Arc intensity evaluation method and device, computer device, readable storage medium and program product

Abstract

The application relates to an arc intensity evaluation method and device, computer equipment, a readable storage medium and a program product. The method comprises the following steps: constructing an initial model of arc intensity, average brightness of an ultraviolet image and arc area; determining the numerical value of the influence factor in the initial model based on acquired calibration data to obtain a target model; acquiring the average brightness of the ultraviolet image and the arc area corresponding to the ultraviolet image of the arc intensity to be evaluated; and inputting the average brightness of the ultraviolet image and the arc area into the target model to obtain the arc intensity. The foregoing method can obtain a target model capable of outputting accurate data by constructing an initial model related to the ultraviolet image and determining the influence factor in the initial model based on calibration data. The average brightness of the ultraviolet image and the arc area corresponding to the ultraviolet image of the arc intensity to be evaluated can reflect the discharge characteristics of the arc from different dimensions, and can reduce the influence of distance, thereby improving the accuracy and reliability of the evaluation result.

Description

Technical Field

[0001] This application relates to the field of electric arc detection technology, and in particular to a method, apparatus, computer equipment, computer-readable storage medium, and computer program product for evaluating electric arc intensity. Background Technology

[0002] In most industrial and power applications, electric arc discharge is a prevalent and significantly hazardous physical phenomenon. Electric arcs not only cause electrical contact erosion and equipment insulation aging and damage, but their high-temperature characteristics also easily ignite surrounding combustibles, leading to fires, explosions, and other safety accidents, seriously threatening production safety and stable equipment operation. To more accurately identify and classify electric arc risks, it is necessary to effectively quantify and evaluate arc intensity.

[0003] Traditional techniques typically utilize ultraviolet (UV) sensors for detection. During an electric arc discharge, specific wavelengths of ultraviolet light are emitted. This light penetrates the UV sensor's casing and illuminates the cathode, generating electrical pulse signals. By collecting the number or amplitude of these pulse signals per unit time, the arc's intensity and strength can be assessed. However, since flame combustion also produces ultraviolet radiation, and its spectral characteristics overlap with those of electric arc ultraviolet radiation, UV sensors lack spectral resolution and cannot effectively distinguish between the ultraviolet signals generated by the arc and the flame. Furthermore, when flame interference is present in the detection environment, the flame's ultraviolet radiation can be misidentified by the UV sensor, leading to an abnormally high number of pulse signals collected per unit time. This reduces the accuracy of arc intensity assessment based on pulse signals. Summary of the Invention

[0004] Therefore, it is necessary to provide a method, apparatus, computer equipment, computer-readable storage medium, and computer program product for evaluating arc intensity that can accurately evaluate the arc intensity, in order to address the above-mentioned technical problems.

[0005] In a first aspect, this application provides a method for evaluating electric arc intensity, the method comprising:

[0006] An initial model was constructed to correlate the arc intensity with the average brightness of the ultraviolet image and the arc area.

[0007] The values ​​of the influencing factors in the initial model are determined based on the acquired calibration data, and the target model is obtained.

[0008] Obtain the average brightness of the ultraviolet image of the arc intensity to be evaluated and the arc area corresponding to the ultraviolet image;

[0009] The average brightness and arc area of ​​the ultraviolet image are input into the target model to obtain the arc intensity.

[0010] In one embodiment, the influence factors include an area influence factor and a brightness influence factor; the calibration data includes a first calibration dataset and a second calibration dataset;

[0011] The process of determining the influencing factors in the initial model based on the acquired calibration data to obtain the target model includes:

[0012] Based on the initial model and the first calibration dataset, the value of the brightness influence factor is obtained by fitting.

[0013] Substituting the values ​​of the brightness influence factors into the initial model yields the intermediate model;

[0014] Based on the intermediate model and the second calibration dataset, the value of the area influence factor is obtained by fitting.

[0015] Substituting the numerical value of the area influence factor into the intermediate model yields the target model.

[0016] In one embodiment, before determining the values ​​of the influence factors in the initial model based on the acquired calibration data to obtain the target model, the method further includes:

[0017] The system controls the arc emitter to emit an electric arc and records the intensity of the current target area as arcpw; the target area is the region that forms an ultraviolet image based on the ultraviolet light generated by the electric arc.

[0018] Data is collected using intensity arcpw as a benchmark and in increments of integer multiples, and calibration data is obtained based on the collected data.

[0019] In one embodiment, the calibration data includes a first calibration dataset and a second calibration dataset; the first calibration dataset includes the calibrated arc intensity, the first arc area, and the first average brightness corresponding to different intensities, and the second calibration dataset includes the calibrated arc intensity, the second arc area corresponding to different intensities, and the second average brightness.

[0020] The process of acquiring data based on the intensity arcpw as a benchmark, with increments of integer multiples, and obtaining calibration data based on the acquired data includes:

[0021] A fixed area is used for calibration, with intensity arcpw as the reference and integer multiples as steps. The brightness of each pixel in the ultraviolet image is collected under different intensities, and the fixed area during calibration is used as the first arc area in the ultraviolet image. A first average brightness is determined based on the brightness of the pixels.

[0022] Using the area and intensity arcpw as a reference during calibration without fixing the time, and taking integer multiples as steps, the second arc area and the brightness of each pixel in the ultraviolet image are collected under different intensities, and the second average brightness is determined based on the brightness of the pixels.

[0023] Different intensities correspond to different calibrated arc intensities.

[0024] In one embodiment, determining the first average brightness or the second average brightness based on the brightness of the pixel includes:

[0025] The sum of the squares of the brightness of all pixels in the same ultraviolet image is used as the first brightness.

[0026] The sum of the brightness of all pixels in the same ultraviolet image is used as the second brightness.

[0027] The ratio of the first brightness to the second brightness is used as the first average brightness or the second average brightness.

[0028] In one embodiment, the area influence factor includes a first area influence factor, a second area influence factor, and a third area influence factor; the brightness influence factor includes a first brightness influence factor, a second brightness influence factor, and a third brightness influence factor.

[0029] The initial model for constructing the relationship between arc intensity and the average brightness and area of ​​the ultraviolet image includes:

[0030] The initial model includes:

[0031]

[0032] in, Indicates the intensity of the electric arc. , , These represent the first area influence factor, the second area influence factor, and the third area influence factor, respectively. , , The sum of is 1. Indicates the reference arc area. Indicates the actual arc area. , , These represent the first brightness influence factor, the second brightness influence factor, and the third brightness influence factor, respectively. , , The sum of is 1. This represents the actual average brightness. This represents the baseline average brightness.

[0033] Secondly, this application also provides an arc intensity evaluation device, the device comprising:

[0034] The model building module is used to construct an initial model of arc intensity and the average brightness and arc area of ​​the ultraviolet image; it is also used to determine the values ​​of the influencing factors in the initial model based on the acquired calibration data to obtain the target model.

[0035] The data acquisition module is used to acquire the average brightness of the ultraviolet image of the arc intensity to be evaluated and the arc area corresponding to the ultraviolet image;

[0036] The arc intensity evaluation module is used to input the average brightness and arc area of ​​the ultraviolet image into the target model to obtain the arc intensity.

[0037] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the above-described method.

[0038] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described method.

[0039] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the above-described method.

[0040] The aforementioned methods, apparatus, computer equipment, computer-readable storage media, and computer program products for evaluating arc intensity can distinguish different ultraviolet light sources and reduce the influence of other ultraviolet light sources by acquiring features corresponding to ultraviolet images. By constructing an initial model related to the ultraviolet image and using calibration data to determine the influencing factors in the initial model, a target model capable of outputting accurate data can be obtained. By acquiring the average brightness and arc area of ​​the ultraviolet image of the arc intensity to be evaluated, the discharge characteristics of the arc can be reflected from different dimensions, and the influence of distance can be reduced, improving the accuracy and reliability of the evaluation results. Attached Figure Description

[0041] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0042] Figure 1This is a flowchart illustrating a method for evaluating arc intensity in one embodiment;

[0043] Figure 2 This is a flowchart illustrating a method for evaluating arc intensity in one embodiment;

[0044] Figure 3 This is a flowchart illustrating a method for evaluating arc intensity in one embodiment;

[0045] Figure 4 This is a flowchart illustrating a method for evaluating arc intensity in one embodiment;

[0046] Figure 5 This is a flowchart illustrating a method for evaluating arc intensity in one embodiment;

[0047] Figure 6 This is a structural block diagram of an arc intensity evaluation device in one embodiment;

[0048] Figure 7 This is an internal structural diagram of a computer device in one embodiment.

[0049] Figure label:

[0050] 102-Model building module; 104-Data acquisition module; 106-Arc intensity evaluation module. Detailed Implementation

[0051] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0052] It should be noted that the terms "first," "second," etc., used in this application can be used to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish the first element from the second element. The terms "comprising" and "having," and any variations thereof, used in this application, are intended to cover non-exclusive inclusion. The term "multiple" used in this application refers to two or more. The term "and / or" used in this application refers to one of the embodiments, or any combination of multiple embodiments.

[0053] UV tubes convert ultraviolet (UV) signals into measurable electrical signals. When UV light emitted from sources such as electric arcs passes through the UV tube shell and irradiates the cathode, if the photon energy exceeds the electron work function of the cathode material, the cathode emits photoelectrons. These photoelectrons are accelerated towards the anode under the influence of a high-voltage electric field (typically several hundred volts) between the cathode and anode. During acceleration, the photoelectrons collide with gas molecules inside the tube, ionizing them and generating new electrons and positive ions. These new electrons, after being accelerated, continue to collide with and ionize other gas molecules, instantly creating an "avalanche effect" or "glow discharge," amplifying the initial weak photocurrent by tens of thousands of times and generating a pulse signal. Traditional methods for evaluating electric arc intensity are based on the pulse signals detected per unit time. This method cannot distinguish the pulse signals formed by UV light emitted from an electric arc, and therefore cannot accurately evaluate based on the pulse signals corresponding to the electric arc. Furthermore, in the presence of multiple electric arcs, the UV tube cannot identify or differentiate them. Moreover, because it cannot distinguish the light source, this evaluation method is easily affected by UV interference in the environment.

[0054] In one exemplary embodiment, such as Figure 1 As shown, a method for evaluating electric arc intensity is provided, including the following steps S102 to S106, wherein:

[0055] Step S102: Construct an initial model of the arc intensity, the average brightness of the ultraviolet image, and the arc area.

[0056] Understandably, the solar-blind ultraviolet light signals generated during electric arc discharge are imperceptible to the human eye. Furthermore, against a background of sunlight, the discharge signals may be obscured, making it impossible to pinpoint the exact location of the discharge. To determine the location of the electric arc discharge and quantify its intensity, ultraviolet images of the arc discharge can be acquired using imaging equipment, such as an ultraviolet imager.

[0057] In the initial model, average brightness refers to the average brightness of the ultraviolet image, and arc area refers to the arc area in the ultraviolet image. Average brightness and arc area can characterize arc intensity from different dimensions. Arc intensity is non-linearly positively correlated with the average brightness of the ultraviolet image, and the arc area in the ultraviolet image can reflect the spatial range of the arc discharge. Generally, the stronger the arc, the larger the arc area in the ultraviolet image, and the two are also non-linearly positively correlated.

[0058] It is also understandable that the average brightness of ultraviolet images is greatly affected by environmental factors such as distance. Evaluating the arc intensity by using two different dimensions of data, average brightness and arc area, can reduce the impact of distance and improve the accuracy and reliability of the evaluation results.

[0059] Step S104: Determine the values ​​of the influencing factors in the initial model based on the obtained calibration data to obtain the target model.

[0060] The calibration data includes data related to the average brightness of the ultraviolet image and the arc area, while the target model is an initial model with the values ​​of the influencing factors substituted into it.

[0061] By determining the values ​​of influencing factors in the initial model through calibration data, the output of the target model can be made closer to the true values, thereby improving the accuracy of arc intensity evaluation.

[0062] Step S106: Obtain the average brightness of the ultraviolet image of the arc intensity to be evaluated and the arc area corresponding to the ultraviolet image.

[0063] Understandably, imaging devices can map each beam of ultraviolet light onto a corresponding pixel point through spatial mapping. This allows the ultraviolet light emitted by the same electric arc to be mapped within a certain range, thus distinguishing between different electric arcs or different ultraviolet light sources. If there is only one electric arc, its ultraviolet image can be directly acquired. If multiple electric arcs or other ultraviolet light sources exist, the resulting ultraviolet image will present multiple separate bright spots, each corresponding to an ultraviolet light source. Based on mapping conditions or experience, the ultraviolet image of the bright spot whose arc intensity is to be evaluated can be selected from the multiple bright spots. This ultraviolet image does not include the light spots from other light sources.

[0064] Step S108: Input the average brightness of the ultraviolet image and the arc area into the target model to obtain the arc intensity.

[0065] The target model is a model related to the average brightness of the ultraviolet image and the arc area. By substituting the average brightness of the ultraviolet image and the arc area into the target model, the corresponding arc intensity can be obtained directly.

[0066] The aforementioned method for evaluating arc intensity can distinguish different ultraviolet light sources and reduce the influence of other ultraviolet light sources by acquiring features corresponding to ultraviolet images. By constructing an initial model related to the ultraviolet image and using calibration data to determine the influencing factors in the initial model, a target model capable of outputting accurate data can be obtained. By acquiring the average brightness and arc area of ​​the ultraviolet image of the arc intensity to be evaluated, the discharge characteristics of the arc can be reflected from different dimensions, and the influence of distance can be reduced, improving the accuracy and reliability of the evaluation results.

[0067] In one embodiment, the influence factors include an area influence factor and a brightness influence factor. The area influence factor includes a first area influence factor, a second area influence factor, and a third area influence factor. The brightness influence factors include a first brightness influence factor, a second brightness influence factor, and a third brightness influence factor. The area influence factor is related to the arc area of ​​the ultraviolet image, and the brightness influence factor is related to the average brightness of the ultraviolet image.

[0068] Step S102: Construct an initial model of arc intensity and the average brightness of the ultraviolet image, and arc area, including:

[0069] The initial model includes:

[0070]

[0071] in, Indicates the intensity of the electric arc. , , These represent the first area influence factor, the second area influence factor, and the third area influence factor, respectively. , , The sum of is 1. Indicates the reference arc area. Indicates the actual arc area. , , These represent the first brightness influence factor, the second brightness influence factor, and the third brightness influence factor, respectively. , , The sum of is 1. This represents the actual average brightness. This represents the baseline average brightness.

[0072] , , , , , Specific values ​​can be obtained by fitting calibration data. Substituting these values ​​into the initial model yields the target model, which is used to calculate the arc intensity. . and The specific value can be determined based on experience or calibration data.

[0073] Understandably, average brightness reflects the instantaneous intensity and energy density of the discharge. Arc area reflects the spatial reach and distribution breadth of the discharge. Constructing an initial model related to average brightness and arc area allows for multi-dimensional evaluation, avoiding the significant influence of environmental factors on a single dimension. Furthermore, by constructing a nonlinear model of average brightness, arc area, and arc intensity, model fit can be improved, underfitting can be avoided, and thus the accuracy of the output data can be enhanced.

[0074] In one embodiment, before step S104, before determining the values ​​of the influence factors in the initial model based on the acquired calibration data and obtaining the target model, refer to... Figure 2The method also includes:

[0075] Step S103: Obtain calibration data.

[0076] See Figure 3 Specifically, it includes steps S202 to S204, wherein:

[0077] Step S202: Control the arc emitter to emit an arc and record the intensity of the current target area as arcpw.

[0078] The target area is the region where an ultraviolet image is formed based on the ultraviolet light generated by the electric arc. An electric arc emitter is a device that can generate and emit an electric arc of varying intensities. The intensity of the electric arc emitter can be detected using a spectrophotometer; intensity represents ultraviolet irradiance, and the unit can be picowatts per square centimeter.

[0079] It is understandable that the spectral intensity illuminometer measures the intensity of the target area, but not all of the ultraviolet light generated by the electric arc is used to form an ultraviolet image. Therefore, the intensity of the target area is not necessarily the intensity of the electric arc emitted by the arc emitter.

[0080] Step S204: Data is collected using intensity arcpw as a reference and in integer multiples as steps, and calibration data is obtained based on the collected data.

[0081] Data is collected in increments of integer multiples of the intensity arcpw. This means that the emission intensity is determined by the detected arcpw, 2 times arcpw, 3 times arcpw, etc. The arc transmitter is controlled to emit an arc, and data at different intensities are collected respectively. Calibration data is obtained based on the collected data.

[0082] Understandably, the data that can be directly collected are the arc area of ​​the ultraviolet image and the brightness of each pixel in the ultraviolet image, while the average brightness needs to be calculated further based on the brightness of each pixel.

[0083] In this embodiment, the intensity, arc area of ​​the ultraviolet image, and average brightness can be substituted into the initial model to fit the values ​​of the influencing factors, thereby obtaining the target model.

[0084] In one embodiment, the calibration data includes a first calibration dataset and a second calibration dataset; the first calibration dataset includes the calibrated arc intensity, the first arc area, and the first average brightness corresponding to different intensities, and the second calibration dataset includes the calibrated arc intensity, the second arc area corresponding to different intensities, and the second average brightness.

[0085] Arc intensity and strength are two distinct characteristics. Intensity refers to the strength of the arc detected in the target area, while arc strength is the intensity defined in this application. Different intensities correspond to different calibrated arc intensities.

[0086] For example, when the intensity is arcpw, the corresponding arc intensity is 1; when the intensity is 2 times arcpw, the corresponding arc intensity is 2; when the intensity is 3 times arcpw, the corresponding arc intensity is 3, and so on.

[0087] Step S204: Using intensity arcpw as a baseline, data is collected in integer multiples of arcpw. Calibration data is obtained based on the collected data. (See attached document). Figure 4 The process includes steps S302 to S304, wherein:

[0088] Step S302: Fix the arc area during calibration, take the intensity arcpw as the reference, and take integer multiples as the step size. Collect the brightness of each pixel in the ultraviolet image under different intensities, and take the fixed area during calibration as the first arc area in the ultraviolet image; determine the first average brightness based on the brightness of the pixels.

[0089] That is, without changing the imaging arc area of ​​the emitted arc, using the first arc area as the fixed arc area of ​​the arc emitted by the arc transmitter, and changing the emission intensity of the arc transmitter to collect data, the brightness of the pixels corresponding to different intensities can be collected, and thus the first average brightness corresponding to different intensities can be obtained. The fixed imaging arc area can be achieved by adding an isolation window to the emitting end of the arc transmitter.

[0090] The ultraviolet light passing through the isolation window can be used to form an ultraviolet image. The corresponding target area is the region where the ultraviolet light passing through the isolation window forms an ultraviolet image; that is, the intensity of the target area is the ultraviolet irradiance of the ultraviolet light passing through the isolation window. By changing the emission intensity of the arc emitter and collecting data at integer multiples of arcpw (arcpw, 2 times arcpw, 3 times arcpw, etc.) in the target area, the first calibration dataset is obtained.

[0091] The corresponding first arc area can be used as the reference arc area in the initial model, and the first average brightness determined by the intensity arcpw corresponding to the first arc area is used as the reference average brightness.

[0092] Step S304: Without fixing the arc area during calibration, and using the intensity arcpw as a benchmark, with integer multiples as steps, collect the second arc area and the brightness of each pixel in the ultraviolet image under different intensities, and determine the second average brightness based on the brightness of the pixels.

[0093] That is, without using an isolation window to fix the area of ​​the emitted arc, by changing the emission intensity of the arc emitter, the actual image arc area under different emission intensities can be obtained, i.e., the second arc area. It is also possible to obtain the brightness of the pixels corresponding to different intensities, and thus obtain the second average brightness corresponding to different intensities.

[0094] Without using an isolation window, the intensity of the target area being detected is the actual ultraviolet irradiance of the ultraviolet rays generated by the electric arc emitted by the arc emitter, which can be detected by a spectrophotometer.

[0095] In this embodiment, the calibrated arc intensity, arc area of ​​the ultraviolet image, and average brightness can be substituted into the initial model to fit the value of the influencing factor, thereby obtaining the target model.

[0096] Understandably, influencing factors include area influencing factors and brightness influencing factors. By fixing the imaging arc area at the calibration time, the impact of the arc area on the results can be reduced during the determination of the brightness influencing factor. After determining the brightness influencing factor, determining the area influencing factor by using the actual arc area and brightness corresponding to different intensities can make the output value of the target model more accurate.

[0097] In one embodiment, determining a first average brightness or a second average brightness based on the brightness of a pixel includes:

[0098] The first average brightness or the second average brightness is obtained by weighted summation of the brightness of each pixel.

[0099] The pixels in the weighted summation are pixels from the same ultraviolet image.

[0100] It is understandable that a complete electric arc is characterized by a bright center and weak edges, and the intensity of the arc depends more on the bright areas in the arc information. Therefore, it is necessary to increase the weight of the bright areas in the ultraviolet image in order to accurately calculate the average brightness of the ultraviolet image (first average brightness or second average brightness).

[0101] In one embodiment, the first average brightness or the second average brightness is obtained by weighted summation of the brightness of each pixel, including:

[0102] The sum of the squares of the brightness of all pixels in the same ultraviolet image is used as the first brightness.

[0103] The sum of the brightness of all pixels in the same ultraviolet image is used as the second brightness.

[0104] The ratio of the first brightness to the second brightness is taken as the first average brightness or the second average brightness.

[0105] Taking the first average brightness as an example, the calculation formula is as follows:

[0106]

[0107] in, This represents the first average brightness, where i represents the pixel index, and I represents the set of pixels. This represents the brightness of the i-th pixel.

[0108] The average brightness in this embodiment is actually an average of the brightness of each pixel, with higher brightness pixels receiving a greater weight. This average brightness more accurately represents the intensity level of the core region of the arc discharge, effectively avoiding background dilution and thus extracting brightness features that are closer to the physical essence.

[0109] In one embodiment, the influence factors include an area influence factor and a brightness influence factor, and the calibration data includes a first calibration dataset and a second calibration dataset.

[0110] Step S104: Determine the influencing factors in the initial model based on the acquired calibration data to obtain the target model. (See [reference]) Figure 5 The process includes steps S402 to S408, wherein:

[0111] Step S402: Based on the initial model and the first calibration dataset, fit the values ​​of the brightness influence factor.

[0112] As mentioned earlier, the first calibration dataset includes the calibrated arc intensity, the first arc area, and the first average brightness corresponding to different intensities. Since the reference arc area in the initial model is equal to the first arc area, and the actual arc area is also equal to the first arc area, the ratio of the actual arc area to the reference arc area is 1 in the initial model.

[0113] because , , The sum of is 1, therefore, the initial model is:

[0114]

[0115] in, Indicates the intensity of the electric arc. , , These represent the first brightness influence factor, the second brightness influence factor, and the third brightness influence factor, respectively. , , The sum of is 1. This represents the actual average brightness. This represents the baseline average brightness.

[0116] Although it exists , , Three different unknowns, but , , The sum of is 1, therefore, the first calibration dataset can include at least two data points corresponding to different intensities. Substitute the first average brightness into the actual average brightness. Substitute the calibrated arc intensity into the arc intensity The reference average brightness is obtained by taking the first arc area and intensity arcpw as the basis.

[0117] Step S404: Substitute the value of the brightness influence factor into the initial model to obtain the intermediate model.

[0118] Understandable, , , The specific values ​​have already been obtained in step S402. The actual arc area, reference arc area, actual average brightness, and reference average brightness can be substituted into the values ​​of the second calibration dataset. Therefore, the unknowns of the intermediate model only include... , , Get , , The target model can then be obtained.

[0119] Step S406: Based on the intermediate model and the second calibration dataset, fit the numerical value of the area influence factor.

[0120] The second calibration dataset includes the calibrated arc intensity, the second arc area corresponding to different intensities, and the second average brightness.

[0121] The reference arc area is the first arc area, and the reference average brightness is the first average brightness obtained by substituting the first arc area and intensity arcpw. The second average brightness is then substituted into the actual average brightness, and the calibrated arc intensity is substituted into the arc intensity. Substitute the second arc area into the actual arc area.

[0122] As mentioned earlier, the intermediate model includes , , With three unknowns, the second calibration dataset requires at least three sets of data to calculate the area influence factor.

[0123] Step S408: Substitute the numerical values ​​of the area influence factor into the intermediate model to obtain the target model.

[0124] If the second calibration dataset is directly substituted into the initial model for multi-parameter nonlinear fitting, the area influence factor and the brightness influence factor may interfere with each other. By determining the influence factors step by step, the influence of the area influence factor on the brightness influence factor can be separated, which not only reduces the complexity of the algorithm, but also improves the accuracy of the influence factor values.

[0125] In one embodiment, when it is necessary to evaluate the arc intensity, an initial model of the average brightness and arc area of ​​the ultraviolet image is first constructed. Next, calibration data is acquired, and the values ​​of the influencing factors in the initial model are determined using the calibration data to obtain the target model. Then, the average brightness and arc area of ​​the ultraviolet image of the arc intensity to be evaluated are obtained. Finally, the acquired average brightness and arc area are substituted into the target model to obtain the arc intensity.

[0126] The calculation method for the average brightness of the ultraviolet image used to evaluate the arc intensity is the same as that for the first average brightness or the second average brightness, and will not be elaborated here.

[0127] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages in other steps. It is understood that the steps in different embodiments can be freely combined as needed, and all non-contradictory solutions formed by such combinations are within the scope of protection of this application.

[0128] Based on the same inventive concept, this application also provides an arc strength evaluation device for implementing the arc strength evaluation method described above. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations of one or more arc strength evaluation device embodiments provided below can be found in the limitations of the arc strength evaluation method described above, and will not be repeated here.

[0129] In one exemplary embodiment, such as Figure 6 As shown, an arc intensity evaluation device is provided, including: a model construction module 102, a data acquisition module 104, and an arc intensity evaluation module 106, wherein:

[0130] The model building module 102 is used to build an initial model of arc intensity and the average brightness and arc area of ​​the ultraviolet image; it is also used to determine the values ​​of the influencing factors in the initial model based on the acquired calibration data to obtain the target model.

[0131] The data acquisition module 104 is used to acquire the average brightness of the ultraviolet image of the arc intensity to be evaluated and the arc area corresponding to the ultraviolet image.

[0132] The arc intensity evaluation module 106 is used to input the average brightness of the ultraviolet image and the arc area into the target model to obtain the arc intensity.

[0133] For details, please refer to the steps in the method implementation examples, which will not be repeated here.

[0134] The aforementioned arc intensity evaluation device can distinguish different ultraviolet light sources and reduce the influence of other ultraviolet light sources by acquiring features corresponding to ultraviolet images. By constructing an initial model related to the ultraviolet image and using calibration data to determine the influencing factors in the initial model, a target model capable of outputting accurate data can be obtained. By acquiring the average brightness and arc area of ​​the ultraviolet image of the arc intensity to be evaluated, the discharge characteristics of the arc can be reflected from different dimensions, and the influence of distance can be reduced, improving the accuracy and reliability of the evaluation results.

[0135] In one embodiment, the influence factors include an area influence factor and a brightness influence factor. The area influence factor includes a first area influence factor, a second area influence factor, and a third area influence factor. The brightness influence factors include a first brightness influence factor, a second brightness influence factor, and a third brightness influence factor. The area influence factor is related to the arc area of ​​the ultraviolet image, and the brightness influence factor is related to the average brightness of the ultraviolet image.

[0136] The initial model built by model building module 102 includes:

[0137]

[0138] in, Indicates the intensity of the electric arc. , , These represent the first area influence factor, the second area influence factor, and the third area influence factor, respectively. , , The sum of is 1. Indicates the reference arc area. Indicates the actual arc area. , , These represent the first brightness influence factor, the second brightness influence factor, and the third brightness influence factor, respectively. , , The sum of is 1. This represents the actual average brightness. This represents the baseline average brightness.

[0139] , , , , , Specific values ​​can be obtained by fitting calibration data. Substituting these values ​​into the initial model yields the target model, which is used to calculate the arc intensity. . and The specific value can be determined based on experience or calibration data.

[0140] In one embodiment, the data acquisition module 104 is further configured to acquire calibration data, specifically including controlling the arc emitter to emit an arc and recording the intensity of the current target area as arcpw, using the intensity arcpw as a reference and incrementing the data in integer multiples, and obtaining calibration data based on the acquired data. The target area is the region where an ultraviolet image is formed based on the ultraviolet light generated by the arc.

[0141] The specific steps are as described in the corresponding method embodiments and will not be repeated here.

[0142] In one embodiment, the calibration data includes a first calibration dataset and a second calibration dataset; the first calibration dataset includes the calibrated arc intensity, the first arc area, and the first average brightness corresponding to different intensities, and the second calibration dataset includes the calibrated arc intensity, the second arc area corresponding to different intensities, and the second average brightness.

[0143] The data acquisition module 104 is also used to fix the arc area during calibration, and, with the intensity arcpw as a reference and integer multiples as steps, collect the brightness of each pixel in the ultraviolet image under different intensities, and use the fixed calibrated area as the first arc area in the ultraviolet image, and determine the first average brightness based on the brightness of the pixels. It is also used to flexibly adjust the arc area during calibration, and, with the intensity arcpw as a reference and integer multiples as steps, collect the second arc area and the brightness of each pixel in the ultraviolet image under different intensities, and determine the second average brightness based on the brightness of the pixels.

[0144] The specific steps are as described in the corresponding method embodiments and will not be repeated here.

[0145] In one embodiment, the data acquisition module 104 is further configured to perform a weighted summation of the brightness of each pixel to obtain a first average brightness or a second average brightness.

[0146] The specific steps are as described in the corresponding method embodiments and will not be repeated here.

[0147] In one embodiment, the data acquisition module 104 is further configured to acquire the sum of the squares of the brightness of all pixels in the same ultraviolet image as a first brightness, acquire the sum of the brightness of all pixels in the same ultraviolet image as a second brightness, and use the ratio of the first brightness and the second brightness as a first average brightness or a second average brightness.

[0148] Taking the first average brightness as an example, the calculation formula is as follows:

[0149]

[0150] in, This represents the first average brightness, where i represents the pixel index, and I represents the set of pixels. This represents the brightness of the i-th pixel.

[0151] The specific steps are as described in the corresponding method embodiments and will not be repeated here.

[0152] In one embodiment, the influence factors include an area influence factor and a brightness influence factor, and the calibration data includes a first calibration dataset and a second calibration dataset.

[0153] The model building module 102 is also used to fit the values ​​of the brightness influence factor based on the initial model and the first calibration dataset, substitute the values ​​of the brightness influence factor into the initial model to obtain the intermediate model, fit the values ​​of the area influence factor based on the intermediate model and the second calibration dataset, substitute the values ​​of the area influence factor into the intermediate model to obtain the target model.

[0154] The specific steps are as described in the corresponding method embodiments and will not be repeated here.

[0155] Each module in the aforementioned arc intensity evaluation device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0156] In one exemplary embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 7As shown, this computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs in the non-volatile storage media. The database stores arc intensity evaluation data. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a method for evaluating arc intensity.

[0157] Those skilled in the art will understand that Figure 7 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0158] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps described above.

[0159] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps described above.

[0160] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps described above.

[0161] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0162] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0163] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for evaluating electric arc intensity, characterized in that, The method includes: An initial model was constructed to correlate the arc intensity with the average brightness of the ultraviolet image and the arc area. The values ​​of the influencing factors in the initial model are determined based on the acquired calibration data, and the target model is obtained. Obtain the average brightness of the ultraviolet image of the arc intensity to be evaluated and the arc area corresponding to the ultraviolet image; The average brightness and arc area of ​​the ultraviolet image are input into the target model to obtain the arc intensity.

2. The method according to claim 1, characterized in that, The influencing factors include area influencing factors and brightness influencing factors; the calibration data includes a first calibration dataset and a second calibration dataset; The process of determining the influencing factors in the initial model based on the acquired calibration data to obtain the target model includes: Based on the initial model and the first calibration dataset, the value of the brightness influence factor is obtained by fitting. Substituting the values ​​of the brightness influence factors into the initial model yields the intermediate model; Based on the intermediate model and the second calibration dataset, the value of the area influence factor is obtained by fitting. Substituting the numerical value of the area influence factor into the intermediate model yields the target model.

3. The method according to claim 1, characterized in that, Before determining the values ​​of the influence factors in the initial model based on the acquired calibration data to obtain the target model, the method further includes: The system controls the arc emitter to emit an electric arc and records the intensity of the current target area as arcpw; the target area is the region that forms an ultraviolet image based on the ultraviolet light generated by the electric arc. Data is collected using intensity arcpw as a benchmark and in increments of integer multiples, and calibration data is obtained based on the collected data.

4. The method according to claim 3, characterized in that, The calibration data includes a first calibration dataset and a second calibration dataset; the first calibration dataset includes the calibrated arc intensity, the first arc area, and the first average brightness corresponding to different intensities; the second calibration dataset includes the calibrated arc intensity, the second arc area corresponding to different intensities, and the second average brightness. The process of acquiring data based on the intensity arcpw as a benchmark, with increments of integer multiples, and obtaining calibration data based on the acquired data includes: A fixed area is used for calibration, with intensity arcpw as the reference and integer multiples as steps. The brightness of each pixel in the ultraviolet image is collected under different intensities, and the fixed area during calibration is used as the first arc area in the ultraviolet image. A first average brightness is determined based on the brightness of the pixels. Using the area and intensity arcpw as a reference during calibration without fixing the time, and taking integer multiples as steps, the second arc area and the brightness of each pixel in the ultraviolet image are collected under different intensities, and the second average brightness is determined based on the brightness of the pixels. Different intensities correspond to different calibrated arc intensities.

5. The method according to claim 4, characterized in that, Determining the first average brightness or the second average brightness based on the brightness of the pixels includes: The sum of the squares of the brightness of all pixels in the same ultraviolet image is used as the first brightness. The sum of the brightness of all pixels in the same ultraviolet image is used as the second brightness. The ratio of the first brightness to the second brightness is used as the first average brightness or the second average brightness.

6. The method according to claim 2, characterized in that, The area influence factor includes a first area influence factor, a second area influence factor, and a third area influence factor; the brightness influence factor includes a first brightness influence factor, a second brightness influence factor, and a third brightness influence factor. The initial model for constructing the relationship between arc intensity and the average brightness and area of ​​the ultraviolet image includes: The initial model includes: in, Indicates the intensity of the electric arc. , , These represent the first area influence factor, the second area influence factor, and the third area influence factor, respectively. , , The sum of is 1. Indicates the reference arc area. Indicates the actual arc area. , , These represent the first brightness influence factor, the second brightness influence factor, and the third brightness influence factor, respectively. , , The sum of is 1. This represents the actual average brightness. This represents the baseline average brightness.

7. A device for evaluating electric arc intensity, characterized in that, The device includes: The model building module is used to construct an initial model of arc intensity and the average brightness and arc area of ​​the ultraviolet image; it is also used to determine the values ​​of the influencing factors in the initial model based on the acquired calibration data to obtain the target model. The data acquisition module is used to acquire the average brightness of the ultraviolet image of the arc intensity to be evaluated and the arc area corresponding to the ultraviolet image; The arc intensity evaluation module is used to input the average brightness and arc area of ​​the ultraviolet image into the target model to obtain the arc intensity.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

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