Plasma gas pressure correction method, apparatus, device, and medium

Abstract

Embodiments of the present application provide a plasma gas pressure correction method, device, equipment and medium. The method comprises: in a controllable gas pressure cavity, collecting a DVS event stream of plasma and corresponding gas pressure parameters; performing spatiotemporal joint representation on the DVS event stream to obtain plasma event time sequence curves and plasma event frame images under each gas pressure parameter; determining a gas pressure sensitive feature of the plasma under each gas pressure parameter according to the plasma event time sequence curves and the plasma event frame images; and performing normalization correction on the gas pressure sensitive feature of the plasma under a second gas pressure according to the gas pressure sensitive feature of the plasma under a first gas pressure to obtain a target gas pressure sensitive feature of the plasma corresponding to the second gas pressure. The method is used to realize unified features under different gas pressure environments and improve the consistency and reliability of the detection results of the dirty plasma in the complex gas pressure scene.

Description

Technical Field

[0001] This application relates to the field of pollution detection in power operation and maintenance, and in particular to a plasma pressure correction method, apparatus, equipment and medium. Background Technology

[0002] In power systems, insulator contamination detection is a crucial step in ensuring the safe operation of the power grid. Laser-induced breakdown spectroscopy (LIBS) is widely used for analyzing the composition of contaminants on insulator surfaces due to its non-contact, rapid response, and remote detection capabilities. However, in actual testing, changes in ambient air pressure (such as in high-altitude areas) can significantly affect the physical properties of laser-induced plasma.

[0003] In related technologies, dynamic vision sensors (DVS) are typically used to acquire the transient evolution process of plasma at the millisecond or even microsecond level through an event-driven mechanism. However, existing DVS detection systems lack a dynamic compensation mechanism for plasma evolution characteristics under varying air pressure scenarios, resulting in significant differences in the DVS event modal characteristics of the same contaminated sample under different air pressure conditions, which affects the stability of contamination detection. Summary of the Invention

[0004] This application provides a plasma pressure correction method, apparatus, equipment, and medium to achieve the effect of unifying characteristic benchmarks under different pressure environments and significantly improving the consistency and reliability of contaminated plasma detection results in complex pressure scenarios.

[0005] In a first aspect, embodiments of this application provide a plasma pressure correction method, including:

[0006] Within a controllable pressure chamber, the DVS event stream of plasma and the corresponding pressure parameters are collected. The DVS event stream includes events excited by laser pulses with event timestamps.

[0007] The DVS event stream is spatiotemporally characterized to obtain plasma event time series curves and plasma event frame images under various pressure parameters;

[0008] Based on the plasma event time series curve and the plasma event frame image, the pressure sensitivity characteristics of the plasma under each pressure parameter are determined.

[0009] Based on the pressure sensitivity characteristics of the plasma under the first pressure, the pressure sensitivity characteristics of the plasma under the second pressure are normalized and corrected to obtain the target pressure sensitivity characteristics of the plasma corresponding to the second pressure.

[0010] Wherein, the first pressure is the pressure parameter in which the fluctuation amplitude of the pressure-sensitive characteristic of the plasma is less than a preset fluctuation amplitude threshold under controllable pressure conditions, and the second pressure is the pressure parameter other than the first pressure under controllable pressure conditions.

[0011] In one possible implementation, the spatiotemporal joint characterization of the DVS event stream to obtain plasma event time series curves and plasma event frame images under various pressure parameters includes:

[0012] Under each pressure parameter, based on the event timestamp, the number of events is counted through a unit time window to determine the peak position of the event excited by the laser pulse;

[0013] Based on the peak position of the event, the continuous event stream is divided into multiple independent single-pulse response processes;

[0014] For each single-pulse response process, the event is spatiotemporally mapped according to a preset time window to reconstruct a plasma event frame image;

[0015] Based on the event statistics within the single-pulse response process, the plasma event time series curve corresponding to the single pulse is generated.

[0016] In one possible implementation, determining the pressure sensitivity characteristics of the plasma under each pressure parameter based on the plasma event time series curve and the plasma event frame image includes:

[0017] Under each pressure parameter, a fixed region of interest (ROI) is determined in the plasma event frame image based on the pre-defined spatial range of the plasma main response region.

[0018] Within the fixed region of interest (ROI), the pressure sensitivity characteristics of the plasma are determined based on the plasma event time series curve.

[0019] In one possible implementation, determining the pressure sensitivity characteristics of the plasma based on the plasma event time series curve within the fixed region of interest (ROI) includes:

[0020] Within the fixed region of interest (ROI), the event stream data is filtered using quantile thresholds to determine the plasma core response region corresponding to the bright spot region;

[0021] In the plasma core response region, the pressure sensitivity characteristics of the plasma are determined based on the plasma event time series curve. The pressure sensitivity characteristics include one or more of the following: bright spot area, event duration, peak response location, event decay slope, and equivalent diffusion radius.

[0022] In one possible implementation, the step of performing quantile threshold filtering on the event stream data within the fixed region of interest (ROI) to determine the plasma core response region corresponding to the bright spot region includes:

[0023] Determine the event density gradient of the main bright spot region within the fixed region of interest (ROI);

[0024] When the event density gradient meets the preset quantile threshold convergence condition, the location where the density gradient changes abruptly is determined as the actual boundary of the bright spot region.

[0025] Based on the actual boundary, the bright spot region is iteratively corrected within the fixed region of interest (ROI) to obtain the corrected bright spot region.

[0026] Based on the corrected bright spot region, the corresponding plasma core response region is determined.

[0027] In one possible implementation, the step of normalizing and correcting the pressure sensitivity characteristics of the plasma at the second pressure based on the pressure sensitivity characteristics of the plasma at the first pressure to obtain the target pressure sensitivity characteristics of the plasma corresponding to the second pressure includes:

[0028] The bright spot area determined under the first air pressure is used as the correction template;

[0029] Spatial registration is performed between the bright spot area determined under the second air pressure and the correction template to determine the registered bright spot area;

[0030] Calculate the scaling factor relative to the correction template for the registered bright spot region;

[0031] When the scaling factor deviates from the preset normalization range, the area of ​​the bright spot region and the equivalent diffusion radius of the plasma under the second gas pressure are linearly transformed according to the scaling factor to obtain the target area of ​​the bright spot region and the target equivalent diffusion radius of the plasma.

[0032] In one possible implementation, the fluctuation amplitude of the pressure-sensitive characteristic of the plasma is determined by the following method:

[0033] For each pressure parameter, the event statistical fluctuation parameter corresponding to the plasma event time series curve is determined, and the statistical fluctuation parameter includes at least one of variance, standard deviation or coefficient of variation;

[0034] The statistical fluctuation parameter is determined as the fluctuation amplitude of the plasma pressure-sensitive characteristic under the corresponding pressure parameter.

[0035] Secondly, embodiments of this application provide a plasma pressure correction device, comprising:

[0036] The acquisition module is used to acquire the DVS event stream of plasma and the corresponding pressure parameters in a controllable pressure cavity. The DVS event stream includes events excited by laser pulses with event timestamps.

[0037] The reconstruction module is used to perform spatiotemporal joint characterization of the DVS event stream to obtain plasma event time series curves and plasma event frame images under various pressure parameters.

[0038] The determination module is used to determine the pressure sensitivity characteristics of the plasma under each pressure parameter based on the plasma event time series curve and the plasma event frame image.

[0039] The correction module is used to normalize and correct the pressure sensitivity characteristics of the plasma under the second pressure based on the pressure sensitivity characteristics of the plasma under the first pressure, so as to obtain the target pressure sensitivity characteristics of the plasma corresponding to the second pressure.

[0040] Wherein, the first pressure is the pressure parameter in which the fluctuation amplitude of the pressure-sensitive characteristic of the plasma is less than a preset fluctuation amplitude threshold under controllable pressure conditions, and the second pressure is the pressure parameter other than the first pressure under controllable pressure conditions.

[0041] Thirdly, embodiments of this application provide an electronic device, including: a memory and a processor;

[0042] The memory stores computer-executed instructions;

[0043] The processor executes computer execution instructions stored in the memory, causing the processor to perform the first aspect and / or various possible implementations of the first aspect as described above.

[0044] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the first aspect and / or various possible implementations of the first aspect.

[0045] Fifthly, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the first aspect and / or various possible implementations of the first aspect.

[0046] The plasma pressure correction method, apparatus, device, and medium provided in this application overcome the problem that existing DVS detection systems lack a dynamic compensation mechanism for plasma evolution characteristics under pressure change scenarios. This leads to significant differences in the DVS event modal characteristics of the same contaminated sample under different pressure conditions, which seriously affects the stability of contamination detection. The method achieves the effects of eliminating the interference of pressure fluctuations on plasma DVS event characteristics, unifying the characteristic benchmark under different pressure environments, and greatly improving the consistency and reliability of contaminated plasma detection results in complex pressure scenarios. Attached Figure Description

[0047] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0048] Figure 1 A schematic diagram of the plasma DVS event stream acquisition system provided in the embodiments of this application;

[0049] Figure 2 A schematic flowchart of the plasma pressure correction method provided in this application;

[0050] Figure 3 A schematic diagram of plasma event frame reconstruction images under different pressure conditions provided in the embodiments of this application;

[0051] Figure 4 A schematic diagram of plasma event time series curves provided in the embodiments of this application;

[0052] Figure 5 A graph showing the variation of bright spot area of ​​plasma under pressure conditions with pressure, provided in an embodiment of this application.

[0053] Figure 6 This is a schematic diagram of plasma bright spot region extraction under different gas pressure conditions provided in the embodiments of this application;

[0054] Figure 7a A schematic diagram illustrating the process of correcting the bright spot region of plasma provided in an embodiment of this application;

[0055] Figure 7bA comparison diagram of plasma bright spot correction results under different gas pressure conditions provided in the embodiments of this application;

[0056] Figure 8 A schematic diagram of the plasma pressure correction device provided in this application;

[0057] Figure 9 A schematic diagram of the structure of the electronic device provided in this application.

[0058] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0059] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application.

[0060] First, let me explain the terms used in this application:

[0061] Plasma pollution detection addresses the need for flashover prevention and control of insulators in power systems. Utilizing technologies such as laser-induced breakdown spectroscopy, it uses a high-energy pulsed laser to focus and ablate contaminants on the insulator surface, generating high-temperature plasma. By collecting and analyzing the emission spectrum of the plasma cooling process, the intensity of spectral characteristics is directly correlated with the equivalent salt density and non-equivalent ash density of the contaminant. This enables non-contact, online, and remote live-line detection of insulator surface contamination, eliminating the need for traditional power outage sampling procedures. It can quickly complete qualitative and quantitative analysis of contaminant composition and content, providing core evidence for assessing the insulation condition outside the power grid.

[0062] DVS event stream is the core output format of dynamic vision sensors. It consists of a large number of asynchronously generated quadruple events arranged in microsecond-level timestamp order. Each event contains pixel coordinates, a microsecond-level timestamp, and the polarity of brightness change (marking whether the brightness increases or decreases). Output is triggered only when the light intensity change perceived by the pixel exceeds a preset threshold. It completely abandons the fixed frame rate sampling mode of traditional frame cameras and has the characteristics of high dynamic range, extremely low data redundancy, and microsecond-level time resolution. In observation scenarios of high-speed transient phenomena such as plasma, it can focus only on the dynamic changes of light intensity, greatly reduce the proportion of invalid background data, and achieve high-precision, low-latency capture of the plasma evolution process.

[0063] In existing insulator pollution detection, in addition to manual sampling and offline analysis, laser pulses are used to excite the surface pollution layer to form plasma, and dynamic visual sensors are used to collect time-stamped event information. Then, through event statistics, event frame reconstruction or time series analysis, features such as bright spot range, response intensity and attenuation process are extracted to determine the pollution status.

[0064] However, changes in ambient air pressure directly affect the spatial expansion morphology and temporal evolution of plasma, causing the same contaminated sample to exhibit different event distributions, frame image morphologies, and time-series curve characteristics under different air pressures. Especially when using fixed thresholds, fixed analysis regions, or fixed time windows, changes caused by air pressure can easily be mistaken for differences in contamination, leading to feature drift and inaccurate judgments.

[0065] Based on this, this application provides a plasma pressure correction method. By collecting the DVS event flow and corresponding pressure parameters of plasma in a controllable pressure chamber, the DVS event flow is spatiotemporally characterized to obtain plasma event time series curves and plasma event frame images under each pressure parameter. Based on this, the pressure sensitivity features under each pressure parameter are determined. Then, based on the pressure sensitivity features corresponding to the first pressure with stable pressure sensitivity feature fluctuation amplitude, the pressure sensitivity features under the second pressure with unstable pressure sensitivity feature fluctuation amplitude are normalized and corrected to obtain the target pressure sensitivity features corresponding to the second pressure, thereby improving the consistency of plasma detection results under different pressure conditions.

[0066] Figure 1 This is a schematic diagram of the plasma DVS event stream acquisition system provided in the embodiments of this application, as shown below. Figure 1 As shown, this system introduces a pressure-controllable detection cavity and a pressure regulation module into the laser-induced plasma detection platform, enabling repeated acquisition of plasma generation and evolution processes under different pressure conditions, thus providing an experimental basis for subsequent pressure disturbance analysis and compensation modeling.

[0067] Specifically, a sealed and controllable pressure chamber is constructed, with continuously adjustable, real-time data acquisition, and precise recording of the internal pressure, providing a stable and controllable closed environment for plasma observation. The contaminated sample to be tested is fixed at the observation station inside the chamber. The pressure is controlled by a main unit to adjust the chamber to the target pressure value and maintain its stability, ensuring that the same sample can be repeatedly excited under different pressure gradients, obtaining raw observation data with lateral comparability. A pulsed laser emission path is arranged, and the focused high-energy laser pulse is vertically struck at a predetermined point on the sample surface. The laser energy instantly ablates the surface material of the sample, exciting a transiently evolving plasma light field. A delay controller coordinates the timing of the laser and sensor, ensuring strict correspondence between laser excitation, event acquisition, and pressure recording in the time dimension.

[0068] A dynamic vision sensor is deployed within the observation field of view. The dynamic vision sensor does not perform fixed frame rate frame exposure operations throughout the entire process. It only asynchronously generates independent events when the light intensity change sensed by the pixel point within the field of view exceeds a preset trigger threshold. Each generated event synchronously records the corresponding pixel position, the event polarity indicating the increase or decrease in light intensity, and an event timestamp with microsecond-level accuracy. All events are arranged in the order of their generation time to form a complete DVS event stream.

[0069] The real-time pressure value inside the cavity at the current moment is collected as the gas pressure parameter for the corresponding acquisition process. This gas pressure parameter is then bound and associated with the synchronously generated DVS event stream to complete the acquisition process of a single set of observation data. The cavity is then adjusted to different target gas pressure values ​​in sequence, and the above laser excitation and data acquisition operations are repeated to obtain a paired dataset of plasma DVS event streams and corresponding gas pressure parameters under full gradient gas pressure.

[0070] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0071] Figure 2 A schematic flowchart of the plasma pressure correction method provided in this application is shown below. Figure 2 As shown, the plasma pressure correction method includes at least steps S201 to S204, which are described in detail below:

[0072] Step S201: In the controllable pressure chamber, the DVS event stream of the plasma and the corresponding pressure parameters are collected. The DVS event stream includes events excited by laser pulses with event timestamps.

[0073] For example, in such Figure 1 Within the controllable pressure chamber of the acquisition system shown, a pulsed laser, controlled by a synchronous trigger control module, ablates the surface of a contaminated sample, inducing plasma generation. Simultaneously, a dynamic visual sensor (DVS) acquires event stream data during the plasma formation, propagation, and decay processes, recording the corresponding pressure conditions, sample number, and laser pulse sequence number. This establishes a corresponding dataset for "pressure conditions, contaminated sample, laser pulse, and DVS event stream." The DVS event stream, as the asynchronous output of the dynamic visual sensor, contains pixel position, event polarity, and timestamps for each individual event, accurately identifying the instantaneous occurrence of brightness changes. The laser pulse, acting as the plasma trigger source, has its corresponding chamber pressure value synchronously recorded and established with the event stream in a strict temporal correspondence.

[0074] Step S202: Perform spatiotemporal joint characterization on the DVS event stream to obtain plasma event time series curves and plasma event frame images under various pressure parameters.

[0075] For example, based on event timestamps and spatial distribution information, the collected DVS event stream is statistically analyzed in the time dimension and reconstructed in the spatial dimension to obtain plasma event time series curves and plasma event frame images under corresponding pressure parameters. Spatiotemporal joint representation refers to the simultaneous reconstruction and mapping of asynchronous events in both the time and spatial dimensions. The plasma event time series curves reflect the dynamic changes in the number of events over time, while the plasma event frame images reflect the spatial distribution of events.

[0076] Optionally, after obtaining the raw DVS event stream under different air pressure conditions, the event stream is first statistically processed based on the event timestamp. By calculating the change in the number of events within a unit time window, the peak position of the event corresponding to the laser pulse is determined, and the continuous event stream is divided into multiple independent single-pulse response processes accordingly.

[0077] During each single-pulse response, events are accumulated according to a preset time window to reconstruct plasma event frame images. The event frame images may include On event frames, Off event frames, and composite event frames, used to characterize plasma brightness enhancement, brightness decay, and overall event activity.

[0078] Furthermore, the responses of multiple laser pulses under different pressure conditions were statistically analyzed to construct an event time series curve of "pulse number and number of events" to characterize the dynamic response stability of plasma under continuous laser action and the law of pressure disturbance.

[0079] Step S203: Determine the pressure sensitivity characteristics of the plasma under each pressure parameter based on the plasma event time series curve and plasma event frame image.

[0080] For example, after obtaining plasma event frame images under different pressure conditions, feature parameters characterizing time changes can be extracted based on plasma event time series curves, and feature parameters characterizing spatial distribution can be extracted based on plasma event frame images, or spatiotemporal joint features can be extracted by combining time and spatial dimension information. For multiple acquisition results under the same pressure parameter, statistical analysis can also be performed on the determined pressure-sensitive features to characterize the stability of the features under that pressure condition. By simultaneously using plasma event time series curves and plasma event frame images to determine pressure-sensitive features, the response differences caused by pressure changes can be uniformly mapped to calculable parameters, thereby providing a basis for subsequently establishing cross-pressure correction relationships.

[0081] Among them, pressure-sensitive characteristics refer to the time, space, or spatiotemporal combined characteristics that exhibit stable and measurable differences with changes in pressure. They are used to describe the degree of change in plasma response under different pressure conditions and serve as input for subsequent normalization correction.

[0082] Step S204: Based on the pressure sensitivity characteristics of the plasma under the first pressure, normalize and correct the pressure sensitivity characteristics of the plasma under the second pressure to obtain the target pressure sensitivity characteristics of the plasma corresponding to the second pressure.

[0083] Wherein, the first pressure is the pressure parameter in which the fluctuation amplitude of the pressure-sensitive characteristic of the plasma is less than a preset fluctuation amplitude threshold under controllable pressure conditions, and the second pressure is the pressure parameter other than the first pressure under controllable pressure conditions.

[0084] For example, the plasma pressure-sensitive feature sequences corresponding to all collected pressure parameters are traversed, and the fluctuation amplitude of each feature sequence is statistically analyzed over the entire observation period. This fluctuation amplitude is then compared with a pre-set fluctuation amplitude threshold to filter out pressure parameters whose feature fluctuation amplitude is less than the preset threshold. This pressure parameter is used as the reference pressure, or the first pressure. Under this first pressure condition, the plasma evolution process is minimally affected by pressure disturbances, and the feature output exhibits extremely high stability and reproducibility. It can serve as a unified benchmark for feature normalization across the entire pressure range. All other pressure parameters within the controllable pressure coverage area, excluding the reference pressure, are designated as the second pressure to be corrected, forming a complete set of pressure parameters to be processed.

[0085] Specifically, the plasma pressure-sensitive features under the first atmospheric pressure can be extracted as a reference template. Using the feature distribution shape and amplitude range of this template as a unified scale, a global mapping correction is performed on each original plasma pressure-sensitive feature under the second atmospheric pressure to eliminate the amplitude shift and shape distortion of plasma features caused by pressure disturbance under different atmospheric pressure environments. All original features under the second atmospheric pressure are uniformly aligned to the feature reference dimension of the reference atmospheric pressure to obtain the corrected target pressure-sensitive feature corresponding to each second atmospheric pressure.

[0086] Furthermore, after normalizing all features across the entire pressure range, plasma features under all pressure conditions have a unified measurement benchmark, eliminating the feature system bias caused by pressure differences and providing consistent feature input for subsequent plasma-related detection and analysis.

[0087] In the embodiments provided in this application, by converting the original asynchronous event data into a computable time series and spatial image, and then using low-fluctuation air pressure conditions as a unified correction benchmark, the pressure-sensitive features extracted under different air pressures are uniformly corrected, so that the influence of air pressure changes on plasma evolution detection results is constrained in the correction process, thereby enabling the features used for subsequent insulator pollution status assessment to have a consistent reference standard.

[0088] In one possible implementation, the specific process of performing spatiotemporal joint characterization of the DVS event stream to obtain plasma event time series curves and plasma event frame images under various pressure parameters may further include steps S301 to S304, which are detailed below:

[0089] Step S301: Under each pressure parameter, based on the event timestamp, count the number of events through a unit time window to determine the peak position of the laser pulse-excited event;

[0090] Step S302: Based on the peak position of the event, the continuous event stream is divided into multiple independent single-pulse response processes;

[0091] Step S303: For each single-pulse response process, the event is spatiotemporally mapped according to a preset time window to reconstruct a plasma event frame image;

[0092] Step S304: Based on the event statistics within the single-pulse response process, generate the plasma event time series curve corresponding to the single pulse.

[0093] For example, the events are grouped and stored based on the timestamped event stream output by the dynamic vision sensor and according to the air pressure parameters. Then, the event timestamps under each air pressure parameter can be counted within a window to obtain a sequence of event counts that change over time. The peak position is then extracted from this sequence, and the continuous event stream is segmented with the peak position as the center, so that the response data corresponding to each laser pulse is independent of each other.

[0094] Subsequently, the event coordinates and polarity information within each single-pulse response process are spatiotemporally mapped, and the events occurring within the same preset time window are projected onto a two-dimensional pixel plane and cumulatively reconstructed to form a plasma event frame image. At the same time, the total number of events, local density, or event intensity changes within each time sub-window are statistically analyzed to form the plasma event time series curve of that single pulse.

[0095] In other words, after acquiring the raw DVS event streams under different atmospheric pressure conditions, the entire event stream can be statistically processed in terms of time sequence based on the high-precision timestamps inherent in the events. Continuous statistical time windows are defined by fixed duration, and the total number of events contained within each window is counted, generating a statistical distribution result showing the time-series variation of the event count. Then, the peak position of the event count corresponding to the laser pulse excitation moment is located from the statistical distribution result. Using this peak position as the time anchor point, a segmentation operation is performed on the continuous long-time-series event stream, breaking the complete event stream into multiple independent single-pulse response processes that do not overlap and have clear boundaries. Each process precisely corresponds to the entire plasma evolution cycle generated by a single laser pulse excitation.

[0096] Please see Figure 3 , Figure 3 This is a schematic diagram illustrating the reconstruction of plasma event frame images under different pressure conditions provided in this application embodiment. For each single-pulse response process obtained from the decomposition, a continuous event accumulation window is defined within the process according to a preset fixed time step. All asynchronously generated events within the window are superimposed according to pixel coordinates, and the reconstruction operation of the plasma event frame image is completed window by window. Figure 3 As shown, the spatial distribution of plasma event response varies significantly under different pressure conditions (e.g., 50 kPa, 60 kPa, 70 kPa, 80 kPa, 90 kPa, and 100 kPa). Under low pressure conditions, the plasma bright spot region is more concentrated and the boundary is clearer. However, as the pressure increases, the plasma region may exhibit phenomena such as enhanced diffusion, increased tail structure, and enhanced background response, indicating that pressure has a significant impact on the spatial morphology of plasma.

[0097] Furthermore, the reconstructed event frames are classified according to event polarity, and On event frames containing only brightness increase events are generated (e.g., ...). Figure 3 As shown, the three types of event frames are: the Off event frame containing only brightness decrease events, and the comprehensive event frame that combines the two types of events. These three types of event frames respectively represent the brightness enhancement evolution process, the brightness decay evolution process, and the overall event activity level throughout the entire cycle.

[0098] Please see Figure 4 , Figure 4 This is a schematic diagram of the plasma event time series curve provided in an embodiment of this application. Full statistics are performed on multiple consecutive laser pulse response processes under the same gas pressure conditions. With the pulse triggering sequence as the horizontal axis and the total number of events within the corresponding single-pulse response process as the dimension, an event time series curve is constructed that corresponds one-to-one between the pulse sequence number and the number of events. For example... Figure 4As shown, the number of events corresponding to each laser pulse under different gas pressure conditions exhibits different fluctuation trends with the pulse sequence number. This event time series can reflect the influence of gas pressure on plasma generation intensity, event triggering stability, and decay process. Furthermore, based on the sequence curve, the dynamic response stability of plasma under continuous laser irradiation is intuitively presented, accurately capturing the perturbation law of plasma event output caused by different gas pressure parameters, and providing standardized time series and image basis data for subsequent extraction of gas pressure sensitive features.

[0099] In the embodiments provided in this application, the reconstruction of standardized plasma event frames through spatiotemporal mapping and the generation of exclusive time-series curves through single-pulse dimension statistics avoid feature distortion caused by cross-pulse event aliasing, and greatly improve the regularity of plasma observation data and the accuracy of subsequent feature extraction.

[0100] In one possible implementation, the specific process of determining the pressure sensitivity characteristics of plasma under various pressure parameters based on plasma event time series curves and plasma event frame images further includes steps S401 and S402, which are detailed below:

[0101] Step S401: Under each pressure parameter, determine a fixed region of interest (ROI) in the plasma event frame image based on the pre-set spatial range of the plasma main response region.

[0102] Step S402: Within a fixed region of interest (ROI), determine the pressure sensitivity characteristics of the plasma based on the plasma event time series curve.

[0103] For example, the reconstructed plasma event frame images under various pressure parameters are first spatially located. The boundary of the Region of Interest (ROI) is extracted based on the pre-defined coordinates, size, or contour information of the main response region, and this boundary is fixed and saved as a unified analysis window under subsequent pressure conditions. Then, within this fixed ROI, the plasma event time series curves are statistically analyzed. The plasma event time series curves can characterize parameters such as the amplitude, peak position, rising slope, decay slope, or duration of the event count within the ROI over time, and these parameters are used as pressure-sensitive features.

[0104] In one possible implementation, after obtaining plasma event frame images under different pressure conditions, to reduce the influence of background regions and non-plasma event responses on the feature extraction results, a fixed region of interest (ROI) is constructed by pre-setting the spatial range of the main plasma response region in the event frame image. This ROI can be determined based on the position, area, and energy concentration of the main plasma spot. Then, within this fixed ROI, sensitive features of plasma pressure are determined based on the plasma event time series curve.

[0105] Please see Figure 5 , Figure 5 The graph showing the variation of bright spot area of ​​plasma under pressure conditions with pressure provided in the embodiments of this application is shown below. Figure 5 As shown, the bright spot area of ​​the plasma at 50 kPa, 60 kPa, 70 kPa, 80 kPa, 90 kPa and 100 kPa varies with the gas pressure. The bright spot area can reflect the degree of plasma spatial diffusion.

[0106] In the embodiments provided in this application, by always confining the pressure-sensitive features within the main plasma response region, the source of the features is clear and the regional consistency is high, thus the obtained pressure-sensitive features have good comparability and stability.

[0107] In one possible implementation, the specific process of determining the pressure sensitivity characteristics of plasma based on plasma event time series curves within a fixed region of interest (ROI) may further include steps S501 and S502, which are detailed below:

[0108] Step S501: Within a fixed region of interest (ROI), perform quantile threshold filtering on the event stream data to determine the plasma core response region corresponding to the bright spot region.

[0109] Step S502: In the plasma core response region, the pressure sensitivity characteristics of the plasma are determined according to the plasma event time series curve. The pressure sensitivity characteristics include one or more of the following: bright spot area, event duration, peak response location, event decay slope, and equivalent diffusion radius.

[0110] For example, the region of interest (ROI) is fixed within the main response region. Event flows within this region are then aggregated by event address, event polarity, and timestamp to form a local event intensity matrix. Subsequently, the intensity matrix is ​​filtered based on a quantile threshold, retaining pixels or event points exceeding the threshold. Connectivity analysis is then used to extract the plasma core response region corresponding to the bright spot region. The number of events in each time window within the plasma core response region is then mapped to a plasma event time series curve, and the peak time, post-peak decay segment, and corresponding time interval are extracted from the plasma event time series curve. The bright spot area and equivalent diffusion radius characterize the degree of plasma spatial expansion; the event duration and event decay slope characterize the plasma temporal evolution process; and the peak response position characterizes the temporal position where the plasma event trigger intensity reaches its maximum.

[0111] Optionally, the area of ​​the bright spot region is obtained by counting the pixels in the plasma core response region and converting it in combination with the calibrated spatial resolution; the event duration is determined by the time difference from the peak to the preset attenuation termination condition; the peak response position is determined by the timestamp corresponding to the peak; the event attenuation slope is obtained by linear fitting or differential calculation of the attenuation segment; and the equivalent diffusion radius is obtained by converting the area of ​​the plasma core response region into the equivalent circle radius.

[0112] It should be noted that other parameters can be selected in practical applications for the quantile threshold, time window width, and decay termination condition, and this application does not limit them.

[0113] Please see Figure 6 , Figure 6 This is a schematic diagram of plasma bright spot region extraction under different gas pressure conditions provided in the embodiments of this application, as shown below. Figure 6 As shown, under the constraint of a fixed region of interest (ROI), the main bright spot region of plasma can be extracted from event frame images under different pressure conditions by means of local event response statistics and threshold screening, thereby reducing the interference of background events and non-main response regions on feature analysis.

[0114] In the embodiments provided in this application, the high-intensity core response within the fixed region of interest (ROI) is stably extracted, and the plasma event time series curve corresponds to the spatial bright spot region, so that the diffusion, attenuation and peak drift caused by pressure changes can be quantitatively characterized, thereby making the features on which the subsequent pressure correction is based more consistent and comparable.

[0115] In one possible implementation, the specific process of determining the plasma core response region corresponding to the bright spot region by performing quantile threshold filtering on event stream data within a fixed region of interest (ROI) may further include steps S601 to S604, which are detailed below:

[0116] Step S601: Determine the event density gradient of the main bright spot region within the fixed region of interest (ROI);

[0117] Step S602: When the event density gradient meets the preset quantile threshold convergence condition, the location where the density gradient changes abruptly is determined as the actual boundary of the bright spot region.

[0118] Step S603: Based on the actual boundary, iteratively correct the bright spot region within the fixed region of interest (ROI) to obtain the corrected bright spot region.

[0119] Step S604: Based on the corrected bright spot region, determine the corresponding plasma core response region.

[0120] For example, the event distribution density can first be statistically analyzed within a fixed Region of Interest (ROI) according to a preset grid, and the event density difference between adjacent statistical intervals can be calculated along the radial or normal direction of the main bright spot region to form an event density gradient curve. Then, the quantile results of this gradient curve are compared with a preset threshold. When the gradient change amplitude in multiple consecutive statistical intervals is less than the threshold, the quantile threshold convergence condition is determined to be met, and the inflection point in the gradient curve that transitions from a gradual to abrupt change is taken as the actual boundary of the bright spot region.

[0121] Then, based on this boundary, the bright spot region within the ROI can be expanded or contracted, and the gradient distribution of the corrected region can be repeatedly calculated until the boundary no longer drifts significantly, thus obtaining the corrected bright spot region. The part with the highest event intensity and most stable temporal continuity within this region is then extracted as the plasma core response region. Isolated noise events scattered outside the boundary are gradually eliminated, and valid plasma events that were mistakenly eliminated within the boundary are supplemented, resulting in a corrected bright spot region with a complete outline and precise boundaries. The bright spot region after iterative correction is the only valid statistical range, eliminating interference from background stray light and invalid noise events outside the region, ultimately accurately locking the core response region of the plasma.

[0122] In the embodiments provided in this application, the boundary of bright spot abrupt change is locked by event density gradient statistics, and the precise bright spot region is obtained by iterative correction. This overcomes the defect of fixed threshold segmentation being easily interfered with by background noise, avoids invalid impurities from entering the feature, and improves the positioning accuracy of the plasma core response region.

[0123] In one possible implementation, the specific process of normalizing and correcting the pressure sensitivity characteristics of the plasma at the second pressure based on the pressure sensitivity characteristics of the plasma at the first pressure to obtain the target pressure sensitivity characteristics of the plasma at the second pressure may further include steps S701 to S704, which are detailed below:

[0124] Step S701: Use the bright spot area determined under the first air pressure as a correction template;

[0125] Step S702: Spatial registration is performed between the bright spot area determined under the second air pressure and the correction template to determine the registered bright spot area;

[0126] Step S703: Calculate the scaling factor relative to the correction template for the registered bright spot region;

[0127] Step S704: When the scaling factor deviates from the preset normalization range, the area of ​​the bright spot region and the equivalent diffusion radius of the plasma under the second gas pressure are linearly transformed according to the scaling factor to obtain the target bright spot region area and the target equivalent diffusion radius of the plasma.

[0128] For example, the outline of the bright spot region can be extracted first based on the plasma event frame image under the first atmospheric pressure, and the corresponding binary region or grayscale threshold region can be written into the template data. After the bright spot region under the second atmospheric pressure is acquired, spatial registration can be completed with the modified template through centroid translation, outline matching, or affine alignment based on feature points. The registration result can be represented as a bright spot mask with unified coordinates. Subsequently, a scaling factor is calculated based on the change in the registered region along the major axis, minor axis, or equivalent diameter. When the factor exceeds the preset normalization range, the area of ​​the bright spot region is linearly corrected according to the proportional relationship corresponding to the scaling factor, and the equivalent diffusion radius is synchronously corrected according to the same scale relationship, thereby outputting the target pressure-sensitive feature consistent with the first atmospheric pressure calibration.

[0129] Specifically, the bright spot area, event duration, event decay slope, and equivalent diffusion radius exhibit varying degrees of regular fluctuations with changes in air pressure. Among these, the bright spot area and diffusion radius can reflect the degree of plasma spatial diffusion, while the event duration and decay slope can reflect the plasma's time decay characteristics, and thus can serve as important components in characterizing air pressure disturbances.

[0130] Therefore, a plasma image under a first pressure with stable response, concentrated main bright spot, clear boundary, and pressure sensitivity fluctuation amplitude less than a preset fluctuation amplitude threshold can be used as a reference image, and a reference bright spot region can be determined based on this reference image. Subsequently, under a second pressure condition, the corresponding plasma candidate region is extracted based on a fixed ROI.

[0131] Furthermore, local threshold screening, background event suppression, and bright spot region constraint processing can be applied to the plasma candidate region. Local threshold screening eliminates low-intensity background events; background event suppression reduces interference from non-main response regions caused by pressure changes; and bright spot region constraint brings the main response region of the plasma under different pressure conditions closer to the reference bright spot region. This allows for a linear transformation of the bright spot region area and equivalent diffusion radius of the plasma under the second pressure, yielding the target bright spot region area and target equivalent diffusion radius.

[0132] Please see Figure 7a , Figure 7a A schematic diagram of the plasma bright spot region correction process provided in the embodiments of this application is shown below. Figure 7a As shown, firstly, bright plasma spot regions are extracted from the original plasma radiation distribution image on the left using an algorithm to generate a "bright spot mask" in the middle (white pixels mark the spot positions, and black represents the background). Then, this mask is used as a spatial weight or filtering template to perform background suppression and signal enhancement on the original image, that is, to retain the original intensity information of the area covered by the mask, while darkening or reducing the high-frequency noise or stray light areas outside the mask to zero. Finally, the "corrected ROI" on the right is obtained, in which the plasma spot outline is clearer and the background is purer, effectively improving the image signal-to-noise ratio and the accuracy of subsequent analysis.

[0133] Please see Figure 7b , Figure 7b Comparison diagrams of plasma bright spot correction results under different gas pressure conditions provided in the embodiments of this application are shown below. Figure 7b As shown, in the original event frame image, the plasma regions under different pressure conditions (such as 50 kPa and 100 kPa) exhibit significant differences in diffusion degree, boundary clarity, and background response. After bright spot region correction, the main plasma response region becomes more concentrated, the boundary becomes clearer, and background disturbances are weakened. This correction result can serve as a stable input for subsequent DVS event modal feature extraction and contamination detection analysis.

[0134] In the embodiments provided in this application, the spatial registration of the bright spot at the second pressure is completed by using the bright spot region at the first pressure as a template, and the size linear transformation is performed based on the calculated scaling factor. This overcomes the problem of large differences in the spatial scale of plasma bright spots at different pressures, avoids the feature error introduced by the size deviation of bright spots across pressures, and improves the consistency of plasma spatial features at different pressures.

[0135] In one possible implementation, the fluctuation amplitude of the pressure-sensitive characteristic of the plasma is determined by the following steps: S801 and S802 are described in detail below:

[0136] Step S801: Under each pressure parameter, determine the event statistical fluctuation parameter corresponding to the plasma event time series curve. The statistical fluctuation parameter includes at least one of variance, standard deviation, or coefficient of variation.

[0137] Step S802: The statistical fluctuation parameter is determined as the fluctuation amplitude of the plasma pressure-sensitive characteristic under the corresponding pressure parameter.

[0138] For example, for each pressure parameter, the plasma event time series curves corresponding to multiple single-pulse response processes collected under the same pressure are summarized, and the mean, discrete deviation or relative discreteness are calculated based on the event number sequence within each time window. Then, at least one of the variance, standard deviation or coefficient of variation is obtained, and the obtained value is directly used as the pressure sensitivity characteristic fluctuation amplitude of the plasma under that pressure parameter.

[0139] Among them, when variance is used, it can be used to quantify the overall dispersion of event distribution; when standard deviation is used, it can directly characterize the scale of event fluctuation; when coefficient of variation is used, it can eliminate the influence of the difference in event mean under different air pressure on the fluctuation characterization, so that the fluctuation amplitude can remain comparable between different air pressure parameters.

[0140] In the embodiments provided in this application, variance, standard deviation or coefficient of variation is used to quantify the time series curve of plasma events, which can transform the time series discrete differences caused by pressure changes into comparable numerical results, thereby improving the stability and consistency of pressure sensitivity characteristics under different pressure conditions.

[0141] Please see Figure 8 , Figure 8 This is a schematic diagram of the plasma pressure correction device provided in the embodiments of this application, as shown below. Figure 8 As shown, the plasma pressure correction device 80 provided in this embodiment includes:

[0142] The acquisition module 810 is used to acquire the DVS event stream of plasma and the corresponding pressure parameters in a controllable pressure cavity. The DVS event stream includes events excited by laser pulses with event timestamps.

[0143] The reconstruction module 820 is used to perform spatiotemporal joint characterization of the DVS event stream to obtain plasma event time series curves and plasma event frame images under various pressure parameters.

[0144] The determination module 830 is used to determine the pressure sensitivity characteristics of the plasma under various pressure parameters based on the plasma event time series curve and plasma event frame image.

[0145] The correction module 840 is used to normalize and correct the pressure sensitivity characteristics of the plasma under the second pressure based on the pressure sensitivity characteristics of the plasma under the first pressure, so as to obtain the target pressure sensitivity characteristics of the plasma corresponding to the second pressure.

[0146] Wherein, the first pressure is the pressure parameter in which the fluctuation amplitude of the pressure-sensitive characteristic of the plasma is less than a preset fluctuation amplitude threshold under controllable pressure conditions, and the second pressure is the pressure parameter other than the first pressure under controllable pressure conditions.

[0147] In one possible embodiment, the aforementioned reconstruction module 820 is further specifically used for,

[0148] Under each pressure parameter, the number of events is counted through a unit time window based on the event timestamp to determine the peak position of the event excited by the laser pulse;

[0149] Based on the peak position of the event, the continuous event stream is divided into multiple independent single-pulse response processes;

[0150] For each single-pulse response process, the event is spatiotemporally mapped according to a preset time window to reconstruct a plasma event frame image;

[0151] Based on the event statistics within the single-pulse response process, the plasma event time series curve corresponding to the single pulse is generated.

[0152] In one possible embodiment, the determining module 830 described above is further specifically used for,

[0153] Under each pressure parameter, a fixed region of interest (ROI) is determined in the plasma event frame image based on the pre-defined spatial range of the main plasma response region.

[0154] Within a fixed region of interest (ROI), the pressure sensitivity characteristics of the plasma are determined based on the plasma event time series curves.

[0155] In one possible embodiment, determining the pressure sensitivity characteristics of plasma based on plasma event time series curves within a fixed region of interest (ROI) includes:

[0156] Within a fixed region of interest (ROI), quantile threshold filtering is performed on the event stream data to determine the plasma core response region corresponding to the bright spot region;

[0157] In the core response region of the plasma, the pressure sensitivity characteristics of the plasma are determined based on the plasma event time series curve. The pressure sensitivity characteristics include one or more of the following: bright spot area, event duration, peak response location, event decay slope, and equivalent diffusion radius.

[0158] In one possible embodiment, the above-mentioned process of performing quantile threshold filtering on event stream data within a fixed region of interest (ROI) to determine the plasma core response region corresponding to the bright spot region includes:

[0159] Determine the event density gradient of the main bright spot region within a fixed region of interest (ROI);

[0160] When the event density gradient meets the preset quantile threshold convergence condition, the location where the density gradient changes abruptly is determined as the actual boundary of the bright spot region.

[0161] Based on the actual boundary, the bright spot region is iteratively corrected within the fixed region of interest (ROI) to obtain the corrected bright spot region.

[0162] Based on the corrected bright spot region, the corresponding plasma core response region is determined.

[0163] In one possible embodiment, the correction module 840 is further specifically used to use the bright spot area determined under the first air pressure as a correction template.

[0164] Spatial registration is performed between the bright spot area determined under the second atmospheric pressure and the correction template to determine the registered bright spot area;

[0165] Calculate the scaling factor relative to the correction template for the registered bright spot region;

[0166] When the scaling factor deviates from the preset normalization range, the area of ​​the bright spot region and the equivalent diffusion radius of the plasma under the second gas pressure are linearly transformed according to the scaling factor to obtain the target area of ​​the bright spot region and the target equivalent diffusion radius of the plasma.

[0167] In one possible embodiment, the fluctuation amplitude of the plasma's pressure-sensitive characteristics is determined in the following way:

[0168] For each pressure parameter, determine the event statistical fluctuation parameter corresponding to the plasma event time series curve. The statistical fluctuation parameter includes at least one of variance, standard deviation or coefficient of variation.

[0169] The statistical fluctuation parameter is defined as the fluctuation amplitude of the plasma pressure-sensitive characteristic under the corresponding pressure parameter.

[0170] The plasma pressure correction device provided in this embodiment can execute the method provided in the above-described method embodiment. Its implementation principle and technical effect are similar, and will not be described in detail here.

[0171] Figure 9 A schematic diagram of the structure of the electronic device provided in this application. Figure 9As shown, the electronic device 90 provided in this embodiment includes at least one processor 901 and a memory 902. Optionally, the electronic device 90 further includes a communication component 903. The processor 901, memory 902, and communication component 903 are connected via a bus 904.

[0172] In the specific implementation process, at least one processor 901 executes computer execution instructions stored in memory 902, causing at least one processor 901 to execute the above-mentioned plasma pressure correction method.

[0173] The specific implementation process of processor 901 can be found in the above method embodiments, and its implementation principle and technical effect are similar. It will not be repeated here.

[0174] In the above embodiments, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.

[0175] The memory may include random access memory (RAM) and may also include non-volatile memory (NVM), such as at least one disk storage device.

[0176] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0177] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the plasma pressure correction method described above.

[0178] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the aforementioned plasma pressure correction method.

[0179] The aforementioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0180] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and the readable storage medium can exist as discrete components in the device.

[0181] The division of units is merely a logical functional division; in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0182] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0183] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0184] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0185] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0186] Finally, it should be noted that other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein, and is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope.

Claims

1. A plasma pressure correction method, characterized in that, include: Within a controllable pressure chamber, the DVS event stream of plasma and the corresponding pressure parameters are collected. The DVS event stream includes events excited by laser pulses with event timestamps. The DVS event stream is spatiotemporally characterized to obtain plasma event time series curves and plasma event frame images under various pressure parameters. Based on the plasma event time series curve and the plasma event frame image, the pressure sensitivity characteristics of the plasma under each pressure parameter are determined. Based on the pressure sensitivity characteristics of the plasma under the first pressure, the pressure sensitivity characteristics of the plasma under the second pressure are normalized and corrected to obtain the target pressure sensitivity characteristics of the plasma corresponding to the second pressure. Wherein, the first pressure is the pressure parameter in which the fluctuation amplitude of the pressure-sensitive characteristic of the plasma is less than a preset fluctuation amplitude threshold under controllable pressure conditions, and the second pressure is the pressure parameter other than the first pressure under controllable pressure conditions.

2. The method according to claim 1, characterized in that, The spatiotemporal joint characterization of the DVS event stream, to obtain plasma event time series curves and plasma event frame images under various pressure parameters, includes: Under each pressure parameter, the number of events is counted through a unit time window based on the event timestamp to determine the peak position of the laser pulse-excited event; Based on the peak position of the event, the continuous event stream is divided into multiple independent single-pulse response processes; For each single-pulse response process, the event is spatiotemporally mapped according to a preset time window to reconstruct a plasma event frame image; Based on the event statistics within the single-pulse response process, the plasma event time series curve corresponding to the single pulse is generated.

3. The method according to claim 1 or 2, characterized in that, The step of determining the pressure sensitivity characteristics of the plasma under various pressure parameters based on the plasma event time series curve and the plasma event frame image includes: Under each pressure parameter, a fixed region of interest (ROI) is determined in the plasma event frame image based on the pre-defined spatial range of the plasma main response region. Within the fixed region of interest (ROI), the pressure sensitivity characteristics of the plasma are determined based on the plasma event time series curve.

4. The method according to claim 3, characterized in that, The step of determining the pressure sensitivity characteristics of plasma within the fixed region of interest (ROI) based on the plasma event time series curve includes: Within the fixed region of interest (ROI), the event stream data is filtered using quantile thresholds to determine the plasma core response region corresponding to the bright spot region; In the plasma core response region, the pressure sensitivity characteristics of the plasma are determined based on the plasma event time series curve. The pressure sensitivity characteristics include one or more of the following: bright spot area, event duration, peak response location, event decay slope, and equivalent diffusion radius.

5. The method according to claim 4, characterized in that, Within the fixed region of interest (ROI), the process of performing quantile threshold filtering on the event stream data to determine the plasma core response region corresponding to the bright spot region includes: Determine the event density gradient of the main bright spot region within the fixed region of interest (ROI); When the event density gradient meets the preset quantile threshold convergence condition, the location where the density gradient changes abruptly is determined as the actual boundary of the bright spot region. Based on the actual boundary, the bright spot region is iteratively corrected within the fixed region of interest (ROI) to obtain the corrected bright spot region. Based on the corrected bright spot region, the corresponding plasma core response region is determined.

6. The method according to claim 4, characterized in that, The step of normalizing and correcting the pressure sensitivity characteristics of the plasma at the second pressure based on the pressure sensitivity characteristics of the plasma at the first pressure to obtain the target pressure sensitivity characteristics of the plasma at the second pressure includes: The bright spot area determined under the first air pressure is used as the correction template; Spatial registration is performed between the bright spot area determined under the second air pressure and the correction template to determine the registered bright spot area; Calculate the scaling factor relative to the correction template for the registered bright spot region; When the scaling factor deviates from the preset normalization range, the area of ​​the bright spot region and the equivalent diffusion radius of the plasma under the second gas pressure are linearly transformed according to the scaling factor to obtain the target area of ​​the bright spot region and the target equivalent diffusion radius of the plasma.

7. The method according to claim 1, characterized in that, The fluctuation amplitude of the pressure-sensitive characteristics of the plasma is determined in the following way: For each pressure parameter, the event statistical fluctuation parameter corresponding to the plasma event time series curve is determined, and the statistical fluctuation parameter includes at least one of variance, standard deviation or coefficient of variation; The statistical fluctuation parameter is determined as the fluctuation amplitude of the plasma pressure-sensitive characteristic under the corresponding pressure parameter.

8. A plasma pressure correction device, characterized in that, include: The acquisition module is used to acquire the DVS event stream of plasma and the corresponding pressure parameters in a controllable pressure cavity. The DVS event stream includes events excited by laser pulses with event timestamps. The reconstruction module is used to perform spatiotemporal joint characterization of the DVS event stream to obtain plasma event time series curves and plasma event frame images under various pressure parameters. The determination module is used to determine the pressure sensitivity characteristics of the plasma under each pressure parameter based on the plasma event time series curve and the plasma event frame image. The correction module is used to normalize and correct the pressure sensitivity characteristics of the plasma under the second pressure based on the pressure sensitivity characteristics of the plasma under the first pressure, so as to obtain the target pressure sensitivity characteristics of the plasma corresponding to the second pressure. Wherein, the first pressure is the pressure parameter in which the fluctuation amplitude of the pressure-sensitive characteristic of the plasma is less than a preset fluctuation amplitude threshold under controllable pressure conditions, and the second pressure is the pressure parameter other than the first pressure under controllable pressure conditions.

9. An electronic device, characterized in that, include: Memory, processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory, causing the processor to perform the method 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 computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1 to 7.

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