A plasma generator health state monitoring and intelligent control system and method

Abstract

The application discloses a kind of plasma generator health state monitoring and intelligent control system and method, it is related to plasma generator health monitoring technical field.The system includes data acquisition module, standard determination module, feature extraction module, numerical evaluation module, loss calculation and evaluation module, life prediction module and analysis control module.The application is by determining the standard health impedance spectrum and standard health temperature field adapted to current working condition, avoid normal operation fluctuation in working condition switching process being misjudged as failure, early weak loss is covered by working condition noise Problem, improve monitoring adaptability and accuracy under complex working condition scene, and in the cooperation of impedance characteristics and temperature characteristics, whether the joint determination of electrode is abnormal, can effectively distinguish electrode real aging loss and external working condition disturbance, through loss index and evaluation confidence calculation, monitoring result is more accurate.

Description

Technical Field

[0001] This application relates to the field of plasma generator health monitoring technology, specifically to a plasma generator health status monitoring and intelligent control system and method. Background Technology

[0002] Plasma generators typically create plasma under varying gas, pressure, and power conditions for processes such as surface treatment, thin film preparation, cleaning, sterilization, and material modification. Electrodes, as the core discharge components of plasma generators, operate under high pressure, high temperature, and strong electric fields for extended periods, making them prone to wear, oxidation, fouling, and structural distortion. These losses directly affect the stability of plasma discharge and the safety of equipment operation, potentially leading to equipment shutdowns or damage. Existing electrode health monitoring technologies often rely on simple electrical parameter thresholds (such as fixed alarm thresholds for current, reflected power, emitted light intensity, or single-point temperature) for fault identification. However, this approach has the following drawbacks in practical applications:

[0003] The physical meaning of the same parameter value is not the same under different operating conditions. When switching operating conditions, normal fluctuations may be misjudged as faults, while early losses may be masked. This not only results in low monitoring accuracy and a high fault misjudgment rate, but also has the problems of monitoring lag and missed detection of hidden losses. Summary of the Invention

[0004] In view of the deficiencies in the existing technology, the technical problem to be solved by this application is: how to achieve accurate and effective monitoring of the health status of electrodes.

[0005] To achieve the above objectives, in a first aspect, embodiments of this application provide a plasma generator health status monitoring and intelligent control system, the system comprising:

[0006] The data acquisition module is used to: acquire the current operating parameters of the plasma generator, the real-time impedance spectrum of the electrode within a preset frequency set, and the real-time temperature field image of the electrode surface.

[0007] The standard determination module is used to: screen standard health impedance spectra and standard health temperature fields from a pre-established health sample library based on current operating condition parameters;

[0008] The feature extraction module is used to: obtain impedance features and temperature features based on current operating parameters, real-time impedance spectrum, real-time temperature field image, standard healthy impedance spectrum and standard healthy temperature field;

[0009] The numerical evaluation module is used to obtain loss consistency characteristic values ​​based on impedance and temperature characteristics during the detection period.

[0010] The loss calculation and evaluation module is used to: obtain the loss index and evaluate the confidence level based on the current operating parameters, impedance characteristics, temperature characteristics and loss consistency characteristics;

[0011] The lifespan prediction module is used to predict the remaining lifespan of the electrode when it reaches the failure loss threshold, based on the loss index.

[0012] The analysis and control module is used to: determine the health level based on the remaining service life and the wear index during the detection period when the assessment confidence level is greater than a preset threshold, and generate control instructions.

[0013] Secondly, embodiments of this application provide a method for monitoring and intelligently controlling the health status of a plasma generator, the method comprising the following steps:

[0014] The current operating parameters of the plasma generator, the real-time impedance spectrum of the electrode within a preset frequency set, and the real-time temperature field image of the electrode surface are collected by the data acquisition module.

[0015] Based on the current operating condition parameters, the standard determination module retrieves similar operating condition samples from the pre-established health sample library and generates standard health impedance spectra and standard health temperature fields corresponding to the current operating condition parameters.

[0016] The feature extraction module obtains impedance characteristics based on the real-time impedance spectrum and the standard health impedance spectrum;

[0017] After the feature extraction module performs coordinate correction on the real-time temperature field image, it obtains the preprocessed real-time temperature field image, compares it with the standard healthy temperature field, and obtains the temperature features.

[0018] The numerical evaluation module obtains loss consistency characteristic values ​​based on impedance and temperature characteristics during the detection period;

[0019] The loss calculation and evaluation module obtains the loss index and evaluation confidence level based on the current operating parameters, impedance characteristics, temperature characteristics, and loss consistency characteristic values;

[0020] The lifespan prediction module predicts the remaining lifespan of the electrode when it reaches the failure and wear threshold based on the wear index.

[0021] When the confidence level is greater than the preset threshold, the analysis and control module determines the health level based on the remaining service life and the wear index during the detection period, and generates control instructions.

[0022] In conjunction with the second aspect, in one embodiment, the impedance characteristics include relative amplitude deviation and spectral shape offset; wherein the relative amplitude deviation is obtained as follows:

[0023] Calculate the absolute amplitude difference between the real-time impedance spectrum and the standard health impedance spectrum at the same diagnostic frequency point, divide it by the impedance amplitude of the corresponding diagnostic frequency point in the standard health impedance spectrum, and obtain the relative amplitude deviation of that diagnostic frequency point.

[0024] After obtaining the relative amplitude deviation of all diagnostic frequency points, the average value is calculated as the relative amplitude deviation at the corresponding sampling time.

[0025] The method for obtaining the spectrum shape offset is as follows:

[0026] Calculate the slope of two adjacent diagnostic frequency points in the real-time impedance spectrum, traverse the diagnostic frequency points, and obtain the first slope set; calculate the slope of two adjacent diagnostic frequency points in the standard health impedance spectrum, traverse the diagnostic frequency points, and obtain the second slope set.

[0027] Calculate the absolute slope difference between the slopes corresponding to the same two diagnostic frequency points in slope set one and slope set two. Traverse slope set one and slope set two to obtain the set of absolute slope difference values, and obtain the average absolute slope difference value based on it. Calculate the ratio of the average absolute slope difference value to the pre-calibrated maximum allowable slope deviation threshold value, and use it as the spectrum shape offset.

[0028] In conjunction with the second aspect, in one embodiment, the method further includes determining whether the impedance at the sampling moment is abnormal based on impedance characteristics, the process of which includes:

[0029] The overall impedance deviation value is obtained by weighted summation of the relative amplitude deviation and the spectral shape offset.

[0030] Determine whether the overall impedance deviation value is greater than the preset safe impedance deviation threshold. If so, determine that an impedance abnormality occurred at that sampling time; otherwise, determine that the impedance is normal at that sampling time.

[0031] In conjunction with the second aspect, in one embodiment, the process of acquiring temperature characteristics includes:

[0032] Calculate the absolute temperature difference between the preprocessed real-time temperature field image and the pixel at the same position in the standard healthy temperature field;

[0033] From the preprocessed real-time temperature field image, pixels with an absolute temperature difference greater than a preset threshold are selected as abnormal pixels.

[0034] The proportion of abnormal pixels in the preprocessed real-time temperature field image is calculated as the proportion of high temperature anomalies.

[0035] Determine if the percentage of abnormal high temperatures is greater than the preset safe temperature percentage. If so, determine that a temperature abnormality has occurred; otherwise, determine that the temperature is normal.

[0036] Based on the proportion of high-temperature anomalies and the temperature determination results, temperature features are generated, including the temperature status at the sampling time and the corresponding proportion of high-temperature anomalies.

[0037] In conjunction with the second aspect, in one embodiment, the process of obtaining loss consistency characteristic values ​​based on impedance characteristics and temperature characteristics during the detection period includes:

[0038] After obtaining the impedance and temperature characteristics at all sampling times within the detection period;

[0039] The time synchronization coefficient is calculated using the following expression:

[0040] ;

[0041] In the formula, This is the time synchronization coefficient. This is the initial moment when the impedance anomaly is first triggered. This represents the initial moment when the temperature anomaly was first triggered. For preset allowed time associated windows;

[0042] The expression for calculating the abnormal persistence overlap coefficient is as follows:

[0043] ;

[0044] In the formula, This is the coefficient for abnormal persistent overlap. The duration of the overlap between impedance anomaly and temperature anomaly. The duration of total impedance anomaly. This represents the total duration of the temperature anomaly. Preset to small positive numbers;

[0045] The anomaly intensity matching coefficient is calculated using the following expression:

[0046] ;

[0047] In the formula, For abnormal intensity matching coefficients, This represents the average composite impedance deviation value during the testing period. The average offset of temperature characteristics within the detection period is calculated as follows:

[0048] ;

[0049] In the formula, This represents the average percentage of abnormally high temperatures during the detection period. The preset safe temperature percentage threshold;

[0050] The loss consistency characteristic value is calculated using the following expression:

[0051] ;

[0052] In the formula, This is the characteristic value of loss consistency. All are weighting coefficients, and , For binary value taking, its expression is:

[0053] ;

[0054] In the formula, To preset the impedance safety deviation threshold, This is a preset temperature safety offset threshold.

[0055] In conjunction with the second aspect, in one embodiment, the formula for calculating the loss index is:

[0056] ;

[0057] In the formula, The loss index, All are weighting coefficients, and .

[0058] In conjunction with the second aspect, in one embodiment, the formula for calculating the assessment confidence level is:

[0059] ;

[0060] In the formula, To assess confidence level, and , This is the stability coefficient under operating conditions.

[0061] In conjunction with the second aspect, in one embodiment, the formula for calculating the remaining useful life is:

[0062] ;

[0063] In the formula, For the remaining service life, Failure loss index threshold This represents the loss index during the most recent detection period. This represents the rate of loss growth.

[0064] In conjunction with the second aspect, in one embodiment, the process of determining the health level and generating control commands based on the remaining service life and the wear index during the detection period includes:

[0065] When the loss index is less than the preset first-level loss threshold and the remaining service life is greater than the preset warning safety time, the health level is determined to be healthy; at this time, the normal operating parameters of the plasma generator are executed.

[0066] When the loss index is greater than the preset first-level loss threshold but less than the preset second-level loss threshold, or when the remaining service life is less than the preset warning safety time but greater than the preset critical danger time, the health level is determined to be a warning level; at this time, a health alarm is issued, and the plasma generator output is limited according to the preset power change slope.

[0067] When the loss index exceeds the preset level 2 loss threshold, or the remaining service life is less than the preset critical danger duration, the health level is determined to be a severe loss level; at this time, an emergency alarm is issued and a shutdown operation is performed, or the system is operated according to the preset derating parameters.

[0068] Compared with the prior art, the advantages of this application are:

[0069] (1) By determining the standard healthy impedance spectrum and standard healthy temperature field that are suitable for the current working conditions, the problem of normal operation fluctuations being misjudged as faults and early weak losses being masked by working condition noise during the working condition switching process is avoided, thereby improving the monitoring adaptability and accuracy in complex working conditions. Furthermore, with the cooperation of impedance characteristics and temperature characteristics, a joint judgment on whether the electrode is abnormal can be made, which can effectively distinguish between the actual aging loss of the electrode and external working condition disturbances. Through loss index and assessment confidence calculation, the monitoring results are more accurate.

[0070] (2) Use the assessment confidence level to determine whether the monitoring results are credible, avoid false matching and false high consistency in the absence of loss, and provide a reliable guarantee for the accurate detection of electrode health status.

[0071] (3) By screening effective detection times, the remaining service life of the electrode can be effectively predicted based on the loss index of historical effective monitoring times. In addition, the electrode health level can be located in conjunction with the loss index, and control commands corresponding to the health level can be generated, providing a reliable guarantee for the stable operation of the plasma generator. Attached Figure Description

[0072] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0073] Figure 1 This is a system architecture block diagram in an embodiment of this application;

[0074] Figure 2 This is a flowchart of the plasma generator health status monitoring and intelligent control method in the embodiments of this application. Detailed Implementation

[0075] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0076] The flowchart shown in the attached diagram is for illustrative purposes only and does not necessarily include all content and operations / steps, nor does it necessarily have to be performed in the order described. For example, some operations / steps can be broken down, combined, or partially merged, so the actual execution order may change depending on the actual situation.

[0077] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings.

[0078] In a first aspect, embodiments of this application provide a plasma generator health status monitoring and intelligent control system, referring to... Figure 1 The system includes a data acquisition module, a standard determination module, a feature extraction module, a numerical evaluation module, a loss calculation and evaluation module, a lifetime prediction module, and an analysis and control module. The data acquisition module is used to collect the current operating parameters of the plasma generator, the real-time impedance spectrum of the electrodes within a preset frequency set, and the real-time temperature field image of the electrode surface.

[0079] The standard determination module is used to screen standard health impedance spectra and standard health temperature fields from a pre-established health sample library based on current operating condition parameters;

[0080] The feature extraction module is used to obtain impedance features and temperature features based on current operating parameters, real-time impedance spectrum, real-time temperature field image, standard health impedance spectrum and standard health temperature field;

[0081] The numerical evaluation module is used to obtain loss consistency characteristic values ​​based on impedance and temperature characteristics during the detection period;

[0082] The loss calculation and evaluation module is used to obtain the loss index and evaluation confidence level based on the current operating parameters, impedance characteristics, temperature characteristics and loss consistency characteristics.

[0083] The lifespan prediction module is used to predict the remaining lifespan of the electrode when it reaches the failure loss threshold, based on the loss index.

[0084] The analysis and control module is used to determine the health level and generate control instructions based on the remaining service life and the wear index during the detection period when the assessment confidence level is greater than a preset threshold.

[0085] By determining the standard healthy impedance spectrum and standard healthy temperature field that are suitable for the current operating conditions, the problem of normal operating fluctuations being misjudged as faults and early weak losses being masked by operating noise during the switching of operating conditions is avoided, thereby improving the monitoring adaptability and accuracy in complex operating scenarios.

[0086] Secondly, embodiments of this application provide a method for monitoring and intelligently controlling the health status of a plasma generator, referring to... Figure 2 The method includes the following steps:

[0087] S1. Collect the current operating parameters of the plasma generator (cavity pressure, working gas ratio, set output power, cavity ambient temperature and cumulative running time), and simultaneously collect the real-time impedance spectrum of the electrode in the preset frequency set (diagnostic frequency points within 10kHz to 10MHz), as well as the real-time temperature field image (i.e. infrared temperature image) of the electrode surface.

[0088] S2. Based on the current operating condition parameters, retrieve similar operating condition samples from the pre-established health sample library (composed of health impedance spectrum data and health temperature field data collected by confirmed healthy electrodes in a predefined conventional operating condition space), and generate standard health impedance spectrum (diagnostic frequency point is the horizontal axis and impedance is the vertical axis) and standard health temperature field corresponding to the current operating condition parameters.

[0089] S3. Obtain impedance characteristics (i.e., relative amplitude deviation and spectral shape shift) based on the real-time impedance spectrum and the standard healthy impedance spectrum; the relative amplitude deviation is obtained as follows:

[0090] Calculate the absolute amplitude difference between the real-time impedance spectrum and the standard health impedance spectrum at the same diagnostic frequency point, divide it by the impedance amplitude of the corresponding diagnostic frequency point in the standard health impedance spectrum, and obtain the relative amplitude deviation of that diagnostic frequency point.

[0091] After obtaining the relative amplitude deviation of all diagnostic frequency points, the average value is calculated as the relative amplitude deviation at the corresponding sampling time (note that when it is greater than 1, it is taken as 1).

[0092] The method for obtaining the spectrum shape offset is as follows:

[0093] Calculate the slope of two adjacent diagnostic frequency points in the real-time impedance spectrum, traverse the diagnostic frequency points, and obtain the first slope set; calculate the slope of two adjacent diagnostic frequency points in the standard health impedance spectrum, traverse the diagnostic frequency points, and obtain the second slope set.

[0094] Calculate the absolute slope difference between the slopes corresponding to the same two diagnostic frequency points in slope set one and slope set two. Traverse slope set one and slope set two to obtain the set of absolute slope difference values, and obtain the average absolute slope difference value based on it. Calculate the ratio of the average absolute slope difference value to the pre-calibrated maximum allowable slope deviation threshold (it should be noted that when the average absolute slope difference value is greater than the pre-calibrated maximum allowable slope deviation threshold, the ratio result is uniformly taken as 1. The larger the ratio value, the greater the degree of offset), and use it as the spectral shape offset.

[0095] Furthermore, based on the impedance characteristics, it is determined whether the impedance at that sampling moment is abnormal. That is, after weighting the relative amplitude deviation and the spectral shape offset (the sum of the total weights is 1) and summing them, the comprehensive impedance deviation value is obtained. It is then determined whether the comprehensive impedance deviation value is greater than the preset safe impedance deviation threshold. If so, it is determined that there is an impedance abnormality at that sampling moment; otherwise, it is determined that the impedance at that sampling moment is normal.

[0096] S4. After performing coordinate correction on the real-time temperature field image, obtain the preprocessed real-time temperature field image and compare it with the standard healthy temperature field to obtain temperature characteristics.

[0097] The coordinate correction steps mentioned above include:

[0098] Electrode contour feature points are extracted from standard health temperature field and real-time temperature field images. Through feature point matching, affine transformation and pixel-level alignment correction, image position deviations caused by shooting shake, installation offset and thermal deformation are eliminated, and spatial alignment between the preprocessed real-time temperature field image and the standard health temperature field is achieved.

[0099] Based on this, the process of comparing the preprocessed real-time temperature field image with the standard healthy temperature field to obtain temperature characteristics includes:

[0100] Calculate the absolute temperature difference between the preprocessed real-time temperature field image and the pixel at the same position in the standard healthy temperature field;

[0101] From the preprocessed real-time temperature field image, pixels with absolute temperature differences greater than a preset threshold are selected as abnormal pixels. The proportion of abnormal pixels in the preprocessed real-time temperature field image is calculated as the high temperature abnormality proportion. It is then determined whether the high temperature abnormality proportion is greater than the preset safe temperature proportion. If so, a temperature abnormality is determined; otherwise, the temperature is determined to be normal.

[0102] Based on the proportion of high temperature anomalies and the temperature determination results, temperature characteristics are generated (i.e., the temperature status at the sampling time: such as abnormal temperature / normal temperature, and the corresponding proportion of high temperature anomalies).

[0103] S5. Based on the impedance and temperature characteristics during the detection period, generate a loss consistency characteristic value (used to characterize the temporal synchronicity, persistence, and anomaly intensity matching between impedance deviation and temperature anomaly); the process for obtaining this loss consistency characteristic value is as follows:

[0104] After obtaining the impedance and temperature characteristics at all sampling times within the detection period;

[0105] The time synchronization coefficient is calculated to characterize the degree of matching between the timing of impedance anomalies and temperature anomalies (i.e., sampling times) within the detection period. Its expression is as follows:

[0106] ;

[0107] In the formula, This is the time synchronization coefficient; the closer the value is to 1, the higher the degree of matching. This is the initial moment when the impedance anomaly is first triggered. This represents the initial moment when the temperature anomaly was first triggered. For preset allowed time associated windows;

[0108] The anomaly persistence overlap coefficient is calculated to characterize the degree of temporal overlap between impedance anomalies and temperature anomalies during the detection period; its expression is:

[0109] ;

[0110] In the formula, This is the abnormal persistence overlap coefficient. The closer this value is to 1, the higher the degree of overlap in time coverage. The duration of the overlap between impedance anomaly and temperature anomaly. The duration of total impedance anomaly. This represents the total duration of the temperature anomaly. This is a preset small positive number to prevent the denominator from being zero;

[0111] The anomaly intensity matching coefficient is calculated to characterize the correlation between the spectral shape shift and the average shift of the temperature anomaly during the detection period; its expression is:

[0112] ;

[0113] In the formula, This is the anomaly strength matching coefficient. The closer this value is to 1, the higher the matching correlation. This represents the average composite impedance deviation value during the testing period. The average offset of temperature characteristics within the detection period is calculated as follows:

[0114] ;

[0115] In the formula, This represents the average percentage of abnormally high temperatures during the detection period. The preset safe temperature percentage threshold;

[0116] The loss consistency characteristic value is calculated using the following expression:

[0117] ;

[0118] In the formula, This is the characteristic value of loss consistency. All are weighting coefficients, and , For binary value taking, its expression is:

[0119] ;

[0120] In the formula, To preset the impedance safety deviation threshold, This is a preset temperature safety offset threshold.

[0121] In this way, when the impedance and temperature characteristics do not simultaneously produce effective anomalies, the value is set to 0, making the loss consistency characteristic value 0, thus avoiding misjudgment in subsequent lossless coupling scenarios.

[0122] S6. Based on the current operating parameters, impedance characteristics, temperature characteristics, and loss consistency characteristics, obtain the loss index and assess the confidence level; the formula for calculating the loss index is:

[0123] ;

[0124] In the formula, The loss index, This represents the average composite impedance deviation value during the testing period. This represents the average offset of temperature characteristics within the detection period. This is the characteristic value of loss consistency. All are weighting coefficients, and ;

[0125] The formula for calculating the confidence level is:

[0126] ;

[0127] In the formula, To assess confidence level, and , The stability coefficient under operating conditions is calculated as follows:

[0128] ;

[0129] In the formula, For the first The standard deviation of the operating parameters during the testing period For the first The maximum permissible standard deviation of the operating condition parameter, This represents the total number of operating parameters.

[0130] By using the confidence level assessment to determine the reliability of the monitoring results, we can avoid misjudgments such as false matching and false high consistency in the absence of loss, thus providing a reliable guarantee for the accurate detection of electrode health status.

[0131] S7. Obtain the loss index of several consecutive historical detection times, screen and evaluate valid detection times with a confidence level greater than a preset threshold, and construct a loss time-series curve. Based on the loss time-series curve, obtain the remaining service life of the electrode when it reaches the failure loss threshold. The calculation formula is as follows:

[0132] ;

[0133] In the formula, For the remaining service life, Failure loss index threshold (determined through electrode accelerated aging test and ultimate loss calibration test). This represents the loss index during the most recent detection period. The loss growth rate is calculated as follows:

[0134] ;

[0135] In the formula, For the first The end timestamp of a valid historical detection period. This represents the loss index for the most recent detection period. This represents the average loss index over the effective historical monitoring period.

[0136] Therefore, by selecting effective detection times and based on the loss index of historical effective monitoring times, the remaining service life of the electrode can be effectively predicted.

[0137] S8. When the assessment confidence level is greater than a preset threshold, determine the health level based on the remaining service life and the wear index during the detection period, and generate control instructions; specifically:

[0138] When the loss index is less than the preset first-level loss threshold and the remaining service life is greater than the preset warning safety time, the health level is determined to be a healthy level. At this time, the electrode loss is extremely low, the operating condition is stable, the evaluation result is reliable, and there is no risk of failure.

[0139] When the loss index is greater than the preset first-level loss threshold and less than the preset second-level loss threshold, or when the remaining service life is less than the preset warning safety time and greater than the preset critical danger time, the health level is determined to be the warning level. At this time, the electrode shows slight continuous loss and the loss status gradually deteriorates, requiring key monitoring and early maintenance.

[0140] When the loss index exceeds the preset secondary loss threshold, or the remaining service life is less than the preset critical danger time, the health level is determined to be a severe loss level. At this time, the electrode has suffered severe structural loss and is close to failure and scrapping, posing a safety hazard to the operation of the equipment.

[0141] Furthermore, when the health level is at the healthy level, the control command is: execute the normal operating parameters of the plasma generator;

[0142] When the health level is at the warning level, the control command is: issue a health alarm and limit the plasma generator output according to the preset power change slope to avoid sudden power changes that could exacerbate electrode wear.

[0143] When the health level is at the severe wear level, the control command is: to issue an emergency alarm and perform a shutdown operation, or to operate according to the preset derated operating parameters (i.e., to operate at a derated rate relative to the normal operating parameters).

[0144] By effectively predicting the remaining service life and combining it with the loss index, the health level of the electrodes can be determined, and corresponding control commands can be generated, providing a reliable guarantee for the stable operation of the plasma generator.

[0145] It should be noted that the sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0146] The terms "comprising" and "having," and any variations thereof, in the specification, claims, and accompanying drawings of this application are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to such process, method, product, or apparatus. The terms "first," "second," and "third," etc., are used to distinguish different objects, etc., and do not indicate a sequence, nor do they limit "first," "second," and "third" to different types.

[0147] In the description of the embodiments of this application, terms such as "exemplary," "for example," or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as "exemplary," "for example," or "for instance" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of terms such as "exemplary," "for example," or "for instance" is intended to present the relevant concepts in a concrete manner.

[0148] In the description of the embodiments of this application, unless otherwise stated, " / " means "or". For example, A / B can mean A or B. The "and / or" in the text is merely a description of the relationship between related objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist simultaneously, and B exists alone. In addition, in the description of the embodiments of this application, "multiple" means two or more.

[0149] In some processes described in the embodiments of this application, multiple operations or steps are included in a specific order. However, it should be understood that these operations or steps may not be executed in the order they appear in the embodiments of this application, or they may be executed in parallel. The sequence number of the operation is only used to distinguish different operations, and the sequence number itself does not represent any execution order. In addition, these processes may include more or fewer operations, and these operations or steps may be executed sequentially or in parallel, and these operations or steps may be combined.

[0150] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes several instructions to cause a terminal device to execute the methods described in the various embodiments of this application.

[0151] The above are merely specific embodiments of this application, but the protection scope of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the protection scope of this application. Therefore, the protection scope of this application should be determined by the scope of the claims.

Claims

1. A health status monitoring and intelligent control system for a plasma generator, characterized in that, The system includes: The data acquisition module is used to: acquire the current operating parameters of the plasma generator, the real-time impedance spectrum of the electrode within a preset frequency set, and the real-time temperature field image of the electrode surface. The standard determination module is used to: screen standard health impedance spectra and standard health temperature fields from a pre-established health sample library based on current operating condition parameters; The feature extraction module is used to: obtain impedance features and temperature features based on current operating parameters, real-time impedance spectrum, real-time temperature field image, standard healthy impedance spectrum and standard healthy temperature field; The numerical evaluation module is used to obtain loss consistency characteristic values ​​based on impedance and temperature characteristics during the detection period. The loss calculation and evaluation module is used to: obtain the loss index and evaluate the confidence level based on the current operating parameters, impedance characteristics, temperature characteristics and loss consistency characteristics; The lifespan prediction module is used to predict the remaining lifespan of the electrode when it reaches the failure loss threshold, based on the loss index. The analysis and control module is used to: determine the health level based on the remaining service life and the wear index during the detection period when the assessment confidence level is greater than a preset threshold, and generate control instructions.

2. A method for monitoring and intelligently controlling the health status of a plasma generator, applied to the plasma generator health status monitoring and intelligent control system described in claim 1, characterized in that, The method includes the following steps: The current operating parameters of the plasma generator, the real-time impedance spectrum of the electrode within a preset frequency set, and the real-time temperature field image of the electrode surface are collected by the data acquisition module. Based on the current operating condition parameters, the standard determination module retrieves similar operating condition samples from the pre-established health sample library and generates standard health impedance spectra and standard health temperature fields corresponding to the current operating condition parameters. The feature extraction module obtains impedance characteristics based on the real-time impedance spectrum and the standard health impedance spectrum; After the feature extraction module performs coordinate correction on the real-time temperature field image, it obtains the preprocessed real-time temperature field image, compares it with the standard healthy temperature field, and obtains the temperature features. The numerical evaluation module obtains loss consistency characteristic values ​​based on impedance and temperature characteristics during the detection period; The loss calculation and evaluation module obtains the loss index and evaluation confidence level based on the current operating parameters, impedance characteristics, temperature characteristics, and loss consistency characteristic values; The lifespan prediction module predicts the remaining lifespan of the electrode when it reaches the failure and wear threshold based on the wear index. When the confidence level is greater than the preset threshold, the analysis and control module determines the health level based on the remaining service life and the wear index during the detection period, and generates control instructions.

3. The method for monitoring and intelligently controlling the health status of a plasma generator according to claim 2, characterized in that, The impedance characteristics include relative amplitude deviation and spectral shape offset; The relative amplitude deviation is obtained as follows: Calculate the absolute amplitude difference between the real-time impedance spectrum and the standard health impedance spectrum at the same diagnostic frequency point, divide it by the impedance amplitude of the corresponding diagnostic frequency point in the standard health impedance spectrum, and obtain the relative amplitude deviation of that diagnostic frequency point. After obtaining the relative amplitude deviation of all diagnostic frequency points, the average value is calculated as the relative amplitude deviation at the corresponding sampling time. The method for obtaining the spectrum shape offset is as follows: Calculate the slope of two adjacent diagnostic frequency points in the real-time impedance spectrum, traverse the diagnostic frequency points, and obtain the first slope set; calculate the slope of two adjacent diagnostic frequency points in the standard health impedance spectrum, traverse the diagnostic frequency points, and obtain the second slope set. Calculate the absolute slope difference between the slopes corresponding to the same two diagnostic frequency points in slope set one and slope set two. Traverse slope set one and slope set two to obtain the set of absolute slope difference values, and obtain the average absolute slope difference value based on it. Calculate the ratio of the average absolute slope difference value to the pre-calibrated maximum allowable slope deviation threshold value, and use it as the spectrum shape offset.

4. The method for monitoring and intelligently controlling the health status of a plasma generator according to claim 3, characterized in that, It also includes determining whether the impedance at a given sampling time is abnormal based on impedance characteristics, the process of which includes: The overall impedance deviation value is obtained by weighted summation of the relative amplitude deviation and the spectral shape offset. Determine whether the overall impedance deviation value is greater than the preset safe impedance deviation threshold. If so, determine that an impedance abnormality occurred at that sampling time; otherwise, determine that the impedance is normal at that sampling time.

5. The method for monitoring and intelligently controlling the health status of a plasma generator according to claim 4, characterized in that, The process for obtaining temperature characteristics includes: Calculate the absolute temperature difference between the pixels at the same location in the preprocessed real-time temperature field image and the standard healthy temperature field; From the preprocessed real-time temperature field image, pixels with an absolute temperature difference greater than a preset threshold are selected as abnormal pixels. The proportion of abnormal pixels in the preprocessed real-time temperature field image is calculated as the proportion of high temperature anomalies. Determine if the percentage of abnormal high temperatures is greater than the preset safe temperature percentage. If so, determine that a temperature abnormality has occurred; otherwise, determine that the temperature is normal. Based on the proportion of high-temperature anomalies and the temperature determination results, temperature features are generated, including the temperature status at the sampling time and the corresponding proportion of high-temperature anomalies.

6. The method for monitoring and intelligently controlling the health status of a plasma generator according to claim 5, characterized in that, The process of obtaining loss consistency characteristic values ​​based on impedance and temperature characteristics during the detection period includes: After obtaining the impedance and temperature characteristics at all sampling times within the detection period; The time synchronization coefficient is calculated using the following expression: ； In the formula, This is the time synchronization coefficient. This is the initial moment when the impedance anomaly is first triggered. This represents the initial moment when the temperature anomaly was first triggered. For preset allowed time associated windows; The expression for calculating the abnormal persistence overlap coefficient is as follows: ； In the formula, This is the coefficient for abnormal persistent overlap. The duration of the overlap between impedance anomaly and temperature anomaly. The duration of total impedance anomaly. This represents the total duration of the temperature anomaly. Preset to small positive numbers; The anomaly intensity matching coefficient is calculated using the following expression: ； In the formula, For abnormal intensity matching coefficients, This represents the average composite impedance deviation value during the testing period. The average offset of temperature characteristics within the detection period is calculated as follows: ； In the formula, This represents the average percentage of abnormally high temperatures during the detection period. The preset safe temperature percentage threshold; The loss consistency characteristic value is calculated using the following expression: ； In the formula, This is the characteristic value of loss consistency. All are weighting coefficients, and , For binary value taking, its expression is: ； In the formula, To preset the impedance safety deviation threshold, This is a preset temperature safety offset threshold.

7. The method for monitoring and intelligently controlling the health status of a plasma generator according to claim 6, characterized in that, The formula for calculating the loss index is: ； In the formula, The loss index, All are weighting coefficients, and .

8. The method for monitoring and intelligently controlling the health status of a plasma generator according to claim 7, characterized in that, The formula for calculating the confidence level is: ； In the formula, To assess confidence level, and , This is the stability coefficient under operating conditions.

9. The method for monitoring and intelligently controlling the health status of a plasma generator according to claim 8, characterized in that, The formula for calculating the remaining useful life is: ； In the formula, For the remaining service life, Failure loss index threshold This represents the loss index during the most recent detection period. This represents the rate of loss growth.

10. A method for monitoring and intelligently controlling the health status of a plasma generator according to claim 9, characterized in that, The process of determining the health level and generating control commands based on the remaining service life and the wear index during the detection period includes: When the loss index is less than the preset first-level loss threshold and the remaining service life is greater than the preset warning safety time, the health level is determined to be healthy; at this time, the normal operating parameters of the plasma generator are executed. When the loss index is greater than the preset first-level loss threshold but less than the preset second-level loss threshold, or when the remaining service life is less than the preset warning safety time but greater than the preset critical danger time, the health level is determined to be a warning level; at this time, a health alarm is issued, and the plasma generator output is limited according to the preset power change slope. When the loss index exceeds the preset level 2 loss threshold, or the remaining service life is less than the preset critical danger duration, the health level is determined to be a severe loss level; at this time, an emergency alarm is issued and a shutdown operation is performed, or the system is operated according to the preset derating parameters.

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