Low-power steady-state Kalman filtering double-loop control ultraviolet laser applied to intestinal flora detection and working method

Abstract

The invention belongs to the technical field of biological experiment irradiation, and particularly relates to a low-power steady-state Kalman filtering double-loop control ultraviolet laser applied to intestinal flora detection and a working method. The continuous laser is used for generating laser and providing energy required for exciting a laser medium for the energy pumping source; the light path control module is used for ensuring the performance and the output power of the laser and realizing accurate control on laser beams; the photoelectric amplification module is used for converting the electric signal into an optical signal, amplifying the optical signal and converting the optical signal back to the electric signal; and the feedback controller is used for keeping the stability of the laser, suppressing interference and improving the precision and robustness of the system. The device is used for solving the problems that part of components in an existing experimental device can form a complex electromagnetic interference environment, and a conventional feedback control system can be influenced by high-frequency noise to generate error adjustment.

Description

Technical Field

[0001] The present application belongs to the field of biological experimental irradiation technology, and specifically relates to a low-power steady-state Kalman filter dual-loop controlled ultraviolet laser and a working method for intestinal flora detection. Background Art

[0002] In the field of laser technology, output power stability has always been one of the core indicators that determine device performance. Traditional lasers are affected by multiple factors, including the nonlinear characteristics of the power supply injection current, mode hopping caused by thermally induced cavity deformation, and gain coefficient drift of semiconductor materials. Their output power fluctuations generally remain above 1%. Especially in ultraviolet applications, the nonlinear crystal frequency doubling efficiency is exponentially sensitive to temperature, making power stability control even more challenging. Existing commercial lasers generally use a closed-loop constant current drive scheme, which adjusts the drive current by real-time detection of changes in the internal resistance of the laser diode to compensate for output power attenuation caused by thermal effects or device aging. However, the hysteresis characteristics of the thermodynamic system result in a temperature response time constant that is usually greater than seconds, while the current regulation bandwidth of the circuit system is limited by the switching rate of the power device. The combined effect of the two creates a dynamic response gap, making it difficult to cope with the rapid environmental disturbances commonly found in biomedical experiments.

[0003] In order to improve power stability, the industry has explored a variety of technical paths. In terms of thermal management, a research team proposed combining a vapor compression variable frequency refrigeration system with electrothermal compensation, and achieved a temperature control accuracy of ±0.01°C by improving the PID algorithm. However, the mechanical vibration of the miniaturized refrigeration system will introduce a new noise source. Another type of solution uses external modulation technology, which uses the Pockels effect of electro-optical crystals or the Bragg diffraction effect of acousto-optic crystals to achieve power closed-loop control by adjusting the modulator attenuation in real time. Although this type of method can suppress power fluctuations to below 0.1%, the modulation device itself will introduce about 15% optical power loss and requires a complex optical path calibration system, which is difficult to meet the needs of low-power application scenarios. It is worth noting that existing research focuses on improving the stability of industrial-grade high-power lasers, and there is still a significant technical gap in the precision control system for milliwatt-level low-power output.

[0004] UV sensitization experiments in the biomedical field place special demands on lasers: maintaining μW / cm 2While maintaining high-level irradiation intensity, it is necessary to ensure that power fluctuations do not exceed 0.5%, which poses a double challenge to traditional control methods. On the one hand, the thermal capacity of the miniaturized culture device is extremely small, and an instantaneous change of 0.1°C in ambient temperature can cause significant power drift; on the other hand, new experimental methods such as optogenetics require the laser to switch between different power modes within seconds, and the existing slow-response system based on thermal compensation cannot meet the dynamic adjustment requirements. More importantly, the precision optical sensors, microfluidic chips and other components in the experimental device will form a complex electromagnetic interference environment, and conventional feedback control systems are easily affected by high-frequency noise and misadjustment.

[0005] Based on this technical background, the present invention proposes an innovative solution that integrates advanced control algorithms with new detection architectures. By constructing a multi-parameter fusion detection module, multi-dimensional state information such as laser junction voltage, heat sink temperature, and output spot morphology is synchronously collected, and the improved Kalman filter is combined to achieve optimal estimation of environmental disturbances. The algorithm can effectively distinguish between slow-changing temperature drift and transient electromagnetic interference, and establish a dynamic compensation model with time-varying gain characteristics. Experimental verification shows that while maintaining milliwatt-level low-power operation, the control system can suppress the fluctuation of ultraviolet laser output power to within 0.3%, which is particularly suitable for biomedical precision experimental scenarios such as cell photoactivation and microfluidic chip detection. Compared with traditional methods, the present invention has made a breakthrough in introducing state observation theory into the field of laser power control, providing a new technical path for the design of optical systems for miniaturized biomedical instruments. Summary of the Invention

[0006] The present invention provides a low-power steady-state Kalman filter dual-loop controlled ultraviolet laser for intestinal flora detection. Some components in existing experimental devices will form a complex electromagnetic interference environment, and conventional feedback control systems will be affected by high-frequency noise and cause misadjustment.

[0007] The present invention provides a working method of a low-power steady-state Kalman filter dual-loop controlled ultraviolet laser applied to intestinal flora detection, so as to apply the low-power steady-state Kalman filter dual-loop controlled ultraviolet laser to intestinal flora detection.

[0008] The present invention is achieved through the following technical solutions:

[0009] A low-power steady-state Kalman filter dual-loop controlled ultraviolet laser for intestinal flora detection, the laser comprising a continuous laser, an optical path control module, a photoelectric amplification module, and a feedback controller, the continuous laser being connected to the optical path control module and the feedback controller, respectively, and the optical path control module being connected to the photoelectric amplification module and the feedback controller, respectively;

[0010] The continuous laser is used to generate laser light and provide the energy pump source with the energy required to excite the laser medium;

[0011] The optical path control module is used to ensure the performance and output power of the laser and achieve precise control of the laser beam;

[0012] The photoelectric amplification module is used to convert electrical signals into optical signals, amplify the optical signals, and convert the optical signals back into electrical signals;

[0013] The feedback controller is used to maintain the stability of the laser, suppress interference, and improve the accuracy and robustness of the system.

[0014] Furthermore, the photoelectric amplification module includes an attenuation plate, a feedback detector and a current amplifier;

[0015] The spectrometer of the optical path control module transmits the reflected laser to the attenuation plate of the photoelectric amplifier module, and the attenuation plate transmits the received reflected laser to the feedback detector, and the feedback detector transmits the received reflected laser to the feedback controller through the current amplifier.

[0016] Furthermore, the feedback controller includes a temperature management module, an A / D converter, an FPGA circuit, a single-chip microcomputer module and an acousto-optic driver;

[0017] The A / D converter receives the signal output by the current amplifier, transmits the signal to the FPGA circuit, and the FPGA circuit transmits the signal to the single-chip microcomputer module and the acousto-optic driver respectively. The acousto-optic driver then transmits the signal to the acousto-optic modulator and continuous laser of the optical path control module.

[0018] Furthermore, the temperature management module includes an LD constant temperature control system, the LD power supply constant temperature control system includes an LD temperature measurement circuit, an LD power supply and a TEC drive circuit, the TEC drive circuit receives the signal converted by the Kalman filter through a D / A converter, the TEC drive circuit transmits the drive signal to the TEC temperature control module, the TEC temperature control module monitors the LD power supply, and the signal emitted by the LD power supply is transmitted to the A / D converter through the LD temperature measurement circuit.

[0019] Furthermore, the power of the laser is 0.1-10 mW, and the wavelength range of the laser is 365-405 nm ultraviolet light.

[0020] A method for operating a low-power steady-state Kalman filter dual-loop controlled ultraviolet laser for intestinal flora detection, the method comprising:

[0021] First, the laser's micro-emitting head accurately guides the generated ultraviolet laser to the required working area for irradiation;

[0022] When the laser is working, the built-in temperature management module monitors the temperature changes inside the laser in real time;

[0023] When the laser is working, its working current value is detected in real time through the Kalman filter model;

[0024] The detected current value is processed through the Kalman filter model. Combined with the pre-set model parameters and the consideration of temperature interference, the current detection value is corrected and filtered to obtain more accurate current feedback information, thus achieving precise control of the laser.

[0025] Furthermore, the temperature compensation control and current closed-loop control steps of the Kalman filter are specifically as follows:

[0026] Steps in model building;

[0027] Steps to make predictions using the established model;

[0028] Steps for updating based on predictions;

[0029] An iterative process based on prediction and update.

[0030] Furthermore, the model establishment step specifically includes a state equation and an observation equation, wherein the state equation describes the change of temperature over time, and the observation equation obtains temperature information from actual measurement values;

[0031] The prediction step is specifically as follows: Kalman filtering predicts the state at the current moment based on the state estimation at the previous moment;

[0032] The updating step is specifically as follows: Kalman filtering corrects the predicted value according to the current observation value to obtain a more accurate state estimate; including calculating the Kalman gain, updating the estimated state and updating the covariance;

[0033] The iterative process is specifically to alternate between prediction and update to recursively estimate the state of the system; each iteration updates the state estimate based on new observation data, thereby achieving continuous prediction of temperature.

[0034] Furthermore, the power supply control step of the LD pump is specifically as follows:

[0035] Steps in model building;

[0036] Steps to make predictions using the established model;

[0037] Steps for updating based on predictions;

[0038] An iterative process based on prediction and update.

[0039] Furthermore, the steps of establishing the model specifically include a state equation and an observation equation, wherein the state equation describes the change of current over time, and the observation equation obtains current information from actual measurement values;

[0040] The prediction step specifically includes predicting the state at the current moment based on the state estimation at the previous moment;

[0041] The updating step specifically includes correcting the predicted value based on the current observation value to obtain a more accurate state estimate; including calculating the Kalman gain, updating the estimated state and updating the covariance;

[0042] The iterative process is specifically to alternate between prediction and update to recursively estimate the state of the system; each iteration updates the state estimate based on new observation data, thereby achieving continuous prediction of temperature.

[0043] The beneficial effects of the present invention are:

[0044] The laser of the present invention can output stably, and can realize fine-tuning control by controlling the power current, can realize precise micro-control, and realize 0.1mw step drive control; the laser has a Kalman filter closed-loop control algorithm to realize current and temperature dual-loop feedback control.

[0045] The present invention can achieve precise fine-tuning through dual-loop feedback control, ensure the accuracy of the experiment, and provide accurate data values. Through Kalman filter dual-loop control, it can solve the demand for precise micro-control and meet the actual needs of experimental testing. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 It is a structural schematic diagram of the present invention.

[0047] Figure 2 It is a block diagram of the LD constant temperature control system of the present invention.

[0048] Figure 3 It is a flow chart of the Kalman filter algorithm execution program of the present invention.

[0049] Figure 4 This is a flow chart of the temperature compensation current control system of the pump source LD power supply of the present invention. DETAILED DESCRIPTION

[0050] In the following description, specific details such as specific system structures and technologies are provided for illustration rather than limitation to facilitate a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid obstructing the description of the present application with unnecessary details.

[0051] It will be understood that when used in this specification and the appended claims, the term "comprising" indicates the presence of described features, integers, steps, operations, elements and / or components, but does not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof.

[0052] It should also be understood that the terms used in this specification are only for the purpose of describing specific embodiments and are not intended to limit the present application. As used in this specification and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms unless the context clearly indicates otherwise.

[0053] The following is a clear and complete description of the technical solutions in the embodiments of this application in conjunction with the drawings in the specification of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0054] In the following description, many specific details are set forth to facilitate a full understanding of the present application. However, the present application may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present application. Therefore, the present application is not limited to the specific embodiments disclosed below.

[0055] Implementation Method 1

[0056] This embodiment provides a low-power steady-state Kalman filter dual-loop controlled ultraviolet laser for intestinal flora detection. Figure 1 As shown, the laser includes a continuous laser, an optical path control module, a photoelectric amplification module and a feedback controller, the continuous laser is connected to the optical path control module and the feedback controller respectively, and the optical path control module is connected to the photoelectric amplification module and the feedback controller respectively;

[0057] The continuous laser is used to generate laser light and provide the energy pump source with the energy required to excite the laser medium;

[0058] The optical path control module is used to ensure the performance and output power of the laser and achieve precise control of the laser beam;

[0059] The photoelectric amplification module is used to convert electrical signals into optical signals, amplify the optical signals, and convert the optical signals back into electrical signals;

[0060] The feedback controller is used to maintain the stability of the laser, suppress interference, and improve the accuracy and robustness of the system.

[0061] The optical path control module includes a first aperture, a first polarizer, an acousto-optic modulator, a second aperture, a spatial filter, a second polarizer, and a beam splitter, which are arranged in sequence. After a continuous laser signal enters the optical path control module, it passes through the first aperture, the first polarizer, the acousto-optic modulator, the second aperture, the spatial filter, and the second polarizer, which are arranged vertically in the optical path, and then reaches the beam splitter placed at a 45-degree angle to the optical path. After passing through the beam splitter, the laser signal is split into a reflected laser signal and a transmitted laser signal and output. At the same time, the acousto-optic modulator receives the radio frequency signal output by the acousto-optic driver in the feedback control module, adjusts the power of the laser signal in real time, and realizes a laser signal with high output power stability.

[0062] Furthermore, the photoelectric amplification module includes an attenuation plate, a feedback detector and a current amplifier;

[0063] The spectrometer of the optical path control module transmits the reflected laser to the attenuation plate of the photoelectric amplifier module, and the attenuation plate transmits the received reflected laser to the feedback detector, and the feedback detector transmits the received reflected laser to the feedback controller through the current amplifier.

[0064] Furthermore, the feedback controller includes a temperature management module, an A / D converter, an FPGA circuit, a single-chip microcomputer module and an acousto-optic driver;

[0065] The A / D converter receives the signal output by the current amplifier, transmits the signal to the FPGA circuit, and the FPGA circuit transmits the signal to the single-chip microcomputer module and the acousto-optic driver respectively. The acousto-optic driver then transmits the signal to the acousto-optic modulator and continuous laser of the optical path control module.

[0066] The voltage signal output by the current amplifier enters the feedback control module and is output as a 24-bit serial digital signal through the A / D converter. The FPGA circuit receives the digital signal, processes it, and outputs it as a real-time power digital signal and a voltage signal. The real-time power digital signal is sent to the single-chip microcomputer module and displayed. The single-chip microcomputer module can also set the corresponding transmitted laser power according to different optical devices to be tested. The acousto-optic driver receives the voltage signal output by the FPGA circuit and outputs a corresponding radio frequency signal.

[0067] Further, such as Figure 2As shown, the temperature management module includes an LD constant temperature control system, and the LD power supply constant temperature control system includes an LD temperature measurement circuit, an LD power supply and a TEC drive circuit. The TEC drive circuit receives the signal converted by the Kalman filter through the D / A converter, and the TEC drive circuit transmits the drive signal to the TEC temperature control module. The TEC temperature control module monitors the LD power supply, and the signal emitted by the LD power supply is transmitted to the A / D converter through the LD temperature measurement circuit.

[0068] Furthermore, the power of the laser is 0.1-10 mW, and the wavelength range of the laser is 365-405 nm ultraviolet light.

[0069] The laser is equipped with a micro-emitting head, which can effectively guide the laser to the designated working area for precise irradiation, ensuring concentrated energy delivery, which is conducive to improving work efficiency and processing accuracy.

[0070] The laser has the ability to adjust the laser power and can flexibly adjust the laser output power according to actual work needs and application scenarios to adapt to different task requirements, such as providing appropriate energy in different material processing or detection tasks.

[0071] The laser detects the current in real time during operation and introduces a Kalman filter model to address current feedback control. In particular, the key factor of temperature interference is taken into account, making current feedback control more precise, thereby ensuring the stability and accuracy of the laser output.

[0072] The photoelectric amplifier module includes an attenuation plate, a feedback detector and a current amplifier. The photosensitive surface of the feedback detector is larger than the area of ​​the laser signal spot to ensure that the feedback detector can accurately measure the power of the laser signal. The reflected laser signal enters the photoelectric amplifier module, passes through the attenuation plate vertically, and reduces the laser power to a range that the feedback detector can withstand. It then enters the feedback detector for photoelectric conversion. The output current signal is output as a voltage signal after passing through the current amplifier.

[0073] The feedback controller mainly includes an A / D converter (Analog to Digital Converter), an FPGA (Field Programmable Gate Array) circuit, a single-chip microcomputer module and an acousto-optic driver. The voltage signal output by the feedback current amplifier enters the feedback control module and is output as a 24-bit serial digital signal through the A / D converter. The FPGA circuit receives the digital signal, processes it and outputs it as a real-time power digital signal and voltage signal. The real-time power digital signal is sent to the single-chip microcomputer module and displayed. The single-chip microcomputer module can also set the corresponding transmitted laser power according to different optical devices to be tested. The acousto-optic driver receives the voltage signal output by the FPGA circuit and outputs the corresponding radio frequency signal.

[0074] To achieve stable control of the system's output laser power, it is imperative that the proportional relationship between the system's output laser power and the current signal generated by the feedback detector remain constant. When the laser power is stably controlled after being reflected by the spectrometer and entering the feedback detector, the actual stable control of the current signal generated by the feedback detector within the feedback loop is achieved. However, the system's output laser power, which truly requires stable control, is outside the feedback loop. Therefore, to ensure that this proportional relationship remains constant, the overall laser power output is estimated through inner-loop feedback. At the same time, the inner-loop feedback is achieved through two feedback loops, the acousto-optic modulator and the continuous laser. Temperature state observation and current compensation closed-loop control are achieved through temperature compensation and closed-loop feedback control of these two links.

[0075] Implementation Method 2

[0076] This embodiment adopts a working method of a low-power steady-state Kalman filter dual-loop controlled ultraviolet laser applied to intestinal flora detection. The working method includes: first, the micro-emitter head of the laser accurately guides the generated ultraviolet laser to the required working area for irradiation; according to the specific task requirements, the power of the laser is adjusted by the relevant control device to set the appropriate power value to meet the energy requirements of the current work.

[0077] Heat dissipation and temperature rise monitoring: During operation, the laser relies on the solid heat sink to dissipate heat. However, due to the lag in conduction time, a certain degree of temperature rise will occur inside the laser. At the same time, the built-in temperature management module monitors the temperature changes inside the laser in real time to keep abreast of the temperature rise dynamics.

[0078] Current detection and feedback: When the laser is working, its operating current value is detected in real time through the Kalman filter model. Due to factors such as temperature interference, relying solely on current detection for feedback control may result in deviations, so the Kalman filter model is introduced.

[0079] like Figure 3As shown in the figure, Kalman filter processing and precise control: The detected current value is processed through the Kalman filter model. Combined with the pre-set model parameters and the consideration of temperature interference, the current detection value is corrected and filtered to obtain more accurate current feedback information. Based on this precise current feedback value, the control system can timely and accurately adjust the relevant operating parameters of the laser, such as power, to achieve precise control of the laser and ensure its stable and efficient operation under low-power steady-state conditions.

[0080] Assume the state equation of the system is:

[0081] x k =F k x k-1 +B k u k +w k Among them, x k is the state vector at the kth moment, F k is the state transition matrix, B k is the control input matrix, u k is the control vector, w k is the process noise, assuming w k The mean is 0 and the covariance is Q k Gaussian distribution.

[0082] The observation equation is:

[0083] z k =H k x k +v k

[0084] Among them, z k is the observation vector at the kth moment, H k is the observation matrix, v k is the observation noise, assuming v k The mean is 0 and the covariance is R k Gaussian distribution.

[0085] Furthermore, the temperature compensation control and current closed-loop control steps of the Kalman filter are specifically as follows:

[0086] Steps in model building;

[0087] Steps to make predictions using the established model;

[0088] Steps for updating based on predictions;

[0089] An iterative process based on prediction and update.

[0090] Furthermore, the steps of establishing the model are specifically as follows: a mathematical model describing temperature changes includes a state equation and an observation equation, wherein the state equation describes the change of temperature over time, and the observation equation describes how to obtain temperature information from actual measurement values; the state equation can be expressed as (X(k)=AcdotX(k-1)+W(k-1)), and the observation equation is (Z(k)=HcdotX(k)+V(k)), wherein (X(k)) is the temperature state at the (k)th moment, (Z(k)) is the corresponding measurement value, (W(k)) and (V(k)) are process noise and observation noise, respectively, and (A) and (H) are the state transfer matrix and observation matrix.

[0091] The prediction step is specifically as follows: Kalman filtering predicts the state at the current moment based on the state estimate at the previous moment; this involves calculating the predicted state and predicted covariance. The predicted state is based on the dynamic characteristics of the model, while the predicted covariance takes into account the influence of process noise;

[0092] The updating step is specifically as follows: the Kalman filter corrects the predicted value based on the current observation value to obtain a more accurate state estimate; this includes calculating the Kalman gain, updating the estimated state and updating the covariance; the Kalman gain is a function of the prediction covariance and the observation noise covariance, which determines the weight of the observation value in the updating process;

[0093] The iterative process is specifically to alternate between prediction and update to recursively estimate the state of the system; each iteration updates the state estimate based on new observation data, thereby achieving continuous prediction of temperature.

[0094] Each iteration updates the state estimate based on new observations, enabling continuous temperature prediction. Kalman filtering can effectively reduce noise and errors in temperature measurements and improve the accuracy of temperature prediction.

[0095] Furthermore, the power supply control step of the LD pump is specifically as follows:

[0096] Steps in model building;

[0097] Steps to make predictions using the established model;

[0098] Steps for updating based on predictions;

[0099] An iterative process based on prediction and update.

[0100] Furthermore, the steps of establishing the model are specifically as follows: a mathematical model describing the current change includes a state equation and an observation equation, wherein the state equation describes the change of current over time, and the observation equation describes how to obtain current information from the actual measurement value; the state equation can be expressed as (x(k+1)=A(k)x(k)+B(k)u(k)+w(k)), and the observation equation is (y(k)=H(k)x(k)+v(k)), wherein (x(k)) is the current state at the (k)th moment, (y(k)) is the corresponding measurement value, (w(k)) and (v(k)) are process noise and observation noise, respectively, and (A(k)), (B(k)) and (H(k)) are the state transfer matrix, control matrix and observation matrix.

[0101] The prediction step specifically involves predicting the state at the current moment based on the state estimate at the previous moment. This involves calculating the predicted state and predicted covariance. The predicted state is based on the dynamic characteristics of the model, while the predicted covariance takes into account the influence of process noise.

[0102] The updating step specifically involves correcting the predicted value based on the current observation value to obtain a more accurate state estimate; this includes calculating the Kalman gain, updating the estimated state, and updating the covariance; the Kalman gain is a function of the predicted covariance and the observation noise covariance, which determines the weight of the observation value in the updating process;

[0103] The iterative process is specifically to alternate between prediction and update to recursively estimate the state of the system; each iteration updates the state estimate based on new observation data, thereby achieving continuous prediction of temperature.

[0104] Each iteration updates the state estimate based on new observations, enabling continuous prediction of current. Kalman filtering can effectively reduce noise and errors in current measurement and improve the accuracy of current prediction.

[0105] like Figure 4 As shown, the temperature loop control

[0106] Temperature loop control primarily involves monitoring equipment temperature changes in real time through various sensors and feeding the temperature signals back to the control system. Based on the difference between the set temperature target and the actual measured value, the control system adjusts the operating state of the heating or cooling device to maintain the equipment operating within the set temperature range.

[0107] Current loop control

[0108] Current loop control uses sensors to monitor the current in the circuit in real time and feeds the current signal back to the control system. Based on the difference between the set current target and the actual measured value, the control system adjusts the power supply output or load impedance to control the current and ensure stable circuit operation.

[0109] Application of random forest algorithm in dual-loop feedback control

[0110] Feature selection and model optimization

[0111] In dual-loop feedback control systems, identifying and selecting the most important features is crucial for improving system performance. The random forest algorithm constructs a decision tree by randomly selecting a subset of features. This approach not only increases model diversity but also helps identify the key features that have the greatest impact on system performance. Furthermore, the random forest algorithm can optimize model parameters by integrating the results of multiple decision trees, further improving system stability and accuracy.

[0112] Model prediction and dynamic adjustment

[0113] The random forest algorithm has excellent predictive power and flexibility and can be used to predict the future behavior of a system. In a dual-loop feedback control system, the random forest model can be used to predict the temperature or current trends of the system and dynamically adjust the control strategy based on the predicted results to achieve more efficient control. For example, in temperature loop control, the random forest model can be used to predict temperature trends and, based on the predicted results, adjust the operating state of the heating or cooling device in advance to prevent the temperature from exceeding the set range.

[0114] Enhanced system robustness

[0115] The random forest algorithm is highly robust to data noise and outliers, and can, to a certain extent, reduce the impact of these factors on control system performance. In dual-loop feedback control systems, introducing the random forest algorithm can improve the system's resistance to uncertainty and external disturbances, enabling the system to maintain good stability and performance in a variety of harsh environments.

[0116] By rationally selecting and utilizing the random forest algorithm, the performance of dual-loop feedback control systems can be effectively improved, enhancing the system's stability and response speed. Future research can further explore the specific application methods and optimization strategies of the random forest algorithm in dual-loop feedback control systems to achieve more efficient and stable control effects.

[0117] The low-power steady-state Kalman filter dual-loop controlled ultraviolet laser is applied to intestinal flora detection. Specifically,

[0118] Preparation before the experiment;

[0119] Turn on the device, hold the probe, insert it from the anus and into the intestine;

[0120] After reaching the designated position, the laser power is adjusted, and then the target area is irradiated with UV light. After the predetermined time is reached, it moves to the next position and the cycle continues.

[0121] Exit the intestine after the experiment.

Claims

1. A low-power steady-state Kalman filter dual-loop controlled ultraviolet laser for intestinal flora detection, characterized in that: The laser includes a continuous laser, an optical path control module, a photoelectric amplification module and a feedback controller, wherein the continuous laser is connected to the optical path control module and the feedback controller respectively, and the optical path control module is connected to the photoelectric amplification module and the feedback controller respectively; The continuous laser is used to generate laser light and provide the energy pump source with the energy required to excite the laser medium; The optical path control module is used to ensure the performance and output power of the laser and achieve precise control of the laser beam; The photoelectric amplification module is used to convert electrical signals into optical signals, amplify the optical signals, and convert the optical signals back into electrical signals; The feedback controller is used to maintain the stability of the laser, suppress interference, and improve the accuracy and robustness of the system.

2. The laser according to claim 1, characterized in that The photoelectric amplification module includes an attenuation plate, a feedback detector and a current amplifier; The beam splitter of the optical path control module transmits the reflected laser to the attenuation plate of the photoelectric amplifier module, and the attenuation plate transmits the received reflected laser to the feedback detector, and the feedback detector transmits the received reflected laser to the feedback controller through the current amplifier.

3. The laser according to claim 2, characterized in that: The feedback controller includes a temperature management module, an A / D converter, an FPGA circuit, a single-chip microcomputer module and an acousto-optic driver; The A / D converter receives the signal output by the current amplifier, transmits the signal to the FPGA circuit, and the FPGA circuit transmits the signal to the single-chip microcomputer module and the acousto-optic driver respectively. The acousto-optic driver then transmits the signal to the acousto-optic modulator and continuous laser of the optical path control module.

4. The laser according to claim 3, characterized in that: The temperature management module includes an LD constant temperature control system, and the LD power supply constant temperature control system includes an LD temperature measurement circuit, an LD power supply and a TEC drive circuit. The TEC drive circuit receives the signal converted by the Kalman filter through the D / A converter, and the TEC drive circuit transmits the drive signal to the TEC temperature control module. The TEC temperature control module monitors the LD power supply, and the signal emitted by the LD power supply is transmitted to the A / D converter through the LD temperature measurement circuit.

5. The laser according to claim 1, characterized in that: The power of the laser is 0.1-10 mW, and the wavelength range of the laser is 365-405 nm ultraviolet light.

6. A method for operating a low-power steady-state Kalman filter dual-loop controlled ultraviolet laser for intestinal flora detection according to any one of claims 1 to 5, characterized in that: The working method comprises: First, the laser's micro-emitting head accurately guides the generated ultraviolet laser to the required working area for irradiation; When the laser is working, the built-in temperature management module monitors the temperature changes inside the laser in real time; When the laser is working, its working current value is detected in real time through the Kalman filter model; The detected current value is processed through the Kalman filter model. Combined with the pre-set model parameters and the consideration of temperature interference, the current detection value is corrected and filtered to obtain more accurate current feedback information, thus achieving precise control of the laser.

7. The working method according to claim 6, characterized in that: The temperature compensation control and current closed-loop control steps of the Kalman filter are specifically as follows: Steps in model building; Steps to make predictions using the established model; Steps for updating based on predictions; An iterative process based on prediction and update.

8. The working method according to claim 7, characterized in that: The steps of establishing the model specifically include a state equation and an observation equation, wherein the state equation describes the change of temperature over time, and the observation equation obtains temperature information from actual measurement values; The prediction step is specifically as follows: Kalman filtering predicts the state at the current moment based on the state estimation at the previous moment; The updating step is specifically as follows: Kalman filtering corrects the predicted value according to the current observation value to obtain a more accurate state estimate; including calculating the Kalman gain, updating the estimated state and updating the covariance; The iterative process is specifically to alternate between prediction and update to recursively estimate the state of the system; each iteration will update the state estimate based on new observation data, thereby achieving continuous prediction of temperature.

9. The working method according to claim 6, characterized in that: The power supply control steps of the LD pump are specifically as follows: Steps in model building; Steps to make predictions using the established model; Steps for updating based on predictions; An iterative process based on prediction and update.

10. The working method according to claim 9, characterized in that: The steps of establishing the model specifically include a state equation and an observation equation, wherein the state equation describes the change of current over time, and the observation equation obtains current information from actual measurement values; The prediction step specifically includes predicting the state at the current moment based on the state estimation at the previous moment; The updating step specifically includes correcting the predicted value based on the current observation value to obtain a more accurate state estimate; including calculating the Kalman gain, updating the estimated state and updating the covariance; The iterative process is specifically to alternate between prediction and update to recursively estimate the state of the system; each iteration will update the state estimate based on new observation data, thereby achieving continuous prediction of temperature.

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