Photocatalytic disinfection efficiency evaluation system based on dynamic detection of microbial activity

Abstract

The invention provides a photocatalytic disinfection efficiency evaluation system based on microbial activity dynamic detection, and relates to the technical field of photocatalytic material detection, the photocatalytic disinfection efficiency evaluation system comprises a signal acquisition module used for acquiring fluorescence intensity signals of live microorganisms and dead microorganisms of a microbial sample subjected to photocatalytic treatment in real time; the signal stability control module is used for calculating a variable coefficient of a fluorescence intensity signal according to a sliding time window and automatically adjusting the injection flow rate of a fluorescent reagent based on an incremental PID control algorithm; the survival rate calculation module is used for carrying out unmixing on the stabilized signals by adopting a constrained least square method based on a pre-calibrated fluorescence signal unmixing matrix so as to obtain an instantaneous survival rate time sequence of the microorganisms in real time; and the efficiency evaluation module is used for fitting the time sequence data to a primary inactivation mechanical model, and solving an inactivation rate constant and a confidence interval and a goodness of fit index thereof. According to the invention, real-time, on-line and standardized quantitative evaluation of the photocatalytic disinfection efficiency is realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of photocatalytic material detection, in particular to a photocatalytic disinfection efficiency evaluation system based on dynamic detection of microbial activity. BACKGROUND

[0002] The photocatalytic disinfection technology is a technology that uses photocatalytic materials (such as nano-titanium dioxide) to generate reactive oxygen species under light to inactivate microorganisms (such as viruses and bacteria), and is widely used in air purification, water treatment and medical environment disinfection; The existing photocatalytic disinfection efficiency evaluation methods mainly rely on plate culture method or offline fluorescence detection method, which has the following disadvantages: long detection period (usually 24-72 hours), dependence on manual sampling and observation, inability to capture the dynamic changes of microbial activity in real time, resulting in the lack of dynamic measurement of the disinfection process; lack of fluorescence signal stability control mechanism, fluorescence intensity signal is easily affected by external disturbances such as light intensity and environmental temperature, resulting in large deviation in survival rate calculation, it is not easy to form precise quantitative evaluation index (such as inactivation rate constant), the evaluation process has no unified standard, the data comparability is poor, and there is no data reliability verification link, it is difficult to accurately evaluate the related photocatalytic materials. SUMMARY

[0003] The technical problem to be solved by the present application is to provide a photocatalytic disinfection efficiency evaluation system based on dynamic detection of microbial activity, which realizes real-time, online and quantitative evaluation of photocatalytic disinfection efficiency by constructing a system integrating dynamic detection, intelligent control and automatic evaluation.

[0004] To solve the above technical problems, the technical scheme of the present application is as follows: A photocatalytic disinfection efficiency evaluation system based on dynamic detection of microbial activity, comprising: A signal acquisition module for acquiring the fluorescence intensity signal of the microorganism sample treated by photocatalysis in real time, wherein the fluorescence intensity signal includes the fluorescence intensity signal of living microorganisms and the fluorescence intensity signal of dead microorganisms; A signal stability control module for calculating the coefficient of variation of the fluorescence intensity signal with a sliding time window, comparing the coefficient of variation of the current sliding window with a preset stability threshold, and automatically adjusting the injection flow rate of the fluorescence reagent based on the incremental PID control algorithm when the coefficient of variation exceeds the stability threshold, and continuously monitoring the coefficient of variation until the coefficient of variation falls below the stability threshold; A survival rate calculation module for calculating the instantaneous survival rate of microorganisms in real time based on a pre-calibrated fluorescence signal demixing matrix, and forming time series data of the instantaneous survival rate changing with time by using the constrained least squares method to demix and calculate the stabilized fluorescence intensity signal. an efficiency evaluation module configured to fit the time series data and corresponding photocatalysis time to a first-order inactivation kinetics model, solve an inactivation rate constant by a non-linear least square method, and calculate a confidence interval and a goodness-of-fit index of the inactivation rate constant; wherein the inactivation rate constant is output as a quantitative evaluation index of photocatalytic disinfection efficiency.

[0005] Further, the preset manner of the stability threshold comprises: obtaining historical fluorescence intensity signal data of a representative microorganism sample under a plurality of photocatalytic treatment conditions, the photocatalytic treatment conditions at least including different light intensities, initial concentrations of microorganisms, and characteristic parameters of catalytic materials, the characteristic parameters including specific surface area and band gap; using a support vector machine regression algorithm with a radial basis function as a kernel function to establish a prediction model between the photocatalytic treatment conditions and the stability threshold; and calculating a corresponding stability threshold for preset parameters of a target photocatalytic application scene based on the prediction model.

[0006] Further, the incremental PID algorithm further comprises an adaptive adjustment mechanism of the integral term: According to the absolute value of the difference between the coefficient of variation and the stability threshold, a plurality of consecutive deviation intervals are predefined, and an integral coefficient value corresponding to each interval is set; wherein the larger the interval where the absolute value of the deviation is located, the larger the integral coefficient value corresponding to it is set; In each control period, according to the specific interval where the real-time absolute value of the deviation is located, the corresponding integral coefficient value is selected and substituted into the calculation process of the incremental PID algorithm to generate a control instruction for adjusting the injection flow rate of the fluorescence reagent.

[0007] Further, the signal stability control module further comprises a feedforward compensation unit for real-time monitoring of at least one external process parameter that causes instantaneous disturbance to the fluorescence labeling efficiency, the external process parameters including light intensity and environmental temperature in the photocatalytic reaction area; a compensation model between the change amount of the external process parameter and the compensation amount of the fluorescence reagent injection flow rate is established in advance through step response test; when the change of any of the external process parameters is monitored to exceed a preset threshold, the feedforward compensation amount is calculated according to the compensation model, and the final control instruction is generated based on the feedforward compensation amount and the feedback control amount calculated by the incremental PID control algorithm to compensate for the instantaneous disturbance of the photocatalytic environment.

[0008] Further, the prediction model is further configured to: predicting an expected coefficient of variation of the fluorescence intensity signal in real time according to current photocatalytic processing conditions; taking the expected coefficient of variation as prior information for adjusting an adaptive adjustment mechanism of an integral term in the incremental PID control algorithm, specifically including: based on the size of the expected coefficient of variation, presetting interval division of absolute value of deviation and corresponding integral coefficient value to enhance integral response sensitivity when expected signal fluctuation is large; multiplying the scaling weight and the feedforward compensation calculated by the compensation model to obtain a final feedforward compensation.

[0009] Further, it also includes a reliability self-evaluation module for grading the reliability of the inactivation rate constant based on the goodness of fit index and the width of the confidence interval; when the reliability grade is lower than a preset standard, automatically backtracking and analyzing the stability control process of the fluorescence intensity signal, outputting potential causes leading to the decrease of the reliability grade; outputting the reliability grade, potential causes and the inactivation rate constant, and generating a comprehensive photocatalytic disinfection efficiency evaluation result with data quality self-evaluation information; the potential causes include: the coefficient of variation of the fluorescence intensity signal continuously exceeds the stability threshold within the photocatalytic action time; the adjustment frequency or amplitude of the incremental PID control algorithm exceeds a preset range; after the step change of the external process parameter, the coefficient of variation of the fluorescence intensity signal continuously exceeds the stability threshold for a time longer than a preset stability time threshold.

[0010] Further, it also includes a decision output module for generating operation instructions for adjusting the photocatalytic reaction process based on the inactivation rate constant and its corresponding reliability grade and potential causes; wherein when the reliability grade is rated as high and the inactivation rate constant is lower than a predefined target threshold, a first type of control instruction is generated, which is configured to directly adjust the operation parameter of the photocatalytic reaction to enhance the reaction intensity; when the reliability grade is rated as low, a second type of control instruction is generated, which is configured to preferentially trigger a subsystem review process directly associated with the potential causes.

[0011] Further, a computer device includes a memory and a processor, the memory stores a computer program, and the processor executes the computer program to realize the functions of each module of a photocatalytic disinfection efficiency evaluation system based on dynamic detection of microbial activity.

[0012] Furthermore, a computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, implements the functions of various modules of a photocatalytic disinfection efficacy evaluation system based on dynamic detection of microbial activity.

[0013] The above-described solution of the present invention has at least the following beneficial effects: The above-mentioned solution of the present invention acquires microbial fluorescence signals in real time through a signal acquisition module and performs instantaneous calculations in conjunction with a survival rate calculation module, overcoming the shortcomings of traditional offline detection, such as long detection cycles and inability to reflect dynamic processes, and realizing continuous capture of changes in microbial activity. The signal stability control module monitors the coefficient of variation through a sliding window and automatically adjusts the reagent flow rate using an incremental PID algorithm, effectively suppressing internal system fluctuations; the feedforward compensation unit performs predictive compensation for external environmental disturbances, significantly improving signal quality and system robustness. The efficacy evaluation module fits the survival rate sequence to a first-order kinetic model, outputs the inactivation rate constant, its confidence interval, and the goodness of fit, forming a unified evaluation standard with clear physical meaning and strong comparability. The system predicts the optimal stability threshold through a machine learning model and adopts an integral term adaptive adjustment and parameter dynamic adjustment mechanism, which can adapt to different media, microorganisms and catalytic material conditions, thereby improving the system's applicability and intelligence level. The reliability self-assessment module grades the results and traces the causes. The decision output module automatically generates optimization instructions or system review instructions based on the assessment results, which improves the system's usability and the confidence of the results. Attached Figure Description

[0014] Fig. 1 This is a block diagram of the overall structure of a photocatalytic disinfection efficiency evaluation system based on dynamic detection of microbial activity provided by the present invention.

[0015] Fig. 2 This is a flowchart of the signal stability control logic provided by the present invention.

[0016] Fig. 3 This is a fluorescence intensity spectrum with each labeled point representing the mean ±1σ under typical calibration. Detailed Implementation

[0017] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0018] likeFigs. 1 to 3 As shown, the embodiment of the present application proposes a photocatalytic disinfection efficiency evaluation system based on dynamic detection of microbial activity, comprising: A signal acquisition module is configured to acquire the fluorescence intensity signals of the microorganism samples subjected to photocatalytic treatment in real time, wherein the fluorescence intensity signals include the fluorescence intensity signals of living microorganisms and the fluorescence intensity signals of dead microorganisms. A signal stability control module is configured to calculate the coefficient of variation of the fluorescence intensity signals in a sliding time window, compare the coefficient of variation of the current sliding window with a preset stability threshold, and automatically adjust the injection flow rate of the fluorescence reagent based on an incremental PID control algorithm when the coefficient of variation exceeds the stability threshold, and continuously monitor the coefficient of variation until the coefficient of variation falls below the stability threshold. A survival rate calculation module is configured to calculate the fluorescence signal demixing matrix based on pre-calibration, demix the stabilized fluorescence intensity signals by using the constrained least squares method, obtain the instantaneous survival rate of the microorganisms in real time, and form time series data of the instantaneous survival rate changing with time. An efficiency evaluation module is configured to fit the time series data and the corresponding photocatalytic action time to a first-order inactivation kinetics model, solve the inactivation rate constant by using a nonlinear least squares method, and calculate the confidence interval of the inactivation rate constant and the goodness-of-fit index. The inactivation rate constant is output as a quantitative evaluation index of the photocatalytic disinfection efficiency.

[0019] In the embodiment of the present application, the photocatalytic disinfection efficiency evaluation system based on dynamic detection of microbial activity is composed of a photocatalytic reaction and fluorescence detection integrated device and a central processing unit. The central processing unit includes four functional modules connected through a data bus: a signal acquisition module, a signal stability control module, a survival rate calculation module, and an efficiency evaluation module, which are applied to the evaluation of the photocatalytic disinfection efficiency of photocatalytic materials such as nano-titanium dioxide on liquid and gas fluid media.

[0020] For liquid media, the fluorescence detection integrated device adopts a transparent quartz reaction tube, the inner wall of which is loaded with a nano-titanium dioxide photocatalytic coating, and an ultraviolet LED light source is arranged around the periphery. The microorganism-containing liquid to be treated, such as an E. coli suspension with an initial concentration of 1×10 6 CFU / mL, is pumped at a constant flow rate and flows through the reaction tube to undergo a photocatalytic disinfection reaction under ultraviolet light.

[0021] A mixing zone is arranged downstream of the reaction tube. SYTO9 solution and iodinated propylidium solution are injected into the fluid synchronously and mixed by two independent micro-injection pumps. The microorganisms flowing through are stained with fluorescent dyes for live and dead microorganisms. The mixed liquid then enters an online fluorescence detection cell, and the fluorescence signals are collected by a double-channel fluorescence sensor in real time. The green fluorescence channel has a central wavelength of 510 nm, corresponding to the live bacteria signal. The red fluorescence channel has a central wavelength of 617 nm, corresponding to the dead bacteria signal. The sampling frequency is set to 1 Hz.

[0022] In this embodiment, the parameters of the transparent quartz reaction tube are as follows: inner diameter: 20 mm; reaction tube length: 300 mm; Effective volume: 94 ml; fluid flow rate: 1-10 ml / min; photocatalytic coating: nano-titanium dioxide coating thickness of 500 nm±50 nm. The parameters of the online fluorescence detection cell and the double-channel fluorescence sensor are based on the fluorescence detection principle to ensure the accuracy and sensitivity of real-time signal acquisition. The detection size is the optical path length of 10 mm and the volume of 0.5 ml.

[0023] The signal stability control module runs in the embedded controller. A sliding time window is set, which is 10 s in this embodiment. The coefficient of variation of the red fluorescence signal intensity is continuously calculated. In this embodiment, the preset stability threshold is 5%. When the real-time calculated coefficient of variation exceeds the stability threshold, the signal stability control module immediately starts the incremental PID control algorithm, generates an adjustment instruction, and dynamically adjusts the flow rate of the micro-injection pump corresponding to the iodinated propylidium solution. In this embodiment, the flow rate adjustment range is 1 to 100 μL / min. The control period is set to 1 second. This adjustment process continues until the calculated coefficient of variation is continuously less than the stability threshold of 5% for 5 cycles, i.e., 5 seconds.

[0024] The survival rate calculation module receives the double-channel fluorescence signals after stability control. Based on the 2x2 fluorescence signal unmixing matrix obtained by standard sample calibration in advance, the calibration method of the unmixing matrix is as follows: in the same liquid medium, the same staining process and the same detection conditions as the actual test, pure live bacteria samples without photocatalytic treatment and pure dead bacteria samples inactivated by high temperature or chemical methods are prepared respectively. The double-channel fluorescence intensity is measured to obtain the live bacteria response vector and the dead bacteria response vector, which constitute the unmixing matrix. In actual calculation, the fluorescence intensity signal vector measured at each time is solved by using the constrained least squares method, and the constraint condition of the algorithm is that the calculated live and dead bacteria fluorescence contribution proportions are not less than zero. The instantaneous survival rate is defined as the ratio of the live bacteria fluorescence contribution proportion to the sum of the live and dead bacteria fluorescence contribution proportions. The survival rate calculation module outputs an instantaneous survival rate data point every second to form a time series data of the survival rate.

[0025] For example, in a typical calibration, based on a typical fluorescence intensity range, unit AU, the average value is taken from multiple repeated experiments, and the standard deviation is considered to ensure reliability. When calibrating, it is necessary to ensure that the fluorescence intensity is within the linear response range of the detector, such as 0-2000 AU, to reduce the effects of saturation or noise. The signal intensity of a pure live bacteria sample without photocatalytic treatment in the green fluorescence channel = 1000 ± 50 AU, there is a small amount of cross-fluorescence signal intensity = 80 ± 10 AU in the red fluorescence channel; there is a small amount of cross-fluorescence signal intensity = 150 ± 20 AU in the green fluorescence channel, and the signal intensity = 900 ± 30 AU in the red fluorescence channel.

[0026] Based on the above calibration data, a demixing matrix is constructed to describe the linear relationship between the fluorescence intensity signal and the contribution of the bacterial community. The measured fluorescence intensity signal vector : ; where G is the green channel fluorescence signal intensity, is the red channel fluorescence signal intensity, and T is the transpose symbol here; The contribution vector H: H= : where is the live bacteria contribution ratio, is the dead bacteria contribution ratio, and T is the transpose symbol.

[0027] Satisfies .

[0028] The demixing matrix F: ; In actual calculation, for each time measured fluorescence intensity signal vector , the constrained least squares method is used to solve , that is, to minimize , with the constraint conditions ≥ 0 and > 0.

[0029] Instantaneous survival rate SR: ; For example, the measured value at a certain time is G = 600 AU, I = 500 AU. Solve the equation group: ; Using the constrained least squares method, such as the lsqlin function of MATLAB, we can get ≈0.55, ≈0.45, survival rate SR ≈ 0.55. Output one data point per second, forming a time series.

[0030] The performance evaluation module fits the time series data of survival rate to a first-order inactivation kinetics model, which represents the natural exponential decay relationship between microbial survival rate and photocatalytic reaction time. The nonlinear least squares method is used to fit and solve the inactivation rate constant, and the confidence interval of the inactivation rate constant is calculated simultaneously, such as 95% and the goodness-of-fit indicators such as the coefficient of determination R 2 . Finally, the inactivation rate constant is output as a quantitative evaluation index of photocatalytic disinfection efficiency.

[0031] For gaseous medium, the fluorescence detection integrated device uses a stainless steel flow cavity with a titanium dioxide photocatalytic coating on the inner wall, and a UV lamp tube is installed inside the cavity. Air containing microbial aerosol passes through the cavity at a flow rate of 0.5-2 m 3 / h, and undergoes photocatalytic inactivation under UV light.

[0032] At the outlet of the cavity, an atomizing mixer is provided to pump the mixed aqueous solution of SYTO9 solution and propidium iodide solution into the atomizer through a liquid supply pump, atomize it into micron-sized droplets, and uniformly mix it with the outgoing aerosol for in-situ fluorescence labeling of the microorganisms in the air. The mixed airflow then enters the aerosol fluorescence detector based on the principle of laser-induced fluorescence. The detector excites, detects, and integrates the intensity of the dual-color fluorescence of each passing microbial particle in the detection zone, outputting fluorescence intensity signals in the 510 nm and 617 nm channels, with a sampling frequency of 1 Hz.

[0033] In this embodiment, the photocatalytic reaction cavity is a stainless steel cavity with an inner diameter of 100 mm, a length of 300 mm, and a total volume of about 2.35 L. The aerosol is allowed to stay in the reaction cavity for 2-8 seconds to meet the photocatalytic reaction requirements. The cavity is equipped with a UV lamp tube with a wavelength of 365 nm and a power of 15 W, and the light intensity can be adjusted in the range of 1-5 mW / cm 2 , which can be adjusted by an incremental PID control algorithm. The aerosol flow rate is set to 0.5-2 m 3 / h, corresponding to a linear flow rate of 0.02-0.07 m / s in the cavity to avoid turbulent interference.

[0034] The atomizer generates droplets with a particle size distribution of 1-5 μm, with a median particle size D50 = 2.5 μm, to ensure efficient mixing of droplets and microbial aerosol. The liquid supply pump uses a micro-ceramic pump with a flow rate adjustable in the range of 0.05-0.5 ml / min, and the residence time of the atomized droplets and aerosol in the mixing zone is more than 0.5 s, with a mixing uniformity variation coefficient of less than 5%.

[0035] The laser-induced fluorescence detector uses a 488 nm solid-state laser with a power of 20 mW, and the focused spot diameter is less than 10 μm.

[0036] It should be noted that the numerical characteristics of the two-channel fluorescence intensity signals output by the aerosol fluorescence detector are consistent with the fluorescence signals output by the liquid system after calibration by the central processing unit of the system, and can be directly input into the survival rate calculation module for demixing processing.

[0037] The signal stability control module uses the same core control logic as the liquid system: the coefficient of variation of the red fluorescence signal is calculated with a 10-second sliding time window, and the stability threshold is set to 5%. When the coefficient of variation exceeds the stability threshold, the adjustment command generated by the incremental PID control algorithm is used to adjust the flow rate of the liquid supply pump of the atomizer. In this embodiment, the adjustment range is 0.05-0.5 mL / min.

[0038] The algorithm logic and implementation of the survival rate calculation module and the performance evaluation module are the same as those of the liquid system: the survival rate calculation module calculates the instantaneous survival rate sequence in real time based on the pre-calibrated demixing matrix and the constrained least squares method, and the calibration method is as follows: under the same conditions as the aerosol generation method, atomization marker parameters, and single-particle detection process used in actual gas detection, aerosol samples containing only live bacteria and only dead bacteria are prepared, and their two-channel fluorescence intensities are measured to obtain the demixing matrix of the gas system; the performance evaluation module performs kinetic model fitting on the sequence and outputs the inactivation rate constant and its confidence interval, fitting goodness, and other reliability indicators.

[0039] By adapting to the specific integrated device for liquid or gas medium, and maintaining consistent signal stability control, survival rate calculation, and performance evaluation algorithms in the central processing unit, the dynamic, online, and quantitative evaluation of photocatalytic disinfection performance in two different fluid media is achieved. The inactivation rate constant output by the system as a standardized indicator has good comparability and repeatability.

[0040] The preset method of the stability threshold includes: Obtain historical fluorescence intensity signal data of representative microbial samples under various photocatalytic treatment conditions, including at least different light intensities, initial concentrations of microorganisms, and characteristic parameters of catalytic materials, including specific surface area and band gap; use a support vector machine regression algorithm with a radial basis function as the kernel function to establish a prediction model between the photocatalytic treatment conditions and the stability threshold; and calculate the corresponding stability threshold based on the prediction model and the preset parameters of the target photocatalytic application scenario.

[0041] In the embodiments of the present application, a plurality of photocatalytic materials such as nano-titanium dioxide, zinc oxide, and cadmium sulfide are selected, and a combination experiment is performed under different processing conditions.

[0042] The conditional factors include: light intensity, such as 1, 2, 5 mW / cm 2 , initial concentration of microorganisms, such as 1×10 5 , 1×10 6 , 1×10 7 CFU / mL.

[0043] For each photocatalytic material, the characteristic parameters related to the photocatalytic sterilization activity are measured and recorded, and in the embodiments of the present application, the characteristic parameters include the specific surface area, which is measured by the BET method, and the unit is m 2 / g, and the band gap, which is calculated by the UV-Vis DRS spectrum, and the unit is eV.

[0044] For each group of conditions, including light intensity, initial concentration of microorganisms, material specific surface area, and material band gap, an automatic search algorithm such as grid search is used to try a plurality of candidate stability thresholds within a preset range, such as 1% to 10%. For each candidate stability threshold, a complete evaluation process is run. A unified signal quality verification standard is set as follows: when the control system runs with the candidate threshold and reaches a stable state, the long-term statistical value of the coefficient of variation of the red fluorescence signal in the subsequent continuous evaluation period, such as 5 minutes after stabilization, is calculated, and the coefficient of variation of the long-term statistics needs to be less than or equal to 3%. Among all the candidate stability thresholds that meet this goal, the one with the largest value is selected as the stability threshold under the working condition. In the embodiments of the present application, the stable state is that the real-time coefficient of variation of the red fluorescence signal is continuously, such as continuously for 5 seconds, below the candidate threshold. Under the premise of ensuring signal quality, the adjustment strength of the controller is reduced as much as possible, thereby improving the robustness of the system. Each historical record contains: light intensity, initial concentration of microorganisms, material specific surface area, material band gap, and stability threshold.

[0045] The required historical data is obtained through offline, artificially guided calibration experiments in the early system development stage. The specific steps are as follows: Step S001, for each specific photocatalytic processing condition, the photocatalytic processing condition includes light intensity, initial concentration of microorganisms, and characteristic parameters of the catalytic material, and the corresponding photocatalytic reaction and detection system is built or configured.

[0046] Step S002, in the case where the signal stability control module has not deployed an automatic threshold, a candidate stability threshold is manually preset by a technician. Then, the system is started to run a complete evaluation process, and during this period, the signal stability control module will use the preset threshold to execute the control logic.

[0047] Step S003, after the system reaches control stability, continuously collect the fluorescence signal in an evaluation period and calculate the long-term statistical value of its coefficient of variation. Observe whether this long-term statistical value meets the predetermined signal quality verification standard.

[0048] Step S004, within a predetermined range, such as within 1% to 10%, manually adjust and try multiple different candidate stability thresholds, repeat steps S002 to S003. Finally, among all candidate thresholds that can meet the signal quality verification standard, select the one with the largest value, and officially record it as the stability threshold label corresponding to the specific photocatalytic treatment condition of this group.

[0049] Step S005, combine each set of experimental conditions: light intensity, initial concentration of microorganisms, specific surface area of materials, and band gap width of materials with its corresponding stability threshold label determined by the above method to form a complete historical data record. By accumulating multiple such records covering a wide range of working conditions, a historical data set for training the prediction model is constructed.

[0050] Prediction model training: Standardize all numerical features in the historical data, and divide the preprocessed data set into training set and test set according to the ratio of 7:3. Take light intensity, initial concentration of microorganisms, specific surface area of materials, and band gap width of materials as input features, and the corresponding stability threshold as prediction target. Adopt support vector machine regression algorithm with radial basis function as kernel function to train the prediction model on the training set. Optimize the key hyperparameters of the model, such as penalty parameter C and kernel function parameter γ, through cross-validation grid search method. Finally, use the independent test set to evaluate the generalization ability of the model, confirm that the predicted stability threshold can effectively guide the control system to achieve the signal quality goal, i.e. the coefficient of variation of fluorescence signal in the evaluation period is less than or equal to 3%. After verification, the prediction model is deployed in the central processing unit.

[0051] When online evaluating the target photocatalytic scene, the system performs the following steps: Parameter input: the user presets the photocatalytic treatment conditions of the current evaluation scene, the parameter range includes: Light intensity: 1-10 mW / cm 2 ; Initial concentration of microorganisms: 1x10 5 -1x10 7 CFU / mL; Specific surface area of photocatalytic materials: 20-200 m 2 / g; Band gap width of materials: 2.5-3.5 eV.

[0052] Training data examples: The historical dataset is obtained through offline calibration experiments, containing multiple sets of photocatalytic treatment conditions and corresponding stability thresholds. For example, four groups of data are arranged according to the light intensity, microbial concentration, specific surface area, band gap, and stability threshold in turn: Historical data 1: 2.0 mW / cm 2 , 1×10 6 CFU / mL, 50 m 2 / g, 3.2 eV, 2.5%; Historical data 2: 5.0 mW / cm 2 , 1×10 5 CFU / mL, 120 m 2 / g, 2.8 eV, 4.2%; Historical data 3: 8.0 mW / cm 2 , 1×10 7 CFU / mL, 80 m 2 / g, 3.0 eV, 5.8%; Historical data 4: 1.5 mW / cm 2 , 1×10 5 CFU / mL, 150 m 2 / g, 3.4 eV, 1.8%; Data standardization uses the Z-score method to make the mean of each feature 0 and the variance 1, avoiding the influence of dimension differences on the model. The dataset size is 2000 groups, divided into training set (1400 groups) and test set (600 groups) according to 7:3. The hyperparameter range optimized by grid search method is based on the typical setting of support vector machine regression: Penalty parameter C: 0.1-100, logarithmic scale, such as 0.1, 1, 10, 100; Kernel function parameter γ: 0.001-1, bandwidth parameter of radial basis function kernel, corresponding to the sensitivity of feature influence.

[0053] The independent test set is used to evaluate the generalization ability of the model, and the typical output results are as follows: Determination coefficient R 2 : 0.92-0.96, indicating that the model can explain more than 92% of the stability threshold variation; Root mean square error: 0.3%-0.5%, average deviation of predicted threshold and actual threshold; Mean absolute error: 0.2%-0.4%, absolute average of prediction error.

[0054] After deployment, the model's prediction example for the target scene is as follows: Input: light intensity is 3.0 mW / cm2 microbial concentration of 2 x 10 6 CFU / mL, specific surface area of 90 m 2 / g, and band gap of 3.1 eV; Output: Predicted stability threshold = 3.2%; Verification result: With the threshold control signal stability, the coefficient of variation of the fluorescence signal within the evaluation period is reduced to 2.8%, meeting the target of ≤3%.

[0055] The prediction model output is directly used to guide the control system: When the predicted threshold is less than or equal to 3%, the system defaults to signal stability, and the PID control parameters adopt a conservative mode, such as reducing the integral coefficient by 20%; When the predicted threshold is greater than 5%, the system automatically enables enhanced control strategies, such as integral interval subdivision and feedforward compensation weight enhancement, to cope with high fluctuation scenarios.

[0056] Threshold prediction: The central processing unit inputs the above-mentioned preset parameters into the deployed prediction model to obtain the corresponding predicted stability threshold, such as 2.2%.

[0057] Threshold application: The signal stability control module uses the predicted value, such as 2.2%, as the stability threshold for this evaluation, and performs incremental PID control to adjust the injection flow rate of the fluorescent reagent.

[0058] The incremental PID algorithm also includes an adaptive adjustment mechanism for the integral term: According to the absolute value of the difference between the coefficient of variation and the stability threshold, multiple consecutive deviation intervals are predefined, and each interval is assigned a corresponding integral coefficient value; the larger the interval of the absolute value of the deviation, the larger the corresponding integral coefficient value is set; In each control cycle, according to the specific interval of the real-time deviation absolute value, the corresponding integral coefficient value is selected and substituted into the calculation process of the incremental PID algorithm to generate a control instruction for adjusting the injection flow rate of the fluorescent reagent.

[0059] In the embodiment of the present application, the integral term adaptive adjustment mechanism is implemented in the embedded controller, and the deviation definition and interval division: Define real-time deviation e: Calculate the absolute value of the difference between the coefficient of variation of the current red fluorescence signal and the preset stability threshold.

[0060] Divide the consecutive deviation intervals: Predefine multiple consecutive deviation absolute value intervals. In this embodiment, three intervals are divided: Small deviation interval: 0 ≤ e < 1% Medium deviation interval: 1% ≤ e < 3% Large deviation interval: e ≥ 3% Adaptive setting of integral coefficient: Set a corresponding integral coefficient for each deviation interval The larger the interval of the absolute value of the deviation, the larger the value of the corresponding integral coefficient is set. By increasing the integral coefficient, the correction speed of the accumulated error can be accelerated when the signal fluctuation is large, thereby shortening the system response time. When the deviation is small, the integral action is weakened to avoid overshoot.

[0061] It should be noted that: this embodiment provides a feasible parameter configuration example to help understanding, in the actual system, the specific integral coefficient value needs to be determined by conventional engineering setting method. The core of the present application lies in the adaptive adjustment mechanism itself, rather than the specific coefficient value.

[0062] Online adaptive adjustment process: In each control period, such as 1s, the signal stability control module performs the following steps: Step S301, calculate the real-time deviation absolute value e, and judge the preset deviation interval to which e belongs; Step S302, according to the interval to which e belongs, select the corresponding integral coefficient ; Step S303, substitute the selected integral coefficient value into the calculation process of the incremental PID control algorithm, to calculate the adjustment instruction of the fluorescence reagent injection flow rate; Step S304, send the adjustment instruction to the actuator, such as peristaltic pump, to control the increase or decrease of the injection flow rate, thereby suppressing signal fluctuation.

[0063] For example, the current stability threshold is 5%. In a certain control period, the real-time calculated coefficient of variation of red fluorescence signal is 7.5%. The real-time deviation absolute value e=2.5% is calculated. According to the preset interval, 2.5% belongs to the medium deviation interval. Therefore, the control system automatically selects and applies the integral coefficient set for this interval. Under this integral coefficient, the algorithm will generate an adjustment instruction to drive the peristaltic pump to increase the injection flow rate of the fluorescence reagent with moderate force, thereby effectively and quickly pulling the large fluctuation signal back to the target threshold value.

[0064] The signal stability control module also includes a feedforward compensation unit, which is used to monitor at least one external process parameter that causes instantaneous disturbances to the fluorescent labeling efficiency in real time. The external process parameters include the light intensity and ambient temperature in the photocatalytic reaction area. Through step response testing, a compensation model is pre-established between the change in the external process parameter and the compensation amount of the fluorescent reagent injection flow rate. When the change of any external process parameter exceeds the preset threshold, the feedforward compensation amount is calculated according to the compensation model, and the final control command is generated based on the feedforward compensation amount and the feedback control amount calculated by the incremental PID control algorithm to compensate for the instantaneous disturbances in the photocatalytic environment.

[0065] In this embodiment of the invention, the feedforward compensation unit is set in the signal stability control module and runs in parallel with the incremental PID control algorithm. The compensation model is established through offline calibration and applied in online control to quickly feedforward compensate for external disturbances such as illumination and temperature, thereby improving the system signal stability.

[0066] Establish the change in light intensity and changes in ambient temperature Compensation amount for fluorescent reagent flow rate The compensation model between them is as follows: When the system is in a steady state, a known step change is applied to one of the external parameters, such as the light intensity, from 2 mW / cm². 2 Increased to 3mW / cm 2 ,Right now =+1mW / cm 2 Meanwhile, the injection flow rate of the fluorescent reagent was kept constant. The steady-state change in fluorescence intensity signal caused by the perturbation was recorded. Then, the reagent injection flow rate was manually adjusted stepwise until the signal returned to the steady-state level before the perturbation. The amount of flow rate change required for this process was recorded; this is the compensation amount required to counteract the perturbation. Multiple sets of data were obtained through multiple step tests of different amplitudes. , Using standard fitting algorithms such as linear regression on the data, the estimated value of the compensation coefficient can be determined. .

[0067] Estimated value of ambient temperature compensation coefficient The calibration follows the same principle and procedure: when the system is in another stable state, a known step change is applied to the ambient temperature, such as raising the ambient temperature from 25°C to 28°C using a constant temperature chamber. Meanwhile, the injection flow rate of the fluorescent reagent was kept constant. The steady-state change of the fluorescence intensity signal caused by temperature perturbation was recorded. Subsequently, the reagent injection flow rate was manually adjusted step by step until the signal returned to the steady-state level before the perturbation.

[0068] According to the light intensity variation amount ΔL, the flow rate compensation amount The typical test data arranged in order of light intensity variation amount ΔL, flow rate compensation amount and corresponding remarks are as follows: Test data 1: 2.0 mW / cm 2 to 3.0 mW / cm 2 , ΔL = +1.0 mW / cm 2 , =+0.48 μL / min; Test data 2: 2.0 mW / cm 2 to 4.0 mW / cm 2 , ΔL = +2.0 mW / cm 2 , =+0.95 μL / min; Test data 3: 2.0 mW / cm 2 to 1.5 mW / cm 2 , ΔL =-0.5 mW / cm 2 , =-0.24 μL / min; Test data 4: 2.0 mW / cm 2 to 3.5 mW / cm 2 , ΔL = +1.5 mW / cm 2 , =+0.72 μL / min; Test data 5: 2.0 mW / cm 2 to 1.0 mW / cm 2 , ΔL =-1.0 mW / cm 2 , =-0.50 μL / min; The fluorescent reagent is propidium iodide solution, the initial flow rate is 10 μL / min; the microbial sample is Escherichia coli, and the photocatalytic reaction temperature is controlled at 25℃. After each step, 5 minutes of stabilization is performed, the red fluorescence channel signal intensity is recorded, the flow rate is manually adjusted until the coefficient of variation is less than 3, the flow rate compensation amount is the average of 3 repeated tests, the linear regression adopts the least square method, and the fitting relationship with ΔL is obtained.

[0069] Temperature compensation coefficient The calibration process is consistent with the light compensation, and the temperature step change is controlled through a thermostat.

[0070] The typical test data are as follows: Test data 6: 25℃ to 28℃, ΔT = +3.0℃, =+0.45 μL / min; Test data 6: 25℃ to 28℃, ΔT = +5.0℃, =+0.75μL / min; Test data 6: 25℃ to 28℃, ΔT = -2.0℃, =-0.30μL / min; Test data 6: 25℃ to 28℃, ΔT = +4.0℃, =+0.60μL / min; Test data 6: 25℃ to 28℃, ΔT = -3.0℃, =-0.45μL / min; Test conditions are the same as light compensation, to ensure consistency. Temperature step is achieved by thermostat, data processing method is the same as light compensation, linear regression uses least square method, fitting relationship with ΔL; Obtain multiple sets of data (a, b) , ) using the same linear regression algorithm, etc. fitting algorithm, you can determine the temperature compensation coefficient estimate In this embodiment, the linear model is used as the basic form of the compensation model, and the parameters of the compensation model are stored in the controller: Light intensity compensation amount : = × ; Ambient temperature compensation amount : = × .

[0071] When the system shows significant nonlinear characteristics in a wide working range, piecewise linearization method can be used, which is: dividing the value range of disturbance change, such as light intensity change ΔL, into multiple continuous intervals, and independently calibrating a linear compensation coefficient for each interval. In online application, the system automatically selects the coefficient corresponding to the interval where the real-time monitored ΔL value falls to calculate.

[0072] In each control cycle, such as 1s, the following steps are executed: Step S401, real-time read the measurement values of light and temperature sensors, calculate the change amount with the last cycle and . If the change amount exceeds the preset perception threshold, such as |>0.5mW / cm 2 or |>1.0℃), the feedforward compensation amount is calculated according to the above compensation model immediately; Step 402, the incremental PID algorithm in parallel with the feedforward compensation unit calculates the feedback control amount according to the deviation of the fluorescence signal coefficient of variation and the stability threshold, which is used to eliminate the overall residual error of the control system and perform accurate calibration; Step 403, superimpose the feedforward compensation amount and the feedback control amount to generate the final control instruction and send it to the peristaltic pump.

[0073] For example, the system has calibrated the illumination compensation coefficient estimate 0.48 (μL / min) / (mW / cm 2 2), the perception threshold 0.5 mW / cm 2 2).

[0074] The initial stable state of the system is the illumination intensity value saved in the last period, which is 2.0 mW / cm 2 2, the fluorescence reagent flow rate is 10.0 μL / min, when the disturbance occurs, the illumination intensity sensor measures the illumination intensity value in the current period as 2.9 mW / cm 2 2, then the illumination intensity change is +0.9 mW / cm 2 2, the preset illumination intensity change exceeds the perception threshold, the feedforward compensation unit immediately calculates the feedforward compensation amount = 0.48 x 0.9 ≈ +0.43 μL / min, at this time the fluorescence intensity signal has not fluctuated, so the feedback control amount is close to 0 at this time, so the final control instruction is to adjust the fluorescence reagent flow rate to 10.43 μL / min. This predictive compensation action is completed before the illumination enhancement causes the fluorescence intensity signal to drop significantly, and after the subsequent fluorescence fluctuation, the feedback control amount is further used to suppress the fluorescence intensity signal fluctuation to maintain the stability of the fluorescence intensity signal.

[0075] The prediction model is also configured to: Real-time predict the expected coefficient of variation of the fluorescence intensity signal according to the current photocatalytic treatment condition; use the expected coefficient of variation as prior information to adjust the adaptive adjustment mechanism of the integral term in the incremental PID control algorithm, specifically including: Based on the size of the expected coefficient of variation, preset the interval division of the deviation absolute value and the corresponding integral coefficient value to enhance the integral response sensitivity when the expected signal fluctuation is large; multiply the scaling weight by the feedforward compensation amount calculated by the compensation model to obtain the final feedforward compensation amount.

[0076] In the embodiment of the present application, the expected coefficient of variation CV is used to quantitatively represent the intrinsic fluctuation level of the fluorescence intensity signal without active control under the current specific photocatalytic treatment condition.

[0077] The expected coefficient of variation CV is obtained as follows: The system models and calibrates the typical operating conditions of the photocatalytic device in the development stage. Under the open-loop state of closing the feedback control function, the system is operated under a set of representative photocatalytic treatment condition combinations, including light intensity, initial microbial concentration, and catalyst material attribute parameters. After the fluorescence intensity signal enters a natural fluctuation state, the corresponding coefficient of variation is measured and recorded as the expected coefficient of variation CV under this working condition. Based on the obtained data set, an expected coefficient of variation prediction model is trained to establish the mapping relationship between the photocatalytic treatment conditions and the expected coefficient of variation CV. Specifically, a regression modeling method is used to establish the mapping relationship between the photocatalytic treatment conditions and the expected coefficient of variation CV. The expected coefficient of variation prediction model takes light intensity, initial microbial concentration, and catalyst material attribute parameters as input variables, and the expected coefficient of variation under the corresponding working condition as output variable, which is trained by supervised learning or regression fitting algorithm, and is used to calculate the expected coefficient of variation CV of the current working condition in real time when the system is online.

[0078] When the system is online, the user inputs the current photocatalytic treatment condition parameters, and the central processing unit calls the trained expected coefficient of variation prediction model to calculate and output the expected coefficient of variation CV corresponding to the current working condition in real time. The central processing unit automatically selects the error deviation interval division scheme according to the output expected coefficient of variation CV, and adjusts the integral term parameters and the feedforward compensation strength.

[0079] When the system is online, the user presets the current photocatalytic treatment condition. The central processing unit calls the above-mentioned expected coefficient of variation prediction model to calculate and output the expected coefficient of variation CV value corresponding to the current working condition in real time.

[0080] The system presets multiple sets of interval division schemes of the absolute value e of the deviation, and automatically switches according to the real-time expected coefficient of variation CV value.

[0081] For example, when CV≤4%, it indicates that the expected fluctuation is gentle, and the basic interval scheme is adopted: Low-grade interval: 0%≤e<1%; Medium-grade interval: 1%≤e<3%; High-grade interval: 3%≤e.

[0082] When CV>4%, it indicates that the expected fluctuation is relatively severe, and the enhanced interval scheme is enabled: Low-grade interval: 0%≤e<0.5%; Low-medium-grade interval: 0.5%≤e<1.5%; Medium-grade interval: 1.5%≤e<2.5%; Medium-high-grade interval: 2.5%≤e<4%; High-grade interval: 4%≤e.

[0083] By more fine interval division to achieve more accurate control in high noise background.

[0084] Configure corresponding integral coefficient value for each interval The principle is: in the expected fluctuation larger working condition, that is, the expected coefficient of variation CV is larger, configure larger integral coefficient value for the same deviation level, to enhance the adjustment of integral term, speed up the signal recovery speed.

[0085] For example, for the absolute value of deviation is 1.5%≤e<2.5%: in the basic scheme, the integral coefficient can be set to 0.02, and in the enhanced scheme, the integral coefficient is set to 0.05.

[0086] In the feedforward compensation control part, in order to further improve the adaptive ability of the system under different fluctuation conditions, a scaling weight W is introduced, which is positively related to the expected coefficient of variation CV. The final feedforward compensation amount is the basic feedforward compensation amount multiplied by the scaling weight W.

[0087] The value of scaling weight W is determined according to the dynamic response characteristics of the system under different disturbance intensities. By adjusting the weight coefficient, the signal overshoot, settling time and steady-state error meet the predetermined performance requirements, so as to realize the balance between disturbance suppression effect and control stability.

[0088] For example, when the expected coefficient of variation CV is less than or equal to 4%, the scaling weight W is set to 1.0 to maintain the original compensation strength.

[0089] When the expected coefficient of variation CV is greater than 4% and less than or equal to 7%, the scaling weight W is set to 1.4.

[0090] When the expected coefficient of variation CV is greater than 7%, the scaling weight W is set to 1.8.

[0091] It should be noted that the corresponding relationship between the scaling weight W and the expected coefficient of variation CV can also be realized by linear function, table lookup or other control parameter tuning means, which is prior art and will not be described in detail here.

[0092] In a typical working condition, the system calculates the expected coefficient of variation CV value as 6.2% according to the user input photocatalytic treatment condition, and determines that it belongs to high fluctuation working condition. The central processing unit automatically enables the enhanced interval scheme, loads the corresponding integral coefficient configuration table, and sets the corresponding scaling weight W to 1.4 according to the expected coefficient of variation CV value.

[0093] In real-time operation process, when it is detected that the external light intensity increases by 0.8mW / cm 2 ​​The feedforward compensation module calculates the compensation flow as 1.0 μL / min according to the basic compensation model. The actual compensation flow output after the scaling weight correction is 1.4 μL / min. At this time, the absolute value of the real-time deviation is 1.8%. The control module determines that the deviation is in the third interval of the enhancement scheme, and the integral coefficient value is 0.05, so that a strong integral adjustment effect is generated to make the signal quickly recover to stability.

[0094] The reliability self-evaluation module is further included, which is used for grading the reliability of the inactivation rate constant based on the goodness of fit index and the width of the confidence interval; when the reliability level is lower than a preset standard, the stability control process of the fluorescence intensity signal is automatically traced back and analyzed, and potential reasons causing the reliability level to decrease are output; the reliability level, the potential reasons and the inactivation rate constant are output, and a comprehensive photocatalytic disinfection and killing efficiency evaluation result with data quality self-evaluation information is generated; The potential reasons include: The coefficient of variation of the fluorescence intensity signal continuously exceeds the stability threshold value within the photocatalysis time; The adjustment frequency or amplitude of the incremental PID control algorithm exceeds the preset range; After a step change in the external process parameter, the coefficient of variation of the fluorescence intensity signal continuously exceeds the stability threshold value for a time longer than a preset stability time threshold value.

[0095] In the embodiment of the present application, the reliability self-evaluation module is additionally arranged in the central processing unit, receives the inactivation rate constant, the coefficient of determination R 2 and the upper and lower limits of the confidence interval output by the efficiency evaluation module, calculates the confidence interval width D, which is the difference between the upper limit and the lower limit of the confidence interval divided by the estimated value of the inactivation rate constant, and jointly determines the reliability of the inactivation rate constant based on the coefficient of determination R 2 and the confidence interval width D: The reliability self-evaluation module divides the reliability of the inactivation rate constant into three levels according to the goodness of fit and the estimation accuracy: High reliability: good fitting and high estimation accuracy. The determination conditions are met at the same time: The coefficient of determination R 2 is greater than or equal to 0.98, and the confidence interval width D is less than or equal to 10%.

[0096] Medium reliability: better fitting and acceptable estimation accuracy. The determination conditions are met at the same time: 0.95≤R 2 <0.98 and the confidence interval width D is less than or equal to 20%.

[0097] Low reliability: none of the conditions of high reliability and medium reliability is met.​

[0098] The threshold value can be adjusted by the user according to the evaluation scene in the system configuration interface. After the evaluation is completed, the system outputs the reliability level together with the original statistical indicators.

[0099] When the reliability level is high reliability, the result is directly output as the final efficiency; when the reliability level is medium reliability or low reliability, the system automatically starts the backtracking analysis process to diagnose the signal and control records in the evaluation period and identify the potential causes leading to the decrease in reliability.

[0100] The backtracking analysis includes three parallel paths: Signal stability analysis: Check the time series of the coefficient of variation of the red fluorescence signal in the evaluation period. If there are consecutive multiple control periods, such as 10 consecutive periods, whose coefficient of variation values all exceed the stability threshold, it indicates that the system has failed to effectively suppress signal noise for a long time, and then it is determined that the signal fluctuation is too large.

[0101] Controller behavior analysis: Analyze the historical record of the fluorescence reagent flow rate adjustment amount. Through sliding window statistics (such as a window length of 20 periods), if one of the following situations is found, it is determined that the controller adjustment is abnormal: Adjustment is too frequent: the number of significant adjustments exceeds the threshold, such as 12 times.

[0102] Controller oscillation: The number of positive and negative sign reversals of the control amount is too large, such as more than 6 times.

[0103] Single adjustment amplitude is too large: The maximum single-step adjustment amount exceeds the safety limit, such as 25% of the set value.

[0104] Feedforward compensation effect analysis: When it is detected that the light intensity or environmental temperature change amplitude exceeds the preset threshold, such as the light change amount exceeding 0.5 mW / cm 2 , the temperature change exceeding 1.0℃, it is determined that external disturbance occurs, and the time required for the fluorescence intensity signal to recover from the disturbance to stability, i.e. the recovery time, is calculated. If the recovery time exceeds the maximum allowed value, such as 15 seconds, it is determined that the compensation response is slow.

[0105] The reliability self-evaluation module generates a structured report based on the above analysis results. If there are multiple potential causes, they are listed in the order of signal fluctuation first, controller abnormality second, and compensation delay third.

[0106] The final report contains the following information: Efficiency indicators: inactivation rate constant, confidence interval, and determination coefficient R 2 .

[0107] Reliability level: high reliability, medium reliability, or low reliability level.

[0108] Potential causes: one or more diagnosed problem descriptions.

[0109] For example, in one evaluation, the inactivation rate constant output by the performance evaluation module is 0.023 , the value of the coefficient of determination R 2 is 0.94, and the confidence interval is [0.021, 0.025].

[0110] The reliability level is low reliability, and the backtracking diagnostic analysis finds that the signal coefficient of variation sequence has been continuously exceeding the stability threshold of 5% for 12 periods, and it is determined that the signal fluctuation is too large. The output adjustment frequency and adjustment amplitude of the incremental PID controller are within the normal range. For a light step disturbance, such as light step disturbance + 0.8mW / cm 2 , the recovery time is 14 seconds, which does not exceed the maximum allowed value.

[0111] Report output: inactivation rate constant: 0.023 ; 95% confidence interval: [0.021, 0.025]; coefficient of determination R 2 : 0.94; reliability level: low reliability; potential causes: signal fluctuation is too large.

[0112] The above report is provided to the user and is also called by the control strategy adjustment module of the central processing unit for parameter setting and system optimization in subsequent experimental periods.

[0113] It also includes a decision output module for generating operation instructions for adjusting the photocatalytic reaction process based on the inactivation rate constant and its corresponding reliability level and potential causes; When the reliability level is rated as high and the inactivation rate constant is lower than the pre-defined target threshold, a first type of control instruction is generated, which is configured to directly adjust the operation parameters of the photocatalytic reaction to enhance the reaction intensity; When the reliability level is rated as low, a second type of control instruction is generated, which is configured to preferentially trigger a subsystem review process directly related to the potential cause.

[0114] In the embodiments of the present application, a decision output module is provided in the central processing unit, which receives the reliability level and potential causes output by the reliability self-evaluation module, and generates control instructions accordingly to drive the photocatalytic reaction system to optimize and adjust.

[0115] The decision output module executes different control paths according to different reliability levels: Optimization decision under high reliability: When the reliability level is high, it means that the evaluation result inactivation rate constant Q has high credibility and can be directly used for performance judgment.

[0116] The system compares the inactivation rate constant with the preset target efficiency threshold .

[0117] If ≥ , the system generates a maintenance instruction to keep the current reaction parameters.

[0118] If < , the system generates a reinforced reaction instruction, i.e., the first type of control instruction. The system first calculates a dimensionless quantity ΔP representing the required adjustment intensity of the control parameter, with the unit being %, and the calculation formula is: ΔP = (K * (K * K * K - K * K * K * K)) / (K * K * K * K) . Wherein, K is a proportional coefficient, which is a constant determined by system tuning, such as Ziegler-Nichols method or trial-and-error method, and its typical value is between 0.8 and 1.2, is the deviation of the current inactivation rate constant from the target efficiency threshold . .

[0119] The system selects an optimization strategy according to the size of the adjustment amplitude ΔP. For example, when ΔP is small, such as less than or equal to 10%, the system extends the light time according to the rule of , wherein is the current light time. When ΔP is large, such as greater than 10%, the system preferentially increases the light source output power, and the adjustment amount is the product of ΔP and the current light source output power.

[0120] Conservative decision under medium reliability: When the reliability level is medium reliability, it indicates that there is a certain uncertainty in the result. At this time, the system generates an extended observation instruction. The extended observation instruction does not change the current photocatalytic reaction parameters, but the instruction efficiency evaluation module extends the data collection time of this evaluation, such as doubling the time, to obtain more abundant data, recalculate the inactivation rate constant value and reevaluate the reliability.

[0121] Review decision under low reliability: When the reliability level is low reliability, it indicates that the current evaluation result is not reliable. The decision output module reads the potential reasons and generates corresponding system review instructions, i.e., the second type of control instruction, and the instruction content is determined according to the potential reasons: Signal sustained fluctuation too large triggers signal stability review instruction: instructs the signal stability control module to recalibrate its stability threshold and PID parameters.

[0122] Controller adjustment abnormality trigger controller parameter review instruction: instructs the controller to enter a safe mode and re-tune.

[0123] Compensation response delay trigger feedforward compensation calibration instruction: instructs the feedforward compensation module to perform a fixed-time, such as a 30-second standardized step response test, and record the fluorescence signal recovery time after disturbance during the test for calculating the compensation coefficient update amount.

[0124] For example, in a liquid photocatalytic evaluation, the performance evaluation module outputs the inactivation rate constant , the reliability level is high reliability, such as the coefficient of determination R 2 =0.985, the confidence interval width D=8%, and the system target performance threshold . Calculate , the decision output module takes the set proportional coefficient , and calculates ΔP=(1.0×0.006 / 0.025)×100%=24%.

[0125] Since ΔP is greater than 10%, the system preferentially selects the light source power increase strategy, generates a reinforcement response instruction: the light source output power is increased by 24%, and the illumination time remains unchanged. After the instruction is sent to the light source control module through the execution interface unit, it takes effect.

[0126] For example, the reliability level is low reliability, such as the coefficient of determination R 2 =0.91, the confidence interval width D=23%, and the potential cause is compensation response delay. The decision output module generates a second type of control instruction to trigger the feedforward compensation recalibration process. The system performs a standardized test and measures the recovery time to be 18 seconds, which exceeds the maximum allowed value of 15 seconds, and accordingly updates the compensation coefficient.

[0127] A computer device includes a memory and a processor, the memory stores a computer program, and the computer program is used to realize the functions of each module of a photocatalytic disinfection performance evaluation system based on dynamic detection of microbial activity.

[0128] A computer readable storage medium stores a computer program, and the computer program is executed by a processor to realize the functions of each module of a photocatalytic disinfection performance evaluation system based on dynamic detection of microbial activity.

[0129] In an embodiment of the present application, a computer device is provided, which includes a processor, a memory, and a communication interface, and the memory stores a computer program. When the processor executes the computer program, it realizes all or part of the function steps of the photocatalytic disinfection performance evaluation system based on dynamic detection of microbial activity as described in any of the above embodiments.

[0130] In addition, the embodiment further provides a computer readable storage medium, which stores a computer program, and when the program is executed by a processor of a computer device, all or part of the function steps of the system of any of the above embodiments are realized. The computer readable storage medium can include but is not limited to read-only memory (ROM), random access memory (RAM), flash memory, memory card, hard disk or other medium capable of storing program code.

[0131] The above is the preferred embodiment of the present application, it should be pointed out that, for those skilled in the art, without departing from the principles of the present application, can make a number of improvements and refinements, these improvements and refinements should also be considered as the protection scope of the present application.

Claims

1. A photocatalytic disinfection efficiency evaluation system based on dynamic detection of microbial activity, characterized in that, The method comprises the following steps: a signal acquisition module is used to acquire the fluorescence intensity signal of the microorganism sample treated by photocatalysis in real time, wherein the fluorescence intensity signal comprises the fluorescence intensity signal of living microorganisms and the fluorescence intensity signal of dead microorganisms; a signal stability control module is used to calculate the coefficient of variation of the fluorescence intensity signal in a sliding time window, compare the coefficient of variation of the current sliding window with a preset stability threshold, and when the coefficient of variation exceeds the stability threshold, automatically adjust the injection flow rate of the fluorescence reagent based on an incremental PID control algorithm, and continuously monitor the coefficient of variation until the coefficient of variation falls below the stability threshold; a survival rate calculation module is used to calculate the stable fluorescence intensity signal based on a pre-calibrated fluorescence signal unmixing matrix, and use a constrained least squares method to calculate the instantaneous survival rate of the microorganism in real time, and form time series data of the instantaneous survival rate over time; an efficiency evaluation module is used to fit the time series data to a first-order inactivation kinetics model corresponding to the photocatalytic time, solve the inactivation rate constant by a nonlinear least squares method, and calculate the confidence interval and goodness-of-fit index of the inactivation rate constant; wherein the inactivation rate constant is output as a quantitative evaluation index of the photocatalytic disinfection efficiency.

2. The photocatalytic sterilization efficiency evaluation system based on dynamic detection of microbial activity according to claim 1, characterized in that, The preset method of the stability threshold comprises the following steps: acquire historical fluorescence intensity signal data of representative microorganism samples under multiple photocatalytic treatment conditions, wherein the photocatalytic treatment conditions at least include different light intensities, initial concentrations of microorganisms and characteristic parameters of catalytic materials, and the characteristic parameters include specific surface area and band gap; use a support vector machine regression algorithm to establish a prediction model between the photocatalytic treatment conditions and the stability threshold, wherein a radial basis function is used as a kernel function; and calculate the corresponding stability threshold for the preset parameters of the target photocatalytic application scene based on the prediction model.

3. The photocatalytic sterilization efficiency evaluation system based on dynamic detection of microbial activity according to claim 2, characterized in that, The incremental PID algorithm further comprises an adaptive adjustment mechanism of the integral term: a plurality of continuous deviation intervals are predefined according to the absolute value of the difference between the coefficient of variation and the stability threshold, and an integral coefficient value corresponding to each interval is set; wherein the larger the interval of the absolute value of the deviation, the larger the integral coefficient value set; in each control period, the integral coefficient value corresponding to the specific interval where the real-time absolute value of the deviation is located is selected, and is substituted into the calculation process of the incremental PID algorithm to generate a control instruction for adjusting the injection flow rate of the fluorescence reagent.

4. The photocatalytic sterilization efficiency evaluation system based on dynamic detection of microbial activity according to claim 3, characterized in that, The signal stability control module further comprises a feedforward compensation unit for monitoring at least one external process parameter that causes instantaneous disturbance to the efficiency of the fluorescent marker in real time, the external process parameter including the light intensity and the ambient temperature in the photocatalytic reaction region; a compensation model between the variation of the external process parameter and the compensation amount of the fluorescent reagent injection flow rate is pre-established through step response test; when the variation of any of the external process parameters is monitored to exceed a preset threshold, a feedforward compensation amount is calculated according to the compensation model, and a final control instruction is generated based on the feedforward compensation amount and the feedback control amount calculated by the incremental PID control algorithm to compensate for the instantaneous disturbance of the photocatalytic environment.

5. The photocatalytic sterilization efficiency evaluation system based on dynamic detection of microbial activity according to claim 4, characterized in that, The prediction model is further configured to: predict the expected coefficient of variation of the fluorescent intensity signal in real time according to the current photocatalytic treatment conditions; and use the expected coefficient of variation as prior information to adjust the adaptive adjustment mechanism of the integral term in the incremental PID control algorithm, specifically including: dividing the interval of the preset deviation absolute value and the corresponding integral coefficient value based on the size of the expected coefficient of variation to enhance the integral response sensitivity when the expected signal fluctuation is large; converting the expected coefficient of variation into a scaling weight; and multiplying the scaling weight by the feedforward compensation amount calculated by the compensation model to obtain the final feedforward compensation amount.

6. The photocatalytic sterilization efficiency evaluation system based on dynamic detection of microbial activity according to claim 5, characterized in that, Further comprising a reliability self-evaluation module for grading the reliability of the inactivation rate constant based on the goodness-of-fit index and the width of the confidence interval; when the reliability grade is lower than a preset standard, automatically backtracking and analyzing the stability control process of the fluorescent intensity signal to output potential causes leading to the decrease of the reliability grade; outputting the reliability grade, potential causes and the inactivation rate constant, and generating a comprehensive photocatalytic disinfection efficiency evaluation result with data quality self-evaluation information; the potential causes include: the coefficient of variation of the fluorescent intensity signal continuously exceeds the stability threshold within the photocatalytic action time; the adjustment frequency or amplitude of the incremental PID control algorithm exceeds the preset range; after the step change of the external process parameter, the coefficient of variation of the fluorescent intensity signal continuously exceeds the stability threshold for longer than a preset stability time threshold.

7. The photocatalytic sterilization efficiency evaluation system based on dynamic detection of microbial activity according to claim 6, characterized in that, Further comprising a decision output module for generating operation instructions for adjusting the photocatalytic reaction process based on the inactivation rate constant and its corresponding reliability grade and potential causes; wherein when the reliability grade is rated as high and the inactivation rate constant is lower than a predefined target threshold, a first type of control instruction is generated, which is configured to directly adjust the operation parameters of the photocatalytic reaction to enhance the reaction strength; when the reliability grade is rated as low, a second type of control instruction is generated, which is configured to preferentially trigger a subsystem review process directly associated with the potential causes.

8. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that, The processor executes the computer program to realize the functions of each module of the photocatalytic disinfection efficiency evaluation system based on dynamic detection of microbial activity according to any one of claims 1 to 7.

9. A computer readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to realize the functions of each module of the photocatalytic sterilization efficiency evaluation system based on dynamic detection of microbial activity according to any one of claims 1 to 7.

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