A method, system, device and terminal for classifying and identifying plasma discharge modes

Abstract

This invention belongs to the field of high-voltage discharge technology and discloses a method, system, device, and terminal for classifying and identifying plasma discharge modes. It acquires the acoustic signal of a plasma source discharge and determines the discharge mode; after preprocessing the acquired acoustic signal, it extracts time-domain and time-frequency domain features and constructs feature vectors; it trains a classification model and uses the trained model to classify and identify the discharge modes. The plasma discharge mode classification method based on acoustic signal features provided by this invention is a non-contact detection method with high detection accuracy. This invention only requires portable sound-receiving tools such as acoustic sensors to obtain the acoustic signal of the plasma source, enabling rapid classification of its discharge modes. It features fast response speed, low cost, and simple operation. This invention does not require expensive detection equipment, is simple to operate, does not require a special detection environment, is not constrained by the discharge device, and has good industrial application value.

Description

Technical Field

[0001] This invention belongs to the field of high-voltage discharge technology, and particularly relates to a plasma discharge mode classification and identification method, system, device and terminal. Background Technology

[0002] Currently, plasma generated by gas discharge is widely used in many fields. Different discharge modes exist within plasma discharge, such as corona discharge, glow discharge-like discharge, spark discharge, and glow discharge mode. Due to the different characteristics of each discharge mode, they have different applications. Electrostatic precipitators based on the corona discharge principle are widely used in industry. Furthermore, corona discharge can also generate ozone, which can be used for disinfection. Glow discharge is uniform, making it well-suited for surface modification and biomedical treatment. Spark discharge is typically used in plasma ignition systems. Therefore, accurately and in real-time determining the plasma discharge mode is crucial when using plasma in industrial applications.

[0003] Currently, existing methods for classifying and identifying plasma discharge patterns mainly include electrical detection, optical detection, and image detection. However, these methods suffer from drawbacks such as high detection costs, stringent requirements for detection conditions, and complex operation. Electrical detection is a contact-based method, demanding sophisticated equipment; optical detection relies on specialized and expensive equipment, is time-consuming, costly, and complex to operate; and image detection is highly sensitive to environmental and lighting conditions. Therefore, to further promote the industrial applications of plasma, it is urgent to develop a new method for classifying and identifying plasma discharge patterns.

[0004] In industrial applications, plasma requires real-time and accurate identification of discharge modes to prevent mode shifts caused by unstable discharges, which could negatively impact or even harm industrial applications. Therefore, existing classification and identification technologies need further improvement and optimization to ensure high accuracy while reducing detection complexity and cost, thus promoting the development of plasma industrial applications.

[0005] Based on the above analysis, the problems and defects of the existing technologies are as follows: Among the existing plasma discharge mode classification and recognition methods, the electrical detection method is a contact detection method, which has high requirements for equipment; the optical detection method relies on dedicated and expensive detection equipment, and the detection time is long, the cost is high, and the operation is relatively complicated; the image detection method is also quite demanding on the detection environment and lighting conditions. Summary of the Invention

[0006] To address the problems existing in the prior art, the present invention provides a plasma discharge mode classification and identification method, system, device and terminal, and particularly relates to a plasma discharge mode classification and identification method, system, medium, device and terminal based on acoustic signal characteristics.

[0007] The present invention is implemented as follows: a plasma discharge mode classification and identification method, which includes: acquiring plasma source discharge acoustic signals and determining discharge modes; preprocessing the acquired plasma source discharge acoustic signals to extract time-domain and time-frequency domain features and construct feature vectors; training a classification model and using the trained classification model to classify and identify discharge modes.

[0008] Furthermore, the plasma discharge mode classification and identification method includes the following steps:

[0009] Step 1: Acquire acoustic signals of the plasma source under different discharge modes;

[0010] Step two: Perform noise reduction filtering on the obtained acoustic signal;

[0011] Step 3: Extract time-domain and time-frequency domain features from the denoised and filtered acoustic signal to construct feature vectors;

[0012] Step 4: Use a classification algorithm to train a classification model for the feature vectors of sample acoustic signals under different modes;

[0013] Step 5: Input the obtained acoustic signal of the unknown mode into the trained classification model to classify and identify the discharge mode of the plasma source.

[0014] Furthermore, the acquisition of acoustic signals from the plasma source under different discharge modes in step one includes:

[0015] (1) Acquisition tools for obtaining acoustic signals from plasma source discharge;

[0016] (2) Obtaining a plasma source;

[0017] (3) Determine the discharge mode of the plasma source.

[0018] Furthermore, the plasma source in step (2) includes needle-needle discharge, needle-plate discharge, surface micro-discharge, dielectric barrier discharge, and jet.

[0019] Furthermore, the discharge modes of the plasma source in step (3) include corona discharge, filament discharge, glow discharge-like discharge, spark discharge, and glow discharge.

[0020] Furthermore, step three involves extracting time-domain and time-frequency domain features from the filtered acoustic signal, including:

[0021] The extracted time-domain feature is the short-time average energy, and the extracted time-frequency domain feature is the STFT;

[0022] The formula for calculating the short-time average energy of the time-domain characteristics is:

[0023]

[0024] The formula for calculating the time-frequency domain characteristic STFT is:

[0025]

[0026]

[0027] The feature vector is in the form of:

[0028] Feature = [E n (k),X n (k),P n (k)].

[0029] Another object of the present invention is to provide a plasma discharge mode classification and identification system applying the aforementioned plasma discharge mode classification and identification method, the plasma discharge mode classification and identification system comprising:

[0030] The acoustic signal acquisition module is used to acquire acoustic signals of the plasma source under different discharge modes;

[0031] The acoustic signal preprocessing module is used to perform noise reduction and filtering on the acquired acoustic signal;

[0032] The feature extraction module is used to extract time-domain and time-frequency domain features from the denoised and filtered acoustic signal and construct feature vectors.

[0033] The classification model training module is used to train a classification model using a classification algorithm on the feature vectors of sample acoustic signals under different modes.

[0034] The discharge mode classification and recognition module is used to input the obtained acoustic signals of unknown modes into the trained classification model to classify and recognize the discharge modes of the plasma source.

[0035] Another object of the present invention is to provide a computer device, the computer device including a memory and a processor, the memory storing a computer program, which, when executed by the processor, causes the processor to perform the steps of the plasma discharge pattern classification and recognition method.

[0036] Another object of the present invention is to provide a computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the steps of the plasma discharge pattern classification and recognition method.

[0037] Another objective of this invention is to provide an information data processing terminal for implementing the plasma discharge mode classification and recognition system.

[0038] Based on the above technical solutions and the technical problems solved, the advantages and positive effects of the technical solution to be protected by this invention are as follows:

[0039] First, addressing the technical problems existing in the prior art and the difficulty of solving them, this paper closely analyzes, in conjunction with the technical solution to be protected by this invention and the results and data obtained during the research and development process, how the technical solution of this invention solves the technical problems, and the inventive technical effects brought about by solving these problems. The specific description is as follows:

[0040] The plasma discharge mode classification method based on acoustic signal characteristics provided by this invention is a non-contact detection method with high detection accuracy. Compared with other traditional detection methods, this invention only requires portable sound-receiving tools such as acoustic sensors to obtain the acoustic signal of the plasma source, and can quickly classify its discharge modes. It has the characteristics of fast response speed, low cost, and simple operation. The plasma discharge mode classification and identification method based on acoustic signal characteristics provided by this invention does not require expensive detection equipment, is simple to operate, does not require a special detection environment, is not restricted by the discharge device, and has good industrial application value.

[0041] Second, considering the technical solution as a whole or from a product perspective, the technical effects and advantages of the technical solution to be protected by this invention are specifically described as follows:

[0042] The plasma discharge mode classification and identification method provided by this invention is a non-contact detection method. It only requires the acquisition of the acoustic signal of the plasma source through portable sound-receiving tools such as acoustic sensors to quickly classify its discharge modes. It has the characteristics of fast response speed, low cost and simple operation.

[0043] Third, as supplementary evidence of the inventive step of the claims of this invention, it is also reflected in the following important aspects:

[0044] (1) The technical solution of this invention fills a technical gap in the industry both domestically and internationally:

[0045] Existing plasma discharge pattern classification and identification methods, both domestically and internationally, mainly include electrical detection, optical detection, and image detection. However, these methods suffer from drawbacks such as high detection costs, stringent requirements for detection conditions, and complex operation. The plasma discharge pattern classification and identification method based on acoustic signal characteristics provided in this invention achieves high identification accuracy while reducing detection complexity and cost. This novel method for plasma discharge pattern identification and classification contributes to promoting the industrial application of plasma.

[0046] (2) Whether the technical solution of the present invention overcomes technical bias:

[0047] The generation of plasma is usually accompanied by sound, but the acoustic signals differ depending on the discharge mode. Glow-like and spark discharges are relatively intense, producing relatively large acoustic signals, while corona discharges produce sounds with smaller amplitudes, sometimes even exceeding the range of human hearing, and are often mistakenly considered to produce no acoustic signal. Therefore, previous plasma discharge mode identification schemes have not received sufficient attention. This invention provides a plasma discharge mode classification and identification method based on acoustic signal characteristics. Using acoustic signal acquisition equipment, this method can effectively identify and classify various discharge modes, overcoming technical biases. Attached Figure Description

[0048] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the embodiments of the present invention will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0049] Figure 1 This is a flowchart of the plasma discharge mode classification and recognition method provided in the embodiments of the present invention;

[0050] Figure 2 This is a schematic diagram of the plasma discharge mode classification and identification method provided in this embodiment of the invention;

[0051] Figure 3 (a) is a photograph of the needle-plasma corona discharge mode and a schematic diagram of the acoustic signal waveform after noise reduction and filtering provided in an embodiment of the present invention; (b) is a photograph of the needle-plasma glow discharge mode and a schematic diagram of the acoustic signal waveform after noise reduction and filtering provided in an embodiment of the present invention; (c) is a photograph of the needle-plasma spark discharge mode and a schematic diagram of the acoustic signal waveform after noise reduction and filtering provided in an embodiment of the present invention; (d) is a photograph of the needle-plasma glow discharge mode and a schematic diagram of the acoustic signal waveform after noise reduction and filtering provided in an embodiment of the present invention.

[0052] Figure 4 (a) is a time-domain and time-frequency domain feature diagram of the acoustic signal in the needle-plasma corona discharge mode provided in the embodiment of the present invention; (b) is a time-domain and time-frequency domain feature diagram of the acoustic signal in the needle-plasma glow discharge mode provided in the embodiment of the present invention; (c) is a time-domain and time-frequency domain feature diagram of the acoustic signal in the needle-plasma spark discharge mode provided in the embodiment of the present invention; (d) is a time-domain and time-frequency domain feature diagram of the acoustic signal in the needle-plasma glow discharge mode provided in the embodiment of the present invention. Detailed Implementation

[0053] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0054] To address the problems existing in the prior art, the present invention provides a plasma discharge mode classification and identification method, system, device and terminal. The present invention will be described in detail below with reference to the accompanying drawings.

[0055] To enable those skilled in the art to fully understand how the present invention is specifically implemented, this section provides an explanatory description of the embodiments that expand upon the technical solutions of the claims.

[0056] like Figure 1 As shown, the plasma discharge mode classification and identification method provided in this embodiment of the invention includes the following steps:

[0057] S101, Acquire acoustic signals of the plasma source under different discharge modes;

[0058] S102, Perform noise reduction filtering on the obtained acoustic signal;

[0059] S103, extract time-domain and time-frequency domain features from the denoised and filtered acoustic signal, and construct a feature vector;

[0060] S104, For the feature vectors of sample acoustic signals under different modes, a classification algorithm is used to train a classification model;

[0061] S105 inputs the obtained acoustic signal of the unknown mode into the trained classification model to classify and identify the discharge mode of the plasma source.

[0062] In a preferred embodiment, the step S101 of the present invention, which involves acquiring acoustic signals of the plasma source under different discharge modes, includes:

[0063] (1) Acquisition tools for obtaining acoustic signals from plasma source discharge;

[0064] (2) The plasma sources include needle-needle discharge, needle-plate discharge, surface micro-discharge, dielectric barrier discharge, and jet, etc.

[0065] (3) The discharge modes of the plasma source may include corona discharge, filament discharge, glow discharge-like discharge, spark discharge, glow discharge, etc.

[0066] In a preferred embodiment, step S103 of this invention involves extracting time-domain and time-frequency domain features from the obtained acoustic signal; wherein the extracted time-domain feature is the short-time average energy, and the extracted time-frequency domain feature is the STFT (Short-Time Fourier Transform), calculated using the following formula:

[0067] The formula for calculating the short-time average energy of the time-domain characteristics is:

[0068]

[0069] The formula for calculating the time-frequency domain characteristic STFT is:

[0070]

[0071]

[0072]

[0073] Feature = [E n (k),X n (k),P n (k)]

[0074] The plasma discharge mode classification and identification system provided in this embodiment of the invention includes:

[0075] The acoustic signal acquisition module is used to acquire acoustic signals of the plasma source under different discharge modes;

[0076] The acoustic signal preprocessing module is used to perform noise reduction and filtering on the acquired acoustic signal;

[0077] The feature extraction module is used to extract time-domain and time-frequency domain features from the denoised and filtered acoustic signal and construct feature vectors.

[0078] The classification model training module is used to train a classification model using a classification algorithm on the feature vectors of sample acoustic signals under different modes.

[0079] The discharge mode classification and recognition module is used to input the obtained acoustic signals of unknown modes into the trained classification model to classify and recognize the discharge modes of the plasma source.

[0080] To demonstrate the inventiveness and technical value of the technical solution of this invention, this section provides specific product or related technology application examples of the technical solution claimed.

[0081] This invention provides a plasma discharge mode classification and identification method based on acoustic signal characteristics, which can be applied to the identification and classification of discharge modes of different plasma sources, including needle-point discharge, needle-plate discharge, surface micro-discharge, dielectric barrier discharge, and jet discharge. Common discharge modes include corona discharge, filament discharge, glow discharge-like discharge, spark discharge, and glow discharge.

[0082] The embodiments of the present invention have achieved some positive results during the research and development or use process, and have indeed great advantages compared with the prior art. The following content describes them in conjunction with the data, charts and other information of the experimental process.

[0083] This invention provides a method for classifying and identifying needle-point discharge plasma discharge modes based on acoustic signal characteristics. In this embodiment, the method first obtains discharge acoustic signals of needle-point discharge under different discharge modes using an acoustic sensor; then, it performs denoising filtering on the obtained acoustic signals; finally, it extracts time-domain and time-frequency domain features from the denoised and filtered acoustic signals to construct feature vectors; and then, it trains a classification model using a classification algorithm on the feature vectors of sample acoustic signals under different modes. Finally, it inputs the obtained acoustic signals of unknown modes into the trained classification model to classify and identify the discharge modes of the plasma source.

[0084] Specifically, such as Figure 2 As shown, the embodiments of the present invention are implemented through the following technical solutions:

[0085] Step 1: Acquire acoustic signals from the plasma source under different discharge modes. In this embodiment of the invention, the electrode material for the needle-needle discharge is stainless steel. By adjusting the distance between the needle electrodes, four plasma discharge modes are obtained: corona discharge, glow discharge-like discharge, spark discharge, and glow discharge. The tool used to obtain the acoustic signals of the needle-needle discharge plasma is an acoustic sensor, and 250 sets of acoustic signals are collected for each of the four discharge modes.

[0086] Step 2 involves denoising and filtering the acoustic signal obtained in Step 1. Specifically, in this embodiment of the invention, the acquired acoustic signal waveform has spikes due to electromagnetic interference; therefore, a median filtering algorithm is used for denoising. Other filtering algorithms, such as wavelet denoising and Kalman filtering, can also be selected.

[0087] Figure 3 The images show four discharge modes of needle-to-needle discharge and the acoustic signal waveforms after noise reduction and filtering, provided for embodiments of the present invention.

[0088] Step 3: Extract time-domain and time-frequency domain features from the acoustic signal obtained in Step 2, which are the short-time average energy and STFT, respectively, and construct feature vectors.

[0089] The formula for calculating the short-time average energy of the time-domain characteristics is:

[0090]

[0091] The formula for calculating the time-frequency domain characteristic STFT is:

[0092]

[0093]

[0094] The feature vector is in the form of:

[0095] Feature = [E n (k),X n (k),P n (k)]

[0096] Figure 4 The acoustic signal time-domain and time-frequency domain feature diagrams for the four discharge modes of needle-to-needle discharge provided in the embodiments of the present invention.

[0097] Step 4: For the feature vectors of sample acoustic signals under different modes, a classification algorithm is used to train a classification model. Specifically, 250 sets of acoustic signal data for each discharge mode are divided into a training set and a test set, with 200 sets used to train the classification model and 50 sets used to test it. In this embodiment, each set of data in the training set is labeled with the correct discharge mode, and the SVM classification algorithm is used to train the classification model. Other classification algorithms such as decision tree classification, Naive Bayes classification, neural network methods, and KNN classification can also be used.

[0098] Step 5: The remaining 50 sets of acoustic signals, without labels, are input into the trained classification model to classify and identify the discharge modes of the plasma source. Table 1 shows the classification and identification results of the four discharge modes of needle-to-needle discharge plasma provided by the embodiment of the present invention. The accuracy rate can reach over 90%, indicating that the plasma discharge mode classification and identification method based on acoustic signal characteristics has high accuracy.

[0099] Table 1. Discharge Mode Classification and Identification Results

[0100] Discharge mode Corona discharge glow discharge Spark discharge glow discharge Classification and recognition accuracy 96％ 98％ 100％ 96％

[0101] It should be noted that embodiments of the present invention can be implemented in hardware, software, or a combination of both. The hardware portion can be implemented using dedicated logic; the software portion can be stored in memory and executed by a suitable instruction execution system, such as a microprocessor or dedicated-design hardware. Those skilled in the art will understand that the above-described devices and methods can be implemented using computer-executable instructions and / or included in processor control code, for example, such code provided on a carrier medium such as a disk, CD, or DVD-ROM, a programmable memory such as read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The devices and modules of the present invention can be implemented by hardware circuitry such as very large-scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, or programmable hardware devices such as field-programmable gate arrays, programmable logic devices, etc., or by software executed by various types of processors, or by a combination of the above-described hardware circuitry and software, such as firmware.

[0102] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications, equivalent substitutions, and improvements made by those skilled in the art within the scope of the technology disclosed in the present invention, and within the spirit and principles of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A method for classifying and identifying plasma discharge modes, characterized in that, The plasma discharge mode classification and identification method includes: acquiring plasma source discharge acoustic signals and determining discharge modes; preprocessing the acquired plasma source discharge acoustic signals to extract time-domain and time-frequency domain features and construct feature vectors; training a classification model and using the trained classification model to classify and identify discharge modes. The plasma discharge mode classification and identification method includes the following steps: Step 1: Acquire acoustic signals of the plasma source under different discharge modes; Step two: Perform noise reduction filtering on the obtained acoustic signal; Step 3: Extract time-domain and time-frequency domain features from the denoised and filtered acoustic signal to construct feature vectors; Step 4: Use a classification algorithm to train a classification model for the feature vectors of sample acoustic signals under different modes; Step 5: Input the obtained acoustic signal of unknown mode into the trained classification model to classify and identify the discharge mode of the plasma source. Step three involves extracting time-domain and time-frequency domain features from the filtered acoustic signal, including: The extracted time-domain feature is the short-time average energy, and the extracted time-frequency domain feature is the STFT; The formula for calculating the short-time average energy of the time-domain characteristics is: ； The formula for calculating the time-frequency domain characteristic STFT is: ； ； The feature vector is in the form of: 。 2. The plasma discharge mode classification and identification method as described in claim 1, characterized in that, Step one involves acquiring acoustic signals from the plasma source under different discharge modes, including: (1) Acquisition tools for obtaining acoustic signals from plasma source discharge; (2) Obtaining a plasma source; (3) Determine the discharge mode of the plasma source.

3. The plasma discharge mode classification and identification method as described in claim 2, characterized in that, The plasma sources in step (2) include needle-needle discharge, needle-plate discharge, surface micro-discharge, dielectric barrier discharge, and jet.

4. The plasma discharge mode classification and identification method as described in claim 2, characterized in that, The discharge modes of the plasma source in step (3) include corona discharge, filament discharge, glow discharge-like discharge, spark discharge, and glow discharge.

5. A plasma discharge mode classification and identification system applying the plasma discharge mode classification and identification method as described in any one of claims 1 to 4, characterized in that, The plasma discharge mode classification and identification system includes: The acoustic signal acquisition module is used to acquire acoustic signals of the plasma source under different discharge modes; The acoustic signal preprocessing module is used to perform noise reduction and filtering on the acquired acoustic signal; The feature extraction module is used to extract time-domain and time-frequency domain features from the denoised and filtered acoustic signal and construct feature vectors. The classification model training module is used to train a classification model using a classification algorithm on the feature vectors of sample acoustic signals under different modes. The discharge mode classification and recognition module is used to input the obtained acoustic signals of unknown modes into the trained classification model to classify and recognize the discharge modes of the plasma source.

6. A computer device, characterized in that, The computer device includes a memory and a processor. The memory stores a computer program, which, when executed by the processor, causes the processor to perform the steps of the plasma discharge mode classification and identification method as described in any one of claims 1 to 4.

7. A computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the steps of the plasma discharge mode classification and identification method as described in any one of claims 1 to 4.

8. An information data processing terminal, characterized in that, The information data processing terminal is used to implement the plasma discharge mode classification and recognition system as described in claim 5.

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