

[0034] The sensing and mapping module acquires real-time influent temperature, real-time influent turbidity, real-time permeate flux, and real-time transmembrane pressure difference. It calculates real-time viscosity and resistance values ​​using preset exponential and logarithmic functions, respectively, and matches them to the corresponding real-time viscosity and resistance ranges. It also matches the corresponding gear based on the preset flux threshold. Furthermore, in the perception mapping module, matching the corresponding gear position specifically includes the following steps: The sensor array collects real-time influent temperature, real-time influent turbidity, real-time permeate flux, and real-time transmembrane pressure difference for the current operating cycle. The real-time inlet water temperature is used as the independent variable to input a preset exponential function to calculate the real-time viscosity value. The real-time viscosity value is compared with each viscosity range to match the corresponding real-time viscosity range. The real-time influent turbidity is used as the independent variable to input a preset logarithmic function to calculate the real-time resistance value. The real-time resistance value is compared with each resistance range to match the corresponding real-time resistance range. Extract the preset flux node array as the preset flux division threshold, compare the real-time water production flux with the preset flux division threshold, and determine the output level of the flux interval to which the real-time water production flux belongs.

[0035] Specifically, during the operation of the membrane separation water treatment equipment, the system uses sensor arrays deployed at pipeline nodes to collect real-time influent temperature, influent turbidity, permeate flux, and transmembrane pressure differential during the current operating cycle. The collected raw physical parameters serve as input sources reflecting the current fluid dynamic boundary conditions for subsequent state mapping analysis. The real-time transmembrane pressure differential, along with the real-time viscosity range, real-time resistance range, and corresponding speed range, is then passed to the subsequent deviation calculation steps.

[0036] To transform the acquired transient physical quantities into characteristic states usable for baseline comparison, the system extracts the factory-preset and experimentally calibrated temperature reference constant and temperature sensitivity coefficient. The real-time inlet water temperature is input as the independent variable into a preset exponential function with the natural constant as its base. The real-time viscosity value is then calculated and output as an equivalent dimensionless viscosity characterization quantity used to represent the effect of temperature on fluid flow characteristics. Its calculation formula is as follows: In the formula This represents the calculated real-time viscosity value. This represents the temperature sensitivity coefficient calibrated experimentally. This indicates the real-time inlet water temperature collected. This indicates the factory-preset temperature reference constant. This represents the natural constant. The system then retrieves a one-dimensional array of scene labels stored as a structured array from the aforementioned baseline array. Extract the viscosity range boundary values ​​of each scene label. and It iterates through all viscosity ranges and compares them with real-time viscosity values. Each viscosity range is constructed as a mutually exclusive partitioned space at the system's bottom layer, with no overlapping regions. When the following conditions are met... When the system determines that this interval is the real-time viscosity interval to which the current moment belongs, the formula is as follows: and These represent the lower and upper boundary values ​​of the viscosity range, respectively.

[0037] Similarly, the system extracts the factory-preset and experimentally calibrated turbidity reference constant, base constant, and turbidity sensitivity coefficient, and inputs the real-time influent turbidity as the independent variable into a preset logarithmic function to calculate and output the real-time resistance value. To ensure the function is legally calculable across the entire operating range, the system performs positive value constraint processing on the input terms of the preset logarithmic function. When the calculation result of the expression within the parentheses is less than or equal to zero, the system forces the calculation to be performed using the factory-preset positive minimum value. The calculation formula is as follows: In the formula This represents the calculated real-time resistance value. This represents a factory-preset base constant that is greater than zero but not equal to one. This represents the experimentally calibrated turbidity sensitivity coefficient. This indicates the real-time turbidity of the influent. This represents the factory-preset turbidity reference constant. The system uses the aforementioned one-dimensional array of scene labels. Extract the resistance range boundary values ​​of each scene label. and It iterates through all resistance intervals and compares them with real-time resistance values. Each resistance interval is also constructed as a mutually exclusive partitioned space, when the condition is met... At that time, the system determines it as the real-time resistance range to which the match belongs, where... and These represent the lower and upper boundary values ​​of the resistance range, respectively.

[0038] After mapping the water quality environmental parameters, the system extracts a preset flux node array, which, together with the scene labeling system, forms a complete operating condition segmentation framework, as the preset flux segmentation threshold. The preset flux node array consists of several flux node values ​​arranged in ascending order, with each adjacent flux node value forming an independent flux quantum interval. The system compares the collected real-time water production flux with the boundary values ​​of each flux quantum interval one by one. When the threshold is met... At that time, the system determines that the quantum interval is the level to which the real-time water production flux belongs, where... This indicates the real-time water production flux collected. and These represent the lower and upper flux thresholds of the quantum interval, respectively.

[0039] By converting the continuously fluctuating transient environmental data into interval identifiers that are completely corresponding to the baseline array index structure, the risk of computational collapse caused by extreme parameters is eliminated, allowing subsequent steps to directly and accurately retrieve benchmark comparison data in the multidimensional matrix using the current state set as coordinates.

[0040] The deviation comparison module matches the target scene label in the baseline array based on the gear position, calculates the difference between the initial transmembrane pressure difference of the target scene label and the real-time transmembrane pressure difference, and outputs the deviation characteristics. Furthermore, in the deviation comparison module, the output deviation features specifically include the following steps: Real-time feature vectors are generated by splicing together the real-time viscosity range, real-time resistance range, and corresponding gear. Match the scene label in the baseline array that matches the real-time feature vector as the target scene label; The initial transmembrane pressure difference corresponding to the target scene label is retrieved from the baseline array as the target initial transmembrane pressure difference; Perform a subtraction operation between the real-time transmembrane pressure difference and the target initial transmembrane pressure difference, and output the deviation characteristics.

[0041] Specifically, after acquiring the mapping features of the current operating cycle, the system sequentially concatenates the real-time viscosity range, real-time resistance range, and boundary values ​​of the corresponding gear determined in the previous step, following the same order as the scene label storage structure in the baseline array, to generate a real-time feature vector for addressing in the baseline array. Its vector expression is as follows: In the formula This represents the constructed real-time feature vector. and This represents the lower and upper boundary values ​​of the real-time viscosity range output in the previous step. and This represents the lower and upper boundary values ​​of the real-time resistance range output in the previous step. and This indicates the lower and upper flux thresholds for the corresponding gear output in the previous step. The system uses this real-time feature vector as the search keyword to read the baseline array stored in the storage space. The system extracts the viscosity range boundary values, drag range boundary values, and flux thresholds corresponding to each scene label in the baseline array to independently form a label boundary set. Each component of the real-time feature vector is then compared one by one with the label boundary sets of each scene label. When the label boundary set of a scene label in the baseline array is completely consistent with the real-time feature vector, the system locks that scene label as the target scene label. If an extreme environmental change results in no matching scene label being found in the baseline array, the system will generate a condition exceeding the limit warning signal and skip the deviation calculation step of the current operating cycle.

[0042] After locking onto the target scene label, the system directly retrieves the initial transmembrane pressure difference stored corresponding to that target scene label from the baseline array, using it as the target initial transmembrane pressure difference under the current matching condition. Subsequently, the system introduces the real-time transmembrane pressure difference, which was synchronously collected and transmitted along with water quality environmental parameters in the previous step, and performs a subtraction operation between the real-time transmembrane pressure difference and the target initial transmembrane pressure difference to calculate the output deviation characteristics. The calculation formula is as follows: In the formula This indicates the deviation characteristics of the calculated output. This represents the real-time transmembrane pressure difference acquired and transmitted in the previous step. This represents the target initial transmembrane pressure difference retrieved from the baseline array.

[0043] By accurately aligning and matching multi-dimensional operating condition features, the difference between real-time pressure data and healthy baseline data under the same boundary conditions is calculated, and the true resistance deviation characteristics that exclude the interference of fluid environment parameter fluctuations are output, providing data support for subsequent determination of the degree of membrane module fouling.

[0044] The attenuation maintenance module issues a cleaning command with a timestamp when the deviation characteristic is greater than the preset deviation alarm threshold, obtains the transmembrane pressure difference after cleaning, calculates the difference between the transmembrane pressure difference after cleaning and the initial transmembrane pressure difference of the target scene label, outputs the attenuation index, and updates the baseline array. Furthermore, in the attenuation maintenance module, updating the baseline array specifically includes the following steps: Extract the pollution tolerance threshold as the preset deviation alarm threshold, compare the deviation characteristics with the preset deviation alarm threshold, and generate a cleaning instruction with a timestamp when the deviation characteristics are greater than the preset deviation alarm threshold. Within the preset recovery period after the time-stamped cleaning command is completed, the differential pressure data of the rerun is collected and the average value is calculated and output as the transmembrane differential pressure after cleaning. The attenuation index is output by subtracting the target initial transmembrane pressure difference from the transmembrane pressure difference after cleaning. Replace the initial transmembrane pressure difference corresponding to the target scene label in the baseline array with the transmembrane pressure difference after cleaning, and output the updated baseline array.

[0045] Specifically, after receiving the deviation characteristics output from the previous step, the system extracts the factory-calibrated or engineering-set contamination tolerance threshold from the underlying storage and uses it as a preset deviation alarm threshold for determining the degree of membrane fouling. The system compares the deviation characteristics calculated in real time with this preset deviation alarm threshold. When the deviation characteristics are determined to be greater than the preset deviation alarm threshold, it indicates that the reversible fouling accumulation on the surface of the membrane module has exceeded the safe boundary for normal operation. At this time, the system automatically generates and sends a timestamped cleaning command through the communication interface. This command encapsulates the absolute system time data of the trigger moment to track maintenance nodes.

[0046] When the system detects continuous output of differential pressure data from the equipment and that the fluctuation rate is below the preset stability threshold, it determines that the cleaning operation is complete and the hydraulic state has returned to stability. It then starts an internal timer to enter the preset recovery period. To eliminate transient fluctuation interference during the initial stage of equipment restart, the system continuously collects differential pressure data during the preset recovery period at a set sampling frequency until the number of collected samples reaches the preset total number of data samples. The recovery period then ends, and the arithmetic mean of these data is calculated and output as the transmembrane differential pressure after cleaning. The calculation formula is as follows: In the formula This represents the calculated transmembrane pressure difference after cleaning. This represents the total number of differential pressure data samples collected during the preset recovery period until the termination conditions are met. Indicates the number of samples collected within the preset recovery period. Differential pressure data.

[0047] After obtaining the transmembrane pressure difference after cleaning, the system introduces the target initial transmembrane pressure difference corresponding to the target scene label determined in the previous step. Since the target initial transmembrane pressure difference represents the resistance to absolute cleanliness under the same fluid boundary conditions, the pressure difference remaining above this initial value after cleaning physically represents contaminant residues that cannot be removed by conventional cleaning. Based on this physical correspondence, the system performs a subtraction operation, subtracting the target initial transmembrane pressure difference from the post-cleaning transmembrane pressure difference, and outputs the attenuation index. The calculation formula is as follows: In the formula This represents the decay exponent in the calculated output. This represents the calculated transmembrane pressure difference after cleaning. This indicates the initial transmembrane pressure difference corresponding to the target scene label retrieved in the previous step. The system writes the decay index into an independent decay record area within the same storage space as the baseline array for archiving, serving as a core indicator for quantifying the aging or irreversible flux decay of the membrane module itself.

[0048] After archiving the decay index, the system locates the mapped address of the target scenario label locked in the previous step within the storage space. The system directly overwrites the initial transmembrane pressure difference data associated with that address with the post-cleaning transmembrane pressure difference calculated in this step. This solidifies the increase in basic resistance caused by irreversible contamination into a new healthy baseline, completes the reference benchmark correction under specific fluid boundary conditions, and finally outputs the updated baseline array for the system to call cyclically in the next cycle of status monitoring and deviation comparison.

[0049] This enables quantitative tracking of the irreversible fouling state of membrane modules and dynamic replacement of underlying baseline data, ensuring the timeliness of the comparison baseline used in subsequent monitoring cycles and maintaining the monitoring accuracy of the system during long-term equipment operation.

[0050] The load assessment module extracts the time interval between adjacent cleaning command timestamps, accumulates the real-time water production flux within the time interval to generate the periodic load, and divides the decay index by the product of the periodic load and the tolerance coefficient of the target scenario label to output the aging equivalent. Furthermore, in the load assessment module, the output of the aging equivalent specifically includes the following steps: Parse the historical instruction log to extract the first timestamp of the last cleaning instruction and the second timestamp of the current cleaning instruction, and calculate the difference between the first timestamp and the second timestamp to generate the time interval; Retrieve multiple real-time water production fluxes within a time interval, perform discrete integration and summation on all real-time water production fluxes within the time interval, and output the periodic load. Extract the tolerance coefficient configured within the target scene label, calculate the product of the periodic load and the tolerance coefficient, and output the equivalent load denominator. Perform a division operation by dividing the attenuation exponent by the denominator of the equivalent load and output the normalized ratio as the aging equivalent.

[0051] Furthermore, the output cycle load specifically includes the following steps: The real-time water production fluxes within the time interval are sorted according to the time series, and the time difference between two adjacent real-time water production fluxes is extracted as the integration step size. Compare the real-time water production flux with the preset abnormal fluctuation threshold, and remove invalid flux data that exceeds the abnormal fluctuation threshold; Based on the integration step size, the trapezoidal integration algorithm is used to perform surface accumulation calculation on the remaining real-time water production flux after removing invalid flux data, and output the periodic load.

[0052] Specifically, after receiving the attenuation index calculated and archived based on the transmembrane pressure difference after cleaning in the previous step, as well as the locked target scenario label, the system parses the underlying historical command log and extracts the first timestamp of the previous cleaning command record and the second timestamp of the current cleaning command record. If it is the first cleaning after the system's initial commissioning, the first timestamp is taken as the device's initial startup timestamp by default. The system performs a difference calculation by subtracting the first timestamp from the second timestamp and outputs the time interval of this cleaning cycle.

[0053] After acquiring the time interval, the system retrieves all real-time permeable flux data within that time interval from the storage space. The system compares each retrieved real-time permeable flux data with a pre-set abnormal fluctuation threshold based on the equipment's rated permeable capacity, discarding invalid flux data exceeding the abnormal fluctuation threshold to eliminate interference from sensor pulse faults in the cumulative calculation. After data cleaning, the system reorders the remaining valid real-time permeable flux data according to the time series and extracts the difference in acquisition time between two adjacent valid real-time permeable flux data as the integration step size. Based on each integration step size, the system uses the trapezoidal integral algorithm to perform surface accumulation calculation on the valid real-time permeable flux data, outputting the periodic load. The trapezoidal integral calculation formula is as follows: In the formula This represents the periodic load calculated from the output. This represents the total number of valid real-time water flux data remaining after removing invalid data. Indicates the sorted order of the first... One effective real-time water production flux, Indicates the sorted order of the first... One effective real-time water production flux, Indicates the first The and the first The time difference between adjacent sampling times corresponding to each effective real-time water production flux.

[0054] After acquiring the periodic load, the system extracts the corresponding tolerance coefficient from the configuration data of the previously locked target scenario label. This tolerance coefficient physically characterizes the differential impact of current specific water quality and resistance conditions on the membrane fouling accumulation rate. The system performs a product operation between the periodic load and the tolerance coefficient to output the equivalent load denominator. The calculation formula is as follows: In the formula This represents the denominator of the calculated equivalent load. This represents the periodic load calculated in the previous step. This represents the tolerance coefficient configured within the target scene label.

[0055] After obtaining the denominator of the equivalent load, the system introduces the decay index, which characterizes the amount of irreversible pollution residue, and performs a division operation by dividing the decay index by the denominator of the equivalent load, outputting the normalized ratio as the aging equivalent. The calculation formula is as follows: In the formula This represents the calculated aging equivalent. This represents the decay index obtained in the previous step. This represents the denominator of the equivalent load calculated in the current step.

[0056] By normalizing the physical degradation index by introducing actual operating water production and scenario tolerance, the evaluation bias caused by differences in operating time, water production intensity and operating difficulty in different cleaning cycles is eliminated, providing an objective and consistent quantitative benchmark for predicting membrane module life under cross-cycle conditions.

[0057] The system also includes a decommissioning determination module, which is used to accumulate each aging equivalent over time to generate a cumulative decay value. When the cumulative decay value exceeds a preset threshold, a device replacement command is triggered, which specifically includes the following steps: Establish a lifecycle data table and store the aging equivalent of each output in the lifecycle data table according to the generation time order; Extract all aging equivalents from the lifecycle data table, sum them, and output the cumulative decay value. Extract the preset end-of-life data as a preset critical value, and compare the cumulative decay value with the preset critical value; When the cumulative attenuation value exceeds the preset threshold, a device replacement instruction containing the device identification code is generated and sent to the operation and maintenance terminal.

[0058] Specifically, after receiving the aging equivalent calculated in the previous step, the system extracts the device identification code embedded in the underlying hardware and uses this device identification code as a unique index key to establish a lifecycle data table in the underlying storage medium. The system obtains the system time at which the calculation is completed and appends the aging equivalent output in this step to the lifecycle data table in chronological order of generation time, forming an aging tracking sequence with cleaning and maintenance as the nodes.

[0059] After the current data is imported into the database, the system extracts all aging equivalent data stored in the lifecycle data table, sums all extracted aging equivalents, and outputs the cumulative decay value, which characterizes the total amount of irreversible damage to the membrane module since its commissioning. The calculation formula is as follows: In the formula This represents the cumulative decay value calculated from the output. This indicates the total number of aging equivalent data entries recorded in the lifecycle data table. This indicates that the lifecycle data table stores the first [item] in chronological order. Each aging equivalent is obtained by writing the aging equivalent calculated and output from the previous step.

[0060] After obtaining the cumulative degradation value, the system extracts the end-of-life data based on the membrane module manufacturer's rated life parameters as a preset critical value. This preset critical value represents the limit of degradation at which the membrane module can no longer maintain safe operation due to its physical structure or water permeability. The system compares the currently calculated cumulative degradation value with this preset critical value. When the cumulative degradation value is determined to be greater than the preset critical value, it indicates that the current equipment has reached the physical scrapping standard, and the system automatically generates an equipment replacement instruction. The system encapsulates the previously extracted equipment identification code into the message of the equipment replacement instruction, and finally sends the instruction to the bound maintenance terminal through the network communication interface.

[0061] This enables quantitative tracking of the health status of membrane modules throughout their entire lifecycle. By using a threshold alarm mechanism based on cumulative physical damage, it effectively avoids the risk of substandard effluent quality caused by equipment exceeding its service life.

[0062] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A full life-cycle operation and maintenance system for membrane separation water treatment equipment, characterized in that, include: The tag generation module obtains the inlet water temperature, inlet water turbidity and product water flux range, converts them into viscosity and resistance ranges using preset exponential and preset logarithmic functions, divides the product water flux levels according to preset flux division thresholds and configures tolerance coefficients, and outputs scene tags. The baseline archiving module obtains the initial transmembrane pressure difference of the target based on each of the scene labels, and constructs a baseline array by mapping the scene labels with the initial transmembrane pressure difference. The sensing and mapping module acquires real-time influent temperature, real-time influent turbidity, real-time permeate flux, and real-time transmembrane pressure difference. It calculates real-time viscosity and resistance values ​​using the preset exponential and logarithmic functions, respectively, and matches them to the corresponding real-time viscosity and resistance ranges. It also matches the corresponding gear according to the preset flux threshold. The deviation comparison module matches the target scene label in the baseline array based on the corresponding gear level, calculates the difference between the initial transmembrane pressure difference of the target scene label and the real-time transmembrane pressure difference, and outputs the deviation feature. The attenuation maintenance module issues a timestamped cleaning command when the deviation feature is greater than a preset deviation alarm threshold, obtains the transmembrane pressure difference after cleaning, calculates the difference between the transmembrane pressure difference after cleaning and the initial transmembrane pressure difference of the target scene label, outputs the attenuation index, and updates the baseline array. The load assessment module extracts the time interval between adjacent cleaning command timestamps, accumulates the real-time water production flux within the time interval to generate a periodic load, and divides the decay index by the product of the periodic load and the tolerance coefficient of the target scene label to output the aging equivalent.

2. The full life-cycle operation and maintenance system for membrane separation water treatment equipment according to claim 1, characterized in that, In the tag generation module, the output scene tags specifically include the following steps: The historical operation database was analyzed to extract temperature boundary values, turbidity boundary values, and flux boundary values, and influent temperature range, influent turbidity range, and product water flux range were constructed. The inlet water temperature range is used as an independent variable and input into the preset exponential function to calculate and output the viscosity range. The influent turbidity range is used as an independent variable and input into the preset logarithmic function to calculate the output resistance range. Extract a preset flux node array as the preset flux division threshold, divide the water production flux interval according to the preset flux division threshold, and output multiple independent flux intervals as water production flux levels; Extract the center value of the flux for each of the water production flux levels, and calculate the ratio of the center value of the flux to the preset rated flux to generate a tolerance coefficient; By splicing together the viscosity range, the resistance range, and the permeable flow rate level configured with the tolerance coefficient, a scene label is generated.

3. The full life-cycle operation and maintenance system for membrane separation water treatment equipment according to claim 2, characterized in that, The calculation of the output viscosity range and the calculation of the output resistance range specifically includes the following steps: Extract the preset temperature reference constant and turbidity reference constant; The deviation ratio between the inlet water temperature range and the temperature reference constant is calculated to generate a temperature independent variable range. The temperature independent variable range is then input into the preset exponential function with the natural constant as the base to output the viscosity range. The deviation ratio between the influent turbidity range and the turbidity reference constant is calculated to generate a turbidity independent variable range. The turbidity independent variable range is then input into the preset logarithmic function with a preset base constant to output the resistance range.

4. The full life-cycle operation and maintenance system for membrane separation water treatment equipment according to claim 1, characterized in that, In the baseline archiving module, the construction of the baseline array specifically includes the following steps: Historical water production logs within the initial preset time period after the extraction membrane separation water treatment equipment is put into operation for the first time; Cluster the historical water production logs according to each scenario label, and filter the target differential pressure data under each scenario label whose fluctuation variance is less than a preset variance threshold. Calculate the arithmetic mean of the target pressure difference data and output the initial transmembrane pressure difference; Using the scene label as index coordinates and the initial transmembrane pressure difference as node values, a multidimensional mapping matrix is ​​constructed to output the baseline array.

5. The full life-cycle operation and maintenance system for membrane separation water treatment equipment according to claim 1, characterized in that, In the perception mapping module, matching the corresponding gear position specifically includes the following steps: The sensor array collects real-time influent temperature, real-time influent turbidity, real-time permeate flux, and real-time transmembrane pressure difference for the current operating cycle. The real-time inlet water temperature is used as an independent variable to input the preset exponential function to calculate the real-time viscosity value. The real-time viscosity value is compared with each viscosity range to match the corresponding real-time viscosity range. The real-time influent turbidity is used as an independent variable to input the preset logarithmic function to calculate the real-time resistance value. The real-time resistance value is compared with each resistance interval to match the corresponding real-time resistance interval. Extract the preset flux node array as the preset flux division threshold, compare the real-time water production flux with the preset flux division threshold, and determine the output level of the flux interval to which the real-time water production flux belongs.

6. The full life-cycle operation and maintenance system for membrane separation water treatment equipment according to claim 1, characterized in that, In the deviation comparison module, the output deviation feature specifically includes the following steps: A real-time feature vector is generated by splicing the real-time viscosity range, the real-time resistance range, and the corresponding gear position. The scene label that matches the real-time feature vector in the baseline array is used as the target scene label; The initial transmembrane pressure difference corresponding to the target scene label is retrieved from the baseline array as the target initial transmembrane pressure difference; Perform the subtraction operation of the real-time transmembrane pressure difference and the target initial transmembrane pressure difference to output the deviation characteristic.

7. The full life-cycle operation and maintenance system for membrane separation water treatment equipment according to claim 1, characterized in that, In the attenuation maintenance module, updating the baseline array specifically includes the following steps: Extract the pollution tolerance threshold as the preset deviation alarm threshold, compare the deviation feature with the preset deviation alarm threshold, and generate a cleaning instruction with a timestamp when the deviation feature is greater than the preset deviation alarm threshold; During the preset recovery period after the timestamped cleaning command is completed, the differential pressure data of the rerun is collected and the average value is calculated and output as the transmembrane differential pressure after cleaning. The attenuation index is output by subtracting the target initial transmembrane pressure difference from the post-cleaning transmembrane pressure difference. Replace the initial transmembrane pressure difference corresponding to the target scene label in the baseline array with the transmembrane pressure difference after cleaning, and output the updated baseline array.

8. The full life-cycle operation and maintenance system for membrane separation water treatment equipment according to claim 1, characterized in that, In the load assessment module, the output aging equivalent specifically includes the following steps: Parse the historical instruction log to extract the first timestamp of the previous cleaning instruction and the second timestamp of the current cleaning instruction, and calculate the difference between the first timestamp and the second timestamp to generate the time interval; Retrieve multiple real-time water production fluxes within the time interval, perform discrete integral summation on all real-time water production fluxes within the time interval, and output the periodic load. Extract the tolerance coefficient configured within the target scene label, and calculate the product of the periodic load and the tolerance coefficient to output the equivalent load denominator; Perform the division operation of the attenuation exponent by the denominator of the equivalent load and output the normalized ratio as the aging equivalent.

9. The full life-cycle operation and maintenance system for membrane separation water treatment equipment according to claim 8, characterized in that, The output cycle load specifically includes the following steps: The real-time water production fluxes within the time interval are sorted according to the time series, and the time difference between two adjacent real-time water production fluxes is extracted as the integration step size. Compare the real-time water production flux with a preset abnormal fluctuation threshold, and remove invalid flux data that exceeds the abnormal fluctuation threshold; Based on the integration step size, the trapezoidal integration algorithm is used to perform surface accumulation calculation on the remaining real-time water production flux after removing invalid flux data, and the cycle load is output.

10. The full life-cycle operation and maintenance system for membrane separation water treatment equipment according to claim 1, characterized in that, The system also includes a decommissioning determination module, which is used to accumulate each aging equivalent over time to generate a cumulative decay value. When the cumulative decay value is greater than a preset threshold, a device replacement command is triggered. The specific steps include: Establish a lifecycle data table, and store the aging equivalent of each output in the lifecycle data table according to the generation time order; Extract all the aging equivalents from the lifecycle data table, sum them up, and output the cumulative decay value. Extract the preset end-of-life data as a preset threshold value, and compare the cumulative decay value with the preset threshold value; When the cumulative attenuation value exceeds the preset threshold, a device replacement instruction containing the device identification code is generated and sent to the operation and maintenance terminal.

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