On-line monitor power supply management system
By enhancing acoustic cavitation and extracting instantaneous phase, combined with ultrasonic transducers and magnetic field strength, the problems of unclear parameters and slow response speed in the power management system of online monitoring instruments are solved, and complementary enhancement of multi-domain signals and improvement of system stability are achieved.
Patent Information
- Application Number
- CN202511436338.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-10-09
- Publication Date
- 2025-11-07
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
Existing online monitoring instrument power management systems rely on model predictive control or fuzzy logic judgment, resulting in unclear physical meaning of parameters, unstable calculation processes, and single-source feedback-based generation of control signals. This makes it impossible to achieve complementary enhancement of acoustic, fluid, and magnetic signals, leading to insufficient control response speed and robustness.
Acoustic cavitation enhancement and Hilbert transform are performed using a collection coupling module. The coupling eigenvector is calculated. Combined with the ultrasonic transducer and magnetic field strength, the acoustic-electromagnetic coupling control parameters are calculated. The state is monitored through adaptive feedback gain and dynamic stability index. Control signals are generated and executed. A visualization interface is constructed and the data is stored.
It improves the ability to identify weak changes in operating data, enhances the system's anti-interference capability and stability, and achieves complementary enhancement of multi-domain signals and improved response speed.
Smart Images
Figure CN120909109A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application relates to the technical field of power management, in particular to a power management system of an online monitoring instrument. BACKGROUND
[0002] The power management technology of the online monitoring instrument is a core component in the field of modern industrial automation, Internet of Things and intelligent devices, and its development is closely related to the progress of microfluidic control, acoustic regulation and electromagnetic field synergy. In recent years, with the wide application of microfluidic technology, especially in the fields of biomedicine, chemical analysis and precision manufacturing, the power management system has gradually evolved from traditional single voltage or current control to multi-physical field coupling regulation. The power management scheme based on ultrasonic transducers and electromagnetic field regulation has achieved a certain degree of integration, and signal processing methods such as Hilbert transform have shown significant advantages in extracting instantaneous phase and feature vectors, providing a theoretical basis for accurate control of power management.
[0003] The existing power management system of the online monitoring instrument still has deficiencies. The mainstream technical path usually relies on model predictive control or fuzzy logic judgment, which is difficult to achieve clear physical meaning of parameters and stable and reliable calculation process. Secondly, the generation of the regulation signal is mostly based on single-source feedback, which cannot realize the complementary enhancement of acoustic, flow and magnetic multi-domain signals, and the response speed and robustness of the regulation are significantly restricted. SUMMARY
[0004] In view of the above existing problems, the present application is proposed.
[0005] Therefore, the present application provides a power management system and system of an online monitoring instrument, which solves the problem that the mainstream technical path usually relies on model predictive control or fuzzy logic judgment, which is difficult to achieve clear physical meaning of parameters and stable and reliable calculation process. Secondly, the generation of the regulation signal is mostly based on single-source feedback, which cannot realize the complementary enhancement of acoustic, flow and magnetic multi-domain signals, and the response speed and robustness of the regulation are significantly restricted.
[0006] To solve the above technical problems, the present application provides the following technical solutions: In a first aspect, the present application provides a power management system of an online monitoring instrument, comprising, The collection and coupling module is used for collecting and preprocessing operation data, performing acoustic cavitation enhancement on the operation data, calculating a cavitation enhancement signal, performing Hilbert transform on the cavitation enhancement signal, generating an analytical signal and extracting an instantaneous phase, generating a preprocessing signal, and calculating a coupling feature vector based on the preprocessing signal. The potential regulation module is used for calculating the acoustic thermophoresis driving voltage based on the coupling feature vector, calculating the plasma regulation potential based on the preprocessed signal, calculating the thermophoretic fluid force based on the acoustic thermophoresis driving voltage and the plasma regulation potential, calculating the acoustic fluid force based on the regulation index combined with the ultrasonic transducer, calculating the electromagnetic heat power based on the regulation index combined with the magnetic field intensity, combining the acoustic fluid force and the electromagnetic heat power to calculate the acoustic-electromagnetic coupling regulation parameter. The monitoring target module is used for calculating the adaptive feedback gain based on the acoustic-electromagnetic coupling regulation parameter combined with the load current and the microfluid flow rate, calculating the dynamic stability index using the acoustic-electromagnetic coupling regulation parameter and the adaptive feedback gain, performing state monitoring on the dynamic stability index, extracting the acoustic-electromagnetic coupling regulation parameter and the adaptive feedback gain corresponding to the abnormal state based on the monitoring result, calculating the target current and the target flow rate, and calculating the control signal. The instruction storage module is used for converting the control signal into a control instruction and performing the control instruction, constructing a visual interface to display the monitoring result, and storing the collected and analyzed operation data.
[0007] As a preferred scheme of the online monitoring instrument power management system, wherein: the acoustic cavitation enhancement is performed on the operation data, the cavitation enhancement signal is calculated, the Hilbert transform is performed on the cavitation enhancement signal to generate an analytical signal and extract an instantaneous phase, and the acoustic-electromagnetic coupling regulation parameter is calculated based on the instantaneous phase. The ultrasonic transducer is used to apply ultrasonic waves to the microfluid channel, the acoustic cavitation enhancement is performed on the operation data, and the cavitation enhancement signal is calculated. The Hilbert transform is performed on the cavitation enhancement signal to generate an analytical signal and extract an instantaneous phase. The induced potential is calculated based on the cavitation enhancement signal, and the preprocessed signal is generated after normalization processing. The coupling feature vector is calculated based on the preprocessed signal.
[0008] As a preferred scheme of the online monitoring instrument power management system, wherein: the acoustic thermophoresis driving voltage and the plasma regulation potential are combined to calculate the thermophoretic fluid force, and the thermophoretic fluid force is calculated based on the coupling feature vector. The acoustic thermophoresis driving voltage is calculated based on the coupling feature vector. The plasma regulation potential is calculated based on the preprocessed signal. The acoustic thermophoresis driving voltage and the plasma regulation potential are combined to calculate the thermophoretic fluid force, and the regulation index is obtained after normalization processing.
[0009] As a preferred scheme of the online monitoring instrument power management system, wherein: the acoustic thermophoresis driving voltage and the plasma regulation potential are combined to calculate the thermophoretic fluid force, and the thermophoretic fluid force is calculated based on the coupling feature vector. The acoustic fluid force is calculated based on the regulation index combined with the ultrasonic transducer. Based on the regulation index combined with the magnetic field strength, the electromagnetic heating power is calculated; The acoustic fluid force and the electromagnetic heating power are combined to calculate the acoustic-electromagnetic coupling regulation parameter.
[0010] As a preferred scheme of the online monitoring instrument power management system of the application, wherein: the dynamic stability index is calculated using the acoustic-electromagnetic coupling regulation parameter and the adaptive feedback gain, and the state monitoring is performed on the dynamic stability index, including: Based on the acoustic-electromagnetic coupling regulation parameter, the adaptive feedback gain is calculated combined with the load current and the microfluid flow rate; The dynamic stability index is calculated using the acoustic-electromagnetic coupling regulation parameter and the adaptive feedback gain; Based on the dynamic stability index, the monitoring range is set using the percentile method, the dynamic stability index is compared with the monitoring range, if the dynamic stability index is within the monitoring range, it is judged as normal state, otherwise it is judged as abnormal state.
[0011] As a preferred scheme of the online monitoring instrument power management system of the application, wherein: the target current and the target flow rate are calculated, and the control signal is calculated, including: Based on the monitoring result, the acoustic-electromagnetic coupling regulation parameter and the adaptive feedback gain corresponding to the abnormal state are extracted, and the target current and the target flow rate are calculated; Based on the target current, the target flow rate and the preprocessed signal, the control signal is calculated; Otherwise, continue to run.
[0012] As a preferred scheme of the online monitoring instrument power management system of the application, wherein: the control signal is converted into a control instruction and executed, including: The control signal is converted into a control instruction using a PID controller, and the control instruction is transmitted and executed using a communication protocol.
[0013] As a preferred scheme of the online monitoring instrument power management system of the application, wherein: a visual interface is constructed to display the monitoring result, including: A visual tool Matplotlib is used to construct a visual interface to display the monitoring result in real time; Allow users to check through real-name verification.
[0014] As a preferred scheme of the online monitoring instrument power management system of the application, wherein: the running data generated by collection and analysis is stored, including: The collected operation data and analysis generated control instructions are stored in the central database, and security access measures are set, the central database stores the data for cloud backup, and the stored data and backup data are periodically detected for integrity, and after the detection is completed, the integrity detection record is generated and stored in the central database.
[0015] As a preferred scheme of the power management system of the online monitoring instrument, the collected operation data are preprocessed, including: Based on the intelligent sensor, the operation data of the online monitoring instrument are collected using the downsampling method according to a unified frequency, and denoising and normalization processing are performed; The intelligent sensor includes a Hall current, a thermocouple, a microfluidic flowmeter and an electromagnetic coil sensor. The operation data include load current, temperature, flow rate and magnetic field strength data.
[0016] The application has the beneficial effects that the application improves the recognition ability of weak change signals in the operation data through acoustic cavitation enhancement and transient phase extraction, evaluates the stability degree in the acoustic-electromagnetic regulation process through the dynamic stability index, and combines the change trend of the load current and the flow rate to improve the anti-interference ability and stability of the system. BRIEF DESCRIPTION OF DRAWINGS
[0017] In order to more clearly illustrate the technical solutions of the embodiments of the application, the following will briefly introduce the drawings needed to be used in the embodiment description, and obviously, the drawings in the following description are only some embodiments of the application, and for those skilled in the art, other drawings can also be obtained without creative labor on the basis of these drawings.
[0018] Figure 1 It is a structure schematic diagram of the power management system of the online monitoring instrument in embodiment 1.
[0019] Figure 2 It is a running flowchart of the power management system of the online monitoring instrument in embodiment 1. DETAILED DESCRIPTION
[0020] In order to make the above-mentioned purposes, features and advantages of the application more obvious and easy to understand, the specific embodiments of the application will be described in detail in combination with the drawings of the specification.
[0021] In the following description, many specific details are set forth in order to provide a thorough understanding of the application, but the application can also be implemented in other ways different from those described herein, and those skilled in the art can make similar generalizations without departing from the connotation of the application, therefore the application is not limited by the specific embodiments disclosed below.
[0022] Second, the "one embodiment" or "an embodiment" referred to herein can include a particular feature, structure, or characteristic. The various embodiments appearing at different places in the specification are not necessarily all cumulative or alternative implementations of the application. In other words, the "in one embodiment" appearing at different places in the specification are not necessarily all referring to the same embodiment, nor are they necessarily mutually exclusive of other embodiments.
[0023] Embodiment 1, Reference Figure 1 and Figure 2 The first embodiment of the present application provides an online monitoring instrument power management system, comprising: S1, a collection coupling module, for collecting and preprocessing operation data, performing cavitation enhancement on the operation data, calculating the cavitation enhancement signal, performing Hilbert transform on the cavitation enhancement signal, generating an analytical signal and extracting an instantaneous phase, generating a preprocessed signal, and calculating a coupling feature vector based on the preprocessed signal; Specifically, collecting and preprocessing operation data includes: Based on intelligent sensors, using a downsampling method to collect operation data of the online monitoring instrument at a uniform frequency, and performing denoising and normalization processing; The intelligent sensor includes a Hall current, a thermocouple, a microfluidic flowmeter, and an electromagnetic coil sensor. The operation data includes load current, temperature, flow rate, and magnetic field strength data.
[0024] Through a unified downsampling processing mechanism, the original data amount can be effectively compressed, the backend processing burden can be reduced, the system response efficiency can be improved, the synchronous acquisition of multi-physical field data (electricity, heat, flow, and magnetism) not only enhances the state observability of the system, but also provides high-quality input for subsequent multi-dimensional coupling modeling. Through normalization and denoising processing, the differences in dimension, sampling frequency, and amplitude characteristics of different data sources are eliminated, the comparability of the data and the stability of feature extraction are improved, and the sensitivity of the system to sudden changes is enhanced.
[0025] Further, the operation data is subjected to cavitation enhancement, the cavitation enhancement signal is calculated, the cavitation enhancement signal is subjected to Hilbert transform, an analytical signal is generated, and an instantaneous phase is extracted, including: An ultrasonic transducer is used to apply ultrasonic waves to the microfluidic channel to perform cavitation enhancement on the operation data, and the cavitation enhancement signal is calculated, with the formula being: , wherein is the cavitation enhancement signal of time t, is the operation data of time t, , and Ultrasonic power, cavitation frequency, and microfluidic channel cross-sectional area were collected from manufacturer specifications, and Fluid density and sound speed were set using experimental measurements, respectively; The Hilbert transform was applied to the cavitation-enhanced signal to generate the analytic signal and extract the instantaneous phase, given by , where is the instantaneous phase at time t, j is the imaginary unit, and N is the time window size, which was set using the fixed time window method, is the sampling interval, which was set using the Nyquist sampling theorem, and k is the convolution index, ; Based on the cavitation-enhanced signal, the induced electric potential was calculated and normalized to generate the preprocessed signal, given by , , where is the induced electric potential at time t, representing the potential change caused by cavitation disturbance, is the number of turns of the induction coil, is the cross-sectional area of the coil, is the cavitation-enhanced magnetic field strength at time t, is the cavitation-enhanced flow velocity at time t, is the preprocessed signal at time t, is the maximum induced electric potential; Based on the preprocessed signal, the coupling feature vector was calculated, given by , where is the i-th coupling feature vector at time t, is the cavitation-enhanced signal at time , is the instantaneous phase at time , is the induced electric potential at time , is the integral window length, which was set using the fixed time window method, is the differential element of the integral variable, used for continuous-time integration, is the integral variable, representing the integration time point.
[0026] Through the nonlinear acoustic cavitation effect, the perturbation signals caused by particles, ions, temperature gradient, etc. in the microfluid are effectively enhanced, breaking the limitation that traditional acoustic signals are difficult to perceive in the low Reynolds number field. The cavitation enhancement introduces high-frequency spectral components, expands the original data frequency domain structure, and provides more abundant spectral information for subsequent signal processing. The traditional Fourier transform cannot reflect the frequency change of the signal over time, while the instantaneous phase reflects the instantaneous disturbance characteristics of the frequency in the non-stationary signal, which is especially suitable for capturing the time-sensitive changes in the microfluid system. The phase information can be used to measure the relative synchronicity and coherence between different signals. The induced electric potential is the embodiment of the interaction between the cavitation flow rate and the magnetic field strength, which quantifies the response relationship between multiple physical fields and provides a measured basis for coupled modeling. By integrating acoustic, electric, and magnetic information, a multi-field situation description of the microscopic physical process is constructed, realizing the structural integration of multi-modal signals. The integral operation has the characteristics of noise reduction, which can suppress the sudden interference signals and improve the stability of the features.
[0027] S2, a potential regulation module, configured to calculate an acoustic-induced thermophoretic driving voltage based on the coupling feature vector, calculate a plasma regulation potential based on the preprocessed signal, calculate a thermophoretic fluid force based on the acoustic-induced thermophoretic driving voltage and the plasma regulation potential, calculate an acoustic-induced fluid force based on the regulation index combined with the ultrasonic transducer, calculate an electromagnetic-induced heat power based on the regulation index combined with the magnetic field strength, and calculate an acoustic-electromagnetic coupling regulation parameter based on the acoustic-induced fluid force and the electromagnetic-induced heat power; Specifically, the thermophoretic fluid force is calculated based on the acoustic-induced thermophoretic driving voltage and the plasma regulation potential, including: The acoustic-induced thermophoretic driving voltage is calculated based on the coupling feature vector, and the formula is: , , , wherein is the acoustic-induced thermophoretic driving voltage, indicating the control voltage applied to the ultrasonic transducer, is the maximum driving voltage, and are the rated power of the transducer and the transducer impedance, respectively, which are collected through the manufacturer's specifications, is the temperature deviation, is the maximum value of the preprocessed temperature, is the preprocessed temperature at time t, which is obtained based on the preprocessed signal, is the target temperature, which is set based on the mean value of the temperature, and are the preprocessed flow rate at time t and the minimum value of the preprocessed flow rate, respectively; The plasma regulation potential is calculated based on the preprocessed signal, and the formula is: , , wherein is the plasma regulating potential at time t, representing the local electric field strength generated by the discharge device, is the discharge voltage, representing the nominal voltage of the micro-plasma discharge device, collected through manufacturer specifications, is the maximum value of the pre-treatment flow rate, and are the flow rate coupling eigenvector at time t and the maximum value of the flow rate coupling eigenvector, respectively; The acoustic thermophoretic driving voltage and the plasma regulating potential are used to calculate the thermophoretic fluid force, and normalization processing is performed to obtain the regulating index, which represents the synergistic effect of thermophoresis and plasma, and the formula is: , wherein is the thermophoretic fluid force at time t.
[0028] The acoustic thermophoretic driving voltage is modeled based on the temperature and flow rate double factors, so that the driving control is no longer dependent on constant power, but dynamically matches the system thermal state, and can automatically adjust the driving force direction and amplitude according to the local thermal gradient, improve the migration efficiency of particles or molecules in the fluid, introduce an impedance matching factor to avoid power overload or loss, improve the energy efficiency of the system, introduce a local electric field through a small-scale plasma discharge, and realize fine regulation of micro-flow rate and interfacial tension, When the thermal gradient is not enough to drive particle motion, the plasma provides an additional energy channel to ensure continuous and effective migration. The thermophoretic fluid force integrates the acoustic thermophoretic driving and the plasma electric field effect, which is the result of superposition of the two mechanisms, and embodies the enhanced migration response. The normalized thermophoretic force can be used as an input index for system controller or optimization algorithm, providing a unified dimensional basis for system learning, prediction and feedback.
[0029] Further, the acoustic fluid force and the electromagnetic heating power are combined to calculate the acoustic-electromagnetic coupling regulating parameter, including: Based on the regulating index, the acoustic fluid force is calculated in combination with the ultrasonic transducer, and the formula is: , wherein is the acoustic fluid force at time t, representing the fluid stress driven by the ultrasonic wave, is the transducer power, collected through manufacturer specifications, is the regulating index at time t, is the ultrasonic frequency, collected through manufacturer specifications; Based on the regulating index, the electromagnetic heating power is calculated in combination with the magnetic field strength, and the formula is: , wherein is the electromagnetic heating power at time t, representing the heating power per unit volume induced by the electromagnetic field, is the fluid conductivity, calculated using Ohm's law; combining the acoustic fluid force and the electromagnetic heating power, the acoustic- electromagnetic coupling regulation parameter is calculated, and the formula is: , wherein is the acoustic- electromagnetic coupling regulation parameter at time t, representing the synergistic strength of the acoustic and electromagnetic effects, and are the maximum acoustic fluid force and the maximum electromagnetic heating power, respectively.
[0030] The acoustic field regulates the flow direction, and the electromagnetic field heats and focuses, which jointly act on a specific area to achieve high spatial resolution control, especially suitable for application in cell screening, nanoparticle enrichment, etc., providing better migration rate and selectivity.
[0031] S3, a monitoring target module, is configured to calculate an adaptive feedback gain based on the acoustic- electromagnetic coupling regulation parameter, in combination with the load current and the microfluidic flow rate, calculate a dynamic stability index using the acoustic- electromagnetic coupling regulation parameter and the adaptive feedback gain, perform state monitoring on the dynamic stability index, based on the monitoring result, extract the acoustic- electromagnetic coupling regulation parameter and the adaptive feedback gain corresponding to the abnormal state, calculate a target current and a target flow rate, and calculate a control signal; Specifically, the dynamic stability index is calculated using the acoustic- electromagnetic coupling regulation parameter and the adaptive feedback gain, and the state monitoring on the dynamic stability index includes: The adaptive feedback gain is calculated based on the acoustic- electromagnetic coupling regulation parameter, in combination with the load current and the microfluidic flow rate, and the formula is: , wherein is the adaptive feedback gain at time t, used to adjust the intensity of the control signal, and are the preprocessed load current at time t and the maximum value of the preprocessed load current, respectively; The dynamic stability index is calculated using the acoustic- electromagnetic coupling regulation parameter and the adaptive feedback gain, and the formula is: , wherein is the dynamic stability index at time t; Based on the dynamic stability index, the monitoring range is set using the percentile method, the dynamic stability index is compared with the monitoring range, if the dynamic stability index is within the monitoring range, it is judged as a normal state, otherwise it is judged as an abnormal state.
[0032] The real-time calculation of the feedback gain is based on the current and flow rate, which ensures that the system can maintain optimal control sensitivity under different load conditions. By dynamically adjusting the gain strength, system oscillation or misoperation caused by sudden changes in control parameters can be avoided. The percentile method determines the monitoring window based on historical sample statistical characteristics, avoiding false judgments caused by manually setting fixed thresholds. By identifying trend anomalies (such as slow instability) in advance, it provides decision-making basis for subsequent control strategy switching.
[0033] Further, the target current and the target flow rate are calculated, and a control signal is calculated, including: Based on the monitoring results, the acoustic-electromagnetic coupling control parameters and the adaptive feedback gain corresponding to the abnormal state are extracted, the target current and the target flow rate are calculated, and the formula is: , , Wherein and are the target current and the target flow rate, , and are the adaptive feedback gain, the acoustic-electromagnetic coupling control parameter and the dynamic stability index corresponding to the abnormal state at time t, is the flow rate step; Based on the target current, the target flow rate and the preprocessed signal, the control signal is calculated, and the formula is: , , Wherein and are the electromagnetic coil control signal and the ultrasonic transducer control signal at time t; Otherwise, continue to run.
[0034] Extracting abnormal parameters and constructing target states enables the system to "pull back" to the stable region from the fault trend. When the hardware cannot be replaced in real time, the system can extend the effective working period by controlling the modified target operating value. By separately regulating electromagnetic and acoustic signals, the interference caused by cross-coupling can be avoided, and the execution response accuracy can be improved. When one physical field is limited, the other channel can strengthen the control according to the stability index to improve the overall stability of the system. The control signal is dynamically generated based on state feedback, which has a natural basis for integration with reinforcement learning or model predictive control (MPC).
[0035] S4, instruction storage module, for converting the control signal into a control instruction and executing it, building a visual interface to display the monitoring results, storing the collected and analyzed operating data; Specifically, the control signal is converted into a control instruction and executed, including: The control signal is converted into a control instruction using a PID controller, and the control instruction is transmitted and executed using the communication protocol EtherCAT.
[0036] PID control avoids sudden changes in the control signal causing mechanical damage to actuators or excessive fluid disturbance, and is particularly suitable for continuous regulation of electromagnetic fields and ultrasonic waves. The integral term can eliminate steady-state errors, and the derivative term can respond to trends in advance. The three terms work together to improve the stability and robustness of the response. EtherCAT can complete the issuance of control instructions within microseconds of delay, ensuring that the sound field and electromagnetic field regulation are synchronized in place, and adapting to high-speed microfluidic environments.
[0037] Further, a visual interface is constructed to display monitoring results, including: A visual interface is constructed using the visualization tool Matplotlib to display monitoring results in real time. Allow users to review through real-name verification.
[0038] The running state is displayed through visual elements such as curves, heat maps, and vector field maps, allowing technicians to intuitively determine whether the system behavior is normal. The data analysis images embedded in the interface provide intuitive evidence for system optimization and maintenance. Only authorized users can view and download monitoring data, effectively protecting technical data property rights and user privacy.
[0039] Further, the running data collected and analyzed is stored, including: The collected running data and the control instructions generated by analysis are stored in a central database, and security access measures are set. The central database stores the data in the cloud backup, and periodically checks the integrity of the stored data and the backup data. After the detection is completed, the integrity detection record is generated and stored in the central database.
[0040] Complete data records facilitate the tracing of running tracks and the analysis of abnormal trends, providing a training sample basis for later machine learning and adaptive control strategies. Multi-cycle data analysis can discover potential patterns for early warning and predictive maintenance.
Claims
1. An online monitor power management system, characterized by: The method comprises the following steps: a collection module is used to collect operation data and perform preprocessing, perform acoustic cavitation enhancement on the operation data, calculate a cavitation enhancement signal, perform Hilbert transform on the cavitation enhancement signal, generate an analytical signal and extract an instantaneous phase, generate a preprocessed signal, calculate a coupling feature vector based on the preprocessed signal; a potential regulation module is used to calculate a sonophoretic driving voltage based on the coupling feature vector, calculate a plasma regulation potential based on the preprocessed signal, calculate a thermophoretic fluid force from the sonophoretic driving voltage and the plasma regulation potential, calculate a sonofluidic force based on the regulation index in combination with an ultrasonic transducer, calculate an electromagnetically induced heat power based on the regulation index in combination with a magnetic field intensity, and calculate a sound-electromagnetic coupling regulation parameter by combining the sonofluidic force and the electromagnetically induced heat power; a monitoring target module is used to calculate an adaptive feedback gain based on the sound-electromagnetic coupling regulation parameter in combination with a load current and a microfluidic flow rate, calculate a dynamic stability index using the sound-electromagnetic coupling regulation parameter and the adaptive feedback gain, perform state monitoring on the dynamic stability index, extract the sound-electromagnetic coupling regulation parameter and the adaptive feedback gain corresponding to an abnormal state based on the monitoring result, calculate a target current and a target flow rate, and calculate a control signal; an instruction storage module is used to convert the control signal into a control instruction and perform execution, build a visual interface to display the monitoring result, and store operation data generated by collection and analysis.
2. The online monitor power management system of claim 1, wherein: The method further comprises the following steps: an ultrasonic transducer is used to apply ultrasonic waves to a microfluidic channel to perform acoustic cavitation enhancement on the operation data and calculate a cavitation enhancement signal; the cavitation enhancement signal is subjected to Hilbert transform to generate an analytical signal and extract an instantaneous phase; an induced potential is calculated based on the cavitation enhancement signal and normalized to generate a preprocessed signal; a coupling feature vector is calculated based on the preprocessed signal.
3. The online monitor power management system of claim 2, wherein: The method further comprises the following steps: the sonophoretic driving voltage is calculated based on the coupling feature vector; the plasma regulation potential is calculated based on the preprocessed signal; the thermophoretic fluid force is calculated from the sonophoretic driving voltage and the plasma regulation potential, and normalized to obtain a regulation index.
4. The online monitor power management system of claim 3, wherein: The method further comprises the following steps: the sonofluidic force is calculated based on the regulation index in combination with the ultrasonic transducer; the electromagnetically induced heat power is calculated based on the regulation index in combination with the magnetic field intensity; the sound-electromagnetic coupling regulation parameter is calculated by combining the sonofluidic force and the electromagnetically induced heat power.
5. The online monitor power management system of claim 4, wherein: The method further comprises the following steps: the adaptive feedback gain is calculated based on the sound-electromagnetic coupling regulation parameter in combination with the load current and the microfluidic flow rate; the dynamic stability index is calculated using the sound-electromagnetic coupling regulation parameter and the adaptive feedback gain; and the state monitoring is performed on the dynamic stability index. Based on the dynamic stability index, the monitoring range is set using the percentile method, the dynamic stability index is compared with the monitoring range, if the dynamic stability index is in the monitoring range, it is judged as normal state, otherwise it is judged as abnormal state.
6. The online monitor power management system of claim 5, wherein: The target current and the target flow rate are calculated, and the control signal is calculated, including: Based on the monitoring result, the abnormal state corresponding acoustic-electromagnetic coupling control parameter and adaptive feedback gain are extracted, the target current and the target flow rate are calculated; Based on the target current, the target flow rate and the preprocessed signal, the control signal is calculated; Otherwise, continue to run.
7. The online monitor power management system of claim 6, wherein: The control signal is converted into a control instruction and executed, including: The control signal is converted into a control instruction using a PID controller, and the control instruction is transmitted and executed using a communication protocol.
8. The online monitor power management system of claim 5, wherein: The visualization interface is constructed to display the monitoring result, including: The visualization interface is constructed using the visualization tool Matplotlib to display the monitoring result in real time; Allowing users to check through real-name verification.
9. The online monitor power management system of claim 7, wherein: The collected and analyzed operation data are stored, including: The collected operation data and the analyzed control instruction are stored in the central database, and security access measures are set, the central database stores the data in the cloud backup, and regularly checks the integrity of the stored data and the backup data, and generates an integrity detection record after the detection is completed, and synchronously stores it in the central database.
10. The online monitor power management system of claim 1, wherein: The operation data are collected and preprocessed, including: Based on the intelligent sensor, the online monitoring instrument operation data are collected using the down-sampling method according to the unified frequency, and denoising and normalization processing are performed; The intelligent sensor includes a Hall current, a thermocouple, a micro-fluid flowmeter and an electromagnetic coil sensor; The operation data include load current, temperature, flow rate and magnetic field strength data.