Method and system for simultaneous monitoring of ph and potential during synthesis of magnetic iron oxide

Abstract

This application provides a method and system for synchronously monitoring pH and potential during the synthesis of magnetic iron oxide, relating to the field of electrochemical monitoring technology. The method includes: acquiring voltage signals output from a pH electrode and an indicator electrode and preprocessing them to form a synchronous time-series data stream; then, after signal conditioning, inputting the signal into a time-frequency transformation module for wavelet packet decomposition to extract characteristic frequency band components corresponding to ion diffusion and electron transfer, and reconstructing pH and potential characteristic curves; further, inputting the two characteristic curves into a dynamic coupling analysis unit to calculate the instantaneous phase difference and amplitude correlation coefficient, and constructing a Lissajous figure accordingly; finally, comparing the Lissajous figure with a standard elliptical trajectory, and obtaining the monitoring results of pH and potential deviations when the figure deviates from the trajectory. This application achieves accurate synchronous monitoring of pH and potential under strong coupling conditions.

Description

Technical Field

[0001] This application relates to the field of electrochemical monitoring technology, and in particular to a method and system for synchronously monitoring pH and potential during the synthesis of magnetic iron oxide. Background Technology

[0002] Magnetic iron oxide, as an important functional material, has wide applications in magnetic recording, biomedicine, and catalysis. Accurate monitoring of pH and potential during its synthesis directly affects product purity and the stability of its magnetic properties. With the increasing demands for material quality from high-end manufacturing industries, developing simultaneous monitoring methods suitable for complex reaction environments has become a current research hotspot.

[0003] Current methods for monitoring pH and potential during the synthesis of magnetic iron oxide typically employ separate measurements using independent electrodes. The voltage signals from both channels are recorded via a data acquisition card and stored in a computer. Offline analysis software is then used to process the recorded data to assess the reaction state. This measurement method meets basic monitoring requirements under steady-state reaction conditions, and the relevant monitoring equipment and data analysis methods have been applied to some extent in laboratory research and industrial production.

[0004] However, in this mode of separate measurement and offline analysis, the monitoring results often fail to accurately reflect the dynamic correlation between pH and potential during the synthesis process, especially when fluctuations or disturbances occur in the reaction system, making it impossible to effectively correlate the changes of the two parameters on the same time scale. The discrepancy between the monitoring data and the actual reaction state affects the accuracy of timely regulation of the synthesis process. Therefore, existing technologies face the technical challenge of accurately monitoring electrochemical parameters under complex reaction conditions. Summary of the Invention

[0005] This application provides a method and system for synchronously monitoring pH and potential during the synthesis of magnetic iron oxide, in order to solve the problem of insufficient accurate synchronous monitoring of pH and potential under strong coupling conditions in the prior art.

[0006] To address the aforementioned technical problems, in a first aspect, this application provides a method for synchronously monitoring pH and potential during the synthesis of magnetic iron oxide, comprising: During the synthesis of magnetic iron oxide, a raw response sequence is collected, which includes a first voltage signal output from the pH electrode and a second voltage signal output from the indicator electrode. The first voltage signal and the second voltage signal are preprocessed to form a synchronous timing data stream, and the synchronous timing data stream is differentially amplified and filtered using a signal conditioning circuit to obtain a conditioned analog signal. The conditioned analog signal is input to the time-frequency conversion module for wavelet packet decomposition to obtain multiple frequency band components, and the first characteristic frequency band component corresponding to the ion diffusion process and the second characteristic frequency band component corresponding to the electron transfer process are extracted from the multiple frequency band components. Signal reconstruction is performed on the first characteristic frequency band component and the second characteristic frequency band component respectively to obtain the pH characteristic curve and the potential characteristic curve; The pH characteristic curve and the potential characteristic curve are input into the dynamic coupling analysis unit. The instantaneous phase difference and amplitude correlation coefficient are calculated by the dynamic coupling analysis unit, and a Lissajous figure is constructed based on the instantaneous phase difference and the amplitude correlation coefficient. The Lissajous figure is used to describe the dynamic coupling relationship between pH value and potential. The Lissajous figure is compared with a preset standard elliptical trajectory. When the comparison result shows that the Lissajous figure deviates from the standard elliptical trajectory, the monitoring results of pH value and potential deviation are obtained.

[0007] Optionally, the step of inputting the pH characteristic curve and the potential characteristic curve to the dynamic coupling analysis unit, calculating the instantaneous phase difference and amplitude correlation coefficient through the dynamic coupling analysis unit, and constructing a Lissajous figure based on the instantaneous phase difference and the amplitude correlation coefficient includes: The pH characteristic curve and the potential characteristic curve are input into the dynamic coupling analysis unit. In the dynamic coupling analysis unit, Hilbert transform is performed on the pH characteristic curve and the potential characteristic curve respectively to extract the instantaneous phase value corresponding to each time point on the pH characteristic curve and the instantaneous phase value corresponding to each time point on the potential characteristic curve, so as to obtain the first instantaneous phase sequence and the second instantaneous phase sequence. The first instantaneous phase sequence and the second instantaneous phase sequence are input into the phase unwrapping module. The phase unwrapping module uses a path tracking algorithm to detect and correct the phase jump points in the first instantaneous phase sequence and the second instantaneous phase sequence to obtain the first unwrapped phase sequence and the second unwrapped phase sequence. Calculate the difference between the first unwrapped phase sequence and the second unwrapped phase sequence at the same time point to obtain the instantaneous phase difference; Calculate the correlation coefficient between the amplitudes of the pH characteristic curve and the potential characteristic curve at the same time point to obtain the amplitude correlation coefficient; Using the instantaneous phase value of the pH characteristic curve as the abscissa and the instantaneous amplitude value of the potential characteristic curve as the ordinate, the coordinate points corresponding to each time point are plotted in a two-dimensional coordinate system, and all coordinate points form a Lissajous figure.

[0008] Secondly, this application provides a system for synchronously monitoring pH and potential during the synthesis of magnetic iron oxide, comprising: The acquisition module is used to acquire the original response sequence during the synthesis of magnetic iron oxide. The original response sequence includes a first voltage signal output by the pH electrode and a second voltage signal output by the indicator electrode. The processing module is used to preprocess the first voltage signal and the second voltage signal to form a synchronous timing data stream, and to use a signal conditioning circuit to perform differential amplification and filtering on the synchronous timing data stream to obtain a conditioned analog signal. The input module is used to input the conditioned analog signal to the time-frequency conversion module for wavelet packet decomposition to obtain multiple frequency band components, and extract the first characteristic frequency band component corresponding to the ion diffusion process and the second characteristic frequency band component corresponding to the electron transfer process from the multiple frequency band components. The reconstruction module is used to reconstruct the first characteristic frequency band component and the second characteristic frequency band component respectively to obtain the pH characteristic curve and the potential characteristic curve; The calculation module is used to input the pH characteristic curve and the potential characteristic curve into the dynamic coupling analysis unit, calculate the instantaneous phase difference and amplitude correlation coefficient through the dynamic coupling analysis unit, and construct a Lissajous figure based on the instantaneous phase difference and the amplitude correlation coefficient. The Lissajous figure is used to describe the dynamic coupling relationship between pH value and potential. The comparison module is used to compare the Lissajous figure with a preset standard elliptical trajectory. When the comparison result shows that the Lissajous figure deviates from the standard elliptical trajectory, the monitoring results of pH value and potential deviation are obtained.

[0009] Thirdly, this application provides an electronic device, comprising: Memory, used to store computer programs; A processor, used to execute the computer program to implement the steps of the method for synchronously monitoring pH and potential during the synthesis of magnetic iron oxide as described in the first aspect above.

[0010] Fourthly, this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, can implement the steps of the method for synchronously monitoring pH and potential during the synthesis of magnetic iron oxide as described in the first aspect above.

[0011] The technical solution provided in this application has the following beneficial effects: This application obtains a synchronous time-series data stream that strictly corresponds to the time axis by acquiring and preprocessing the raw voltage signals output by the pH electrode and indicator electrode. Then, the synchronous time-series data is differentially amplified and filtered using a signal conditioning circuit to eliminate common-mode interference and high-frequency noise in the field environment, resulting in a clean analog signal. The conditioned signal is then input into a time-frequency conversion module for wavelet packet decomposition, extracting the first characteristic frequency band corresponding to the ion diffusion process and the second characteristic frequency band corresponding to the electron transfer process from the decomposition results. Next, the two extracted characteristic frequency bands are reconstructed to obtain pH characteristic curves and potential characteristic curves that accurately reflect the reaction characteristics. Subsequently, the two characteristic curves are input into a dynamic coupling analysis unit to calculate the instantaneous phase difference and amplitude correlation coefficient, and a Lissajous figure is constructed based on the calculation results to describe the dynamic coupling relationship between pH value and potential. Finally, the constructed Lissajous figure is compared with a preset standard elliptical trajectory. When the figure deviates from the standard trajectory, the monitoring results of pH value and potential deviation are output, thereby realizing the synchronous monitoring of two key parameters in the synthesis process.

[0012] Furthermore, this application performs Hilbert transform on the pH characteristic curve and the potential characteristic curve to extract their respective instantaneous phase values, obtaining a first instantaneous phase sequence and a second instantaneous phase sequence. Then, the two instantaneous phase sequences are input into a phase unwrapping module, and a path tracking algorithm is used to detect and correct phase jump points in the sequences, obtaining continuously changing first and second unwrapped phase sequences. Next, the difference between the two unwrapped phase sequences at the same time point is calculated to obtain the instantaneous phase difference, and the correlation coefficient between the amplitudes of the two characteristic curves at the same time point is calculated to obtain the amplitude correlation coefficient. Finally, the instantaneous phase value of the pH characteristic curve is plotted on the x-axis, and the instantaneous amplitude value of the potential characteristic curve is plotted on the y-axis, forming a Lissajous figure to describe the dynamic coupling relationship. This method accurately extracts the phase relationship characteristics between pH and potential, eliminates the interference of phase jumps on the analysis results, and provides a stable and reliable graphical basis for subsequent state judgment.

[0013] These or other aspects of this application will become more apparent from the description of the following embodiments. Attached Figure Description

[0014] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0015] Figure 1A flowchart illustrating a method for synchronously monitoring pH and potential during the synthesis of magnetic iron oxide, provided in this application embodiment; Figure 2 This is a schematic diagram illustrating a specific implementation of a method for synchronously monitoring pH and potential during the synthesis of magnetic iron oxide, provided in an embodiment of this application. Figure 3 This is a schematic diagram of a system for synchronously monitoring pH and potential during the synthesis of magnetic iron oxide, provided in an embodiment of this application. Detailed Implementation

[0016] To address the problems existing in the prior art, this application proposes a method for synchronously monitoring pH and potential during the synthesis of magnetic iron oxide. The core of this method lies in constructing a collaborative chain for signal acquisition and processing: First, time-aligned time-series data is obtained through synchronous acquisition and preprocessing to eliminate common-mode interference and high-frequency noise in the field environment. Then, a time-frequency transformation module is used to perform wavelet packet decomposition on the signal, extracting characteristic frequency band components corresponding to the ion diffusion and electron transfer processes and reconstructing them to obtain pure pH and potential characteristic curves. Next, the two characteristic curves are input into a dynamic coupling analysis unit to calculate the instantaneous phase difference and amplitude correlation coefficient, and based on this, a Lissajous figure describing the dynamic coupling relationship between pH and potential is constructed. Finally, the constructed figure is compared with a preset standard elliptical trajectory, and the monitoring result is output when the figure deviates from the trajectory.

[0017] This method achieves synchronous and accurate monitoring of two electrochemical parameters under strong coupling conditions through the synergistic effect of time-frequency transformation and dynamic coupling analysis. It fundamentally solves the problem of deviation between monitoring results and reaction state caused by time-division acquisition and offline analysis in existing technologies, and provides a reliable data foundation for the precise control of the synthesis process.

[0018] To enable those skilled in the art to better understand the present application, the present application will be further described in detail below with reference to the accompanying drawings and specific embodiments. Obviously, the described embodiments are merely some embodiments of the present application, and not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0019] The core of this application is to provide a method for synchronously monitoring pH and potential during the synthesis of magnetic iron oxide, and a flowchart of one specific embodiment is shown below. Figure 1 As shown, the method includes: Step 101: During the synthesis of magnetic iron oxide, the original response sequence is collected, which includes a first voltage signal output by the pH electrode and a second voltage signal output by the indicator electrode.

[0020] In step 101, the original response sequence refers to the set of unprocessed electrical signals directly acquired by the sensor array during the synthesis of magnetic iron oxide; the first voltage signal is the potential signal output by the pH electrode corresponding to the hydrogen ion activity in the solution, and the second voltage signal is the potential signal formed by converting the current signal output by the indicator electrode corresponding to the ratio of ferrous ions and ferric ions in the solution.

[0021] In this embodiment of the application, a first voltage signal and a second voltage signal during the synthesis process are first collected by a pH electrode and an indicator electrode installed in the reactor, respectively. These two signals together constitute the original response sequence for subsequent analysis.

[0022] Step 102: Preprocess the first voltage signal and the second voltage signal to form a synchronous timing data stream, and use a signal conditioning circuit to perform differential amplification and filtering on the synchronous timing data stream to obtain a conditioned analog signal.

[0023] Among them, the synchronous timing data stream refers to the combined data of the first voltage signal and the second voltage signal that are strictly aligned on the time axis after being timestamped and normalized; the signal conditioning circuit is a hardware unit used to amplify, filter and other processes the electrical signal; the conditioned analog signal refers to the pure electrical signal whose noise components are effectively suppressed after differential amplification and filtering.

[0024] In this embodiment, step 102 includes the following process: Step 1021: The synchronous timing data stream is sent to the signal conditioning circuit, and the synchronous timing data stream is differentially processed by the differential amplifier in the signal conditioning circuit to eliminate common-mode noise in the synchronous timing data stream.

[0025] In step 1021, the differential amplifier is a component in the signal conditioning circuit used to implement differential operations. This component amplifies the difference between two input signals, thereby suppressing the same interference components in the two signals. Common-mode noise refers to the same interference components that exist simultaneously in the first voltage signal and the second voltage signal.

[0026] In this embodiment, the synchronous timing data stream is first sent to the differential amplifier in the signal conditioning circuit. The differential amplifier performs differential operation on the first voltage signal and the second voltage signal in the synchronous timing data stream. By calculating the difference between the two signals, the common-mode noise components in the two signals are canceled out, thereby obtaining the signal after eliminating common-mode noise.

[0027] Step 1022: The signal after common-mode noise elimination is sent to the low-pass filter in the signal conditioning circuit. The low-pass filter filters out high-frequency noise higher than the set frequency and outputs the conditioned analog signal.

[0028] In step 1022, the low-pass filter is a component in the signal conditioning circuit used to filter out high-frequency components. This component allows signals with frequencies below a set frequency to pass through while attenuating signal components with frequencies above a set frequency. High-frequency noise refers to interference components with frequencies higher than the characteristic frequency of the reaction process. For example, the cutoff frequency of the low-pass filter is set to 10 Hz to filter out high-frequency noise components with frequencies higher than 10 Hz in the signal.

[0029] In this embodiment, the signal after common-mode noise elimination is sent to a low-pass filter in the signal conditioning circuit. The low-pass filter sets a cutoff frequency according to the characteristic frequency of the reaction process, filters out high-frequency noise components in the input signal that are higher than the cutoff frequency, retains the low-frequency effective components related to the reaction process, and finally obtains a pure conditioned analog signal from the output of the low-pass filter.

[0030] This application effectively eliminates common-mode interference and high-frequency noise in synchronous time-series data streams through the combined processing of differential amplification and low-pass filtering, providing a clean and reliable analog signal foundation for subsequent analysis.

[0031] Step 103: Input the conditioned analog signal into the time-frequency conversion module for wavelet packet decomposition to obtain multiple frequency band components, and extract the first characteristic frequency band component corresponding to the ion diffusion process and the second characteristic frequency band component corresponding to the electron transfer process from the multiple frequency band components.

[0032] The time-frequency conversion module is a processing unit used to convert time-domain signals to the time-frequency domain for analysis. The frequency band component refers to the signal component within a specific frequency range obtained after wavelet packet decomposition. The ion diffusion process refers to the migration of ions in the reaction system driven by the concentration gradient. The electron transfer process refers to the electron exchange reaction that occurs between the electrode and the solution interface. The first characteristic frequency band component refers to the signal component corresponding to the ion diffusion process selected from multiple frequency band components. The second characteristic frequency band component refers to the signal component corresponding to the electron transfer process selected from multiple frequency band components.

[0033] This application does not impose specific limitations on the type of time-frequency conversion module, its internal structure design, parameter design, training process, etc., and can be set accordingly based on the actual situation.

[0034] In this embodiment, step 103 includes the following process: Step 1031: Input the conditioned analog signal into the time-frequency conversion module. The time-frequency conversion module performs wavelet packet decomposition on the pH time-series data and potential time-series data in the conditioned analog signal. Each decomposition divides the conditioned analog signal into a first frequency band and a second frequency band. The first frequency band and the second frequency band are then decomposed into the next level. After a preset number of decompositions, multiple frequency band components in different frequency ranges are obtained.

[0035] In step 1031, pH time-series data refers to the portion of the conditioned analog signal corresponding to the pH electrode, potential time-series data refers to the portion of the conditioned analog signal corresponding to the indicator electrode, the first frequency band refers to the low-frequency signal component obtained after each decomposition, and the second frequency band refers to the high-frequency signal component obtained after each decomposition.

[0036] In this embodiment, the conditioned analog signal is first input to the time-frequency conversion module, which performs wavelet packet decomposition on the pH time-series data and potential time-series data in the conditioned analog signal. In each decomposition process, the input signal is divided into a first frequency band and a second frequency band, and then the first and second frequency bands are further decomposed at the next level. Through this layer-by-layer decomposition method, after a preset number of decomposition layers, multiple frequency band components in different frequency ranges are finally obtained from the pH time-series data and the potential time-series data.

[0037] Step 1032: Obtain the first frequency range corresponding to the ion diffusion process in the frequency domain, and select the frequency band components whose center frequency is within the first frequency range from the multiple frequency band components, and mark them as the first characteristic frequency band components.

[0038] In step 1032, the center frequency refers to the frequency value corresponding to the center position of the frequency range covered by each frequency band component, which is used to characterize the location of the main frequency component of the frequency band component; the first frequency range refers to the frequency interval predetermined according to the kinetic characteristics of the ion diffusion process.

[0039] In this embodiment of the application, a first frequency range corresponding to the ion diffusion process is obtained in advance, and then the frequency band components obtained by wavelet packet decomposition are screened one by one to extract those frequency band components whose center frequency is within the first frequency range. These screened frequency band components are marked as first characteristic frequency band components.

[0040] Step 1033: Obtain the second frequency range corresponding to the electron transfer process in the frequency domain, and select the frequency band components whose center frequency is within the second frequency range from the multiple frequency band components, and mark them as the second characteristic frequency band components.

[0041] In step 1033, the second frequency range refers to a frequency interval predetermined based on the kinetic characteristics of the electron transfer process.

[0042] In this embodiment of the application, a pre-set second frequency range corresponding to the electron transfer process is obtained, and then the frequency band components obtained by wavelet packet decomposition are screened one by one to extract those frequency band components whose center frequency is within the second frequency range. These screened frequency band components are marked as second characteristic frequency band components.

[0043] This application uses wavelet packet decomposition to decompose pH time series data and potential time series data layer by layer, and selects the corresponding characteristic frequency band components according to the frequency ranges of ion diffusion process and electron transfer process, thereby realizing the effective separation of different physical process components in complex electrochemical signals.

[0044] Step 104: Reconstruct the signals of the first characteristic frequency band component and the second characteristic frequency band component respectively to obtain the pH characteristic curve and the potential characteristic curve.

[0045] In step 104, the pH characteristic curve refers to the curve that reflects the change of pH value over time after reconstructing the signal of the first characteristic frequency band component, and the potential characteristic curve refers to the curve that reflects the change of potential value over time after reconstructing the signal of the second characteristic frequency band component.

[0046] In this embodiment, the extracted first characteristic frequency band component is first subjected to signal reconstruction processing, and the frequency band component is converted from the frequency domain back to the time domain to obtain a pH characteristic curve reflecting the characteristics of the ion diffusion process; then, the extracted second characteristic frequency band component is subjected to signal reconstruction processing in the same way, and the frequency band component is converted from the frequency domain back to the time domain to obtain a potential characteristic curve reflecting the characteristics of the electron transfer process.

[0047] Step 105: Input the pH characteristic curve and the potential characteristic curve into the dynamic coupling analysis unit, calculate the instantaneous phase difference and amplitude correlation coefficient through the dynamic coupling analysis unit, and construct a Lissajous figure based on the instantaneous phase difference and the amplitude correlation coefficient. The Lissajous figure is used to describe the dynamic coupling relationship between pH value and potential.

[0048] The dynamic coupling analysis unit is a processing module used to analyze the dynamic relationship between two signals. The instantaneous phase difference refers to the difference between the instantaneous phase values ​​of two signals at the same time point. The amplitude correlation coefficient refers to the degree of correlation between the amplitudes of two signals at the same time point. The Lissajous figure is a trajectory graph drawn on a two-dimensional plane with the instantaneous values ​​of two signals as the horizontal and vertical coordinates. The shape of the graph reflects the phase and amplitude relationship between the two signals.

[0049] This application does not impose specific limitations on the type of dynamic coupling analysis unit, the structural design of its internal structure, the parameter design, the training process, etc., and can be set accordingly based on the actual situation.

[0050] In this embodiment, step 105 includes the following process, such as... Figure 2 As shown: Step 1051: Input the pH characteristic curve and the potential characteristic curve into the dynamic coupling analysis unit. Perform Hilbert transform on the pH characteristic curve and the potential characteristic curve respectively in the dynamic coupling analysis unit to extract the instantaneous phase value corresponding to each time point on the pH characteristic curve and the instantaneous phase value corresponding to each time point on the potential characteristic curve, so as to obtain the first instantaneous phase sequence and the second instantaneous phase sequence.

[0051] In step 1051, the instantaneous phase value refers to the phase angle of the signal at a specific time point. The first instantaneous phase sequence refers to the sequence formed by arranging the instantaneous phase values ​​of each time point on the pH characteristic curve in chronological order. The second instantaneous phase sequence refers to the sequence formed by arranging the instantaneous phase values ​​of each time point on the potential characteristic curve in chronological order.

[0052] In this embodiment, the pH characteristic curve and potential characteristic curve obtained during the synthesis of magnetic iron oxide are first input to a dynamic coupling analysis unit. The dynamic coupling analysis unit performs Hilbert transform processing on the input pH characteristic curve and potential characteristic curve respectively. The instantaneous phase value corresponding to each time point in the synthesis of magnetic iron oxide is extracted from the pH characteristic curve through Hilbert transform, and these instantaneous phase values ​​are arranged in the time order of the synthesis of magnetic iron oxide to form a first instantaneous phase sequence. At the same time, the instantaneous phase value corresponding to each time point in the synthesis of magnetic iron oxide is extracted from the potential characteristic curve through Hilbert transform, and these instantaneous phase values ​​are arranged in the time order of the synthesis of magnetic iron oxide to form a second instantaneous phase sequence.

[0053] In practical applications, during the initial reaction stage of the magnetic iron oxide synthesis process, the pH characteristic curve contains 201 data points, with one data point collected every 0.5 seconds from 0 seconds to 100 seconds after the start of the reaction. After performing a Hilbert transform on the pH characteristic curve, the instantaneous phase value corresponding to each data point in the magnetic iron oxide synthesis process is obtained. These instantaneous phase values ​​are arranged in chronological order according to the reaction process to form the first instantaneous phase sequence, which contains 201 instantaneous phase values. Similarly, after performing a Hilbert transform on the potential characteristic curve, the corresponding second instantaneous phase sequence is obtained, which also contains 201 instantaneous phase values.

[0054] Step 1052: Input the first instantaneous phase sequence and the second instantaneous phase sequence into the phase unwrapping module. The phase unwrapping module uses a path tracking algorithm to detect and correct the phase transition points in the first instantaneous phase sequence and the second instantaneous phase sequence to obtain the first unwrapped phase sequence and the second unwrapped phase sequence.

[0055] The specific structural design examples of each module in the phase unwrapping module are as follows: The phase unwrapping module uses a field-programmable gate array (FPGA) chip as its core processor. This chip integrates sequentially connected submodules for difference calculation, comparator array, region identification, direction determination, and phase correction. The difference calculation submodule consists of multiple parallel subtractor units, the comparator array submodule consists of twenty parallel digital comparators, the region identification submodule has a built-in first-in-first-out memory for temporarily storing the coordinates of continuous jump points, the direction determination submodule is implemented using a sign detection circuit, and the phase correction submodule includes a combinational logic circuit of adders and multipliers. The construction process of this phase unwrapping module includes: firstly, collecting 50,000 sets of instantaneous phase sequence data containing phase jumps as training samples, using the original instantaneous phase sequence in each set of samples as input data, and using the manually corrected unwrapped phase sequence as label data; then, inputting the input data into the phase unwrapping module, calculating the difference between adjacent points through the difference calculation submodule, identifying jump points through the comparator array submodule, marking jump regions through the region identification submodule, determining the jump direction through the direction determination submodule, performing period value addition and subtraction operations through the phase correction submodule, and outputting the predicted unwrapped phase sequence; calculating the mean square error between the predicted unwrapped phase sequence and the label data as the loss function, adjusting the comparison threshold parameter in the comparator array submodule according to the loss function value, and repeating the above process until the loss function converges, thus completing the training of the phase unwrapping module.

[0056] It should be noted that the above structure is exemplary. This application does not impose specific limitations on the internal structure design of the phase unwrapping module, and can make corresponding settings according to the actual situation.

[0057] A phase jump point refers to the position in the instantaneous phase sequence during the synthesis of magnetic iron oxide where the phase difference between two adjacent points exceeds the normal range. The first unwrapped phase sequence refers to the continuous phase sequence obtained after phase jump correction of the first instantaneous phase sequence during the synthesis of magnetic iron oxide. The second unwrapped phase sequence refers to the continuous phase sequence obtained after phase jump correction of the second instantaneous phase sequence during the synthesis of magnetic iron oxide.

[0058] Step 1052 may specifically include the following steps: A1: Input the first instantaneous phase sequence and the second instantaneous phase sequence into the phase unwrapping module. The phase unwrapping module calculates the difference between the instantaneous phase values ​​of two adjacent time points in the first instantaneous phase sequence and the second instantaneous phase sequence, respectively, to obtain the first difference sequence and the second difference sequence.

[0059] In step A1, the first difference sequence refers to the sequence formed by arranging the differences in instantaneous phase values ​​at adjacent time points in the first instantaneous phase sequence during the synthesis of magnetic iron oxide in chronological order, and the second difference sequence refers to the sequence formed by arranging the differences in instantaneous phase values ​​at adjacent time points in the second instantaneous phase sequence during the synthesis of magnetic iron oxide in chronological order.

[0060] In this embodiment, after receiving the first instantaneous phase sequence and the second instantaneous phase sequence obtained during the synthesis of magnetic iron oxide through the phase unwrapping module, the two sequences are processed respectively. For the first instantaneous phase sequence, the instantaneous phase value at the second time point is subtracted from the instantaneous phase value at the first time point to obtain the first difference value, the instantaneous phase value at the third time point is subtracted from the instantaneous phase value at the second time point to obtain the second difference value, and so on until all adjacent time points are calculated. All differences are arranged in the time order of the magnetic iron oxide synthesis process to form the first difference value sequence. The second instantaneous phase sequence is processed in the same way to obtain the second difference value sequence.

[0061] In practical applications, during the initial reaction stage of the magnetic iron oxide synthesis process, the first instantaneous phase sequence contains instantaneous phase values ​​arranged in chronological order of the reaction time. For example, the instantaneous phase value at the first time point corresponding to 0.5 seconds of reaction is 0.2 radians, the instantaneous phase value at the second time point corresponding to 1.0 seconds of reaction is 0.5 radians, and the instantaneous phase value at the third time point corresponding to 1.5 seconds of reaction is 0.8 radians. The difference between the instantaneous phase values ​​at the second and first time points is calculated to be 0.3 radians, and the difference between the instantaneous phase values ​​at the third and second time points is also calculated to be 0.3 radians. These differences are arranged in chronological order of the reaction process to form the first difference sequence, which contains 200 difference data points.

[0062] A2: Identify the differences between the first difference sequence and the second difference sequence whose absolute values ​​are greater than a preset jump threshold, and mark the time points corresponding to the differences as candidate jump points.

[0063] In step A2, the preset jump threshold is a pre-set critical value used to determine whether a phase jump occurs during the synthesis of magnetic iron oxide. The candidate jump point refers to the time point position of the magnetic iron oxide synthesis process corresponding to the difference whose absolute value exceeds the preset jump threshold.

[0064] The embodiments of this application do not specifically limit the value of the preset transition threshold; it can be set according to the actual situation.

[0065] In this embodiment, the phase unwrapping module compares the absolute value of each difference in the first difference sequence with a preset jump threshold. When the absolute value of a difference is greater than the preset jump threshold, the next time point corresponding to the difference is marked as a candidate jump point in the magnetic iron oxide synthesis process. The second difference sequence is processed in the same way to identify the candidate jump points in the second difference sequence.

[0066] In practical applications, a preset jump threshold of 0.8 radians is set for the magnetic iron oxide synthesis process. The first difference sequence contains 200 difference data. For example, the 50th difference is 0.3 radians, and its absolute value is less than 0.8 radians, so it is not marked. The 51st difference is 0.9 radians, and its absolute value is greater than 0.8 radians. Therefore, the next time point corresponding to the 51st difference, i.e., the 52nd time point corresponding to the 26th second of the reaction, is marked as a candidate jump point.

[0067] A3: Starting from the candidate jump point, scan the adjacent differences forward and backward respectively, and mark the time point region corresponding to the consecutive differences whose absolute values ​​are all greater than the preset jump threshold as the jump region.

[0068] In step A3, the transition region refers to the time interval formed by multiple consecutive candidate transition points during the synthesis of magnetic iron oxide.

[0069] In this embodiment, the phase unwrapping module takes each candidate jump point as the starting point, scans forward the difference value corresponding to the previous time point adjacent to the candidate jump point, and determines whether the absolute value of the difference is greater than a preset jump threshold; at the same time, it scans backward the difference value corresponding to the next time point adjacent to the candidate jump point, and determines whether the absolute value of the difference is greater than the preset jump threshold; all time points corresponding to consecutive differences whose absolute values ​​are all greater than the preset jump threshold are combined together and marked as a jump region in the magnetic iron oxide synthesis process.

[0070] In practical applications, the 52nd time point, at 26 seconds into the synthesis of magnetic iron oxide, is used as a candidate jump point. Scanning forward to the 51st time point, the difference between the reaction at 25.5 seconds and the current difference is 0.9 radians, greater than 0.8 radians. Continuing forward to the 50th time point, the difference between the reaction at 25 seconds and the current difference is 0.3 radians, less than 0.8 radians. Scanning backward to the 53rd time point, the difference between the reaction at 26.5 seconds and the current difference is 0.85 radians, greater than 0.8 radians. Continuing backward to the 54th time point... The difference at the 27th second of the reaction corresponding to the point is 0.88 radians, which is greater than 0.8 radians. Scanning forward to the 55th time point, the difference at the 27.5th second of the reaction corresponding to the point is 0.3 radians, which is less than 0.8 radians. Therefore, the four consecutive time points of the magnetic iron oxide synthesis process corresponding to the 25.5th second of the reaction at the 51st time point, the 26th second of the reaction at the 52nd time point, the 26.5th second of the reaction at the 53rd time point, and the 27th second of the reaction at the 54th time point are marked as a transition region.

[0071] A4: Determine the transition direction corresponding to the transition region based on the difference between the instantaneous phase value at each time point within the transition region and the instantaneous phase value at the previous time point.

[0072] In step A4, the jump direction refers to the direction in which the phase value increases or decreases during the synthesis of magnetic iron oxide.

[0073] In this embodiment, the phase unwrapping module obtains the instantaneous phase value of the first time point within the transition region and the instantaneous phase value of the previous time point, and calculates the difference between the two instantaneous phase values; the transition direction is determined according to the sign of the difference. When the difference is positive, it indicates that the phase transitions in the positive direction during the synthesis of magnetic iron oxide, and when the difference is negative, it indicates that the phase transitions in the negative direction during the synthesis of magnetic iron oxide.

[0074] In practical applications, the transition region includes four time points from the 25.5th second to the 27th second of the reaction. The instantaneous phase value of the first time point in the transition region, i.e., the 51st time point when the reaction reaches 25.5 seconds, is 6.1 radians. The instantaneous phase value of the previous time point, i.e., the 50th time point when the reaction reaches 25 seconds, is 0.8 radians. The calculated difference is 5.3 radians, which is a positive value. Therefore, the transition direction corresponding to this transition region is determined to be the positive direction.

[0075] A5: Based on the jump direction, the instantaneous phase values ​​of all time points within and after the jump region are uniformly increased or decreased by a fixed phase period value, so that the absolute value of the difference corresponding to each time point within the jump region is less than or equal to a preset jump threshold, thereby completing the correction of the phase jump points in the first instantaneous phase sequence and the second instantaneous phase sequence.

[0076] In step A5, the fixed phase period value refers to the angle value corresponding to a complete phase period.

[0077] In this embodiment, the phase unwrapping module selects to add or subtract a fixed phase period value to the instantaneous phase value according to the determined jump direction; when the jump direction is positive, the instantaneous phase value of all time points within the jump region and after the jump region is uniformly subtracted by a fixed phase period value; when the jump direction is negative, the instantaneous phase value of all time points within the jump region and after the jump region is uniformly added by a fixed phase period value; after the correction is completed, the difference between each adjacent time point within the jump region is recalculated, and it is verified whether the absolute value of these differences is less than or equal to the preset jump threshold.

[0078] In practical applications, a fixed phase period value of 2π, or 6.28 radians, is set. During the synthesis of magnetic iron oxide, a transition region is identified, encompassing time points 51 to 54, with the transition direction determined to be positive. First, a fixed phase period value of 6.28 radians is uniformly subtracted from all instantaneous phase values ​​from time point 51 to 201. After correction, the instantaneous phase value at time point 50 remains unchanged at 0.80 radians, while the instantaneous phase value at time point 51 changes from 7.05 radians to 0.77 radians. The difference between time point 51 and time point 50 is calculated to be 0.77 - 0.80 = -0.03 radians. The absolute value of this difference, 0.03 radians, is less than the preset transition threshold of 0.80 radians. At this point, the absolute values ​​of the differences between other adjacent time points within the transition region are also less than the preset transition threshold. Therefore, the correction of the phase transition point within this transition region during the synthesis of magnetic iron oxide is completed.

[0079] A6: Filter the corrected first instantaneous phase sequence and the corrected second instantaneous phase sequence to remove residual noise, and obtain the first unwrapped phase sequence and the second unwrapped phase sequence.

[0080] In this embodiment, the phase unwrapping module filters the first instantaneous phase sequence after phase jump correction and uses a moving average method to remove minor fluctuations that may be introduced during the correction process, resulting in a smooth and continuous first unwrapped phase sequence suitable for the analysis of the magnetic iron oxide synthesis process. The same method is used to filter the corrected second instantaneous phase sequence to obtain a smooth and continuous second unwrapped phase sequence suitable for the analysis of the magnetic iron oxide synthesis process.

[0081] In practical applications, a three-point moving average method is used to filter the corrected first instantaneous phase sequence. For example, the filtered value at time point 50 when the reaction reaches 25 seconds is equal to the average of the three instantaneous phase values ​​at time points 49 (24.5 seconds), 50 (25 seconds), and 51 (25.5 seconds). This method is applied to all time points in the synthesis of magnetic iron oxide to obtain the first untangled phase sequence.

[0082] Step 1053: Calculate the difference between the first unwrapped phase sequence and the second unwrapped phase sequence at the same time point to obtain the instantaneous phase difference.

[0083] In this embodiment, the dynamic coupling analysis unit corresponds the first unwrapped phase sequence and the second unwrapped phase sequence to the same time point in the magnetic iron oxide synthesis process. For each time point, the instantaneous phase difference at that time point is obtained by subtracting the phase value of the second unwrapped phase sequence from the phase value of the first unwrapped phase sequence at that time point. The instantaneous phase differences of all time points are arranged in the time order of the magnetic iron oxide synthesis process to form an instantaneous phase difference sequence.

[0084] In practical applications, for the 50th time point when the reaction proceeds to the 25th second during the synthesis of magnetic iron oxide, the phase value at this time point in the first untangling phase sequence is 0.82 radians, and the phase value at this time point in the second untangling phase sequence is 0.35 radians. The calculated difference is 0.47 radians, which is the instantaneous phase difference when the reaction proceeds to the 25th second.

[0085] Step 1054: Calculate the correlation coefficient between the amplitudes of the pH characteristic curve and the potential characteristic curve at the same time point to obtain the amplitude correlation coefficient.

[0086] In this embodiment, the dynamic coupling analysis unit maps the pH characteristic curve and the potential characteristic curve to the same time point in the magnetic iron oxide synthesis process. For each time point, it acquires the amplitude data of the pH characteristic curve and the amplitude data of the potential characteristic curve at that time point, calculates the correlation coefficient between the two amplitude data, and obtains the amplitude correlation coefficient at that time point. The amplitude correlation coefficients of all time points are arranged in the time order of the magnetic iron oxide synthesis process to form an amplitude correlation coefficient sequence.

[0087] In practical applications, for the 50th time point when the reaction proceeds to the 25th second during the synthesis of magnetic iron oxide, the amplitude of this time point in the pH characteristic curve is 0.75 mV, and the amplitude of this time point in the potential characteristic curve is 0.60 mV. The correlation coefficient between these two amplitudes is calculated to be 0.8, which is the amplitude correlation coefficient when the reaction proceeds to the 25th second.

[0088] Step 1055: Using the instantaneous phase value of the pH characteristic curve as the abscissa and the instantaneous amplitude value of the potential characteristic curve as the ordinate, plot the coordinate points corresponding to each time point in a two-dimensional coordinate system. All coordinate points constitute a Lissajous figure.

[0089] In step 1055, the instantaneous amplitude refers to the magnitude of the signal at a specific time point during the synthesis of magnetic iron oxide, and the coordinate point refers to a planar position determined by the horizontal and vertical coordinate values.

[0090] In this embodiment, the dynamic coupling analysis unit establishes a two-dimensional coordinate system. The instantaneous phase value of the pH characteristic curve corresponding to each time point in the magnetic iron oxide synthesis process is used as the abscissa value of that time point, and the instantaneous amplitude value of the potential characteristic curve corresponding to that time point is used as the ordinate value of that time point. A coordinate point corresponding to that time point is plotted in the two-dimensional coordinate system. The same operation is performed on all time points in the magnetic iron oxide synthesis process, and the coordinate points corresponding to all time points are plotted in the two-dimensional coordinate system. These coordinate points together constitute a Lissajous figure reflecting the dynamic coupling relationship between pH value and potential in the magnetic iron oxide synthesis process.

[0091] In practical applications, for the 50th time point when the reaction proceeds to 25 seconds during the synthesis of magnetic iron oxide, the instantaneous phase value of the pH characteristic curve (0.82 radians) is used as the abscissa, and the instantaneous amplitude value of the potential characteristic curve (0.60 mV) is used as the ordinate. A point with coordinates (0.82, 0.60) is plotted in a two-dimensional coordinate system. For the 51st time point when the reaction proceeds to 25.5 seconds, the instantaneous phase value of the pH characteristic curve (0.85 radians) is used as the abscissa, and the instantaneous amplitude value of the potential characteristic curve (0.62 mV) is used as the ordinate. Another point with coordinates (0.85, 0.62) is plotted in a two-dimensional coordinate system. After plotting the coordinates corresponding to all time points during the synthesis of magnetic iron oxide in this way, the closed trajectory formed by connecting all the coordinates is the Lissajous figure reflecting the dynamic characteristics of the synthesis process.

[0092] This application extracts the instantaneous phase during the synthesis of magnetic iron oxide using Hilbert transform, performs phase untangling correction using a path tracing algorithm, calculates the instantaneous phase difference and amplitude correlation coefficient, and finally constructs a Lissajous figure. This achieves an accurate description of the dynamic coupling relationship between pH and potential during the synthesis of magnetic iron oxide, providing a reliable graphical basis for subsequent judgment of the synthesis reaction state.

[0093] Step 106: Compare the Lissajous figure with a preset standard elliptical trajectory. When the comparison result shows that the Lissajous figure deviates from the standard elliptical trajectory, the monitoring results of pH value and potential deviation are obtained.

[0094] The standard elliptical trajectory refers to a pre-defined ideal Lissajous figure corresponding to the target stoichiometry during the synthesis of magnetic iron oxide, while the monitoring results refer to information used to characterize the degree of deviation of pH and potential from the target state during the synthesis of magnetic iron oxide.

[0095] It should be noted that the embodiments of this application do not specifically limit the specific form of the standard elliptical trajectory, and can be set accordingly according to the actual situation.

[0096] In this embodiment, step 106 includes the following process: Step 1061: Extract the coordinate values ​​of all coordinate points from the Lissajous figure to form a first point set, and extract the coordinate values ​​of all coordinate points from the standard elliptical trajectory to form a second point set.

[0097] In step 1061, the first point set refers to the set of coordinate values ​​of all coordinate points on the Lissajous figure obtained by actual measurement during the synthesis of magnetic iron oxide, and the second point set refers to the set of coordinate values ​​of all coordinate points on the preset standard elliptical trajectory.

[0098] In this embodiment, the abscissa and ordinate values ​​of each coordinate point are first extracted from the Lissajous figure constructed during the synthesis of magnetic iron oxide, and these coordinate values ​​are arranged in chronological order to form a first point set; at the same time, the abscissa and ordinate values ​​of each coordinate point are extracted from the preset standard elliptical trajectory, and these coordinate values ​​are arranged in the same chronological order to form a second point set.

[0099] In practical applications, during the reaction period from the 25th to the 30th second in the synthesis of magnetic iron oxide, the Lissajous figure contains 120 coordinate points. The coordinate values ​​of these 120 coordinate points are extracted to form the first point set, which contains 120 coordinate data pairs, for example, the first coordinate point is (0.82, 0.60) and the second coordinate point is (0.85, 0.62). At the same time, the coordinate values ​​of 120 coordinate points corresponding to the time points are extracted from the preset standard elliptical trajectory to form the second point set, which contains 120 coordinate data pairs, for example, the first coordinate point is (0.80, 0.58) and the second coordinate point is (0.83, 0.60).

[0100] Step 1062: Calculate the Euclidean distance between each coordinate point in the first point set and the corresponding time point coordinate point in the second point set to obtain the distance sequence.

[0101] In step 1062, Euclidean distance refers to the straight-line distance between two coordinate points on a two-dimensional plane, and the distance sequence refers to the sequence composed of the Euclidean distances corresponding to all time points arranged in chronological order.

[0102] In this embodiment of the application, for each time point, the coordinate values ​​of that time point in the first point set and the coordinate values ​​of the same time point in the second point set are obtained, and the Euclidean distance between the two coordinate points is calculated; the Euclidean distances calculated for all time points are arranged in chronological order to form a distance sequence.

[0103] In practical applications, for the first time point, the coordinates of this time point in the first point set are (0.82, 0.60), and the coordinates of this time point in the second point set are (0.80, 0.58). The difference in the horizontal coordinates is calculated as 0.82 - 0.80 = 0.02, and the difference in the vertical coordinates is 0.60 - 0.50 = 0.02. The Euclidean distance is... For the second time point, the coordinates of this time point in the first point set are (0.85, 0.62), and the coordinates of this time point in the second point set are (0.83, 0.60). The difference in the horizontal coordinates is calculated to be 0.85-0.83=0.02, and the difference in the vertical coordinates is 0.62-0.60=0.02. The Euclidean distance is also 0.028. Arrange the Euclidean distances calculated for all 120 time points in chronological order to form a distance sequence.

[0104] Step 1063: Input the distance sequence into the Isolation Forest algorithm. The Isolation Forest algorithm constructs multiple isolated trees through random partitioning, calculates the path length of each distance value in the multiple isolated trees, and calculates the anomaly score of each distance value based on the path length.

[0105] In step 1063, path length refers to the number of edges a data point traverses from the root node to a leaf node in an isolated tree, and anomaly score refers to a value calculated based on path length to measure the degree of anomaly of the data point.

[0106] In this embodiment, the distance sequence is first input into the Isolation Forest algorithm. The algorithm randomly selects segmentation features and segmentation values ​​to randomly segment each distance value in the distance sequence multiple times, constructing multiple isolated trees. For each distance value, its path length in each isolated tree is calculated, and then the path lengths of the distance value in all isolated trees are averaged to obtain the average path length. The anomaly score of the distance value is calculated based on the average path length. The closer the anomaly score is to 1, the more likely the distance value is to be an anomaly point, and the closer the anomaly score is to 0, the more likely the distance value is to be a normal point.

[0107] In practical applications, the Isolation Forest algorithm constructs 100 isolated trees, and each isolated tree randomly divides the 120 distance values ​​in the distance sequence. For the distance value of 0.15 corresponding to the 50th time point, its path length in the 100 isolated trees is calculated to be 5.2, 6.1, 4.8, etc. After summing these path lengths and dividing by 100, the average path length is 5.6. Based on the average path length of 5.6, the anomaly score is calculated to be 0.82.

[0108] Step 1064: Compare the abnormal score with a preset abnormal threshold. When the distance values ​​of the abnormal score greater than the abnormal threshold appear consecutively in the distance sequence and the preset number is greater than the preset number threshold, it is determined that the Lissajous figure deviates from the standard elliptical trajectory.

[0109] In step 1064, the abnormal threshold is a pre-set critical value used to determine whether the abnormal score represents an abnormal state, and the preset quantity threshold is a pre-set critical quantity used to determine whether consecutive abnormalities constitute a deviation state.

[0110] The embodiments of this application do not specifically limit the numerical values ​​of the preset abnormal threshold and the preset quantity threshold, which can be set according to the actual situation.

[0111] In this embodiment of the application, the abnormal score corresponding to each distance value is compared with a preset abnormal threshold, and distance values ​​with abnormal scores greater than the abnormal threshold are filtered out; then the position of these distance values ​​in the distance sequence is checked. When there are multiple consecutive time points where the abnormal scores of the distance values ​​are all greater than the abnormal threshold, and the number of these consecutive distance values ​​is greater than a preset quantity threshold, it is determined that the Lissajous figure in the current magnetic iron oxide synthesis process deviates from the preset standard elliptical trajectory.

[0112] In practical applications, an anomaly threshold of 0.80 and a preset number threshold of 5 were set. The anomaly scores corresponding to the 48th to 55th time points in the distance sequence were 0.81, 0.82, 0.83, 0.85, 0.84, 0.82, 0.81, and 0.80, respectively. The anomaly scores of these 8 consecutive time points were all greater than 0.80, and the number of consecutive anomalies was greater than the preset number threshold of 5. Therefore, it was determined that the Lissajous figure in the current magnetic iron oxide synthesis process deviated from the standard elliptical trajectory.

[0113] Step 1065: When deviating from the standard elliptical trajectory, calculate the pH value deviation and the potential value deviation based on the relative positional relationship between each coordinate point in the first point set and the corresponding coordinate point in the second point set, and output the pH value deviation and the potential value deviation as monitoring results.

[0114] In step 1065, pH deviation refers to the difference between the actual pH value and the target pH value during the synthesis of magnetic iron oxide, and potential deviation refers to the difference between the actual potential value and the target potential value during the synthesis of magnetic iron oxide.

[0115] In this embodiment, when it is determined that the Lissajous figure deviates from the standard elliptical trajectory, the abscissa value of each coordinate point in the first point set (i.e., the instantaneous phase value of the pH characteristic curve) and the ordinate value of each coordinate point in the second point set (i.e., the instantaneous amplitude value of the potential characteristic curve) are obtained. The abscissa value and ordinate value of the corresponding time point coordinate point in the second point set are obtained. For each time point, the difference between the abscissa value of the first point set and the abscissa value of the second point set are calculated as the instantaneous pH deviation value at that time point, and the difference between the ordinate value of the first point set and the ordinate value of the second point set are calculated as the instantaneous potential deviation value at that time point. The average value of the instantaneous pH deviation values ​​at all time points is used to obtain the pH value deviation, and the average value of the instantaneous potential deviation values ​​at all time points is used to obtain the potential value deviation. These two deviation values ​​are output as monitoring results.

[0116] In practical applications, for the eight consecutive time points from the 48th to the 55th time point, the instantaneous pH deviation and instantaneous potential deviation are calculated for each time point. Taking the 50th time point as an example, the x-axis value of the first set of points is 0.85, the x-axis value of the second set of points is 0.80, and the instantaneous pH deviation is 0.05; the y-axis value of the first set of points is 0.62, the y-axis value of the second set of points is 0.58, and the instantaneous potential deviation is 0.04. The instantaneous pH deviation values ​​of the eight time points are added together and divided by 8 to obtain an average value of 0.06, and the instantaneous potential deviation values ​​of the eight time points are added together and divided by 8 to obtain an average value of 0.05. The pH deviation of 0.06 and the potential deviation of 0.05 are output as monitoring results.

[0117] In this embodiment, after step 106, the following process is also included: B1: The monitoring results are input into the gradient boosting decision tree model. The gradient boosting decision tree model uses the monitoring results as input data and processes the input data through multiple decision trees in the gradient boosting decision tree model. Each decision tree allocates the input data to the corresponding leaf node according to the node splitting condition and outputs the leaf node value corresponding to each decision tree.

[0118] In step B1, the decision tree is the basic prediction unit in the model; the leaf node refers to the node at the very end of the decision tree, and each leaf node corresponds to an output value.

[0119] In this embodiment, the monitoring results output in step 1065 are first input into a pre-trained gradient boosting decision tree model, which contains multiple decision trees constructed in sequence. The first decision tree receives the monitoring results as input data and distributes the input data layer by layer to the corresponding paths according to the splitting conditions of each node within the decision tree, eventually reaching a leaf node and outputting the value corresponding to the leaf node. Then, the output of the first decision tree and the monitoring results are used together as the input of the second decision tree. The second decision tree also distributes the input data to the leaf nodes and outputs the values ​​according to the node splitting conditions, and so on until all decision trees are processed.

[0120] This application does not impose specific restrictions on the model type, internal structure design, parameter design, training process, etc. of the gradient boosting decision tree model, and corresponding settings can be made according to the actual situation.

[0121] B2: Sum the values ​​of all leaf nodes output by the decision tree to obtain the deviation level between the current reaction state and the target measurement ratio.

[0122] In step B2, the deviation level refers to a numerical value used to quantify the degree of difference between the current reaction state and the target stoichiometry during the synthesis of magnetic iron oxide.

[0123] In this embodiment, the leaf node values ​​of all decision tree outputs in the gradient boosting decision tree model are summed up. The summed result is the deviation level between the reaction state and the target stoichiometry in the current magnetic iron oxide synthesis process. The larger the deviation level value, the more serious the deviation, and the smaller the value, the closer to the target state.

[0124] B3: Input the deviation level into the ant colony optimization algorithm. The ant colony optimization algorithm initializes multiple artificial ant colony individuals. Each artificial ant colony individual carries a set of candidate values ​​for pH adjustment parameters and potential adjustment parameters. Each artificial ant colony individual moves in the parameter space based on pheromone concentration and heuristic information.

[0125] In step B3, the artificial ant colony is the basic search unit in this algorithm. The pH adjustment parameter refers to the control variable used to adjust the pH value during the synthesis of magnetic iron oxide, and the potential adjustment parameter refers to the control variable used to adjust the potential value during the synthesis of magnetic iron oxide. The pheromone concentration refers to the concentration of chemical substances left by simulated ants on the path, which is used to guide subsequent ants to choose a path. Heuristic information refers to the auxiliary information that guides the search direction in advance based on the characteristics of the problem. The parameter space refers to the two-dimensional space composed of all possible values ​​of the pH adjustment parameter and the potential adjustment parameter.

[0126] In this embodiment, the deviation level obtained in step B2 is first input into the ant colony optimization algorithm. The algorithm initializes a set number of artificial ant colony individuals. Each artificial ant colony individual randomly generates a set of candidate values ​​for pH adjustment parameters and potential adjustment parameters as its initial position. Each artificial ant colony individual calculates the probability of moving in various directions in the parameter space based on the pheromone concentration at the current parameter position and the pre-set heuristic information. It selects the direction of movement according to the probability and moves one step in the parameter space. After reaching the new parameter position, it updates the candidate value it carries.

[0127] B4: During the movement, update the candidate values ​​it carries, and after multiple iterations, converge to the optimal parameter combination, which includes the target value for pH adjustment and the target value for potential adjustment.

[0128] In step B4, the optimal parameter combination refers to the combination of pH adjustment parameter and potential adjustment parameter values ​​that make the objective function optimal after multiple iterations. The pH adjustment target value refers to the pH adjustment parameter value in the optimal parameter combination, and the potential adjustment target value refers to the potential adjustment parameter value in the optimal parameter combination.

[0129] In this embodiment, each artificial ant colony individual updates its own candidate values ​​for pH adjustment parameters and potential adjustment parameters after each movement, and simultaneously updates the pheromone concentration along the path it traverses. The movement and update operations are repeated for multiple iterations. During the iteration process, the pheromone concentration gradually accumulates on the high-quality path, guiding the artificial ant colony individuals to gather in the optimal area. When the preset number of iterations is reached or all artificial ant colony individuals converge to the same area, the candidate values ​​carried by the artificial ant colony individual corresponding to the path with the highest current pheromone concentration are selected as the optimal parameter combination output. This optimal parameter combination includes the target values ​​for pH adjustment and potential adjustment for subsequent control.

[0130] This application compares Lissajous figures with standard elliptical trajectories and calculates the Euclidean distance, combines anomaly detection with the isolated forest algorithm to determine the deviation state of the figures, calculates the deviation of pH value and potential as the monitoring result, evaluates the deviation level through a gradient boosting decision tree model, and finally uses the ant colony optimization algorithm to search for the optimal adjustment parameters. This achieves accurate identification of pH value and potential deviation state and optimization of adjustment parameters during the synthesis of magnetic iron oxide.

[0131] Figure 3 This application provides a schematic diagram of a system for synchronously monitoring pH and potential during the synthesis of magnetic iron oxide, as shown in the embodiments of the present application. Figure 3 As shown, the system includes: The acquisition module 31 is used to acquire the original response sequence during the synthesis of magnetic iron oxide. The original response sequence includes a first voltage signal output by the pH electrode and a second voltage signal output by the indicator electrode.

[0132] The processing module 32 is used to preprocess the first voltage signal and the second voltage signal to form a synchronous timing data stream, and to use a signal conditioning circuit to perform differential amplification and filtering on the synchronous timing data stream to obtain a conditioned analog signal.

[0133] The input module 33 is used to input the conditioned analog signal to the time-frequency conversion module for wavelet packet decomposition to obtain multiple frequency band components, and to extract the first characteristic frequency band component corresponding to the ion diffusion process and the second characteristic frequency band component corresponding to the electron transfer process from the multiple frequency band components.

[0134] The reconstruction module 34 is used to reconstruct the first characteristic frequency band component and the second characteristic frequency band component respectively to obtain the pH characteristic curve and the potential characteristic curve.

[0135] The calculation module 35 is used to input the pH characteristic curve and the potential characteristic curve into the dynamic coupling analysis unit, calculate the instantaneous phase difference and amplitude correlation coefficient through the dynamic coupling analysis unit, and construct a Lissajous figure based on the instantaneous phase difference and the amplitude correlation coefficient. The Lissajous figure is used to describe the dynamic coupling relationship between pH value and potential.

[0136] The comparison module 36 is used to compare the Lissajous figure with a preset standard elliptical trajectory. When the comparison result shows that the Lissajous figure deviates from the standard elliptical trajectory, the monitoring result of pH value and potential deviation is obtained.

[0137] The pH and potential synchronous monitoring system in the magnetic iron oxide synthesis process of this application embodiment is used to implement the aforementioned method for synchronous monitoring of pH and potential in the magnetic iron oxide synthesis process. Therefore, the specific implementation of the pH and potential synchronous monitoring system in the magnetic iron oxide synthesis process can be found in the embodiment section of the method for synchronous monitoring of pH and potential in the magnetic iron oxide synthesis process above. The specific implementation can be referred to the description of the corresponding embodiments, and will not be repeated here.

[0138] This application also provides an electronic device, comprising: a memory for storing a computer program; and a processor for executing the computer program to implement the steps of the method for synchronously monitoring pH and potential during the synthesis of magnetic iron oxide as described above.

[0139] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the method for synchronously monitoring pH and potential during the synthesis of magnetic iron oxide as described above.

[0140] In one exemplary embodiment, the aforementioned computer-readable storage medium may include, but is not limited to, various media capable of storing computer programs, such as USB flash drives, read-only memory, random access memory, portable hard drives, magnetic disks, or optical disks.

[0141] The embodiments of this application also provide a computer program product, which includes a computer program that, when executed by a processor, implements the steps in any of the embodiments of the method for synchronous monitoring of pH value and potential during the synthesis of magnetic iron oxide.

[0142] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0143] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in one or more embodiments of this specification are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, use and processing of related data must comply with relevant laws, regulations and standards, and corresponding operation entry points are provided for users to choose to authorize or refuse.

[0144] The foregoing has provided a detailed description of a method and system for synchronously monitoring pH and potential during the synthesis of magnetic iron oxide, as provided in this application. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the embodiments above are merely for the purpose of helping to understand the method and its core ideas. It should be noted that those skilled in the art can make various improvements and modifications to this application without departing from its principles, and these improvements and modifications also fall within the protection scope of this application.

Claims

1. A method for synchronously monitoring pH and potential during the synthesis of magnetic iron oxide, characterized in that, include: During the synthesis of magnetic iron oxide, a raw response sequence is collected, which includes a first voltage signal output from the pH electrode and a second voltage signal output from the indicator electrode. The first voltage signal and the second voltage signal are preprocessed to form a synchronous timing data stream, and the synchronous timing data stream is differentially amplified and filtered using a signal conditioning circuit to obtain a conditioned analog signal. The conditioned analog signal is input to the time-frequency conversion module for wavelet packet decomposition to obtain multiple frequency band components, and the first characteristic frequency band component corresponding to the ion diffusion process and the second characteristic frequency band component corresponding to the electron transfer process are extracted from the multiple frequency band components. Signal reconstruction is performed on the first characteristic frequency band component and the second characteristic frequency band component respectively to obtain the pH characteristic curve and the potential characteristic curve; The pH characteristic curve and the potential characteristic curve are input into the dynamic coupling analysis unit. The instantaneous phase difference and amplitude correlation coefficient are calculated by the dynamic coupling analysis unit, and a Lissajous figure is constructed based on the instantaneous phase difference and the amplitude correlation coefficient. The Lissajous figure is used to describe the dynamic coupling relationship between pH value and potential. The Lissajous figure is compared with a preset standard elliptical trajectory. When the comparison result shows that the Lissajous figure deviates from the standard elliptical trajectory, the monitoring results of pH value and potential deviation are obtained.

2. The method according to claim 1, characterized in that, The step of inputting the pH characteristic curve and the potential characteristic curve into the dynamic coupling analysis unit, calculating the instantaneous phase difference and amplitude correlation coefficient through the dynamic coupling analysis unit, and constructing a Lissajous figure based on the instantaneous phase difference and the amplitude correlation coefficient includes: The pH characteristic curve and the potential characteristic curve are input into the dynamic coupling analysis unit. In the dynamic coupling analysis unit, Hilbert transform is performed on the pH characteristic curve and the potential characteristic curve respectively to extract the instantaneous phase value corresponding to each time point on the pH characteristic curve and the instantaneous phase value corresponding to each time point on the potential characteristic curve, so as to obtain the first instantaneous phase sequence and the second instantaneous phase sequence. The first instantaneous phase sequence and the second instantaneous phase sequence are input into the phase unwrapping module. The phase unwrapping module uses a path tracking algorithm to detect and correct the phase jump points in the first instantaneous phase sequence and the second instantaneous phase sequence to obtain the first unwrapped phase sequence and the second unwrapped phase sequence. Calculate the difference between the first unwrapped phase sequence and the second unwrapped phase sequence at the same time point to obtain the instantaneous phase difference; Calculate the correlation coefficient between the amplitudes of the pH characteristic curve and the potential characteristic curve at the same time point to obtain the amplitude correlation coefficient; Using the instantaneous phase value of the pH characteristic curve as the abscissa and the instantaneous amplitude value of the potential characteristic curve as the ordinate, the coordinate points corresponding to each time point are plotted in a two-dimensional coordinate system, and all coordinate points form a Lissajous figure.

3. The method according to claim 2, characterized in that, The first instantaneous phase sequence and the second instantaneous phase sequence are input into the phase unwrapping module. The phase unwrapping module uses a path tracking algorithm to detect and correct phase transition points in the first instantaneous phase sequence and the second instantaneous phase sequence to obtain the first unwrapped phase sequence and the second unwrapped phase sequence, including: The first instantaneous phase sequence and the second instantaneous phase sequence are input into the phase unwrapping module. The phase unwrapping module calculates the difference between the instantaneous phase values ​​of two adjacent time points in the first instantaneous phase sequence and the second instantaneous phase sequence, respectively, to obtain the first difference sequence and the second difference sequence. Identify the differences between the first difference sequence and the second difference sequence whose absolute values ​​are greater than a preset jump threshold, and mark the time points corresponding to the differences as candidate jump points; Starting from the candidate jump point, scan the adjacent differences forward and backward respectively, and mark the time point region corresponding to the consecutive differences whose absolute values ​​are all greater than the preset jump threshold as the jump region; The jump direction corresponding to the jump region is determined based on the difference between the instantaneous phase value at each time point within the jump region and the instantaneous phase value at the previous time point. According to the jump direction, the instantaneous phase values ​​of all time points within and after the jump region are uniformly increased or decreased by a fixed phase period value, so that the absolute value of the difference corresponding to each time point within the jump region is less than or equal to a preset jump threshold, thereby completing the correction of the phase jump points in the first instantaneous phase sequence and the second instantaneous phase sequence; The corrected first instantaneous phase sequence and the corrected second instantaneous phase sequence are filtered to remove residual noise, resulting in the first unwrapped phase sequence and the second unwrapped phase sequence.

4. The method according to claim 1, characterized in that, After obtaining the monitoring results of pH value and potential deviation, the following is also included: The monitoring results are input into the gradient boosting decision tree model. The gradient boosting decision tree model takes the monitoring results as input data and processes the input data through multiple decision trees in the gradient boosting decision tree model. Each decision tree allocates the input data to the corresponding leaf node according to the node splitting condition and outputs the leaf node value corresponding to each decision tree. The values ​​of the leaf nodes output by all decision trees are summed to obtain the deviation level between the current reaction state and the target measurement ratio; The deviation level is input into the ant colony optimization algorithm, which initializes multiple artificial ant colony individuals. Each artificial ant colony individual carries a set of candidate values ​​for pH adjustment parameters and potential adjustment parameters. Each artificial ant colony individual moves in the parameter space based on pheromone concentration and heuristic information. During the movement, the candidate values ​​carried by the device are updated, and after multiple iterations, the device converges to the optimal parameter combination, which includes the target value for pH adjustment and the target value for potential adjustment.

5. The method according to claim 1, characterized in that, The step of inputting the conditioned analog signal into a time-frequency conversion module for wavelet packet decomposition to obtain multiple frequency band components, and extracting a first characteristic frequency band component corresponding to the ion diffusion process and a second characteristic frequency band component corresponding to the electron transfer process from the multiple frequency band components, includes: The conditioned analog signal is input to the time-frequency conversion module. The time-frequency conversion module performs wavelet packet decomposition on the pH time series data and potential time series data in the conditioned analog signal. Each decomposition divides the conditioned analog signal into a first frequency band and a second frequency band. The first frequency band and the second frequency band are then decomposed into the next level. After a preset number of decompositions, multiple frequency band components in different frequency ranges are obtained. Obtain the first frequency range corresponding to the ion diffusion process in the frequency domain, and select the frequency band components whose center frequency is within the first frequency range from the multiple frequency band components, and mark them as the first characteristic frequency band components; Obtain the second frequency range corresponding to the electron transfer process in the frequency domain, and select the frequency band components whose center frequency is within the second frequency range from the multiple frequency band components, and mark them as the second characteristic frequency band components.

6. The method according to claim 1, characterized in that, The step of comparing the Lissajous figure with a preset standard elliptical trajectory, and obtaining the monitoring results of pH value and potential deviation when the comparison result shows that the Lissajous figure deviates from the standard elliptical trajectory, includes: The coordinate values ​​of all coordinate points are extracted from the Lissajous figure to form a first point set, and the coordinate values ​​of all coordinate points are extracted from the standard elliptical trajectory to form a second point set. Calculate the Euclidean distance between each coordinate point in the first point set and the corresponding time point coordinate point in the second point set to obtain a distance sequence; The distance sequence is input into the Isolation Forest algorithm, which constructs multiple isolation trees through random partitioning, calculates the path length of each distance value in the multiple isolation trees, and calculates the anomaly score of each distance value based on the path length. The anomaly score is compared with a preset anomaly threshold. When the distance values ​​with an anomaly score greater than the anomaly threshold appear consecutively in the distance sequence and the preset number is greater than the preset number threshold, it is determined that the Lissajous figure deviates from the standard elliptical trajectory. When the deviation from the standard elliptical trajectory is made, the pH value deviation and the potential value deviation are calculated based on the relative positional relationship between each coordinate point in the first set of points and the corresponding coordinate point in the second set of points, and the pH value deviation and the potential value deviation are output as monitoring results.

7. The method according to claim 1, characterized in that, The step of using a signal conditioning circuit to differentially amplify and filter the synchronous timing data stream to obtain a conditioned analog signal includes: The synchronous timing data stream is sent to the signal conditioning circuit, and the differential amplifier in the signal conditioning circuit performs differential operation on the synchronous timing data stream to eliminate common-mode noise in the synchronous timing data stream. The signal after common-mode noise elimination is sent to the low-pass filter in the signal conditioning circuit. The low-pass filter filters out high-frequency noise higher than the set frequency and outputs the conditioned analog signal.

8. A system for synchronously monitoring pH and potential during the synthesis of magnetic iron oxide, characterized in that, include: The acquisition module is used to acquire the original response sequence during the synthesis of magnetic iron oxide. The original response sequence includes a first voltage signal output by the pH electrode and a second voltage signal output by the indicator electrode. The processing module is used to preprocess the first voltage signal and the second voltage signal to form a synchronous timing data stream, and to use a signal conditioning circuit to perform differential amplification and filtering on the synchronous timing data stream to obtain a conditioned analog signal. The input module is used to input the conditioned analog signal to the time-frequency conversion module for wavelet packet decomposition to obtain multiple frequency band components, and extract the first characteristic frequency band component corresponding to the ion diffusion process and the second characteristic frequency band component corresponding to the electron transfer process from the multiple frequency band components. The reconstruction module is used to reconstruct the first characteristic frequency band component and the second characteristic frequency band component respectively to obtain the pH characteristic curve and the potential characteristic curve; The calculation module is used to input the pH characteristic curve and the potential characteristic curve into the dynamic coupling analysis unit, calculate the instantaneous phase difference and amplitude correlation coefficient through the dynamic coupling analysis unit, and construct a Lissajous figure based on the instantaneous phase difference and the amplitude correlation coefficient. The Lissajous figure is used to describe the dynamic coupling relationship between pH value and potential. The comparison module is used to compare the Lissajous figure with a preset standard elliptical trajectory. When the comparison result shows that the Lissajous figure deviates from the standard elliptical trajectory, the monitoring results of pH value and potential deviation are obtained.

9. An electronic device, characterized in that, include: Memory, used to store computer programs; A processor, configured to execute the computer program to implement the steps of the method for synchronously monitoring pH and potential during the synthesis of magnetic iron oxide as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, enables the method for synchronously monitoring pH and potential during the synthesis of magnetic iron oxide as described in any one of claims 1 to 7.

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