Error dynamic compensation method and system for water quality biotoxicity on-line monitoring, analyzer and application

Abstract

The invention is suitable for the technical field of water quality monitoring, and provides a dynamic error compensation method and system for water quality biotoxicity online monitoring, an analyzer and application of the dynamic error compensation method and system for water quality biotoxicity online monitoring. A dynamic reference value reflecting the current device state is obtained, then normalization compensation is carried out on the signal, and finally an accurate toxicity index is obtained through calculation. The invention also provides an analyzer for realizing the method, and the analyzer is provided with a high-precision temperature control dual optical path, an automatic calibration module and a control unit for executing a compensation algorithm, and is expanded to an Internet of Things monitoring system and a health early warning method. According to the scheme, the long-term stability and accuracy of monitoring data are remarkably improved, and the maintenance cost is greatly reduced.

Description

Technical Field

[0001] This invention belongs to the field of water quality monitoring technology, and particularly relates to an error dynamic compensation method for online monitoring of water biotoxicity, an online water biotoxicity analyzer, an Internet of Things monitoring system, a health status early warning method for photoelectric detection devices, electronic equipment, a computer-readable storage medium, and a standard reagent kit. Background Technology

[0002] Water biotoxicity monitoring is a crucial means of assessing the degree of water pollution and ensuring ecological and drinking water safety. The luminescent bacteria method, as a rapid and sensitive biotoxicity detection method, has been widely used in online water quality monitoring. Its basic principle is to utilize the phenomenon that the intensity of bioluminescence emitted by luminescent bacteria (such as Vibrio fischeri or Luteinobacillus luminifera) during metabolism is inhibited and weakened upon contact with toxic substances, thereby quantitatively assessing the overall toxicity of water samples.

[0003] Currently, online water quality biotoxicity monitoring equipment based on luminescent bacteria is susceptible to measurement errors due to various factors during actual operation, mainly including: Performance degradation of photoelectric detection devices (such as photomultiplier tubes): As the cumulative operating time increases and the operating temperature changes, the sensitivity of photoelectric detection devices will drift, resulting in deviations in the detection signal under the same luminous intensity; Environmental factors: Environmental factors such as temperature fluctuations and changes in light at the monitoring site can affect the metabolic activity of luminescent bacteria and the stability of the detection system; Systematic error accumulation: After multiple water sample tests, factors such as residues in the liquid system and changes in reagent activity can lead to the gradual accumulation of systematic errors. Signal noise interference: Electronic noise and liquid flow noise during the detection process can affect the accuracy of the luminescence intensity signal.

[0004] In existing technologies, errors are usually corrected by periodic manual calibration. However, this method has problems such as fixed calibration cycles, inability to respond to error changes in real time, and cumbersome operation, making it difficult to meet the requirements of online monitoring for data accuracy and real-time performance.

[0005] Therefore, there is an urgent need for a technical solution that can dynamically compensate for measurement errors and improve monitoring accuracy and stability. Summary of the Invention

[0006] The purpose of this invention is to provide an error dynamic compensation method for online monitoring of water biotoxicity, an online water biotoxicity analyzer, an Internet of Things monitoring system, a health status early warning method for photoelectric detection devices, electronic equipment, a computer-readable storage medium, and a standard reagent kit, aiming to solve the technical problems of fixed calibration cycles, inability to respond to error changes in real time, and cumbersome operation in existing technologies.

[0007] This invention is implemented as follows: a dynamic error compensation method for online monitoring of water quality biotoxicity, the dynamic error compensation method comprising the following steps: S1. Within a single detection cycle, the real-time luminous intensity signal Rt of the reference reaction unit containing the reference medium is simultaneously measured using the first photoelectric detection channel, and the real-time luminous intensity signal St of the sample reaction unit containing the water sample to be tested is simultaneously measured using the second photoelectric detection channel. S2. Obtain the dynamic reference value corresponding to the current detection time. The dynamic reference value is used to characterize the expected signal reference of the first photoelectric detection channel and the second photoelectric detection channel under ideal conditions without interference from toxic substances. S3. Based on the dynamic reference value, normalize or differentially calculate the real-time luminous intensity signals Rt and St to obtain the compensated reference signal and sample signal; S4. Calculate the biotoxicity index of the water sample to be tested based on the compensated reference signal and the sample signal.

[0008] A further technical solution of the present invention is that the dynamic reference value is determined by any of the following methods: a1. Based on a pre-constructed performance degradation model of optoelectronic detection devices, and according to the cumulative running time, operating temperature and historical signal drift data of the devices, predict and generate the dynamic reference value at the current moment. a2. By executing an automatic calibration procedure, a known non-toxic standard solution is injected into the reference reaction unit and the sample reaction unit, and the corresponding luminescence intensity signal is measured to update the dynamic reference value with the measured value.

[0009] A further technical solution of the present invention is that the performance degradation model is an exponential degradation model based on the semiconductor device aging theory, a Weibull distribution model, or a prediction model trained by a machine learning algorithm; the machine learning algorithm includes neural networks, support vector machines, or random forests.

[0010] A further technical solution of the present invention is that the triggering conditions of the automatic calibration program include at least one of the following: b1. Periodic time triggering; b2. Triggered after a predetermined number of water sample tests have been completed; b3. Triggered when the signal-to-noise ratio is detected to be lower than the set threshold in real time; b4. Triggered by receiving calibration instructions from a remote server.

[0011] A further technical solution of the present invention is: step S1 includes a signal preprocessing step: filtering and denoising the real-time luminous intensity signal Rt and the real-time luminous intensity signal St respectively, wherein the filtering and denoising process includes, but is not limited to, sliding window averaging filtering, Kalman filtering or wavelet threshold denoising.

[0012] A further technical solution of the present invention is: the biotoxicity index in step S4 includes at least one of relative luminescence, inhibition rate, and toxic equivalent concentration calculated based on the standard toxicant dose-response curve; the standard toxicant includes zinc sulfate, 3,5-dichlorophenol, or potassium dichromate.

[0013] Another object of the present invention is to provide an online water quality biotoxicity analyzer, comprising: An optical detection unit, comprising an excitation source, a beam splitter, a first photomultiplier tube, and a second photomultiplier tube, is used to implement step S1 in the error dynamic compensation method; The fluid reaction unit includes a reference reaction cell, a sample reaction cell, a high-precision injection pump, and a multi-way valve, which are used to contain the reference medium and the water sample to be tested and mix them with the luminescent bacteria solution for reaction. A control and processing unit, electrically connected to the optical detection unit and the fluid reaction unit, is configured to execute a computer program stored in its memory to implement the steps of the error dynamic compensation method.

[0014] A further technical solution of the present invention is: the first photomultiplier tube and the second photomultiplier tube are symmetrically integrated and packaged in the same temperature control module, and the temperature control accuracy of the temperature control module is better than ±0.5℃.

[0015] A further technical solution of the present invention is: it also includes a built-in automatic calibration module, the automatic calibration module comprising: Standard solution storage tank; The calibration fluid flow path connected to the multi-way valve; The control and processing unit controls the automatic calibration module to execute the automatic calibration program.

[0016] A further technical solution of the present invention is that the control and processing unit further includes: A performance degradation model storage area is used to store the parameters of the performance degradation model; The self-learning algorithm module is used to automatically optimize the parameters of the performance degradation model based on historical calibration data.

[0017] Another object of the present invention is to provide an Internet of Things (IoT) monitoring system for water quality biotoxicity, comprising: At least one of the aforementioned online water quality biotoxicity analyzers shall be used as an on-site monitoring terminal; The cloud platform server is connected to the on-site monitoring terminal via a wired or wireless communication network; The cloud platform server is configured as follows: Receive the change data of the dynamic benchmark reference value and the equipment operation log reported by each field monitoring terminal; Based on big data analysis, the performance degradation model is collaboratively trained and globally optimized. The optimized model parameters or algorithm update package will be sent to the corresponding field monitoring terminal.

[0018] Another objective of this invention is to provide a method for early warning of the health status of photoelectric detection devices based on an online water quality biotoxicity analyzer, comprising: Continuously record the sequence of changes in the dynamic benchmark reference value over time or number of uses; Extract trend features from the changing sequence, including decay rate, fluctuation variance, or nonlinearity; The trend characteristics are compared with a preset health threshold range. When the trend exceeds the threshold range, an early warning report is generated that includes the device performance degradation level, recommended maintenance time, or remaining service life.

[0019] Another object of the present invention is to provide an electronic device comprising: processor; Memory, used to store computer programs; When the processor executes the computer program, it implements the steps of the error dynamic compensation method or the photoelectric detection device health status early warning method.

[0020] Another object of the present invention is to provide a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the steps of an error dynamic compensation method or a health status early warning method for a photoelectric detection device are implemented.

[0021] Another object of the present invention is to provide a standard reagent kit for calibrating water quality biotoxicity monitoring equipment, the standard reagent kit comprising: Lyophilized luminescent bacteria preparation with stable luminescence intensity; Resuscitation dilution for resuscitation agents; Osmotic pressure regulating fluid; Non-toxic standard control solutions for automated calibration procedures; The luminescence intensity value of the standard quality control liquid has been pre-calibrated and stored in the control and processing unit of the monitoring equipment, serving as the basis for updating the dynamic reference value.

[0022] The beneficial effects of this invention are: high error compensation accuracy: through dual-channel synchronous detection, dynamic reference value update, signal filtering and noise reduction, etc., the measurement error caused by factors such as performance degradation of photoelectric detection devices, environmental interference, and accumulation of system errors is effectively compensated, so that the repeatability of toxicity index measurement is ≤10% and the actual water sample comparison test error is ≤20%; High long-term accuracy: Through dynamic compensation, the systematic drift caused by asynchronous attenuation of photoelectric detection devices is effectively suppressed, so that the instrument can maintain high accuracy without manual calibration during continuous operation for weeks or even months. High degree of automation: Built-in automatic calibration module, supports multiple calibration trigger conditions, can complete the benchmark value update without manual intervention, and also supports functions such as automatic bacterial resuscitation and automatic liquid path cleaning, with a minimum maintenance cycle of ≥240 hours; Wide monitoring range: It can respond to more than a number of toxic substances, including pesticides and heavy metals, and is suitable for online monitoring of various scenarios such as surface water, drinking water sources, and pollution sources; High level of intelligence: It has self-calibration, self-diagnosis and self-learning capabilities, realizes multi-device collaborative work and model optimization through IoT technology, has a health status early warning function for photoelectric detection devices, facilitates users to maintain and manage equipment, and realizes remote monitoring and collaborative optimization through IoT, which reflects a high degree of intelligence; Strong early warning capability: It can provide early warning of component aging failures by analyzing the compensation parameters themselves, enabling predictive maintenance and avoiding sudden downtime; High compatibility: Supports free switching between national standard mode and ISO mode, conforms to the requirements of GB / T 15441-1995 and ISO11348-3:2007 standards, and the communication interface supports multiple methods such as 4-20mA, RS485, and RS232, which facilitates integration with existing monitoring systems; Low maintenance costs: Significantly reduces the frequency of on-site calibration and maintenance due to decreased accuracy, thereby reducing maintenance manpower and material costs. Attached Figure Description

[0023] Figure 1 This is a flowchart of an error dynamic compensation method for online monitoring of water biotoxicity provided in an embodiment of the present invention. Detailed Implementation

[0024] Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention.

[0025] In the description of this invention, it should be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicating orientation or positional relationships, are based on the orientation or positional relationships shown in the accompanying drawings and are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, in the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0026] Definitions and Explanations Photoelectric detection channel: refers to a signal detection unit composed of photoelectric detection devices (such as photomultiplier tubes), signal amplification circuits, analog-to-digital conversion modules, etc., used to convert the luminescence intensity of luminescent bacteria into a processable electrical signal.

[0027] Reference reaction unit: refers to the reaction vessel containing the reference medium (a mixture of non-toxic standard solution and luminescent bacterial solution) to provide a stable reference signal for luminescence intensity.

[0028] Sample reaction unit: refers to the reaction vessel that holds the mixture of the water sample to be tested and the luminescent bacteria solution, and is used to generate a luminescence intensity signal related to the toxicity of the water sample.

[0029] Dynamic reference value: refers to the expected signal reference value of the first and second photoelectric detection channels determined based on the current actual performance state of the photoelectric detection device under ideal conditions free from toxic interference. It is a quantity that changes dynamically with factors such as device usage time and environmental conditions, and is used to correct real-time measurement signals.

[0030] Performance degradation model: A mathematical or statistical model used to describe and predict how the gain (sensitivity) of photoelectric detection devices such as photomultiplier tubes (PMTs) changes with factors such as cumulative operating time, operating temperature, and power-on history.

[0031] Relative luminescence: refers to the ratio of the luminescence intensity of the sample reaction unit to the luminescence intensity of the reference reaction unit, used to characterize the degree of influence of the water sample on the luminescence of luminescent bacteria.

[0032] Inhibition rate: refers to the degree to which the water sample inhibits the luminescence of luminescent bacteria. The calculation formula is: Inhibition rate = (1 - relative luminescence) × 100%.

[0033] Toxic equivalent: refers to the toxicity intensity expressed as the concentration of a standard toxic substance (such as zinc sulfate) that the toxic effect of the water sample is equivalent to. It is a standardized method of toxicity characterization.

[0034] Automatic calibration procedure: A series of fluid actions and measurement steps that are automatically initiated and executed by the instrument control unit, designed to introduce standard substances with known properties into the measurement system to update system parameters (such as dynamic reference values) or verify measurement accuracy.

[0035] like Figure 1 As shown, the error dynamic compensation method for online monitoring of water biotoxicity provided by the present invention is described in detail below: Step S1, Synchronous Measurement Step: Within a single detection cycle, the real-time luminescence intensity signal Rt of the reference reaction unit containing the reference medium is simultaneously measured using the first photoelectric detection channel, and the real-time luminescence intensity signal St of the sample reaction unit containing the water sample to be tested is simultaneously measured using the second photoelectric detection channel. The instrument control unit drives a high-precision syringe pump to inject equal amounts of regenerating luminescent bacteria solution into the reference reaction cell (containing blank reference water) and the sample reaction cell (containing the water sample to be tested), respectively. The excitation light source (such as an LED of a specific wavelength) is evenly distributed to the two reaction cells through a beam splitter. After a specific reaction time, such as 5, 15, or 30 minutes, the fluorescence from the two reaction cells is synchronously received by the first and second PMTs and converted into electrical signals. After amplification and analog-to-digital conversion, the digital signals real-time luminescence intensity signal Rt and real-time luminescence intensity signal St are obtained. Here, "synchronization" means that the signal acquisition of the two channels is triggered by the same clock to ensure strict time alignment in order to capture the same instantaneous state.

[0036] Step S2, Dynamic Reference Acquisition Step: Acquire the dynamic reference value corresponding to the current detection time. The dynamic reference value is used to characterize the expected signal reference of the first photoelectric detection channel and the second photoelectric detection channel under ideal conditions without interference from toxic substances. The control unit retrieves the dynamic reference value related to the current time from the memory. This value contains at least a pair of values ​​(R0_dyn, S0_dyn), corresponding to the current reference of the first and second channels, respectively.

[0037] In this step, the dynamic reference value is determined in the following way: Based on a pre-built performance degradation model of the photoelectric detection device, the dynamic reference value for the current moment is predicted and generated according to the device's cumulative operating time, operating temperature, and historical signal drift data. The performance degradation model is pre-stored in the system memory or generated through self-learning. This model can be a simplified exponential decay function: G(t) = G0 * exp(-k * T_avg * t), where G(t) is the gain factor at time t, G0 is the initial gain, k is the material-related attenuation coefficient, T_avg is the average operating temperature, and t is the cumulative power-on time. The control unit reads the cumulative operating time t of the PMT and the T_avg recorded by the temperature sensor in real time, and calculates the gain degradation factors G1(t) and G2(t) of the two PMTs respectively using the model. The initial reference values ​​(R0_initial, S0_initial) are obtained through precise calibration at the factory or after the last major overhaul. Therefore, the dynamic reference values ​​are: R0_dyn = R0_initial / G1(t), S0_dyn = S0_initial / G2(t). More advanced models can employ machine learning models, such as those using t, T, and historical signal variance as features, and training them with recent self-calibrated measured values ​​as labels to achieve more accurate predictions. The performance degradation model can be an exponential degradation model based on semiconductor device aging theory, a Weibull distribution model, or a prediction model trained using machine learning algorithms; these machine learning algorithms include neural networks, support vector machines, or random forests.

[0038] By executing an automatic calibration procedure, a known non-toxic standard solution is injected into the reference reaction unit and the sample reaction unit, and the corresponding luminescence intensity signal is measured to update the dynamic reference value with the measured value. When the triggering conditions are met, such as: every 24 hours; or after every 100 water sample measurements; or when the signal-to-noise ratio of Rt / St is monitored to be below 30dB for three consecutive times, the control unit starts the automatic calibration procedure. The procedure is as follows: a) Switch the flow path to the standard quality control solution, a known non-toxic solution with stable luminescence intensity; b) Inject the quality control solution as the "sample" and "reference solution" into the two reaction cells respectively, perform a complete measurement, and obtain a set of measured values ​​(Rt_cal, St_cal); c) Since the quality control solution is non-toxic, theoretically Rt_cal and St_cal should be equal and equal to their calibration value I_std. The dynamic benchmark value is updated to a correction factor of R0_dyn = I_std * (Rt_cal / St_cal), and S0_dyn = I_std (or symmetrically processed). This directly refreshes the benchmark with the latest measured data. The automatic calibration program is triggered by at least one of the following conditions: b1, periodic time triggering; b2, triggering after a predetermined number of water sample tests are completed; b3, triggering when the signal-to-noise ratio is lower than a set threshold in real time; b4, triggering upon receiving a calibration command from a remote server.

[0039] Step S3, Compensation Calculation Step: Based on the dynamic reference value, normalize or differentially calculate the real-time luminous intensity signals Rt and St to obtain the compensated reference signal and sample signal; after obtaining the dynamic reference value (R0_dyn, S0_dyn), compensate the real-time measured value (Rt, St). An effective compensation algorithm is the normalization method: Rt_compensated = Rt / R0_dyn, St_compensated = St / S0_dyn; based on this, even if the absolute values ​​of R0_dyn and S0_dyn decrease due to device attenuation, their ratio can be corrected back to the true theoretical ratio. Other algorithms such as the differential method can also be used.

[0040] Step S4, toxicity assessment step: Calculate the biotoxicity index of the water sample to be tested based on the compensated reference signal and sample signal; the error dynamic compensation method also includes a signal preprocessing step: filter and denoise the real-time luminous intensity signal Rt and the real-time luminous intensity signal St respectively, the filtering and denoising process includes but is not limited to sliding window averaging filtering, Kalman filtering or wavelet threshold denoising.

[0041] Before compensation calculation, the original real-time emission intensity signals Rt and St can be preprocessed. For example, a sliding window averaging filter (with a window size of 10 sampling points) can be used to smooth random noise; and an outlier removal algorithm based on the 3σ criterion can be used to remove abnormal pulses caused by accidental interference.

[0042] Then, the relative luminosity (RL) is calculated using the compensated signal: RL = (St_compensated / Rt_compensated) * 100%.

[0043] Calculate the inhibition rate (IR): IR = (1 - St_compensated / Rt_compensated) * 100%.

[0044] Furthermore, standard curves stored in the instrument can be queried based on the RL (Reactivity Level), such as the relationship curve between zinc sulfate concentration and RL. The fitting equation may be a Logistic model. By interpolation or back-calculation, the equivalent toxicant concentration (TEQ) of zinc sulfate in mg / L can be obtained, achieving quantitative characterization of toxicity. Biotoxicity indicators include at least one of relative luminescence, inhibition rate, and toxicant equivalent concentration calculated based on the standard toxicant dose-response curve; the standard toxicant includes zinc sulfate, 3,5-dichlorophenol, or potassium dichromate.

[0045] Another object of the present invention is to provide an online water quality biotoxicity analyzer, comprising: The optical detection unit includes an excitation source, a beam splitter, a first photomultiplier tube, and a second photomultiplier tube, used to implement step S1 in the error dynamic compensation method. It includes a high-stability LED excitation source with a center wavelength of ~470nm. Following this is the beam splitter, which splits the excitation light into a 50:50 ratio and guides it through optical fibers to the bottom of the reference reaction cell and the sample reaction cell, respectively. The reaction cells are positioned above the light-receiving windows of the first and second PMTs. The two PMTs are integrated and packaged within a metal heat sink, which can be a Hamamatsu H10721 series model. This heat sink is in close contact with the thermoelectric cooler (TEC), forming a temperature control module. A temperature sensor (PT1000) feeds back the temperature to the PID controller, stabilizing the PMT's operating temperature at 25±0.3℃, greatly reducing gain variations caused by temperature fluctuations.

[0046] The fluid reaction unit, comprising a reference reaction cell, a sample reaction cell, a high-precision syringe pump, and a multi-way valve, is used to contain the reference medium and the water sample to be tested, and to mix and react with the luminescent bacterial solution. The reference and sample reaction cells are made of quartz glass and have a volume of approximately 1.5 mL. The high-precision syringe pump is used to accurately deliver the bacterial solution, water sample, and standard solution, with an accuracy of ±0.5%. High-precision syringe pumps such as the CETONI neMESYS series are used. The multi-way valve is used to switch the flow path between states such as "sampling," "bacterial addition," "reaction," "cleaning," and "calibration." Multi-way valves such as the VICI Valco multi-position valve are used.

[0047] A control and processing unit, electrically connected to the optical detection unit and the fluid reaction unit, is configured to execute a computer program stored in its memory to implement the steps of the dynamic error compensation method. This is an embedded industrial computer, such as one based on an ARM Cortex-A series processor running a Linux system. Its memory (e.g., eMMC) stores: a main control program for coordinating the actions of the optical and fluid units; and the memory itself is an eMMC.

[0048] The first photomultiplier tube and the second photomultiplier tube are symmetrically integrated and packaged in the same temperature control module, and the temperature control accuracy of the temperature control module is better than ±0.5℃.

[0049] It also includes a built-in automatic calibration module, which includes: Standard solution storage tank; The calibration fluid flow path is connected to the multi-port valve; The control and processing unit controls the automatic calibration module to execute the automatic calibration program. As part of the fluid unit, it includes a separate standard quality control liquid storage tank containing a dedicated, non-toxic, long-term calibrated buffer bacterial solution. It is connected to the main fluid path through one port of a multi-port valve. When the control unit issues a calibration command, the quality control liquid is drawn up and measured according to a preset program.

[0050] The control and processing unit also includes: A performance degradation model storage area is used to store the parameters of the performance degradation model; The self-learning algorithm module is used to automatically optimize the parameters of the performance degradation model based on historical calibration data.

[0051] Another object of the present invention is to provide an Internet of Things (IoT) monitoring system for water quality biotoxicity, comprising: Multiple online water quality biotoxicity analyzers are deployed at different monitoring sections as on-site monitoring terminals; each terminal is connected to the cloud platform server via 4G / 5G or fiber optic networks.

[0052] The cloud platform server is connected to the on-site monitoring terminal via a wired or wireless communication network; The cloud platform server is configured as follows: Receive the change data of the dynamic benchmark reference value and the equipment operation log reported by each field monitoring terminal; Based on big data analysis, the performance degradation model is collaboratively trained and globally optimized. The optimized model parameters or algorithm update package will be sent to the corresponding field monitoring terminal.

[0053] The cloud platform server performs the following collaborative tasks: Data aggregation: Receive historical sequences of dynamic baseline reference values ​​(R0_dyn, S0_dyn), ambient temperature, cumulative working time, etc., reported by each terminal.

[0054] Model co-training: Utilizing aggregated data from across the network, performance degradation models can be retrained or optimized using batch or incremental learning methods. For example, if it is found that the degradation coefficient k of a certain PMT model is generally higher than the theoretical value in high-temperature and high-humidity regions, the platform can generate an optimized model parameter package for the climatic conditions of that region.

[0055] Update and distribution: Securely distribute the optimized model parameters to the terminals in the corresponding regions to update their local models and achieve "more accurate with use" swarm intelligence optimization.

[0056] Another objective of this invention is to provide a method for early warning of the health status of photoelectric detection devices based on an online water quality biotoxicity analyzer, comprising: Continuously record the sequence of changes in the dynamic benchmark reference value over time or number of uses; Extract trend features from the changing sequence, including decay rate, fluctuation variance, or nonlinearity; The trend characteristics are compared with a preset health threshold range. When the trend exceeds the threshold range, an early warning report is generated that includes the device performance degradation level, recommended maintenance time, or remaining service life.

[0057] The control unit continuously records the dynamic reference value R0_dyn after each measurement or calibration. It analyzes its changes over time and extracts trend features: for example, it calculates the linear regression slope of R0_dyn over the past 30 days as the "attenuation rate"; and calculates the standard deviation of its diurnal fluctuation as the "variance variance". These feature values ​​are compared with preset health thresholds (e.g., attenuation rate > 0.5% / month, or variance variance > twice the normal value). If these thresholds are exceeded, a warning report is generated on the local touchscreen and remote platform: "PMT-A attenuation accelerates, check within 1 month recommended," or "PMT-B stability decreases, possibly contaminated, clean optical path recommended."

[0058] Another object of the present invention is to provide an electronic device comprising: processor; Memory, used to store computer programs; When the processor executes the computer program, it implements the steps of the error dynamic compensation method or the photoelectric detection device health status early warning method.

[0059] Another object of the present invention is to provide a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the steps of an error dynamic compensation method or a health status early warning method for a photoelectric detection device are implemented.

[0060] Another object of the present invention is to provide a standard reagent kit for calibrating water quality biotoxicity monitoring equipment, the standard reagent kit comprising: Lyophilized luminescent bacteria preparation with stable luminescence intensity; Resuscitation dilution for resuscitation agents; Osmotic pressure regulating fluid; Non-toxic standard control solutions for automated calibration procedures; The luminescence intensity value of the standard quality control liquid has been pre-calibrated and stored in the control and processing unit of the monitoring equipment, serving as the basis for updating the dynamic reference value.

[0061] To ensure the reliability of dynamic compensation, especially the accuracy of the automatic calibration path, a standard reagent kit is essential. This kit includes: Freeze-dried luminescent bacteria preparation: 3 vials, the bacterial strain is luminescent bacillus T3 variant (compliant with GB / T15441), the viable count of each vial is consistent to ensure stable luminescence intensity after thawing.

[0062] Resuscitation diluent: A special buffer solution for resuscitating lyophilized bacteria, providing optimal osmotic pressure and nutrients.

[0063] Osmotic pressure adjusting solution: 2M NaCl solution, used to adjust the osmotic pressure of the sample to avoid interference with toxicity detection.

[0064] Non-toxic standard control solution: a key component. This is a specially formulated buffer solution that contains no toxic substances but maintains stable luminescence in luminescent bacteria. When mixed with a specific batch of lyophilized bacterial resuscitation solution for the instrument, the resulting luminescence intensity value I_std is precisely calibrated at 25±0.1℃ before shipment. The calibration value is affixed to the bottle as a QR code, which can be scanned and entered by the instrument or pre-stored directly in its memory. This control solution is used in the automatic calibration program as a "ruler" for updating the dynamic reference value.

[0065] Example 1: Specific Implementation of the Dynamic Error Compensation Method Step S1: Simultaneously acquire luminous intensity signals through dual channels Detection cycle setting: Based on actual monitoring needs, a single detection cycle can be set to 30 minutes, which is adjustable within the range of 30 minutes to 24 hours. The detection cycle includes the mixing reaction time of the luminescent bacteria solution and the water sample, the signal acquisition time, etc.

[0066] Preparation of reference medium and water sample to be tested: Reference medium: Mix the non-toxic standard control solution and the revived luminescent bacteria solution at a volume ratio of 1:1 and inject the mixture into the reference reaction tank. Water sample to be tested: The water sample to be tested is extracted by a high-precision injection pump. After pretreatment to remove impurities with a diameter greater than 3mm, it is mixed evenly with the revived luminescent bacterial solution at a volume ratio of 1:1 and injected into the sample reaction cell.

[0067] Dual-channel synchronous measurement: The first photoelectric detection channel is aligned with the reference reaction cell, and the second photoelectric detection channel is aligned with the sample reaction cell. Under the synchronous control of the control and processing unit, the two channels simultaneously acquire the luminescence intensity signal. The signal acquisition frequency was set to 10Hz, and a total of 18,000 data points were collected in each detection cycle. The results were obtained in 30 minutes × 60 seconds × 10Hz, which yielded the real-time luminous intensity signal Rt sequence and the real-time luminous intensity signal St sequence.

[0068] Signal preprocessing steps The acquired real-time luminous intensity signal sequences Rt and St are subjected to filtering and noise reduction processing. In this embodiment, the Kalman filtering algorithm is used, and the specific process is as follows: Establish the state equation and observation equation for the Kalman filter: The state equation is X(k) = A × X(k-1) + W(k), where X(k) is the actual luminous intensity at time k, A is the state transition matrix with a value of 1, and W(k) is the process noise. The observation equation is Z(k) = H × X(k) + V(k), where Z(k) is the observed value at time k, i.e., the real-time luminous intensity signal Rt or the real-time luminous intensity signal St, H is the observation matrix with a value of 1, and V(k) is the observation noise.

[0069] Initialize filter parameters: Set the initial state estimate X(0) to the average of the first 100 data points collected, the initial covariance matrix P(0) to 0.01, the process noise variance Q to 0.001, and the observation noise variance R to 0.01.

[0070] Iterative calculation: Following the prediction and update steps of Kalman filtering, each data point is processed to obtain the filtered real-time luminous intensity signal Rt' sequence and the real-time luminous intensity signal St' sequence, effectively removing electronic noise and liquid flow noise from the signal.

[0071] Step S2: Obtain dynamic baseline reference values This embodiment uses a combination of methods a1 and a2 to determine the dynamic reference value, as detailed below: Calculation of predicted values ​​based on the performance degradation model: This embodiment uses a neural network algorithm to train a performance degradation model. The input parameters of the model include the cumulative running time t (in hours), operating temperature T (in °C), and historical signal drift data D (in %) of the photoelectric detection device. The neural network model has a three-layer structure: an input layer (3 neurons), a hidden layer (10 neurons), and an output layer (1 neuron). The output is the predicted value B_pred of the dynamic baseline reference. The model training data comes from the historical operation logs of the equipment, including calibration data at different operating times and operating temperatures. A total of 1,000 training samples were collected, and the prediction error of the trained model is less than ±2%.

[0072] Updated measured values ​​based on the automatic calibration procedure: The automatic calibration program is set to trigger once every 24 hours (condition b1), and additionally once after 50 water sample tests are completed (condition b2). Automatic calibration process: The control and processing unit controls the multi-way valve to switch to the calibration liquid flow path, injects a mixture of non-toxic standard quality control solution and luminescent bacteria solution into both the reference reaction cell and the sample reaction cell, and simultaneously collects the luminescence intensity signals of the two channels. After filtering, the measured reference value B_meas is obtained. The fusion calculation of dynamic benchmark reference value is: B = α × B_pred + (1-α) × B_meas, where α is the weighting coefficient with a value of 0.7. By fusing the predicted value and the measured value, both dynamic responsiveness and accuracy are taken into account.

[0073] Step S3: Signal compensation calculation This embodiment uses a normalization calculation method to compensate the filtered signal, and the specific formula is as follows: The compensated reference signal R_comp = Rt' / B The compensated sample signal S_comp = St' / B Where Rt' is the filtered real-time emission intensity signal of the reference channel, St' is the filtered real-time emission intensity signal of the sample channel, and B is the dynamic reference value.

[0074] By normalizing the signal, systematic errors caused by factors such as performance degradation of photoelectric detection devices and changes in ambient temperature are eliminated, so that the compensated signal can accurately reflect the true luminescence state of the luminescent bacteria.

[0075] Step S4: Calculation of biotoxicity indicators Relative luminance calculation: Relative luminance L = (average value of S_comp) / (average value of R_comp) × 100%, where the average value of S_comp and the average value of R_comp are the arithmetic mean of the compensated signal sequence, respectively.

[0076] Inhibition rate calculation: Inhibition rate I = (1 - L / 100) × 100%.

[0077] Toxic equivalent concentration calculation: Dose-response curves of a standard poison (zinc sulfate) were pre-plotted: a series of zinc sulfate standard solutions of different concentrations (0.1 mg / L, 0.5 mg / L, 1.0 mg / L, 2.0 mg / L, 5.0 mg / L) were prepared, and their inhibition rates were measured. A standard curve was plotted with zinc sulfate concentration on the x-axis and inhibition rate on the y-axis, and the fitting equation was obtained as I = a × C + b (where a and b are fitting coefficients). Based on the inhibition rate I of the water sample to be tested, the toxic equivalent concentration C_eq=(I - b) / a is calculated by substituting it into the fitting equation.

[0078] Example 2: Specific Structure of the Online Water Biotoxicity Analyzer This embodiment details the specific structure of the online water quality biotoxicity analyzer and the implementation details of each component as follows: The optical detection unit includes an excitation source, a beam splitter, a first photomultiplier tube, and a second photomultiplier tube, used to implement step S1 in the error dynamic compensation method. The excitation source uses an LED light source with a wavelength of 470nm, providing stable luminous intensity, adjustable power ranging from 1 to 10mW, and a lifespan greater than 10,000 hours. The beam splitter uses a semi-transparent mirror to divide the excitation source beam into two beams of equal intensity, which are respectively irradiated into the reference reaction cell and the sample reaction cell, ensuring that the excitation conditions of the two reaction cells are consistent. Both the first and second photomultiplier tubes are imported high-sensitivity photomultiplier tubes, model R928, with a response wavelength range of 185–900nm, dark current less than 1nA, and adjustable gain ranging from 10³ to 10⁻⁶. 6 The two photomultiplier tubes are symmetrically integrated and packaged in the same temperature control module. The temperature control module uses a semiconductor cooler (TEC) combined with a PID temperature control algorithm, with a temperature control range of 15 to 30°C and a control accuracy better than ±0.5°C, effectively avoiding the impact of temperature changes on the performance of the photomultiplier tubes.

[0079] The fluid reaction unit includes a reference reaction cell, a sample reaction cell, a high-precision syringe pump, and a multi-way valve. It is used to contain the reference medium and the water sample to be tested, and to mix and react them with the luminescent bacterial solution. Both the reference and sample reaction cells are made of quartz, with a volume of 10 mL, offering excellent light transmittance and corrosion resistance. The cell walls are specially treated to reduce the adsorption of the luminescent bacterial solution. The high-precision syringe pump uses a plunger pump structure, with an injection accuracy of ±1.0% and a range of 0.1–10 mL, enabling precise control of the dispensing volume of water sample, bacterial solution, and calibration solution. The multi-way valve uses an imported diaphragm valve with a lifespan of up to 1 million cycles. It has 8 channels for connecting to water sample lines, bacterial solution lines, calibration solution lines, pure water lines, and waste liquid lines, allowing for rapid switching of fluid paths. Each reaction cell is equipped with a micro magnetic stirrer at the bottom, with an adjustable stirring speed range of 500–1500 rpm, ensuring uniform mixing of the reaction system.

[0080] The control and processing unit is electrically connected to the optical detection unit and the fluid reaction unit, and is configured to execute a computer program stored in its memory to implement the steps of the error dynamic compensation method. The hardware architecture uses an ARM Cortex-A9 processor with a clock speed of 1GHz, equipped with 1GB of DDR3 memory and 16GB of flash memory, and supports multiple communication interfaces such as USB, RS485, RS232, and Ethernet.

[0081] The automatic calibration module includes a standard solution storage tank and a calibration fluid flow path connected to a multi-way valve. The control and processing unit controls the automatic calibration module to execute the automatic calibration program. The standard solution storage tank, made of corrosion-resistant PP material, has a volume of 2L and is used to store non-toxic standard quality control solution. The tank is equipped with a level sensor to monitor the remaining solution level in real time, issuing a reagent shortage alarm when the level falls below 10%. The calibration fluid flow path uses a rigid PVC pipe with a diameter of 6mm, connected to one channel of the multi-way valve. A solenoid valve is installed on the flow path to control the flow of the calibration fluid. After receiving the calibration trigger signal, the control and processing unit opens the solenoid valve, uses a high-precision syringe pump to draw the standard quality control solution, injects it into the reference reaction cell and sample reaction cell, completes signal acquisition and baseline value update, closes the solenoid valve, and discharges the calibration fluid in the reaction cell to the waste liquid pipeline.

[0082] The computer-executed program in this analyzer is functionally divided into: a performance degradation model storage area, which stores the parameters of the performance degradation model, the weight parameters and thresholds of the neural network performance degradation model, and supports the updating and backup of model parameters; a self-learning algorithm module, which automatically optimizes the parameters of the performance degradation model based on historical calibration data. After each automatic calibration, the performance degradation model is fine-tuned using new calibration data to optimize model parameters and improve prediction accuracy; a signal processing module, which implements functions such as Kalman filtering, normalization calculation, and toxicity index calculation; and a control module, which controls the collaborative work of the optical detection unit, fluid reaction unit, and automatic calibration module to automate the detection process.

[0083] Example 3: Specific Implementation of a Water Quality Biotoxicity Internet of Things Monitoring System This embodiment details the specific structure and workflow of the water quality biotoxicity IoT monitoring system as follows: A water quality biotoxicity IoT monitoring system includes: at least one online water quality biotoxicity analyzer as a field monitoring terminal; a cloud platform server connected to the field monitoring terminal via a wired or wireless communication network; wherein the cloud platform server is configured to: receive change data of the dynamic benchmark reference value and equipment operation logs reported by each field monitoring terminal; perform collaborative training and global optimization of the performance degradation model based on big data analysis; and distribute the optimized model parameters or algorithm update package to the corresponding field monitoring terminal.

[0084] The monitoring system includes on-site monitoring terminals, a communication network, and a cloud platform server. It utilizes 10 of the aforementioned online water quality biotoxicity analyzers, installed at different surface water monitoring sections. The analyzers communicate via RS485 combined with Ethernet, supporting the HJ212 protocol standard. A dual-network architecture, primarily wired Ethernet with 4G wireless communication as backup, ensures reliable data transmission and network communication.

[0085] The cloud platform server utilizes Alibaba Cloud and Tencent Cloud servers, configured with 8 cores, 16GB of memory, and a 1TB hard drive, running a Linux operating system and IoT platform software. It receives data reported from various field monitoring terminals, including dynamic baseline reference value B, compensated toxicity indicators (relative luminescence, inhibition rate, toxic equivalent concentration), and equipment operation logs (cumulative operating time, operating temperature, calibration records, etc.). Data is stored in a MySQL database with a retention period of at least two years, supporting data querying and statistics by monitoring terminal number, time range, and other criteria. Historical calibration and operational data from all field monitoring terminals are periodically (e.g., weekly) aggregated to construct a large-scale training dataset. A distributed machine learning framework is used to collaboratively train the performance degradation model, optimizing its generalization ability. The optimized model parameters are packaged into an algorithm update package and distributed to each field monitoring terminal via the network; the terminal automatically updates its local model parameters upon receipt. Web and mobile monitoring interfaces are provided, allowing users to view the real-time operating status and monitoring data of each terminal. Remote calibration commands are supported, allowing users to manually trigger the automatic calibration program of the field monitoring terminals as needed.

[0086] Example 4: Specific Implementation of a Health Status Early Warning Method for Photoelectric Detection Devices The specific process of the health status early warning method for photoelectric detection devices in this embodiment is described in detail below. A method for early warning of the health status of photoelectric detection devices based on the above-mentioned analyzer includes: continuously recording the change sequence of dynamic reference values ​​over time or usage frequency, with a recording interval of 1 hour, forming a B(t) time series; extracting trend features of the change sequence, including decay rate, fluctuation variance, or nonlinearity, where decay rate v is calculated by the linear fitting slope of B(t) over the most recent 30 days, v=(B(t0)-B(t30)) / 30, where t0 is the current time and t30 is the time 30 days ago; fluctuation variance σ² is calculated by the variance of the B(t) sequence over the most recent 30 days, reflecting the stability of the reference value; and nonlinearity γ is achieved by fitting the B(t) sequence with a cubic polynomial, γ=|a3|, where a3 is the coefficient of the cubic term, reflecting the degree of nonlinearity of the decay trend; and comparing the trend features with a preset health threshold range, and when the threshold range is exceeded, generating an early warning report containing the device performance degradation level, recommended maintenance time, or remaining service life. In the health status early warning system, health thresholds are pre-set, including a decay rate threshold of v ≤ 0.5% / day, a fluctuation variance threshold of σ² ≤ 0.01, and a nonlinearity threshold of γ ≤ 0.0001. The execution program compares these thresholds and generates an early warning report. When any trend feature exceeds the corresponding threshold range, the photoelectric detection device is deemed to have degraded. Based on the degree to which the feature exceeds the threshold, the performance degradation level is categorized as mild, moderate, and severe. Mild degradation refers to exceeding the threshold by less than 10%, moderate degradation by 10%–50%, and severe degradation by more than 50%. An early warning report is generated based on the degree of performance degradation. The report includes: device number, performance degradation level, current decay rate, fluctuation variance, nonlinearity, recommended maintenance time, and predicted remaining service life. Mild degradation recommends maintenance within one month, moderate degradation within one week, and severe degradation immediately. The remaining service life is calculated based on decay trend fitting. The early warning report is pushed to the user's mobile app and email via a cloud platform server.

[0087] Example 5: Specific Implementation of Electronic Devices This embodiment details the specific structure of the electronic device, which can serve as the control and processing unit of an online water quality biotoxicity analyzer. It includes a processor and a memory for storing computer programs. When the processor executes the computer program, it implements the steps of any of the following methods: the error dynamic compensation method and the photoelectric detection device health status early warning method. The processor of this electronic device uses an STM32H743VI microcontroller based on an ARM Cortex-M7 core, with a main frequency of 480MHz, supporting single-precision and double-precision floating-point operations. The memory is divided into a program memory and a data memory. The program memory uses 16MB of SPI Flash to store the computer program and performance degradation model parameters; the data memory uses 64MB of SDRAM to store temporary data and signal sequences during the detection process. The electronic device is equipped with analog signal interfaces, digital interfaces, and display interfaces. The analog signal interface includes a signal amplification circuit and an analog-to-digital conversion circuit (16-bit precision, 1MSps sampling rate) for receiving the output signal of the photomultiplier tube. The digital interface includes a GPIO interface, a UART interface (supporting RS232 and RS485 communication), an SPI interface, and an I2C interface for connecting peripherals such as multi-way valves, syringe pumps, and level sensors. The display interface supports an LCD touch screen interface and can connect to a 10-inch color LCD touch screen.

[0088] In electronic devices, computer programs are written in C language and based on the FreeRTOS operating system. They mainly include an initialization module, which is used to complete the initialization configuration of the processor, memory, and peripheral interfaces. The signal acquisition module is used to control the analog-to-digital conversion circuit to acquire photoelectric detection signals; The signal processing module is used to implement functions such as filtering and noise reduction, dynamic reference value calculation, signal compensation, and toxicity index calculation. The equipment control module is used to control the operation of peripherals such as multi-way valves, injection pumps, and temperature control modules; The communication module is used to enable data interaction with the cloud platform server; The early warning module is used to assess the health status of photoelectric detection devices and provide early warning functions.

[0089] Example 6: Specific Composition of the Standard Reagent Kit This embodiment details the specific composition and technical parameters of the standard reagent kit.

[0090] The standard reagent kit includes a lyophilized luminescent bacterial preparation with stable luminescence intensity; a resuscitation diluent for resuscitating the bacterial preparation; an osmotic pressure regulating solution; and a non-toxic standard quality control solution for the automatic calibration procedure. The luminescence intensity value of the standard quality control solution has been pre-calibrated and stored in the control and processing unit of the monitoring device as the basis for updating the dynamic reference value.

[0091] The strain of the freeze-dried luminescent bacteria preparation is *Bacillus luminifera*, which conforms to the standard GB / T 15441-1995. Each vial contains 0.5 mL and needs to be freeze-dried for storage. The shelf life is 1 year at -20℃. The luminescence intensity is stable after thawing. Under non-toxic conditions, the half-life of luminescence intensity 1 is greater than 8 hours.

[0092] The main component of the resuscitation diluent is phosphate buffer (pH=7.0), which contains an appropriate amount of nutrients. Each bottle contains 100mL and can be stored at room temperature. It has a shelf life of 6 months and is used to revive lyophilized luminescent bacteria preparations. Each vial of lyophilized bacteria preparation requires the addition of 5mL of resuscitation diluent.

[0093] The osmotic pressure regulating solution is a sodium chloride solution with a concentration of 0.9%, 500 mL per bottle, stored at room temperature, with a shelf life of 1 year. It is used to regulate the osmotic pressure of the reaction system to ensure the normal metabolic activity of the luminescent bacteria.

[0094] The non-toxic standard control solution is a glucose solution prepared with deionized water at a concentration of 10 mg / L, 1 L per bottle, stored at room temperature, with a shelf life of 6 months. It is non-toxic and does not inhibit the luminescence intensity of luminescent bacteria. Its luminescence intensity value has been pre-calibrated with a calibration error of less than ±2%. This calibration value is stored in the control and processing unit of the monitoring equipment as the basis for updating the dynamic benchmark reference value.

[0095] The procedure for using standard reagents: Remove the lyophilized bacterial agent from the -20℃ freezer, let it stand at room temperature for 5 minutes, add 5mL of recovery diluent, gently shake to mix evenly, and let it recover at room temperature for 15 minutes; add an appropriate amount of osmotic pressure adjusting solution to the recovered bacterial solution to match the osmotic pressure of the bacterial solution with that of the water sample to be tested. Calibration and testing: Non-toxic standard control solution is mixed with bacterial solution for automatic calibration; water sample to be tested is mixed with bacterial solution for toxicity testing.

[0096] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for dynamic error compensation in online monitoring of water biotoxicity, characterized in that, The dynamic error compensation method includes the following steps: S1. Within a single detection cycle, the real-time luminous intensity signal Rt of the reference reaction unit containing the reference medium is simultaneously measured using the first photoelectric detection channel, and the real-time luminous intensity signal St of the sample reaction unit containing the water sample to be tested is simultaneously measured using the second photoelectric detection channel. S2. Obtain the dynamic reference value corresponding to the current detection time. The dynamic reference value is used to characterize the expected signal reference of the first photoelectric detection channel and the second photoelectric detection channel under ideal conditions without interference from toxic substances. S3. Based on the dynamic reference value, normalize or differentially calculate the real-time luminous intensity signal Rt and the real-time luminous intensity signal St to obtain the compensated reference signal and sample signal. S4. Calculate the biotoxicity index of the water sample to be tested based on the compensated reference signal and the sample signal.

2. The error dynamic compensation method according to claim 1, characterized in that, The dynamic reference value is determined in any of the following ways: a1. Based on a pre-constructed performance degradation model of optoelectronic detection devices, and according to the cumulative running time, operating temperature and historical signal drift data of the devices, predict and generate the dynamic reference value at the current moment. a2. By executing an automatic calibration procedure, a known non-toxic standard solution is injected into the reference reaction unit and the sample reaction unit, and the corresponding luminescence intensity signal is measured to update the dynamic reference value with the measured value. The performance degradation model is an exponential degradation model based on semiconductor device aging theory, a Weibull distribution model, or a prediction model trained by a machine learning algorithm; the machine learning algorithm includes neural networks, support vector machines, or random forests. The triggering conditions for the automatic calibration procedure include at least one of the following: b1. Periodic time triggering; b2. Triggered after a predetermined number of water sample tests have been completed; b3. Triggered when the signal-to-noise ratio is detected to be lower than the set threshold in real time; b4. Triggered by receiving calibration instructions from a remote server.

3. The error dynamic compensation method according to claim 4, characterized in that, Step S1 includes a signal preprocessing step: filtering and noise reduction processing is performed on the real-time light intensity signal Rt and the real-time light intensity signal St respectively. The filtering and noise reduction processing includes, but is not limited to, sliding window averaging filtering, Kalman filtering or wavelet thresholding. The biotoxicity indicators in step S4 include at least one of relative luminescence, inhibition rate, and toxic equivalent concentration calculated based on the standard toxicant dose-response curve; the standard toxicant includes zinc sulfate, 3,5-dichlorophenol, or potassium dichromate.

4. An online water quality biotoxicity analyzer, characterized in that, include: An optical detection unit, comprising an excitation source, a beam splitter, a first photomultiplier tube, and a second photomultiplier tube, is used to implement step S1 in the error dynamic compensation method; The fluid reaction unit includes a reference reaction cell, a sample reaction cell, a high-precision injection pump, and a multi-way valve, which are used to contain the reference medium and the water sample to be tested and mix them with the luminescent bacteria solution for reaction. A control and processing unit, electrically connected to the optical detection unit and the fluid reaction unit, is configured to execute a computer program stored in its memory to implement the steps of the error dynamic compensation method according to any one of claims 1 to 6.

5. The online water quality biotoxicity analyzer according to claim 7, characterized in that, The first photomultiplier tube and the second photomultiplier tube are symmetrically integrated and packaged in the same temperature control module, and the temperature control accuracy of the temperature control module is better than ±0.5℃; It also includes a built-in automatic calibration module, which comprises: Standard solution storage tank; The calibration fluid flow path connected to the multi-way valve; The control and processing unit controls the automatic calibration module to execute the automatic calibration program. The control and processing unit further includes: A performance degradation model storage area is used to store the parameters of the performance degradation model; The self-learning algorithm module is used to automatically optimize the parameters of the performance degradation model based on historical calibration data.

6. A water quality biotoxicity IoT monitoring system, characterized in that, include: At least one online water quality biotoxicity analyzer as described in any one of claims 4 to 5 shall be used as a field monitoring terminal; The cloud platform server is connected to the on-site monitoring terminal via a wired or wireless communication network; The cloud platform server is configured as follows: Receive the change data of the dynamic benchmark reference value and the equipment operation log reported by each field monitoring terminal; Based on big data analysis, the performance degradation model is collaboratively trained and globally optimized. The optimized model parameters or algorithm update package will be sent to the corresponding field monitoring terminal.

7. A method for early warning of the health status of a photoelectric detection device based on the analyzer described in any one of claims 4 to 5, characterized in that, include: Continuously record the sequence of changes in the dynamic benchmark reference value over time or number of uses; Extract trend features from the changing sequence, including decay rate, fluctuation variance, or nonlinearity; The trend characteristics are compared with a preset health threshold range. When the trend exceeds the threshold range, an early warning report is generated that includes the device performance degradation level, recommended maintenance time, or remaining service life.

8. An electronic device, characterized in that, include: processor; Memory, used to store computer programs; When the processor executes the computer program, it implements the steps of the method as described in any one of claims 1 to 3 and 7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 3 and 7.

10. A standard reagent kit for calibrating water quality biotoxicity monitoring equipment, characterized in that, The standard reagent kit includes: Lyophilized luminescent bacteria preparation with stable luminescence intensity; Resuscitation dilution for resuscitation agents; Osmotic pressure regulating fluid; Non-toxic standard control solutions for automated calibration procedures; The luminescence intensity value of the standard quality control liquid has been pre-calibrated and stored in the control and processing unit of the monitoring equipment, serving as the basis for updating the dynamic reference value.

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