A photocatalytic disinfection efficiency evaluation system based on dynamic detection of microbial activity

Abstract

This invention provides a photocatalytic disinfection efficacy evaluation system based on dynamic detection of microbial activity, belonging to the field of photocatalytic material detection technology. It includes a signal acquisition module for real-time acquisition of fluorescence intensity signals of live and dead microorganisms in photocatalytically treated microbial samples; a signal stability control module for calculating the coefficient of variation of the fluorescence intensity signal using a sliding time window and automatically adjusting the injection flow rate of the fluorescent reagent based on an incremental PID control algorithm; a survival rate calculation module for unmixing the stabilized signal using a constrained least squares method based on a pre-calibrated fluorescence signal unmixing matrix to obtain the instantaneous survival rate time series of microorganisms in real time; and an efficacy evaluation module for fitting the time series data to a first-order inactivation kinetic model, solving for the inactivation rate constant, its confidence interval, and goodness-of-fit index. This invention achieves real-time, online, standardized quantitative evaluation of photocatalytic disinfection efficacy.

Description

Technical Field

[0001] This invention relates to the field of photocatalytic material detection technology, and in particular to a photocatalytic disinfection efficiency evaluation system based on dynamic detection of microbial activity. Background Technology

[0002] Photocatalytic disinfection technology is a technology that uses photocatalytic materials (such as nano titanium dioxide) to generate active oxygen species under light to inactivate microorganisms (such as viruses and bacteria). It is widely used in air purification, water treatment and medical environment disinfection.

[0003] Existing methods for evaluating the efficacy of photocatalytic disinfection mainly rely on plate culture or offline fluorescence detection, which have the following shortcomings: the detection cycle is long (usually 24-72 hours), relying on manual sampling and observation, and cannot capture the dynamic changes in microbial activity in real time, resulting in a lack of dynamic measurement of the disinfection process; there is a lack of fluorescence signal stability control mechanism, and the fluorescence intensity signal is easily affected by external disturbances such as light intensity and ambient temperature, resulting in large deviations in survival rate calculation, making it difficult to form accurate quantitative evaluation indicators (such as inactivation rate constant); the evaluation process lacks a unified standard, the data comparability is poor, and there is a lack of data reliability verification, making it difficult to accurately evaluate the relevant photocatalytic materials. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a photocatalytic disinfection efficacy evaluation system based on dynamic detection of microbial activity. By constructing a system that integrates dynamic detection, intelligent control and automatic evaluation, the system realizes real-time, online and quantitative evaluation of photocatalytic disinfection efficacy.

[0005] To solve the above-mentioned technical problems, the technical solution of the present invention is as follows:

[0006] A photocatalytic disinfection efficacy evaluation system based on dynamic detection of microbial activity includes:

[0007] The signal acquisition module is used to acquire the fluorescence intensity signal of the photocatalytically treated microbial sample in real time, the fluorescence intensity signal including the fluorescence intensity signal of live microorganisms and the fluorescence intensity signal of dead microorganisms;

[0008] The signal stability control module is used to calculate the coefficient of variation of the fluorescence intensity signal with 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 fluorescent reagent based on an incremental PID control algorithm, and continuously monitor the coefficient of variation until the coefficient of variation falls back below the stability threshold.

[0009] The survival rate calculation module is used to perform unmixing calculation on the stabilized fluorescence intensity signal based on the pre-calibrated fluorescence signal unmixing matrix and the constrained least squares method to obtain the instantaneous survival rate of microorganisms in real time and form time series data of the instantaneous survival rate changing over time.

[0010] The efficiency evaluation module is used to fit the time series data and the corresponding photocatalytic reaction time to a first-order inactivation kinetic model, solve the inactivation rate constant by nonlinear least squares method, and calculate the confidence interval and goodness-of-fit index of the inactivation rate constant.

[0011] The inactivation rate constant is output as a quantitative evaluation index of photocatalytic sterilization efficiency.

[0012] Furthermore, the preset method for the stability threshold includes:

[0013] Historical fluorescence intensity signal data of representative microbial samples under various photocatalytic treatment conditions are obtained. The photocatalytic treatment conditions include at least different light intensities, initial microbial concentrations, and characteristic parameters of the catalytic material, including specific surface area and band gap. A support vector machine regression algorithm is used with radial basis functions as kernel functions to establish a predictive model between the photocatalytic treatment conditions and the stability threshold. Based on the predictive model, the corresponding stability threshold is calculated for preset parameters of the target photocatalytic application scenario.

[0014] Furthermore, the incremental PID algorithm also includes an adaptive adjustment mechanism for the integral term:

[0015] Based on the absolute value of the difference between the coefficient of variation and the stability threshold, multiple consecutive deviation intervals are predefined, and a corresponding integral coefficient value is set for each interval; wherein, the larger the interval in which the absolute value of the deviation is located, the larger the corresponding integral coefficient value is set.

[0016] In each control cycle, based on the specific interval where the absolute value of the real-time deviation lies, the corresponding integral coefficient value is selected and substituted into the calculation process of the incremental PID algorithm to generate control instructions for adjusting the injection flow rate of the fluorescent reagent.

[0017] Furthermore, the signal stability control module also includes a feedforward compensation unit for real-time monitoring of at least one external process parameter that causes instantaneous disturbances to the fluorescent labeling efficiency. The external process parameters include the light intensity and ambient temperature within 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 for the fluorescent reagent injection flow rate. When the change in any of the external process parameters exceeds a preset threshold, the feedforward compensation amount is calculated based on the compensation model, and a 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.

[0018] Furthermore, the prediction model is also configured as follows:

[0019] The expected coefficient of variation of the fluorescence intensity signal is predicted in real time based on the current photocatalytic treatment conditions; the expected coefficient of variation is used as prior information to adjust the adaptive adjustment mechanism of the integral term in the incremental PID control algorithm, specifically including:

[0020] Based on the expected coefficient of variation, a preset interval division of the absolute value of the deviation and the corresponding integral coefficient value are established to enhance the integral response sensitivity when the expected signal fluctuates greatly; the scaling weight is multiplied by the feedforward compensation amount calculated by the compensation model to obtain the final feedforward compensation amount.

[0021] Furthermore, it also includes a reliability self-assessment module, used to assess 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, it automatically backtracks and analyzes the stability control process of the fluorescence intensity signal, outputs the potential reasons for the decrease in reliability level; outputs the reliability level, potential reasons and the inactivation rate constant, and generates a comprehensive photocatalytic elimination efficiency assessment result with data quality self-assessment information;

[0022] The potential reasons include:

[0023] The coefficient of variation of the fluorescence intensity signal continues to exceed the stability threshold during the photocatalytic reaction time;

[0024] The adjustment frequency or amplitude of the incremental PID control algorithm exceeds the preset range;

[0025] After a step change in the external process parameters, the coefficient of variation of the fluorescence intensity signal remains above the stability threshold for a longer period than the preset stability time threshold.

[0026] Furthermore, it also includes a decision output module, which is used to generate operation instructions for adjusting the photocatalytic reaction process based on the inactivation rate constant and its corresponding reliability level and potential causes;

[0027] When the reliability level is rated as high and the inactivation rate constant is lower than a predefined target threshold, a first type of control instruction is generated. The first type of control instruction is configured to directly adjust the operating parameters of the photocatalytic reaction to enhance the reaction intensity.

[0028] When the reliability level is rated as low, a second type of control instruction is generated, which is configured to prioritize triggering the subsystem review process directly related to the potential cause.

[0029] Furthermore, a computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the functions of various modules of a photocatalytic disinfection efficacy evaluation system based on dynamic detection of microbial activity.

[0030] 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.

[0031] The above-described solution of the present invention has at least the following beneficial effects:

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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

[0037] Figure 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.

[0038] Figure 2 This is a flowchart of the signal stability control logic provided by the present invention.

[0039] Figure 3 This is a fluorescence intensity spectrum with each labeled point representing the mean ±1σ under typical calibration. Detailed Implementation

[0040] 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.

[0041] like Figures 1 to 3 As shown, embodiments of the present invention propose a photocatalytic disinfection efficacy evaluation system based on dynamic detection of microbial activity, comprising:

[0042] The signal acquisition module is used to acquire the fluorescence intensity signal of the photocatalytically treated microbial sample in real time. The fluorescence intensity signal includes the fluorescence intensity signal of live microorganisms and the fluorescence intensity signal of dead microorganisms.

[0043] The signal stability control module is used to calculate the coefficient of variation of the fluorescence intensity signal with 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 fluorescent reagent based on the incremental PID control algorithm, and continuously monitor the coefficient of variation until the coefficient of variation falls back below the stability threshold.

[0044] The survival rate calculation module is used to calculate the stable fluorescence intensity signal based on the pre-calibrated fluorescence signal unmixing matrix using the constrained least squares method, thereby obtaining the instantaneous survival rate of microorganisms in real time and generating time series data of the instantaneous survival rate changing over time.

[0045] The efficiency evaluation module is used to fit time series data and corresponding photocatalytic reaction time to a first-order inactivation kinetic model, solve the inactivation rate constant using nonlinear least squares method, and calculate the confidence interval and goodness-of-fit index of the inactivation rate constant.

[0046] Among them, the inactivation rate constant is output as a quantitative evaluation index of photocatalytic sterilization efficiency.

[0047] In this embodiment of the invention, the photocatalytic disinfection efficacy evaluation system based on dynamic detection of microbial activity consists of an integrated photocatalytic reaction and fluorescence detection device and a central processing unit. The central processing unit includes four functional modules connected via a data bus: a signal acquisition module, a signal stability control module, a survival rate calculation module, and an efficacy evaluation module. It is applied to the evaluation of the photocatalytic disinfection efficacy of photocatalytic materials, such as nano-titanium dioxide, for both liquid and gaseous fluid media.

[0048] For liquid media, the integrated fluorescence detection device uses a transparent quartz reaction tube with a nano-titanium dioxide photocatalytic coating on its inner wall and surrounded by an ultraviolet LED light source. The liquid to be treated contains microorganisms, such as an initial concentration of 1×10⁻⁶. 6 A CFU / mL E. coli suspension was pumped into and flowed through the reaction tube at a constant flow rate, where it underwent a photocatalytic sterilization reaction under ultraviolet light.

[0049] A mixing zone was set up downstream of the reaction tube. Two independent micro-injection pumps were used to simultaneously inject and mix SYTO9 solution and propidium iodide solution into the fluid, respectively, to perform fluorescent staining of live and dead microorganisms flowing through the fluid. The mixed liquid then entered an online fluorescence detection cell, where a dual-channel fluorescence sensor acquired fluorescence signals in real time: green fluorescence channel (center wavelength 510 nm, corresponding to live bacteria signal) and red fluorescence channel (center wavelength 617 nm, corresponding to dead bacteria signal), with a sampling frequency set to 1 Hz.

[0050] In this embodiment, the parameters of the transparent quartz reaction tube are: inner diameter: 20 mm; reaction tube length: 300 mm;

[0051] Effective volume: 94ml; fluid flow rate: 1-10ml / min; photocatalytic coating: nano-titanium dioxide coating thickness of 500nm±50nm. The parameters of the online fluorescence detection cell and dual-channel fluorescence sensor are based on the fluorescence detection principle to ensure the accuracy and sensitivity of real-time signal acquisition. The detection scale dimensions are an optical path length of 10mm and a volume of 0.5ml.

[0052] The signal stability control module runs within the embedded controller. A sliding time window is set, which is 10 seconds in this embodiment, to continuously calculate the coefficient of variation (COP) of the red fluorescence signal intensity. In this embodiment, the preset stability threshold is 5%. When the COP calculated in real time exceeds the stability threshold, the signal stability control module immediately activates the incremental PID control algorithm to generate adjustment commands and dynamically adjust the flow rate of the micro-injection pump corresponding to the propidium iodide solution. In this embodiment, the flow rate adjustment range is 1 to 100 μL / min. The control cycle is set to 1 second, and this adjustment process continues until the calculated COP is below the stability threshold of 5% for five consecutive cycles (5 seconds).

[0053] The survival rate calculation module receives the dual-channel fluorescence signal after stability control. Based on a 2×2 fluorescence signal unmixing matrix pre-calibrated using standard samples, the unmixing matrix is ​​calibrated as follows: under the same liquid medium, staining procedure, and detection conditions as the actual test, pure live bacterial samples without photocatalytic treatment and pure dead bacterial samples after high-temperature or chemical inactivation are prepared, respectively. The dual-channel fluorescence intensity is measured to obtain the live bacterial response vector and the dead bacterial response vector, forming the unmixing matrix. In the actual calculation, the fluorescence intensity signal vector measured at each moment is solved using constrained least squares. The algorithm's constraint condition is that the calculated fluorescence contribution ratios of both live and dead bacteria are not less than zero. The instantaneous survival rate is defined as the ratio of the live bacterial fluorescence contribution ratio to the sum of the live and dead bacterial fluorescence contribution ratios. The survival rate calculation module outputs one instantaneous survival rate data point per second, forming a time-series data of the survival rate.

[0054] For example, in a typical calibration, based on a typical fluorescence intensity range (in AU), the average value is obtained through multiple repeated experiments, and the standard deviation is considered to ensure reliability. During calibration, 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 influence of saturation or noise. The signal intensity of a pure live bacterial sample without photocatalytic treatment in the green fluorescence channel is shown. =1000±50AU, a small amount of cross-fluorescence signal intensity exists in the red fluorescence channel. =80±10AU; Pure dead bacterial samples showed a small amount of cross-fluorescence signal intensity in the green fluorescence channel. =150±20AU, signal intensity in the red fluorescence channel =900±30AU.

[0055] The unmixing matrix is ​​constructed based on the above calibration data. This is used to describe the linear relationship between fluorescence intensity signal and bacterial community contribution; the measured fluorescence intensity signal vector... :

[0056] ;

[0057] Wherein, G represents the fluorescence signal intensity of the green channel. The value represents the fluorescence signal intensity in the red channel, where T stands for transpose.

[0058] Contribution vector H:

[0059] H= :

[0060] in, Contribution ratio of live bacteria The percentage of dead bacteria is represented by T, where T is the transpose symbol.

[0061] satisfy .

[0062] Demixing matrix F:

[0063] ;

[0064] In actual calculations, the fluorescence intensity signal vector measured at each time step... The constrained least squares method is used to solve the problem. That is, minimize The constraints are ≥0 and >0.

[0065] Instantaneous Survival Rate (SR):

[0066] ;

[0067] For example, at a certain moment, the measured values ​​are G = 600 AU and I = 500 AU. Solve the system of equations:

[0068] ;

[0069] Using constrained least squares, such as the lsqlin function in MATLAB, we can obtain... ≈0.55, The instantaneous survival rate (SR) is approximately 0.45, and the instantaneous survival rate (SR) is approximately 0.55. One data point is output per second to form a time series.

[0070] The efficacy evaluation module fits the survival rate time-series data to a primary inactivation kinetic model, which characterizes the natural exponential decay of microbial survival rate with photocatalytic reaction time. A nonlinear least squares method is used to fit and solve for the inactivation rate constant, and the confidence interval (e.g., 95%) and goodness-of-fit indices (e.g., coefficient of determination R) are calculated simultaneously. 2 Ultimately, the inactivation rate constant is output as a quantitative evaluation index of photocatalytic sterilization efficiency.

[0071] For gaseous media, the integrated fluorescence detection device employs a stainless steel flow chamber with an inner wall coated with titanium dioxide for photocatalysis, and an ultraviolet lamp is installed inside the chamber. Air containing microbial aerosols is detected at a velocity of 0.5–2 m. 3 A flow rate of / h continuously passes through the cavity, where photocatalytic inactivation occurs under ultraviolet light.

[0072] An atomizing mixer is installed at the cavity outlet. A mixed aqueous solution of SYTO9 solution and propidium iodide solution is pumped into the atomizer via a liquid supply pump, atomizing it into micron-sized droplets. These droplets are then uniformly mixed with the outflowing aerosol for in-situ fluorescent labeling of airborne microorganisms. The mixed airflow then enters an aerosol fluorescence detector based on the principle of laser-induced fluorescence. This detector excites, detects, and integrates the intensity of the dual-color fluorescence of each microbial particle passing through the detection zone, outputting fluorescence intensity signals for the 510nm and 617nm channels, with a sampling frequency of 1Hz.

[0073] In this embodiment, the photocatalytic reaction chamber is made of stainless steel, with an inner diameter of 100 mm, a length of 300 mm, and a total volume of approximately 2.35 L. This allows the aerosol to remain within the reaction chamber for 2-8 seconds, thus meeting the requirements of the photocatalytic reaction. The chamber contains a built-in ultraviolet lamp with a wavelength of 365 nm, a power of 15 W, and an adjustable light intensity range of 1-5 mW / cm². 2 This allows for adjustment via incremental PID control. The aerosol flow rate is set to 0.5-2 m / s. 3 / h corresponds to a linear flow velocity of 0.02-0.07m / s within the cavity, in order to avoid turbulence interference.

[0074] The atomizer generates droplets with a droplet size distribution of 1-5 μm and a median droplet size D50 of 2.5 μm to ensure efficient mixing of droplets and microbial aerosols. A micro-peristaltic pump is used for liquid supply, with an adjustable flow rate range of 0.05-0.5 ml / min. After atomization, the residence time of droplets and aerosols in the mixing zone is greater than 0.5 s, and the coefficient of variation for mixing uniformity is less than 5%.

[0075] The laser-induced fluorescence detector uses a 488nm solid-state laser with a power of 20mW and a focused spot diameter of less than 10μm.

[0076] It should be noted that the dual-channel fluorescence intensity signal output by the aerosol fluorescence detector, after being calibrated by the central processing unit of the system of this invention, has the same numerical characteristics as the fluorescence signal output by the liquid system, and can be directly input into the survival rate calculation module for demixing.

[0077] The signal stability control module uses the same core control logic as the liquid system: it calculates the coefficient of variation of the red fluorescence signal with a sliding time window of 10 seconds, and sets the stability threshold 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 nebulizer. In this embodiment, the adjustment range is 0.05–0.5 mL / min.

[0078] 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 is based on the pre-calibrated unmixing matrix and the constrained least squares method. It calculates the instantaneous survival rate sequence in real time from the stable fluorescence signal. The calibration method is as follows: under the same conditions as the aerosol generation method, atomization labeling parameters and single particle detection process used in actual gas detection, aerosol samples of pure live bacteria and pure dead bacteria are prepared respectively, and their dual-channel fluorescence intensity is measured to obtain the unmixing matrix of the gas system. The performance evaluation module fits the sequence with a kinetic model and outputs the inactivation rate constant and its confidence interval, goodness of fit and other reliability indicators.

[0079] By adapting to specific integrated devices for liquid or gaseous media and maintaining consistent signal stability control, survival rate calculation, and performance evaluation algorithms in the central processing unit, the system provides dynamic, online, and quantitative evaluation of photocatalytic sterilization efficiency in two different fluid media. The inactivation rate constant output by the system serves as a standardized indicator, exhibiting good comparability and repeatability.

[0080] The preset methods for the stability threshold include:

[0081] Historical fluorescence intensity signal data of representative microbial samples under various photocatalytic treatment conditions were obtained. The photocatalytic treatment conditions included at least different light intensities, initial microbial concentrations, and characteristic parameters of the catalytic material, including specific surface area and band gap. A support vector machine regression algorithm was used with radial basis functions as kernel functions to establish a predictive model between photocatalytic treatment conditions and stability thresholds. Based on the predictive model, the corresponding stability thresholds were calculated for preset parameters of the target photocatalytic application scenario.

[0082] In this embodiment of the invention, a variety of photocatalytic materials, such as nano-titanium dioxide, zinc oxide, and cadmium sulfide, as well as processing conditions, were selected for combined experiments.

[0083] Conditional factors include: light intensity, such as 1, 2, or 5 mW / cm². 2 Initial concentration of microorganisms, such as 1×10 5 1×10 6 1×10 7 CFU / mL.

[0084] For each photocatalytic material, its characteristic parameters strongly correlated with photocatalytic sterilization activity were measured and recorded. In this embodiment, the characteristic parameters included specific surface area, which was measured by the BET method, with units of m². 2 / g and bandgap width, calculated using UV-VisDRS spectroscopy, in eV.

[0085] For each set of conditions, including light intensity, initial microbial concentration, material specific surface area, and material bandgap, an automatic search algorithm, such as grid search, is used to try multiple 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 operates at the candidate threshold and reaches a stable state, the long-term statistical value of the coefficient of variation of the red fluorescence signal is calculated over a subsequent continuous evaluation period, such as 5 minutes after stabilization. The long-term statistical coefficient of variation must be less than or equal to 3%. Among all candidate stability thresholds that meet this objective, the one with the largest value is selected as the stability threshold for this operating condition. In this embodiment, a stable state is defined as the real-time coefficient of variation of the red fluorescence signal continuously falling below this candidate threshold for 5 consecutive seconds. To ensure signal quality, the controller's adjustment intensity is reduced as much as possible, thereby improving system robustness. Each historical record includes: light intensity, initial microbial concentration, material specific surface area, material bandgap, and stability threshold.

[0086] The required historical data was obtained through offline, manually-led calibration experiments during the early stages of system development. The specific steps are as follows:

[0087] Step S001: For each set of specific photocatalytic treatment conditions, including light intensity, initial concentration of microorganisms and characteristic parameters of catalytic materials, build or configure the corresponding photocatalytic reaction and detection system.

[0088] In step S002, if the signal stability control module has not yet deployed an automatic threshold, a candidate stability threshold is manually preset by a technician. Subsequently, the system is started to run the complete evaluation process, during which the signal stability control module will use the preset threshold to execute control logic.

[0089] Step S003: After the system reaches stable control, continuously collect fluorescence signals during an evaluation period and calculate the long-term statistical value of its coefficient of variation. Observe whether the long-term statistical value meets the predetermined signal quality verification standard.

[0090] Step S004: Within a preset range, such as 1% to 10%, manually adjust and try multiple different candidate stability thresholds, repeating steps S002 to S003. Finally, among all candidate thresholds that meet the signal quality verification criteria, select the one with the largest value and officially record it as the stability threshold label corresponding to this specific photocatalytic treatment condition.

[0091] Step S005 involves combining each set of experimental conditions—light intensity, initial microbial concentration, material specific surface area, material bandgap—with their corresponding stability threshold labels determined using the methods described above, to form a complete historical data record. By accumulating multiple such records covering a wide range of operating conditions, a historical dataset is constructed for training the prediction model.

[0092] Predictive model training:

[0093] All numerical features in the historical data were standardized, and the preprocessed dataset was divided into training and test sets in a 7:3 ratio. Using light intensity, initial microbial concentration, material specific surface area, and material bandgap as input features, and the corresponding stability thresholds as prediction targets, a support vector machine regression algorithm with a radial basis function as the kernel function was employed to train the prediction model on the training set. Key hyperparameters of the model, such as the penalty parameter C and the kernel function parameter γ, were optimized using a cross-validation grid search method. Finally, the model's generalization ability was evaluated using an independent test set, confirming that its predicted stability thresholds could effectively guide the control system to achieve the signal quality target, namely, a fluorescence signal variation coefficient of less than or equal to 3% during the evaluation period. After validation, the prediction model was deployed in the central processing unit.

[0094] When evaluating the target photocatalytic scenario online, the system performs the following steps:

[0095] Parameter input: User-preset photocatalytic treatment conditions for the current evaluation scenario. The parameter range includes:

[0096] Light intensity: 1–10 mW / cm 2 ;

[0097] Initial microbial concentration: 1×10 5 –1×10 7 CFU / mL;

[0098] Specific surface area of ​​photocatalytic materials: 20–200 m² 2 / g;

[0099] Material band gap: 2.5–3.5 eV.

[0100] Training data example:

[0101] Historical datasets were obtained through offline calibration experiments and include multiple sets of photocatalytic treatment conditions and their corresponding stability thresholds. For example, four sets of data are arranged in the following order: light intensity, microbial concentration, specific surface area, band gap, and stability threshold.

[0102] Historical data 1: 2.0 mW / cm 2 1×10 6 CFU / mL, 50m 2 / g, 3.2eV, 2.5%;

[0103] Historical data 2: 5.0 mW / cm 2 1×10 5 CFU / mL, 120m 2 / g, 2.8eV, 4.2%;

[0104] Historical data 3: 8.0 mW / cm 2 1×10 7 CFU / mL, 80m 2 / g, 3.0eV, 5.8%;

[0105] Historical data 4: 1.5 mW / cm 2 1×10 5 CFU / mL, 150m 2 / g, 3.4eV, 1.8%;

[0106] Data standardization was performed using the Z-score method, ensuring that the mean of each feature was 0 and the variance was 1, thus avoiding the impact of differences in units on the model. The dataset consisted of 2000 sets, divided into a training set (1400 sets) and a test set (600 sets) in a 7:3 ratio.

[0107] The range of hyperparameters optimized by the grid search method is based on the typical settings for support vector machine regression:

[0108] Penalty parameter C: 0.1–100, logarithmic scale, such as 0.1, 1, 10, 100;

[0109] Kernel function parameter γ: 0.001–1, the bandwidth parameter of the radial basis function kernel, corresponding to the sensitivity of the characteristic influence.

[0110] The generalization ability of the model is evaluated using an independent test set. Typical output results are as follows:

[0111] Coefficient of determination R 2 The values ​​of 0.92–0.96 indicate that the model can explain more than 92% of the variation in the stability threshold.

[0112] Root mean square error: 0.3%–0.5%, the average deviation between the predicted threshold and the actual threshold;

[0113] Mean absolute error: 0.2%–0.4%, the absolute average of the prediction error.

[0114] After deployment, the model's predictions for the target scenario are shown in the following example:

[0115] Input: Illuminance is 3.0 mW / cm² 2 The microbial concentration was 2×10 6 CFU / mL, specific surface area 90 m² 2 / g, with a band gap of 3.1 eV;

[0116] Output: Prediction stability threshold = 3.2%;

[0117] Verification results: Using this threshold to control signal stability, the coefficient of variation of fluorescence signal decreased to 2.8% during the evaluation period, meeting the target of ≤3%.

[0118] The output of the predictive model is directly used to guide the control system:

[0119] When the prediction threshold is less than or equal to 3%, the system defaults to a stable signal, and the PID control parameters adopt a conservative mode, such as reducing the integral coefficient by 20%.

[0120] When the prediction threshold is greater than 5%, the system automatically activates enhanced control strategies, such as subdividing the integral interval and increasing the feedforward compensation weight, to cope with high-fluctuation scenarios.

[0121] Threshold prediction: The central processing unit inputs the above preset parameters into the deployed prediction model to obtain the corresponding prediction stability threshold, such as 2.2%.

[0122] Threshold application: The signal stability control module uses this 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.

[0123] Incremental PID algorithms also include an adaptive adjustment mechanism for the integral term:

[0124] Based on the absolute value of the difference between the coefficient of variation and the stability threshold, multiple continuous deviation intervals are predefined, and a corresponding integral coefficient value is set for each interval; the larger the interval containing the absolute value of the deviation, the larger the corresponding integral coefficient value is set.

[0125] In each control cycle, based on the specific interval where the absolute value of the real-time deviation lies, the corresponding integral coefficient value is selected and substituted into the calculation process of the incremental PID algorithm to generate control instructions for adjusting the injection flow rate of the fluorescent reagent.

[0126] In this embodiment of the invention, the adaptive adjustment mechanism of the integral term is implemented in the embedded controller, and the deviation is defined and the interval is divided as follows:

[0127] 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.

[0128] Divide the deviation into continuous intervals: Predefine multiple continuous absolute deviation value intervals. In this embodiment, it is divided into three intervals:

[0129] Small deviation interval: 0 ≤ e < 1%

[0130] Medium deviation range: 1% ≤ e < 3%

[0131] Large deviation range: e≥3%

[0132] Adaptive setting of integral coefficients:

[0133] Set a corresponding integral coefficient for each deviation interval. The larger the range of the absolute value of the deviation, the larger the corresponding integral coefficient should be. 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.

[0134] It should be noted that this embodiment provides a feasible parameter configuration example to aid understanding. In a real system, the specific integral coefficients... The values ​​need to be determined using conventional engineering tuning methods. The core of this invention lies in the adaptive adjustment mechanism itself, rather than the specific coefficient values.

[0135] Online adaptive adjustment process:

[0136] In each control cycle, such as 1 second, the signal stability control module performs the following steps:

[0137] Step S301: Calculate the absolute value of the real-time deviation e, and determine the preset deviation interval to which e belongs;

[0138] Step S302: Select the corresponding integration coefficient based on the interval to which e belongs. ;

[0139] Step S303, select the integral coefficients The values ​​are substituted into the calculation process of the incremental PID control algorithm to calculate the adjustment command for the injection flow rate of the fluorescent reagent.

[0140] Step S304: The adjustment command is sent to the actuator, such as a peristaltic pump, to control the injection flow rate to increase or decrease, thereby suppressing signal fluctuations.

[0141] For example, the current stability threshold is 5%. In a certain control cycle, the real-time calculated coefficient of variation of the red fluorescence signal is 7.5%. The calculated real-time deviation absolute value e = 2.5%. According to the preset range, 2.5% falls within the medium deviation range. Therefore, the control system automatically selects and applies the integral coefficient set for this range. Under this integral coefficient, the algorithm will generate adjustment instructions to drive the peristaltic pump to increase the injection rate of the fluorescent reagent with appropriate force, thereby effectively and quickly and smoothly pulling the fluctuating signal back to near the target threshold.

[0142] 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.

[0143] 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.

[0144] 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:

[0145] 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. .

[0146] 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 in 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.

[0147] According to the change in light intensity ΔL and the flow velocity compensation The typical test data, arranged in order with corresponding notes, are as follows:

[0148] Test data 1: 2.0 mW / cm 2 Increased to 3.0 mW / cm 2 ΔL = +1.0 mW / cm 2 , =+0.48μL / min;

[0149] Test data 2: 2.0 mW / cm 2 Increased to 4.0 mW / cm 2 ΔL = +2.0 mW / cm 2 , =+0.95μL / min;

[0150] Test data 3: 2.0 mW / cm 2 Reduced to 1.5 mW / cm 2 ΔL = -0.5 mW / cm 2 , =-0.24μL / min;

[0151] Test data 4: 2.0 mW / cm 2 Increased to 3.5mW / cm 2 ΔL = +1.5mW / cm 2 , =+0.72μL / min;

[0152] Test data 5: 2.0 mW / cm 2 Reduced to 1.0 mW / cm 2 ΔL = -1.0 mW / cm 2 , =-0.50μL / min;

[0153] The fluorescent reagent was propidium iodide solution, with an initial flow rate of 10 μL / min; the microbial sample was *E. coli*, and the photocatalytic reaction temperature was controlled at 25℃. After each step change, the flow rate was stabilized for 5 minutes, and the signal intensity of the red fluorescence channel was recorded. The flow rate was manually adjusted until the coefficient of variation was below 3. The flow rate compensation was the mean of three repeated tests. Linear regression was performed using the least squares method, and the results were fitted... The relationship with ΔL.

[0154] Temperature compensation coefficient The calibration process is the same as that for illumination compensation, and the temperature step change is controlled by a constant temperature chamber.

[0155] Typical test data is as follows:

[0156] Test data 6: From 25℃ to 28℃, ΔT = +3.0℃. =+0.45μL / min;

[0157] Test data 6: From 25℃ to 28℃, ΔT = +5.0℃. =+0.75μL / min;

[0158] Test data 6: From 25℃ to 28℃, ΔT = -2.0℃. =-0.30μL / min;

[0159] Test data 6: From 25℃ to 28℃, ΔT = +4.0℃. =+0.60μL / min;

[0160] Test data 6: From 25℃ to 28℃, ΔT = -3.0℃. =-0.45μL / min;

[0161] The test conditions were the same as those for illumination compensation to ensure consistency. Temperature steps were achieved using a constant temperature chamber, and data processing was the same as for illumination compensation. Linear regression employed the least squares method for fitting. Relationship with ΔL;

[0162] Obtain multiple groups ( , Using the same standard fitting algorithm such as linear regression, the estimated value of the temperature compensation coefficient can be determined from the data. In this embodiment, a linear model is used as the basic form of the compensation model, and the parameters of the compensation model are stored in the controller:

[0163] Light intensity compensation :

[0164] = × ;

[0165] Ambient temperature compensation :

[0166] = × .

[0167] When the system exhibits significant nonlinear characteristics over a wide operating range, a piecewise linearization method can be used. Specifically, the range of the disturbance change, such as the change in light intensity ΔL, is divided into multiple continuous intervals, and a linear compensation coefficient is independently assigned to each interval. In online applications, the system automatically selects the corresponding coefficient for calculation based on which interval the real-time monitored ΔL value falls into.

[0168] In each control cycle, such as 1 second, the following steps are performed:

[0169] Step S401: Read the measured values ​​of the light and temperature sensors in real time and calculate their changes compared to the previous cycle. and If the change exceeds a preset sensing threshold, such as | |>0.5mW / cm 2 or | If the temperature exceeds 1.0℃, then the feedforward compensation amount is immediately calculated based on the above compensation model;

[0170] Step 402: The incremental PID algorithm, which runs in parallel with the feedforward compensation unit, calculates the feedback control quantity based on the deviation between the fluorescence signal variation coefficient and the stability threshold. This quantity is used to eliminate the overall residual error of the control system and perform precise calibration.

[0171] Step 403: The feedforward compensation amount and the feedback control amount are superimposed to generate the final control command and send it to the peristaltic pump.

[0172] For example, the system has calibrated the estimated value of the illumination compensation coefficient. It is 0.48 (μL / min) / (mW / cm) 2 Perception threshold | |>0.5mW / cm 2 .

[0173] The initial steady-state of the system was based on a light intensity value of 2.0 mW / cm² stored from the previous cycle. 2 The flow rate of the fluorescent reagent was 10.0 μL / min. When the disturbance occurred, the light intensity measured by the light intensity sensor in the current cycle was 2.9 mW / cm². 2 The change in light intensity is +0.9 mW / cm². 2If the change in light intensity exceeds the preset sensing threshold, the feedforward compensation unit immediately calculates the feedforward compensation amount. =0.48×0.9≈+0.43μL / min. At this point, the fluorescence intensity signal has not yet fluctuated, so the feedback control quantity is close to 0. Therefore, the final control command is to adjust the flow rate of the fluorescent reagent to 10.43μL / min. This predictive compensation action is completed before the fluorescence intensity signal drops significantly due to the enhancement of light. After the fluorescence fluctuation occurs, the feedback control quantity further suppresses the fluctuation of the fluorescence intensity signal to maintain the stability of the fluorescence intensity signal.

[0174] The prediction model is also configured as follows:

[0175] The expected coefficient of variation of the fluorescence intensity signal is predicted in real time based on the current photocatalytic treatment conditions; the expected coefficient of variation is used as prior information to adjust the adaptive adjustment mechanism of the integral term in the incremental PID control algorithm, specifically including:

[0176] Based on the expected coefficient of variation, the interval division of the absolute value of the deviation and the corresponding integral coefficient value are preset to enhance the sensitivity of the integral response when the expected signal fluctuates greatly; the scaling weight is multiplied by the feedforward compensation amount calculated by the compensation model to obtain the final feedforward compensation amount.

[0177] In this embodiment of the invention, the expected coefficient of variation (CV) is used to quantitatively characterize the intrinsic fluctuation level of the fluorescence intensity signal without active control under the current specific photocatalytic treatment conditions.

[0178] The expected coefficient of variation (CV) is obtained as follows:

[0179] During the development phase, the system models and calibrates typical operating conditions for the photocatalytic device. For a representative combination of photocatalytic treatment conditions, including light intensity, initial microbial concentration, and catalytic material properties, the system operates in an open-loop state with feedback control disabled. Once the fluorescence intensity signal enters a natural fluctuation state, the corresponding coefficient of variation (CV) is measured and recorded, and the result is used as the expected CV for that operating condition. Based on the obtained dataset, an expected CV prediction model is trained to establish a mapping relationship between the photocatalytic treatment conditions and the expected CV. Specifically, regression modeling is used to establish this mapping relationship. The expected CV prediction model uses light intensity, initial microbial concentration, and catalytic material properties as input variables, and the expected CV for the corresponding operating condition as the output variable. It is trained using supervised learning or regression fitting algorithms and is used to calculate the expected CV for the current operating condition in real time during online system operation.

[0180] When the system is running online, the user inputs the current photocatalytic processing condition parameters, the central processing unit calls the pre-trained expected coefficient of variation prediction model, calculates and outputs the expected coefficient of variation CV corresponding to the current operating condition in real time, and the central processing unit automatically selects the error deviation interval division scheme based on the output expected coefficient of variation CV, and adjusts the integral term parameters and feedforward compensation intensity.

[0181] When the system is running online, the user presets the current photocatalytic treatment conditions. The central processing unit calls the aforementioned expected coefficient of variation prediction model to calculate and output the expected coefficient of variation (CV) value corresponding to the current operating conditions in real time.

[0182] The system has multiple preset interval division schemes for the absolute value of deviation e, and automatically switches between them based on the real-time expected coefficient of variation (CV).

[0183] For example, when CV ≤ 4%, it indicates that the expected volatility is moderate, and the basic range scheme is adopted:

[0184] Low-level range: 0% ≤ e < 1%;

[0185] Medium-level range: 1% ≤ e < 3%;

[0186] High-level range: 3%≤e.

[0187] When CV > 4%, it indicates that the expected volatility is relatively high, and the enhanced interval scheme is activated:

[0188] Low-level range: 0% ≤ e < 0.5%;

[0189] Low to medium grade range: 0.5% ≤ e < 1.5%;

[0190] Medium-level range: 1.5% ≤ e < 2.5%;

[0191] Medium to high grade range: 2.5% ≤ e < 4%;

[0192] High-level range: 4%≤e.

[0193] More precise control can be achieved in high-noise environments by using finer interval divisions.

[0194] Configure corresponding integral coefficients for each interval The setting principle is as follows: under operating conditions with large expected fluctuations, i.e., when the expected coefficient of variation (CV) is large, a larger integral coefficient value is configured for the same deviation level to enhance the adjustment power of the integral term and accelerate the signal recovery speed.

[0195] For example, when the absolute value of the deviation is 1.5% ≤ e < 2.5%: an integral coefficient can be set in the basic scheme. =0.02, in the enhancement scheme, the integral coefficient is set to... =0.05.

[0196] In the feedforward compensation control section, a scaling weight W, positively correlated with the expected coefficient of variation (CV), is introduced to further enhance the system's adaptability under different fluctuation conditions. The final feedforward compensation amount is the base feedforward compensation amount multiplied by the scaling weight W.

[0197] The scaling weight W is determined based on 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 are made to meet the predetermined performance requirements, thereby achieving a balance between disturbance suppression effect and control stability.

[0198] 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.

[0199] 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.

[0200] When the expected coefficient of variation (CV) is greater than 7%, the scaling weight (W) is set to 1.8.

[0201] It should be noted that the correspondence between the scaling weight W and the expected coefficient of variation CV can also be achieved through linear functions, lookup tables, or other control parameter tuning methods. This is existing technology and will not be elaborated on here.

[0202] Under a typical operating condition, the system calculates the expected coefficient of variation (CV) to be 6.2% based on the photocatalytic treatment conditions input by the user. This indicates a high-fluctuation operating condition. The central processing unit automatically activates the enhanced interval scheme and loads the corresponding integral coefficient configuration table. At the same time, it sets the corresponding scaling weight W to 1.4 based on the expected CV value.

[0203] During real-time operation, when an increase of 0.8 mW / cm² in external light intensity was detected... 2 At this time, the feedforward compensation module calculates a compensation flow rate of 1.0 μL / min based on the basic compensation model. After scaling and weighting correction, the actual output compensation flow rate is 1.4 μL / min. At this point, the absolute value of the real-time deviation is 1.8%. The control module determines that this deviation falls within the third interval of the enhancement scheme, corresponding to the integral coefficient... The value is 0.05, which produces a strong integral regulation effect, allowing the signal to quickly recover and stabilize.

[0204] It also includes a reliability self-assessment module, which is used to evaluate 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 the preset standard, it automatically backtracks and analyzes the stability control process of the fluorescence intensity signal, outputs the potential reasons for the decrease in reliability level; outputs the reliability level, potential reasons and inactivation rate constant, and generates a comprehensive photocatalytic sterilization efficiency evaluation result with data quality self-assessment information.

[0205] Potential reasons include:

[0206] The coefficient of variation of the fluorescence intensity signal continued to exceed the stability threshold during the photocatalytic reaction time;

[0207] The adjustment frequency or amplitude of the incremental PID control algorithm exceeds the preset range;

[0208] After a step change in external process parameters, the coefficient of variation of the fluorescence intensity signal exceeds the stability threshold for a longer period than the preset stability time threshold.

[0209] In this embodiment of the invention, a reliability self-assessment module is added to the central processing unit. This module receives the inactivation rate constant and the coefficient of determination R output by the performance assessment module. 2 And the upper and lower limits of the confidence interval, calculate the confidence interval width D, which is an estimate of the difference between the upper and lower confidence limits divided by the inactivation rate constant. Based on the coefficient of determination R... 2 The reliability of the inactivation rate constant is graded based on the combined condition of the confidence interval width D:

[0210] The reliability self-assessment module classifies the reliability of the inactivation rate constant into three levels based on goodness of fit and estimation accuracy:

[0211] High reliability: good fit and high estimation accuracy. The decision criteria are met simultaneously:

[0212] Coefficient of determination R 2 ≥0.98. The confidence interval width D is less than or equal to 10%.

[0213] Moderate reliability: The fit is good, and the estimation accuracy is acceptable. The decision criteria are met simultaneously:

[0214] 0.95≤R 2 <0.98 and the confidence interval width D is less than or equal to 20%.

[0215] Low reliability: Neither the conditions for high reliability nor medium reliability are met.

[0216] The aforementioned thresholds can be adjusted by the user in the system configuration interface according to the evaluation scenario. After the evaluation is completed, the system will output the reliability level and the original statistical indicators together.

[0217] When the reliability level is high, the result is directly used as the final performance output; when the reliability level is medium or low, the system automatically starts the backtracking analysis process to diagnose the signal and control records within the evaluation period and identify potential causes of reliability degradation.

[0218] Backtracking analysis includes three parallel paths:

[0219] Signal stability analysis: Examine the time series of the coefficient of variation of the red fluorescence signal during the evaluation period. If the coefficient of variation exceeds the stability threshold for multiple consecutive control cycles, such as 10 consecutive cycles, it indicates that the system has failed to effectively suppress signal noise for a long period of time, and is therefore judged as having excessively large continuous signal fluctuations.

[0220] Controller behavior analysis: Analyze the historical records of fluorescent reagent flow rate adjustments. Using a sliding window (e.g., window length 20 cycles), if any of the following conditions are found, the controller is considered to be abnormally adjusted: Adjustments are too frequent: The number of adjustments significantly exceeds the threshold, such as 12 times.

[0221] Controller oscillation: The number of times the positive and negative signs of the control quantity are reversed is too many, such as more than 6 times.

[0222] Single adjustment range is too large: The maximum single-step adjustment exceeds the safety limit, such as 25% of the set value.

[0223] Feedforward compensation effect analysis: When the detected change in light intensity or ambient temperature exceeds a preset threshold, such as a change in light intensity exceeding 0.5 mW / cm², the effect is assessed. 2 When the temperature change exceeds 1.0℃, it is determined that an external disturbance has occurred. The time required for the fluorescence intensity signal to recover from the occurrence of the disturbance to stability is calculated, i.e., the recovery time. If the recovery time exceeds the maximum allowable value, such as 15 seconds, it is determined to be a slow compensation response.

[0224] The reliability self-assessment module synthesizes the above analysis results and generates a structured report. If multiple potential causes exist, they are listed in the order of signal fluctuation taking precedence over controller malfunction over compensation delay.

[0225] The final report contains the following information:

[0226] Performance indicators: inactivation rate constant, confidence interval, coefficient of determination R 2 .

[0227] Reliability level: High reliability, medium reliability, or low reliability.

[0228] Potential cause: Description of one or more diagnosed problems.

[0229] For example, in one evaluation, the inactivation rate constant output by the efficacy evaluation module was 0.023. Coefficient of determination R 2 The value is 0.94, and the confidence interval is [0.021, 0.025].

[0230] The reliability level is low. Backtracking diagnostic analysis revealed that the signal variation coefficient sequence exceeded the stability threshold of 5% for 12 consecutive periods, indicating excessively large signal fluctuations. The incremental PID controller's output adjustment frequency and amplitude are both within the normal range. For a single illumination step disturbance, such as an illumination step disturbance of +0.8mW / cm²,... 2 The recovery time is 14 seconds, which does not exceed the maximum allowable value.

[0231] Report Output: Inactivation Rate Constant: 0.023 95% confidence interval: [0.021, 0.025]; Coefficient of determination R0 2 0.94; Reliability level: Low reliability; Potential cause: Excessive and continuous signal fluctuations.

[0232] In addition to being provided to users, the above report is also available for use by the control strategy adjustment module of the central processing unit for parameter tuning and system optimization in subsequent experimental cycles.

[0233] It also includes a decision output module, which generates operation instructions for adjusting the photocatalytic reaction process based on the inactivation rate constant and its corresponding reliability level and potential causes;

[0234] When the reliability level is rated as high and the inactivation rate constant is lower than the predefined target threshold, a first type of control instruction is generated. The first type of control instruction is configured to directly adjust the operating parameters of the photocatalytic reaction to enhance the reaction intensity.

[0235] When the reliability level is rated as low, a second type of control instruction is generated. This second type of control instruction is configured to prioritize triggering the subsystem review process directly related to the potential cause.

[0236] In this embodiment of the invention, a decision output module is provided in the central processing unit. This module receives the reliability level and potential causes output by the reliability self-assessment module, and generates control commands accordingly to drive the photocatalytic reaction system to perform optimization and adjustment.

[0237] The decision output module executes different control paths based on different reliability levels:

[0238] Optimization decisions under high reliability:

[0239] When the reliability level is high reliability, it indicates that the inactivation rate constant Q of the evaluation result is highly reliable and can be directly used for performance judgment.

[0240] The system will inactivation rate constant Compared with the preset target performance threshold Compare them.

[0241] like ≥ Generate a maintenance command to maintain the current reaction parameters.

[0242] like < This generates a strengthening response command, i.e., a first-type control command. The system first calculates a dimensionless quantity ΔP, in percentage (%), characterizing the required adjustment intensity of the control parameter. The calculation formula is: = .in, This is a proportionality constant, determined through system tuning, such as the Ziegler-Nichols method or trial and error, with typical values ​​between 0.8 and 1.2. The current inactivation rate constant With target performance threshold deviation .

[0243] The system selects an optimization strategy based on the magnitude of the adjustment ΔP. For example, when ΔP is small (e.g., less than or equal to 10%), the system selects the appropriate strategy. The rules extend the duration of illumination, among which This represents the current illumination time. When ΔP is large, such as greater than 10%, the output power of the light source is increased first, and the adjustment amount is calculated as the product of ΔP and the current output power of the light source.

[0244] Conservative decision-making under moderate reliability:

[0245] When the reliability level is medium, it indicates that there is some uncertainty in the results. At this point, an extended observation command is generated. The extended observation command does not change the current photocatalytic reaction parameters, but the command performance evaluation module extends the data acquisition time for this evaluation, such as doubling the time, to obtain more data and recalculate the inactivation rate constant. The value was then reassessed and its reliability was re-evaluated.

[0246] Review decisions under low reliability:

[0247] When the reliability level is low, it indicates that the current assessment results are unreliable. The decision output module reads the potential causes and generates corresponding system review instructions, i.e., the second type of control instructions. The content of the instructions is determined based on the potential causes.

[0248] Excessive continuous signal fluctuation triggers a signal stability verification command: instructing the signal stability control module to recalibrate its stability threshold and PID parameters.

[0249] An abnormal controller adjustment triggers a controller parameter verification command: instructing the controller to enter safe mode and readjust.

[0250] Slow response triggers feedforward compensation calibration command: instructs the feedforward compensation module to perform a standardized step response test for a fixed period of time, such as 30 seconds. During the test, the fluorescence signal recovery time after the disturbance is applied is recorded and used to calculate the compensation coefficient update.

[0251] For example, in a liquid photocatalysis evaluation, the efficiency evaluation module outputs the inactivation rate constant. The reliability level is high reliability, such as the coefficient of determination R. 2 =0.985, confidence interval width D=8%, system target performance threshold .calculate The decision output module rounds down the predetermined proportional coefficient. Calculate ΔP = (1.0 × 0.006 / 0.025) × 100% = 24%.

[0252] Since ΔP is greater than 10%, the system prioritizes increasing the light source power, generating an enhanced response command: the light source output power is increased by 24%, while the illumination time remains unchanged. The command takes effect after being sent to the light source control module via the execution interface unit.

[0253] For example, a reliability level of low reliability, such as the coefficient of determination R... 2 =0.91, confidence interval width D=23%, the potential cause is slow compensation response. The decision output module generates a second type of control command, triggering the feedforward compensation recalibration process. The system performs standardized testing, measuring the recovery time as 18 seconds, which exceeds the maximum allowable value of 15 seconds, and updates the compensation coefficient accordingly.

[0254] A computer device includes a memory and a processor, the memory storing a computer program for implementing the functions of various modules of a photocatalytic disinfection efficacy evaluation system based on dynamic detection of microbial activity.

[0255] A computer-readable storage medium having a computer program stored thereon, 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.

[0256] In this embodiment of the invention, a computer device is provided, including a processor, a memory, and a communication interface. The memory stores a computer program. When the processor executes the computer program, it implements all or part of the functional steps of the photocatalytic disinfection efficacy evaluation system based on dynamic detection of microbial activity as described in any of the above embodiments.

[0257] Furthermore, this embodiment also provides a computer-readable storage medium storing a computer program thereon. When the program is executed by the processor of a computer device, it implements all or part of the functional steps of the system as described in any of the above embodiments. The computer-readable storage medium may include, but is not limited to, read-only memory (ROM), random access memory (RAM), flash memory, memory card, hard disk, or other media capable of storing program code.

[0258] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A photocatalytic disinfection efficiency evaluation system based on dynamic detection of microbial activity, characterized in that, include: The signal acquisition module is used to acquire the fluorescence intensity signal of the photocatalytically treated microbial sample in real time, the fluorescence intensity signal including the fluorescence intensity signal of live microorganisms and the fluorescence intensity signal of dead microorganisms; The signal stability control module is used to calculate the coefficient of variation of the fluorescence intensity signal with 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 fluorescent reagent based on an incremental PID control algorithm, and continuously monitor the coefficient of variation until the coefficient of variation falls back below the stability threshold. The survival rate calculation module is used to perform unmixing calculation on the stabilized fluorescence intensity signal based on the pre-calibrated fluorescence signal unmixing matrix and the constrained least squares method to obtain the instantaneous survival rate of microorganisms in real time and form time series data of the instantaneous survival rate changing over time. The efficiency evaluation module is used to fit the time series data and the corresponding photocatalytic reaction time to a first-order inactivation kinetic model, solve the inactivation rate constant by nonlinear least squares method, and calculate the confidence interval and goodness-of-fit index of the inactivation rate constant. The inactivation rate constant is output as a quantitative evaluation index of photocatalytic sterilization efficiency; The preset methods for the stability threshold include: Historical fluorescence intensity signal data of representative microbial samples under various photocatalytic treatment conditions are obtained. The photocatalytic treatment conditions include at least different light intensities, initial microbial concentrations, and characteristic parameters of the catalytic material, including specific surface area and band gap. A support vector machine regression algorithm is used with radial basis functions as kernel functions to establish a predictive model between the photocatalytic treatment conditions and the stability threshold. Based on the predictive model, the corresponding stability threshold is calculated for preset parameters of the target photocatalytic application scenario.

2. The photocatalytic disinfection efficacy evaluation system based on dynamic detection of microbial activity according to claim 1, characterized in that, The incremental PID control algorithm also includes an adaptive adjustment mechanism for the integral term: Based on the absolute value of the difference between the coefficient of variation and the stability threshold, multiple consecutive deviation intervals are predefined, and a corresponding integral coefficient value is set for each interval; wherein, the larger the interval in which the absolute value of the deviation is located, the larger the corresponding integral coefficient value is set. In each control cycle, based on the specific interval where the absolute value of the real-time deviation lies, the corresponding integral coefficient value is selected and substituted into the calculation process of the incremental PID control algorithm to generate control instructions for adjusting the injection flow rate of the fluorescent reagent.

3. The photocatalytic disinfection efficacy evaluation system based on dynamic detection of microbial activity according to claim 2, characterized in that, The signal stability control module further includes a feedforward compensation unit for real-time monitoring of at least one external process parameter that causes instantaneous disturbances to the fluorescent labeling efficiency. The external process parameters include the light intensity and ambient temperature within 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 for the fluorescent reagent injection flow rate. When the change in any of the external process parameters exceeds a preset threshold, the feedforward compensation amount is calculated based on the compensation model, and a 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.

4. The photocatalytic disinfection efficacy evaluation system based on dynamic detection of microbial activity according to claim 3, characterized in that, The prediction model is also configured to: The expected coefficient of variation of the fluorescence intensity signal is predicted in real time based on the current photocatalytic treatment conditions; the expected coefficient of variation is used as prior information to adjust the adaptive adjustment mechanism of the integral term in the incremental PID control algorithm, specifically including: Based on the magnitude of the expected coefficient of variation, a preset interval division of the absolute value of the deviation and the corresponding integral coefficient value are established to enhance the integral response sensitivity when the expected signal fluctuation is large; the expected coefficient of variation is converted into a scaling weight; the scaling weight is multiplied by the feedforward compensation amount calculated by the compensation model to obtain the final feedforward compensation amount.

5. The photocatalytic disinfection efficacy evaluation system based on dynamic detection of microbial activity according to claim 4, characterized in that, It also includes a reliability self-assessment module, which is used to evaluate 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 the preset standard, it automatically backtracks and analyzes the stability control process of the fluorescence intensity signal and outputs the potential reasons for the decrease in reliability level. Output the reliability level, potential causes, and inactivation rate constant, and generate a comprehensive photocatalytic disinfection efficiency evaluation result with data quality self-assessment information; The potential reasons include: The coefficient of variation of the fluorescence intensity signal continues to exceed the stability threshold during the photocatalytic reaction time; The adjustment frequency or amplitude of the incremental PID control algorithm exceeds the preset range; After a step change in the external process parameters, the coefficient of variation of the fluorescence intensity signal remains above the stability threshold for a longer period than the preset stability time threshold.

6. The photocatalytic disinfection efficacy evaluation system based on dynamic detection of microbial activity according to claim 5, characterized in that, It also includes a decision output module, which generates 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 a predefined target threshold, a first type of control instruction is generated. The first type of control instruction is configured to directly adjust the operating 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 prioritize triggering the subsystem review process directly related to the potential cause.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, The processor executes the computer program to implement the functions of each module of the photocatalytic disinfection efficacy evaluation system based on dynamic detection of microbial activity as described in any one of claims 1 to 6.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the functions of each module of the photocatalytic disinfection efficacy evaluation system based on dynamic detection of microbial activity as described in any one of claims 1 to 6.

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