Water purifier organic matter detection and control method and system

Abstract

The application discloses a kind of organic matter detection and control method and system of water purifier, belong to water quality purification and monitoring technical field;It aims at solving the problem of inaccurate pollutant identification, extensive adsorption saturation determination, poor regeneration process adaptability;Including: collecting water sample to construct initial characteristic vector and dimension reduction by principal component analysis method, match with pollution source fingerprint database to determine target pollutant combination type, adapt adsorption temperature interval to realize targeted adsorption;Collecting adsorption quality to draw change broken line, analyze adsorption saturation comprehensive determination value, judge whether to trigger regeneration-backwashing program;Adjust temperature to change adsorbent form during regeneration, detect pollutant viscosity coefficient to adapt backwashing parameters, match desorption temperature and verify desorption rate, unqualified retry, out-of-limit prompt to replace adsorbent;The application improves purification pertinence and effect stability, optimizes adsorbent utilization rate, ensures system operation coherence and reliability, applicable to various water quality purification scenarios.

Description

Technical Field

[0001] This invention belongs to the field of water purification and monitoring technology, specifically relating to a method and system for detecting and controlling organic matter in a water purifier. Background Technology

[0002] In the field of water purification, organic pollutants can easily affect drinking water safety. Water purifiers have become the mainstream purification equipment in both home and commercial settings due to their convenience and efficiency. Their ability to remove organic pollutants directly affects the purification effect. However, existing water purifiers still have the following shortcomings in the process of treating organic pollutants:

[0003] Existing water purification technologies lack the accuracy to identify the types and characteristics of organic pollutants in water, making it impossible to accurately classify pollutants. This results in a lack of specificity in the adsorption and purification process, making it difficult to adapt to the purification needs of different pollutants, and thus affecting the purification effect and efficiency.

[0004] Traditional solutions rely solely on "time to reach" or "flow rate to reach" to determine whether the adsorbent is saturated. This not only fails to show the actual weight gain curve of the adsorbent but also ignores dynamic signals such as adsorption rate decay, inflection point frequency, and fluctuation amplitude. This leads to premature regeneration wasting energy, while delayed regeneration allows pollutants to penetrate, resulting in a sudden deterioration of the purified water quality.

[0005] Conventional adsorbent regeneration processes lack dynamic adaptability in parameter design, making it impossible to adjust regeneration conditions according to pollutant desorption characteristics. Furthermore, the regeneration effect lacks scientific and effective quantitative verification. In addition, the coordination between the regeneration stage and subsequent adsorption stages is insufficient, affecting the overall continuity and reliability of the system operation. To address this, we propose a method and system for detecting and controlling organic matter in water purifiers. Summary of the Invention

[0006] The purpose of this invention is to provide a method and system for detecting and controlling organic matter in a water purifier, so as to solve the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a method for detecting and controlling organic matter in a water purifier, comprising the following steps:

[0008] Step 1: Collect water samples from the water body to be purified, construct an initial feature vector, and use principal component analysis to reduce the dimensionality to obtain the principal component feature vector; match it with the pollution source fingerprint database to determine the target pollutant combination type, and match the optimal suitable adsorption temperature range accordingly, adjust the reaction chamber temperature to enable the adsorbent to target adsorption.

[0009] Step 2: Collect the total mass of adsorbed contaminants from the supramolecular porous adsorbent, and simultaneously obtain the net mass of the adsorbent. Plot a line graph showing the change in adsorbed contaminant mass, extract the adsorption saturation evaluation parameters, and perform comprehensive analysis to obtain the comprehensive adsorption saturation judgment value. Based on this, determine whether the adsorbent is saturated. If it is saturated, trigger the regeneration-backwashing synergistic procedure.

[0010] Step 3: When the regeneration-backwashing synergistic program is triggered, the inlet water passage is cut off, the water to be purified remaining in the reaction chamber is drained, the temperature of the reaction chamber is adjusted to change the form of the adsorbent, and the viscosity coefficient of the pollutants is detected simultaneously to analyze and match the backwashing intensity and duration; the appropriate temperature for pollutant desorption is matched, and the backwash water is heated and then subjected to high-pressure reverse rinsing; the desorption rate is verified, and if it is qualified, the target pollutant combination and the appropriate temperature are re-matched and reset, if it is not qualified, the test is repeated, and if the limit is exceeded, the adsorbent is replaced.

[0011] Preferably, the specific process of collecting water samples from the water body to be purified and constructing the initial feature vector is as follows:

[0012] Multiple water samples were collected from the water body to be purified according to the preset collection cycle. Each set of samples was then sequentially transported to a three-dimensional fluorescence spectrometer to obtain the original fluorescence intensity data of each set of samples under different excitation and emission wavelengths.

[0013] All raw fluorescence intensity data under the same excitation and emission wavelengths are statistically fused to obtain comprehensive fluorescence intensity data within the acquisition period.

[0014] The combined fluorescence intensities are arranged in a matrix according to the excitation wavelength and emission wavelength to construct the original fluorescence signal matrix; the original fluorescence signal matrix is ​​preprocessed to obtain the purified signal matrix.

[0015] Based on the purified signal matrix, key parameters of the fluorescence peak are extracted using the local maximum method; the extracted key parameters of the fluorescence peak are used as basic feature components to construct an initial feature vector.

[0016] Preferably, the process of obtaining the principal component eigenvectors is as follows:

[0017] Calculate the covariance matrix of the initial eigenvectors, perform eigenvalue decomposition on the covariance matrix, and solve for all eigenvalues ​​and the eigenvector corresponding to each eigenvalue.

[0018] For each feature value, calculate its proportion to the sum of all feature values ​​to obtain a single contribution rate;

[0019] Arrange all feature values ​​in descending order of value. For each feature value after sorting, sum up the individual contribution rates of the feature value and the feature values ​​that precede it in the sorting position to obtain the cumulative contribution rate of the corresponding position.

[0020] Select the first few feature values ​​whose cumulative contribution rate is greater than or equal to the corresponding preset threshold, and obtain the feature vectors corresponding to these feature values ​​as principal components to be used.

[0021] For each selected principal component, its corresponding eigenvector component is operated on with the corresponding basic eigenvector component in the initial eigenvector to obtain the score value of each principal component; the scores of all principal components are integrated to obtain the principal component eigenvector.

[0022] Preferably, the specific process of matching and determining the target pollutant combination type, matching the optimal suitable adsorption temperature range accordingly, and adjusting the reaction chamber temperature to achieve targeted adsorption by the adsorbent is as follows:

[0023] A pollution source fingerprint database is constructed, which includes a basic standard library, a localized dynamic library, and a manual input interface. The basic standard library stores standard three-dimensional fluorescence spectrum sample sets of various typical organic pollutant combinations and corresponding standard principal component feature vectors. The localized dynamic library accesses regional water quality monitoring data and is updated at a fixed period.

[0024] Preset high thresholds for precise matching and low thresholds for potential pollutants, substitute the principal component feature vector of the current collection period into the pollution source fingerprint database, and calculate the cosine similarity matching degree with each standard principal component feature vector;

[0025] If there is a pollutant combination with a matching degree greater than or equal to the high threshold of precise matching, it is directly identified as the target pollutant combination type.

[0026] If only pollutant combinations with a matching degree greater than or equal to the low threshold of potential pollutants and less than the high threshold of precise matching exist, the combination with "relatively high matching degree + optimal physicochemical parameter compatibility" is selected as the target pollutant combination type. The optimal suitable adsorption temperature range is matched through the target pollutant combination type-suitable adsorption temperature mapping table. The temperature of the supramolecular porous adsorbent reaction chamber is adjusted to this range using a PID temperature controller. After the temperature stabilizes, the adsorbent switches to the optimal adsorption form to achieve targeted adsorption.

[0027] Preferably, the specific process for plotting the line graph of changes in sludge suction quality is as follows:

[0028] Taking the moment when the supramolecular porous adsorbent was regenerated most recently as the initial moment, after the water flow in the reaction chamber stabilizes, the mass of the supramolecular porous adsorbent in the reaction chamber is collected at the preset collection interval to obtain the total mass of the adsorbent at each collection moment. At the same time, the net mass of the adsorbent before it was first loaded into the reaction chamber and before it was introduced into the water body is obtained.

[0029] A two-dimensional rectangular coordinate system is constructed, with the total mass of sludge suctioned as the vertical axis and time as the horizontal axis. The total mass of sludge suctioned at each collection moment from the initial moment to the current moment is extracted, and several data points are marked in the coordinate system. The data points are connected sequentially with line segments in chronological order to obtain a broken line curve of sludge suction mass change.

[0030] Preferably, the comprehensive adsorption saturation judgment value is analyzed; based on this, it is determined whether the adsorbent is saturated. If saturated, the specific process of triggering the regeneration-backwashing synergistic procedure is as follows:

[0031] From the line graph of changes in adsorption quality, five adsorption saturation evaluation parameters corresponding to the current moment are extracted: current cumulative adsorption amount, adsorption rate decay coefficient, frequency of adsorption inflection point, recent adsorption quality fluctuation range, and degree of slowdown in cumulative adsorption growth rate. After normalizing and dimensionless processing of the above parameters, the comprehensive adsorption saturation judgment value is obtained by weighting according to the preset weight coefficient.

[0032] If the comprehensive adsorption saturation judgment value is greater than or equal to the corresponding preset threshold, the adsorbent is determined to have reached saturation, triggering the regeneration-backwashing synergistic procedure.

[0033] Preferably, the specific process for analyzing the viscosity coefficient of contaminants and adapting the backwash intensity and duration when the regeneration-backwash synergistic procedure is triggered is as follows:

[0034] First, cut off the water inlet passage of the adsorption reaction chamber and keep the water outlet passage temporarily open to drain the water to be purified remaining in the chamber until only supramolecular porous adsorbent remains in the reaction chamber. At the same time, lock the weighing sensor's data acquisition function.

[0035] Adjust the internal temperature of the adsorption reaction chamber to the preset format transformation temperature based on the physical properties of the adsorbent, i.e.:

[0036] If the optimal adsorption morphology of the current supramolecular porous adsorbent is homogeneous, the morphology transition temperature is set to a value higher than the suitable adsorption temperature range; if the optimal adsorption morphology is heterogeneous, the morphology transition temperature is set to a value lower than the suitable adsorption temperature range.

[0037] After temperature adjustment, maintain the temperature for a preset time to ensure that the adsorbent is completely converted from the adsorbed state into easily separable heterogeneous solid particles.

[0038] During the morphological transformation and heat preservation stage, the rheometer built into the reaction chamber is activated simultaneously to detect the viscosity coefficient of the cumulative pollutant mixture system to be desorbed in the chamber; based on the detected viscosity coefficient, a backwashing parameter prediction model is constructed to analyze the appropriate backwashing intensity and backwashing duration.

[0039] Preferably, the specific process of reverse high-pressure rinsing after heating the backwash water at the appropriate temperature for pollutant desorption is as follows:

[0040] Construct an adsorbent-pollutant matching parameter library. This library contains all pollutant combination types for target purification, and each pollutant combination type has a preset set of pollutant desorption matching temperature ranges.

[0041] Substitute the target pollutant combination type corresponding to the current moment into the adsorbent-pollutant matching parameter library, match the corresponding pollutant desorption matching temperature range, and heat the backwash water source to that temperature range.

[0042] High-pressure backwash water is introduced into the adsorption reaction chamber according to the current suitable backwash intensity, backwash duration and backwash water temperature. The water flow flows counter-directionally along the adsorbent packing layer to remove the accumulated pollutants on the surface and in the pores of the adsorbent. The pollutant-containing wastewater generated during the backwash is discharged through a dedicated sewage discharge channel.

[0043] Preferably, the process involves verifying the desorption rate. If it passes, the target pollutant combination and suitable temperature are re-matched and reset. If it fails, the test is repeated. If the limit is exceeded, the adsorbent is replaced. The specific process is as follows:

[0044] After the backwashing is completed, drain the backwash water from the reaction chamber. When the temperature inside the chamber returns to the appropriate adsorption temperature range, collect the net mass of the adsorbent after desorption and obtain the current cumulative adsorption amount before backwashing. Calculate the difference between the total mass of pollutants adsorbed before backwashing and the net mass of the adsorbent after desorption, and compare it with the cumulative adsorption amount to obtain the pollutant desorption rate.

[0045] If the desorption rate is greater than or equal to the corresponding preset threshold, the regeneration is deemed qualified; otherwise, the complete process of morphological transformation insulation, viscosity coefficient detection, backwashing parameter adaptation and high-pressure backwashing is repeated until the desorption rate meets the standard.

[0046] If the target is not met after more than the preset threshold number of re-executions, an adsorbent replacement prompt will be triggered.

[0047] Once the regeneration-backwashing synergistic procedure or adsorbent replacement is completed, the target pollutant combination type and optimal suitable adsorption temperature range corresponding to the current moment are rematched, and the internal temperature of the adsorption reaction chamber is heated to the optimal suitable adsorption temperature range.

[0048] Compared with the prior art, the beneficial effects of the present invention are:

[0049] (1) The organic matter detection and control method and system of the water purifier, through the collection of multiple water samples and the detection of three-dimensional fluorescence spectroscopy, extracts key parameters of fluorescence peaks to construct an initial feature vector, and after dimensionality reduction by principal component analysis, performs cosine similarity matching with the pollution source fingerprint database to accurately determine the type of target pollutant combination; then, by adjusting the reaction chamber temperature through the mapping relationship between pollutant combination and suitable adsorption temperature range, the adsorbent is switched to the optimal adsorption form, which completely solves the problem of lack of specificity in traditional adsorption and significantly improves the water purification efficiency and consistency of effect.

[0050] (2) The organic matter detection and control method and system of this water purifier extracts five core parameters by drawing a broken line of the change of adsorption mass: the current cumulative adsorption amount, the adsorption rate decay coefficient, the frequency of adsorption inflection point, the fluctuation range of adsorption mass in the recent period, and the degree of slowdown of the cumulative adsorption amount growth rate. The saturation judgment value is obtained by comprehensive calculation, realizing a full-view characterization of the adsorbent state. Compared with the traditional coarse judgment method of "time / flow", it can accurately capture the adsorption dynamic signal, which not only avoids the waste of resources due to premature regeneration of the adsorbent before it is fully utilized, but also prevents purification failure caused by oversaturation, and ensures that the adsorbent can give full play to its effectiveness within the optimal period.

[0051] (3) The organic matter detection and control method and system of this water purifier forms targeted regeneration conditions by matching the desorption temperature based on the target pollutant combination type and dynamically adjusting the backwash intensity and duration in combination with the pollutant viscosity coefficient; the desorption rate is used as the quantitative standard for qualified regeneration, and a retest mechanism and adsorbent replacement prompt are provided to ensure thorough desorption; after regeneration, the pollutant combination and the appropriate adsorption temperature are rematched to achieve seamless connection between regeneration and adsorption, solving the problems of fixed regeneration parameters, no effect verification and poor connection in traditional regeneration, and improving the overall reliability and continuity of the system operation. Attached Figure Description

[0052] Figure 1 This is a flowchart of the present invention. Detailed Implementation

[0053] The technical solutions of 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.

[0054] Example 1;

[0055] Please see Figure 1 The present invention provides a method for detecting and controlling organic matter in a water purifier, comprising:

[0056] Step 1: Multiple water samples are collected from the water body to be purified. After three-dimensional fluorescence spectroscopy detection, data processing, and fluorescence peak parameter extraction, an initial feature vector is constructed. Principal component analysis is used to reduce the dimensionality and obtain the principal component feature vector. The target pollutant combination type is determined by matching with the pollution source fingerprint database. Based on this, the optimal suitable adsorption temperature range is matched, and the reaction chamber temperature is adjusted to enable targeted adsorption of the adsorbent. The specific process is as follows:

[0057] The water quality sample collection cycle is preset, and the water body to be purified is continuously collected by the sampling pump configured at the water inlet of the water purifier within the collection cycle to obtain multiple sets of water quality samples to be tested.

[0058] Multiple water quality samples were sequentially transported to a three-dimensional fluorescence spectrometer. Three-dimensional fluorescence spectroscopy was performed on each water quality sample to obtain the original fluorescence intensity data of each water quality sample under different excitation wavelengths and emission wavelengths.

[0059] The raw fluorescence intensity data of all groups of water quality samples under the same excitation wavelength and emission wavelength were statistically fused (such as arithmetic mean, weighted average, etc.) to obtain the comprehensive fluorescence intensity data of water quality samples within the collection period.

[0060] The combined fluorescence intensities are arranged in a matrix according to the dimensions of excitation wavelength and emission wavelength to construct the original fluorescence signal matrix corresponding to the acquisition cycle;

[0061] The original fluorescence signal matrix was preprocessed with baseline correction and noise filtering to obtain the purified signal matrix.

[0062] Based on the purified signal matrix, key parameters of the fluorescence peak are extracted using the local maximum method, including peak intensity, excitation peak wavelength, emission peak wavelength, peak half width at half maximum (FWHM), and peak area.

[0063] The extracted key parameters of the fluorescence peak are used as basic feature components to construct an initial feature vector;

[0064] Principal component analysis (PCA) is used to reduce the dimensionality of the initial eigenvectors and optimize them, constructing the final eigenvectors as follows:

[0065] Calculate the covariance matrix of the initial eigenvectors, perform eigenvalue decomposition on the covariance matrix, and solve for all the eigenvalues ​​corresponding to the covariance matrix, as well as the eigenvectors corresponding to each eigenvalue (which can be solved using methods such as QR decomposition).

[0066] For each feature value, calculate the proportion of the current feature value to the sum of all feature values ​​to obtain the individual contribution rate corresponding to the current feature value;

[0067] Arrange all feature values ​​in descending order to obtain a descending sequence of feature values; for each feature value in this sequence, sum the individual contribution rates of all feature values ​​whose sort positions precede the feature value and the feature value itself, to obtain the cumulative contribution rate of the corresponding feature value position;

[0068] A preset cumulative contribution rate threshold is set. From all feature values ​​arranged in descending order of numerical value, the first M feature values ​​whose cumulative contribution rate is greater than or equal to the preset threshold are selected. At the same time, the feature vectors corresponding to these M feature values ​​are obtained, thus obtaining the first M principal components to be used.

[0069] For each selected principal component, use the formula: The principal component scores are obtained. Where t is the label of the principal component, t=1,2,…,M; M is the total number of principal components selected. Let f be the f-th component of the eigenvector corresponding to the t-th principal component. Let f be the f-th basic feature component in the initial feature vector, where f = 1, 2, ..., F; and F be the total number of basic feature components.

[0070] The scores of all principal components are integrated to obtain the principal component feature vector;

[0071] Construct a pollution source fingerprint database, which includes a basic standard library and a localized dynamic library:

[0072] The basic standard library stores standard three-dimensional fluorescence spectrum sample sets of various typical combinations of organic pollutants and corresponding standard principal component feature vectors;

[0073] The localized dynamic library accesses regional water quality monitoring data and updates the combinations of pollutants and their variation characteristics that frequently occur in the region at fixed intervals. All updated pollutant combinations are simultaneously generated with corresponding standard principal component feature vectors (the generation logic is consistent with the basic standard library). At the same time, a manual input interface is reserved to allow users to supplement specific pollutant combinations and their corresponding standard principal component feature vectors according to actual water use scenarios, ensuring that all pollutant combinations in the database have matching feature data.

[0074] Two sets of cosine similarity thresholds are preset: a high threshold for accurate matching and a low threshold for potential pollutants (low threshold < high threshold).

[0075] Obtain the principal component feature vector corresponding to the current collection period, substitute it into the above dynamically updated pollution source fingerprint database, and use the cosine similarity algorithm to calculate the cosine similarity matching degree between the current principal component feature vector and the standard principal component feature vector corresponding to each combination of organic pollutants in the database.

[0076] The database employs a dual-mode approach, combining fuzzy matching and precise matching, to determine the combination of target pollutants.

[0077] Precise matching mode: If there is a combination of pollutants with a cosine similarity matching degree greater than or equal to the precise matching high threshold, then the combination is directly identified as the target pollutant combination of the water body to be purified in the current collection period.

[0078] Fuzzy matching mode: If there is no pollutant combination that meets the exact matching condition, but there are multiple pollutant combinations whose cosine similarity matching degree is ≥ the low threshold of potential pollutants (and < the high threshold of exact matching), then the compatibility of these potential pollutant combinations with the current water quality pH value, hardness and other physicochemical parameters is further calculated, and finally the combination with "relatively high cosine similarity matching degree + optimal physicochemical parameter compatibility" is selected as the target pollutant combination;

[0079] If there is only one pollutant combination whose cosine similarity matching degree is greater than or equal to the low threshold of potential pollutants, then the combination is directly identified as the target pollutant combination.

[0080] A target pollutant combination type-adaptive adsorption temperature mapping table is constructed, in which each target pollutant combination type corresponds to an optimal adaptive adsorption temperature range. This mapping table is updated synchronously with the dynamically updated pollution source fingerprint database to ensure that newly added and updated pollutant combinations in the database can be matched with the corresponding temperature range.

[0081] The target pollutant combination type is matched with the entries in the mapping table, and the corresponding optimal adsorption temperature range is output.

[0082] Based on this optimal adsorption temperature range, the PID temperature controller is activated to adjust the temperature of the supramolecular porous adsorbent reaction chamber of the water purifier, so that the actual temperature of the reaction chamber is controlled to be maintained within the optimal adsorption temperature range.

[0083] Once the temperature stabilizes, the supramolecular porous adsorbent automatically switches to the optimal adsorption form to achieve targeted adsorption of the target pollutant combination.

[0084] It should be noted that by continuously collecting multiple sets of water samples at a preset collection cycle, and combining this with three-dimensional fluorescence spectroscopy to obtain raw fluorescence intensity data under different excitation and emission wavelengths, after statistical fusion, baseline correction, and noise filtering preprocessing, the local maximum method was used to extract key parameters of the fluorescence peaks. This ensured the comprehensiveness and accuracy of the organic matter characteristic information of the water body, avoiding the characteristic distortion problem caused by single water sample detection or unprocessed data. Subsequently, principal component analysis was used to reduce the dimensionality of the initial feature vector, removing redundant information while retaining the core features. This simplified the data processing flow and improved the representativeness of the feature vector, providing high-quality data support for the subsequent accurate identification of pollutants.

[0085] A pollution source fingerprint database is constructed, integrating a basic standard library (typical pollutant combinations), a localized dynamic library (regional water quality data updated periodically), and a manual input interface to adapt to dynamic changes in water quality and special water use scenarios. A dual-mode approach of "precise matching (direct judgment based on high threshold) + fuzzy matching (screening based on low threshold + physicochemical parameter compatibility)" is adopted, combined with a cosine similarity algorithm, to significantly improve the accuracy of pollutant combination identification under complex water quality conditions.

[0086] By using a target pollutant-adaptive temperature mapping table and a PID temperature controller, the reaction chamber temperature is precisely controlled, prompting the supramolecular porous adsorbent to switch to the optimal adsorption form, thus solving the problem of poor adaptability of traditional adsorption. At the same time, the target pollutant combination type determined in this module provides a core basis for the desorption temperature matching of the subsequent regeneration module, constructing a collaborative link of parameters for the entire process of "identification-adsorption-regeneration" to ensure the continuity of system operation.

[0087] The target pollutant combination type identified in this step provides a core technical basis for matching the appropriate temperature range for pollutant desorption in the subsequent regeneration-backwashing synergistic process. It constructs a parameter synergy link for the entire process of "pollutant identification-targeted adsorption-regeneration desorption", ensuring the continuity and synergy of each link in the entire technical solution. It provides key support for the closed-loop operation of the solution and further improves the operational reliability and practicality of the entire water purifier organic matter detection and control system.

[0088] Step 2: Collect the total mass of adsorbed contaminants from the supramolecular porous adsorbent, and simultaneously obtain the net mass of the adsorbent. Plot a line graph showing the change in adsorbed contaminant mass, extract the adsorption saturation evaluation parameters, and perform comprehensive analysis to obtain the comprehensive adsorption saturation judgment value. Based on this, determine whether the adsorbent is saturated. If saturated, trigger the regeneration-backwashing synergistic procedure. The specific process is as follows:

[0089] A high-precision weighing sensor is installed at the bottom of the supramolecular porous adsorbent reaction chamber in the main purification channel of the water purifier. An anti-interference design for mass acquisition is adopted: a waterproof and biofilm-resistant coating and a water flow buffer are added to the outside of the sensor to reduce the interference of water flow impact and biofilm growth on the measurement. A moving average filtering algorithm is used when collecting data to remove abnormal fluctuations. Simultaneously, a calibration procedure is automatically executed at fixed intervals, and the measurement deviation is corrected by comparing the adsorbent mass after rinsing with blank water with the initial net mass.

[0090] Taking the moment when the supramolecular porous adsorbent completed its regeneration as the initial moment, and when the water flow in the reaction chamber was stable, the mass of the supramolecular porous adsorbent in the reaction chamber was collected at preset collection intervals to obtain the total mass of pollutants absorbed at each collection moment. At the same time, the net mass of the adsorbent after it was first loaded into the reaction chamber and before any water was introduced was also obtained. The moment when the supramolecular porous adsorbent completed its regeneration refers to the moment when the regeneration-backwashing synergistic procedure was completed, the desorption rate reached the preset threshold, and the adsorbent had removed the accumulated pollutants and restored its adsorption capacity.

[0091] A two-dimensional rectangular coordinate system is constructed, with the total mass of the adsorbed waste as the vertical axis and time as the horizontal axis. The total mass of the adsorbed waste corresponding to each collection moment from the initial moment to the current moment is extracted (the total mass of the adsorbed waste at the initial moment is the net mass of the adsorbent). Several data points are marked in the coordinate system. The data points are connected sequentially with line segments in time order to obtain a broken line of the change in adsorbed waste mass.

[0092] From the line graph showing the change in adsorption mass, the following adsorption saturation evaluation parameters corresponding to the current moment are extracted:

[0093] Current cumulative adsorption capacity LX: The difference between the total mass of adsorbed pollutants and the net mass of the adsorbent at the current moment (reflecting the total mass of pollutants captured by the supramolecular porous adsorbent up to the current moment);

[0094] Adsorption rate decay coefficient SK: The adsorption initial stage is defined as the period corresponding to 30% of the total adsorption cycle time, and the current stage is defined as the period corresponding to 30% of the total adsorption cycle time traced back from the current moment. The average slope of the broken line of the change in adsorption mass in the two periods is calculated, and the ratio of the average slope of the adsorption initial stage to the average slope of the current stage is taken. This coefficient directly reflects the decay trend of adsorption efficiency (reflects the degree of decay of the adsorption capacity of the supramolecular porous adsorbent).

[0095] Adsorption inflection point frequency (GP): Calculate the slope difference of the line segment representing the change in adsorbed mass between two adjacent collection periods. Set a slope change threshold (based on a fixed proportion of the maximum adsorption rate of the adsorbent). When the slope difference between two adjacent collection periods is greater than or equal to the preset threshold, the connection point between the two collection periods is determined to be an adsorption inflection point. The correlation logic between the number of inflection points and sudden changes in water quality pollution is as follows: a single inflection point corresponds to a sudden change in pollutant concentration reaching a fixed proportion. Two or more consecutive inflection points are determined to be a change in pollution type, and water quality re-testing is triggered simultaneously.

[0096] The recent adsorption mass fluctuation amplitude XF: extracts the difference between the maximum and minimum values ​​of the ordinate of the line graph within the most recent preset time period; its correlation logic with the stability of the adsorption process and the pollutant mixing characteristics is as follows: the stability of the adsorption process is quantified by the stability coefficient, which is calculated by the ratio of the fluctuation amplitude to the average adsorption amount in the initial period. When the stability coefficient reaches the preset value, the adsorption is considered stable; the pollutant mixing characteristics are distinguished by the ratio of the fluctuation amplitude to the average adsorption amount. When the ratio is lower than the preset value, it is determined that the adsorption is dominated by a single pollutant, and when the ratio is higher than the preset value, it is determined that the adsorption is competitive by multiple pollutants.

[0097] The degree of slowdown in the cumulative adsorption growth rate (SV): Calculate the difference between the average slope of the most recent s consecutive collection periods and the average slope of the initial s consecutive collection periods, and then use the ratio of this difference to the average slope of the initial s consecutive collection periods as this parameter (reflecting the degree to which the supramolecular porous adsorbent is close to the adsorption saturation state), where s is the preset number of consecutive collection periods.

[0098] After normalizing and dimensionlessly processing the current cumulative adsorption amount LX, adsorption rate decay coefficient SK, adsorption inflection point frequency GP, recent adsorption mass fluctuation amplitude XF, and cumulative adsorption amount growth rate slowdown degree SV corresponding to the current moment, the adsorption saturation comprehensive judgment value XFB is obtained using the formula: XFB=LX×b1+SK×b2+GP×b3+XF×b4+SV×b5; where b1, b2, b3, b4, and b5 are preset weight coefficients.

[0099] A preset adsorption saturation comprehensive judgment threshold is set. If the current adsorption saturation comprehensive judgment value is greater than or equal to the corresponding preset threshold, it is determined that the adsorbent has reached the adsorption saturation state, and the regeneration-backwashing coordinated program is triggered.

[0100] It should be noted that the initial benchmark is the time when the last regeneration is completed, and the net mass standard is the first loading without water flow. This ensures the consistency of the adsorption mass data collection and avoids the judgment distortion caused by the confusion of benchmarks. The weighing sensor adopts a physical anti-interference design with a waterproof and biofilm-proof coating and a water flow buffer device. Combined with a moving average filtering algorithm and a periodic automatic calibration program, it further improves the mass acquisition accuracy.

[0101] By plotting a line graph showing the change in adsorption quality, five core parameters are extracted: current cumulative adsorption amount, adsorption rate decay coefficient, frequency of adsorption inflection point occurrence, recent fluctuation range, and degree of slowdown in growth rate. This multi-dimensional approach covers the total amount of pollutants, adsorption efficiency decay, impact of sudden changes in water quality, process stability, and degree of saturation approach. After normalization, a comprehensive judgment value is calculated by weighting according to preset weights. Compared with the traditional single threshold judgment, this significantly improves the scientificity and pertinence of saturation identification, effectively avoiding "premature regeneration waste" or "oversaturation purification failure".

[0102] This module clarifies the judgment criteria of "regeneration completion = desorption rate meets standard + adsorbent restores adsorption capacity", providing a unified benchmark for subsequent regeneration effect verification; based on the comparison of comprehensive judgment value and preset threshold, the regeneration program is precisely triggered, and a closed-loop synergy of "adsorption-saturation detection-regeneration trigger" is constructed to ensure that the adsorbent fully exerts its effectiveness within the optimal cycle.

[0103] Step 3: When the regeneration-backwashing synergistic program is triggered, the inlet water passage is cut off, the water to be purified remaining in the reaction chamber is drained, the temperature of the reaction chamber is adjusted to change the adsorbent morphology, and the viscosity coefficient of the pollutants is simultaneously detected to analyze the appropriate backwashing intensity and duration; the appropriate temperature for pollutant desorption is matched, and the backwash water is heated and then subjected to reverse high-pressure rinsing; the desorption rate is verified, and if it is qualified, the target pollutant combination and appropriate temperature are rematched and reset; if it is unqualified, the test is repeated, and if the limit is exceeded, the adsorbent is replaced. The specific process is as follows:

[0104] If the current adsorption saturation comprehensive judgment value XFB is greater than or equal to the preset adsorption saturation comprehensive judgment threshold, the water purifier control system immediately triggers the regeneration-backwashing coordinated program, and the specific execution process is as follows:

[0105] First, cut off the water inlet passage of the adsorption reaction chamber, and keep the water outlet passage temporarily open to drain the water to be purified remaining in the chamber until only supramolecular porous adsorbent (containing the accumulated pollutants) remains in the reaction chamber; at the same time, lock the weighing sensor acquisition function (the purpose is to avoid the sensor generating invalid data due to water flow and temperature disturbances during program execution).

[0106] The PID temperature controller is activated to adjust the internal temperature of the adsorption reaction chamber to the preset morphology transition temperature based on the physical properties of the adsorbent. Specifically, if the optimal adsorption morphology of the current supramolecular porous adsorbent is homogeneous, the morphology transition temperature is set to a value higher than the suitable adsorption temperature range; if the optimal adsorption morphology is heterogeneous, the morphology transition temperature is set to a value lower than the suitable adsorption temperature range.

[0107] After temperature adjustment, the temperature is maintained for a preset time to ensure that the adsorbent is completely converted from the adsorbed state into easily separable heterogeneous solid particles, so that the accumulated pollutants are initially separated from the adsorbent matrix.

[0108] During the morphological transformation and heat preservation stage, the rheometer built into the reaction chamber is activated simultaneously to detect the viscosity coefficient μ of the accumulated pollutant mixture to be desorbed in the chamber (multiple sets of data are continuously collected and the arithmetic mean is taken).

[0109] A backwashing parameter prediction model is constructed based on the viscosity coefficient μ, specifically as follows:

[0110] The backwash intensity PF is obtained using the formula: PF=K1×μ+P0, where K1 is a preset proportional coefficient and P0 is the basic backwash intensity (the parameters involved in this formula are normalized and dimensionless before calculation).

[0111] The backwashing time TF is obtained using the formula: TF=K2×ln(μ)+t0, where K2 is a preset proportional coefficient and t0 is the basic backwashing time (the parameters involved in this formula are normalized and dimensionless before calculation).

[0112] Construct an adsorbent-pollutant matching parameter library. This library contains all pollutant combination types for target purification, and each pollutant combination type has a preset set of pollutant desorption matching temperature ranges.

[0113] By substituting the target pollutant combination type corresponding to the current moment into the adsorbent-pollutant matching parameter library, the corresponding pollutant desorption matching temperature range is matched, and the backwash water source is heated to that temperature range.

[0114] The high-pressure water pump of the water purifier's backwash module pumps high-pressure backwash water into the adsorption reaction chamber according to the currently adapted backwash intensity, backwash duration, and backwash water temperature. The water flow flows in the opposite direction (opposite to the normal purification water flow) along the adsorbent packing layer to wash away the accumulated pollutants on the surface and in the pores of the adsorbent. The wastewater containing pollutants generated during washing is discharged through a dedicated sewage discharge channel to avoid secondary pollution.

[0115] After the backwashing is completed, drain the backwash water from the reaction chamber. When the temperature inside the chamber returns to the appropriate adsorption temperature range, collect the net mass of the adsorbent after desorption and obtain the current cumulative adsorption amount before backwashing. The pollutant desorption rate is obtained by dividing the difference between the total mass of pollutants adsorbed before backwashing and the net mass of the adsorbent after desorption by the current cumulative adsorption amount.

[0116] A preset pollutant desorption rate threshold is set. If the desorption rate is greater than or equal to the corresponding preset threshold, the regeneration is deemed qualified. Otherwise, the complete process of morphological transformation insulation, viscosity coefficient detection, backwashing parameter adaptation, and high-pressure backwashing is repeated until the desorption rate meets the standard.

[0117] A threshold is set for the number of times the regeneration process can be re-executed. If the number of re-executions exceeds this threshold and the pollutant desorption rate still fails to meet the standard, an adsorbent replacement prompt will be triggered.

[0118] Once the regeneration-backwashing synergistic process or adsorbent replacement is completed, the target pollutant combination type and the corresponding optimal adsorption temperature range are rematched at the current moment, and the internal temperature of the adsorption reaction chamber is heated to that range.

[0119] It should be noted that the form transition temperature is precisely set according to the optimal adsorption form of the adsorbent: if the optimal adsorption form of the adsorbent is homogeneous, the form transition temperature is higher than the suitable adsorption temperature; if it is heterogeneous, the form transition temperature is lower than the suitable adsorption temperature. The preset holding time is maintained by the PID temperature controller to ensure that the adsorbent is completely converted into easily separable heterogeneous solid particles, thereby achieving the initial separation of pollutants from the adsorbent matrix.

[0120] The module simultaneously activates a rheometer during the adsorbent morphology transformation stage to detect the viscosity coefficient of the pollutant mixture. Based on this core characteristic, it dynamically adjusts the intensity and duration of backwashing to avoid the problems of incomplete desorption or energy waste caused by traditional fixed-parameter rinsing. At the same time, combined with the targeted desorption temperature matched by the adsorbent-pollutant adaptation parameter library, the backwash water is heated to the corresponding temperature, forming a dual desorption support of "morphology transformation + temperature field synergy". With the reverse high-pressure rinsing design and dedicated sewage discharge channel, it can not only efficiently remove pollutants from the pores of the adsorbent, but also avoid secondary pollution.

[0121] This module uses the desorption rate as the core standard for successful regeneration. The desorption rate is calculated precisely by dividing the mass difference before and after backwashing by the cumulative adsorption amount. It also includes fault-tolerant mechanisms such as "retry if not meeting standard" and "replace if exceeding limits": the retry process ensures sufficient adsorbent regeneration, and the preset retry threshold avoids resource consumption caused by ineffective cycles; if the desorption rate consistently fails to meet the standard, an adsorbent replacement prompt is triggered, providing timely warning of adsorbent failure. After the regeneration process is completed or the adsorbent is replaced, the module rematches the current target pollutant combination with the corresponding optimal adsorption temperature, adjusting the reaction chamber temperature to the optimal state, achieving a seamless connection between "regeneration and adsorption," constructing a closed-loop process, and allowing the system to dynamically adapt to real-time changes in water pollutants.

[0122] An organic matter detection and control system for a water purifier, comprising:

[0123] Pollutant identification and targeted adsorption module: Water samples are collected from the water body to be purified, an initial feature vector is constructed, and the principal component feature vector is obtained by dimensionality reduction using principal component analysis; the target pollutant combination type is determined by matching with the pollution source fingerprint database, and the optimal suitable adsorption temperature range is matched accordingly, and the temperature of the reaction chamber is adjusted to enable the adsorbent to target adsorption.

[0124] Adsorption saturation monitoring trigger module: Collects the total mass of adsorbed contaminants of the supramolecular porous adsorbent, and simultaneously obtains the net mass of the adsorbent, plots the change in adsorbed contaminant mass, extracts adsorption saturation evaluation parameters and performs comprehensive analysis to obtain a comprehensive adsorption saturation judgment value; based on this, it determines whether the adsorbent is saturated. If it is saturated, it triggers the regeneration-backwashing collaborative program.

[0125] Regeneration-backwash-desorption verification module: When the regeneration-backwash coordinated program is triggered, the inlet water passage is cut off, the water to be purified remaining in the reaction chamber is drained, the temperature of the reaction chamber is adjusted to change the form of the adsorbent, and the viscosity coefficient of the pollutants is detected simultaneously to analyze the appropriate backwash intensity and duration; the appropriate temperature for pollutant desorption is matched, and the backwash water is heated and then subjected to high-pressure reverse rinsing; the desorption rate is verified, and if it is qualified, the target pollutant combination and appropriate temperature are re-matched and reset, if it is unqualified, the test is repeated, and if the limit is exceeded, the adsorbent is replaced.

[0126] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for detecting and controlling organic matter in a water purifier, characterized in that, Includes the following steps: Step 1: Collect water samples from the water body to be purified, construct an initial feature vector, and use principal component analysis to reduce the dimensionality to obtain the principal component feature vector; match it with the pollution source fingerprint database to determine the target pollutant combination type, and match the optimal suitable adsorption temperature range accordingly, adjust the reaction chamber temperature to enable the adsorbent to target adsorption. Step 2: Collect the total mass of adsorbed contaminants from the supramolecular porous adsorbent, and simultaneously obtain the net mass of the adsorbent. Plot a line graph showing the change in adsorbed contaminant mass, extract the adsorption saturation evaluation parameters, and perform comprehensive analysis to obtain the comprehensive adsorption saturation judgment value. Based on this, determine whether the adsorbent is saturated. If it is saturated, trigger the regeneration-backwashing synergistic procedure. Step 3: When the regeneration-backwashing synergistic program is triggered, the inlet water passage is cut off, the water to be purified remaining in the reaction chamber is drained, the temperature of the reaction chamber is adjusted to change the form of the adsorbent, and the viscosity coefficient of the pollutants is detected simultaneously to analyze and adapt the backwashing intensity and duration. Match the appropriate temperature for pollutant desorption, heat the backwash water and then perform a high-pressure reverse flush; verify the desorption rate, if it is qualified, rematch the target pollutant combination and reset the appropriate temperature, if it is not qualified, repeat the test, and if it exceeds the limit, prompt to replace the adsorbent.

2. The method for detecting and controlling organic matter in a water purifier according to claim 1, characterized in that: The specific process of collecting water samples from the water body to be purified and constructing the initial feature vector is as follows: Multiple water samples were collected from the water body to be purified according to the preset collection cycle. Each set of samples was then sequentially transported to a three-dimensional fluorescence spectrometer to obtain the original fluorescence intensity data of each set of samples under different excitation and emission wavelengths. All raw fluorescence intensity data under the same excitation and emission wavelengths are statistically fused to obtain comprehensive fluorescence intensity data within the acquisition period. The combined fluorescence intensities are arranged in a matrix according to the excitation wavelength and emission wavelength to construct the original fluorescence signal matrix; the original fluorescence signal matrix is ​​preprocessed to obtain the purified signal matrix. Based on the purified signal matrix, key parameters of the fluorescence peak are extracted using the local maximum method; the extracted key parameters of the fluorescence peak are used as basic feature components to construct an initial feature vector.

3. The method for detecting and controlling organic matter in a water purifier according to claim 2, characterized in that: The process of obtaining the principal component eigenvectors is as follows: Calculate the covariance matrix of the initial eigenvectors, perform eigenvalue decomposition on the covariance matrix, and solve for all eigenvalues ​​and the eigenvector corresponding to each eigenvalue. For each feature value, calculate its proportion to the sum of all feature values ​​to obtain a single contribution rate; Arrange all feature values ​​in descending order of value. For each feature value after sorting, sum up the individual contribution rates of the feature value and the feature values ​​that precede it in the sorting position to obtain the cumulative contribution rate of the corresponding position. Select the first few feature values ​​whose cumulative contribution rate is greater than or equal to the corresponding preset threshold, and obtain the feature vectors corresponding to these feature values ​​as principal components to be used. For each selected principal component, its corresponding eigenvector component is operated on with the corresponding basic eigenvector component in the initial eigenvector to obtain the score value of each principal component; the scores of all principal components are integrated to obtain the principal component eigenvector.

4. The method for detecting and controlling organic matter in a water purifier according to claim 3, characterized in that: The specific process of matching and determining the target pollutant combination type, matching the optimal suitable adsorption temperature range based on this, and adjusting the reaction chamber temperature to achieve targeted adsorption by the adsorbent is as follows: A pollution source fingerprint database is constructed, which includes a basic standard library, a localized dynamic library, and a manual input interface. The basic standard library stores standard three-dimensional fluorescence spectrum sample sets of various typical organic pollutant combinations and corresponding standard principal component feature vectors. The localized dynamic library accesses regional water quality monitoring data and is updated at a fixed period. Preset high thresholds for precise matching and low thresholds for potential pollutants, substitute the principal component feature vector of the current collection period into the pollution source fingerprint database, and calculate the cosine similarity matching degree with each standard principal component feature vector; If there is a pollutant combination with a matching degree greater than or equal to the high threshold of precise matching, it is directly identified as the target pollutant combination type. If only pollutant combinations with a matching degree greater than or equal to the low threshold of potential pollutants and less than the high threshold of precise matching exist, the combination with a relatively high matching degree and the best physicochemical parameter compatibility is selected as the target pollutant combination type. The optimal compatibility adsorption temperature range is matched using the target pollutant combination type-adaptive adsorption temperature mapping table. The temperature of the supramolecular porous adsorbent reaction chamber is adjusted to this range using a PID temperature controller. After the temperature stabilizes, the adsorbent switches to the optimal adsorption form to achieve targeted adsorption.

5. The method for detecting and controlling organic matter in a water purifier according to claim 4, characterized in that: The specific process for plotting the line graph showing the change in suction quality is as follows: Taking the moment when the supramolecular porous adsorbent was regenerated most recently as the initial moment, after the water flow in the reaction chamber stabilizes, the mass of the supramolecular porous adsorbent in the reaction chamber is collected at the preset collection interval to obtain the total mass of the adsorbent at each collection moment. At the same time, the net mass of the adsorbent before it was first loaded into the reaction chamber and before it was introduced into the water body is obtained. A two-dimensional rectangular coordinate system is constructed, with the total mass of sludge suctioned as the vertical axis and time as the horizontal axis. The total mass of sludge suctioned at each collection moment from the initial moment to the current moment is extracted, and several data points are marked in the coordinate system. The data points are connected sequentially with line segments in chronological order to obtain a broken line curve of sludge suction mass change.

6. The method for detecting and controlling organic matter in a water purifier according to claim 5, characterized in that: Analyze the comprehensive adsorption saturation judgment value; based on this, determine whether the adsorbent is saturated. If saturated, the specific process of triggering the regeneration-backwashing synergistic procedure is as follows: From the line graph of changes in adsorption quality, five adsorption saturation evaluation parameters corresponding to the current moment are extracted: current cumulative adsorption amount, adsorption rate decay coefficient, frequency of adsorption inflection point, recent adsorption quality fluctuation range, and degree of slowdown in cumulative adsorption growth rate. After normalizing and dimensionless processing of the above parameters, the comprehensive adsorption saturation judgment value is obtained by weighting according to the preset weight coefficient. If the comprehensive adsorption saturation judgment value is greater than or equal to the corresponding preset threshold, the adsorbent is determined to have reached saturation, triggering the regeneration-backwashing synergistic procedure.

7. The method for detecting and controlling organic matter in a water purifier according to claim 6, characterized in that: The specific process of analyzing the viscosity coefficient of contaminants and adapting the backwash intensity and duration when the regeneration-backwash synergistic procedure is triggered is as follows: First, cut off the water inlet passage of the adsorption reaction chamber and keep the water outlet passage temporarily open to drain the water to be purified remaining in the chamber until only supramolecular porous adsorbent remains in the reaction chamber. At the same time, lock the weighing sensor's data acquisition function. Adjust the internal temperature of the adsorption reaction chamber to the preset format transformation temperature based on the physical properties of the adsorbent, i.e.: If the optimal adsorption morphology of the current supramolecular porous adsorbent is homogeneous, the morphology transition temperature is set to a value higher than the suitable adsorption temperature range; if the optimal adsorption morphology is heterogeneous, the morphology transition temperature is set to a value lower than the suitable adsorption temperature range. After temperature adjustment, maintain the temperature for a preset time to ensure that the adsorbent is completely converted from the adsorbed state into easily separable heterogeneous solid particles. During the morphological transformation and heat preservation stage, the rheometer built into the reaction chamber is activated simultaneously to detect the viscosity coefficient of the cumulative pollutant mixture system to be desorbed in the chamber; based on the detected viscosity coefficient, a backwashing parameter prediction model is constructed to analyze the appropriate backwashing intensity and backwashing duration.

8. The method for detecting and controlling organic matter in a water purifier according to claim 7, characterized in that: The specific process of matching the appropriate temperature for pollutant desorption and then heating the backwash water for reverse high-pressure rinsing is as follows: Construct an adsorbent-pollutant matching parameter library. This library contains all pollutant combination types for target purification, and each pollutant combination type has a preset set of pollutant desorption matching temperature ranges. Substitute the target pollutant combination type corresponding to the current moment into the adsorbent-pollutant matching parameter library, match the corresponding pollutant desorption matching temperature range, and heat the backwash water source to that temperature range. High-pressure backwash water is introduced into the adsorption reaction chamber according to the current appropriate backwash intensity, backwash duration and backwash water temperature. The water flow flows in a counter-directional direction along the adsorbent packing layer to remove the accumulated pollutants on the surface and in the pores of the adsorbent. Wastewater containing pollutants generated during rinsing is discharged through a dedicated sewage discharge channel.

9. The method for detecting and controlling organic matter in a water purifier according to claim 8, characterized in that: The specific process for verifying the desorption rate is as follows: If it passes, the target pollutant combination and suitable temperature are re-matched; if it fails, the test is repeated; if the limit is exceeded, the adsorbent should be replaced. After the backwashing is completed, drain the backwash water from the reaction chamber. When the temperature inside the chamber returns to the appropriate adsorption temperature range, collect the net mass of the adsorbent after desorption and obtain the current cumulative adsorption amount before backwashing. Calculate the difference between the total mass of pollutants adsorbed before backwashing and the net mass of the adsorbent after desorption, and compare it with the cumulative adsorption amount to obtain the pollutant desorption rate. If the desorption rate is greater than or equal to the corresponding preset threshold, the regeneration is deemed qualified; otherwise, the complete process of morphological transformation insulation, viscosity coefficient detection, backwashing parameter adaptation and high-pressure backwashing is repeated until the desorption rate meets the standard. If the target is not met after more than the preset threshold number of re-executions, an adsorbent replacement prompt will be triggered. Once the regeneration-backwashing synergistic procedure or adsorbent replacement is completed, the target pollutant combination type and optimal suitable adsorption temperature range corresponding to the current moment are rematched, and the internal temperature of the adsorption reaction chamber is heated to the optimal suitable adsorption temperature range.

10. An organic matter detection and control system for a water purifier, applied to the organic matter detection and control method for a water purifier proposed in any one of claims 1-9, characterized in that, include: Pollutant identification and targeted adsorption module: Water samples are collected from the water body to be purified, an initial feature vector is constructed, and the principal component feature vector is obtained by dimensionality reduction using principal component analysis; the target pollutant combination type is determined by matching with the pollution source fingerprint database, and the optimal suitable adsorption temperature range is matched accordingly, and the temperature of the reaction chamber is adjusted to enable the adsorbent to target adsorption. Adsorption saturation monitoring trigger module: Collects the total mass of adsorbed contaminants of the supramolecular porous adsorbent, and simultaneously obtains the net mass of the adsorbent, plots the change in adsorbed contaminant mass, extracts adsorption saturation evaluation parameters and performs comprehensive analysis to obtain a comprehensive adsorption saturation judgment value; based on this, it determines whether the adsorbent is saturated. If it is saturated, it triggers the regeneration-backwashing collaborative program. Regeneration backwash desorption verification module: When the regeneration-backwash collaborative program is triggered, the water inlet passage is cut off, the water to be purified remaining in the reaction chamber is drained, the temperature of the reaction chamber is adjusted to change the form of the adsorbent, and the viscosity coefficient of the pollutants is detected simultaneously to analyze and adapt the backwash intensity and duration. Match the appropriate temperature for pollutant desorption, heat the backwash water and then perform a high-pressure reverse flush; verify the desorption rate, if it is qualified, rematch the target pollutant combination and reset the appropriate temperature, if it is not qualified, repeat the test, and if it exceeds the limit, prompt to replace the adsorbent.

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