Method and equipment for monitoring aluminum leakage in deep well casting and storage medium

Abstract

The invention relates to the technical field of aluminum processing safety monitoring, in particular to a deep well casting aluminum leakage monitoring method and device and a storage medium, and the method comprises the steps: selecting a visual monitoring target area, and carrying out the visual analysis of the obtained visual features based on the visual monitoring target area, so as to obtain a visual monitoring result, the object of the visual monitoring result comprises a worker and a work piece; acquiring a sound signal, and preprocessing the sound signal; performing time-frequency processing on the preprocessed sound signal to obtain a time-frequency graph; calculating an intensity value corresponding to the sound signal, counting a high-intensity component ratio in the time-frequency diagram based on the intensity value, and comparing the high-intensity component ratio with a preset value to obtain a sound monitoring result; and aluminum leakage monitoring is carried out based on the visual monitoring result and the sound monitoring result, and an alarm signal is generated under the abnormal condition. The method and the device are used for improving the monitoring effect during man-machine aluminum leakage monitoring.

Description

Technical Field

[0001] The present application relates to the technical field of aluminum processing safety monitoring, and in particular to a method, equipment and storage medium for monitoring aluminum leakage in deep well casting. Background Art

[0002] The aluminum processing industry is a high-risk industry, especially in the casting workshop, where there is often a high risk of explosion. The safety hazards in the aluminum processing industry are often caused by aluminum leakage. The reasons for aluminum liquid leakage usually include two factors: one is the leakage that is difficult to avoid in the production process itself, and the other is the leakage caused by improper behavior of workers in production operations.

[0003] Initially, the aluminum processing industry had a low level of informatization in production safety monitoring. Production safety warnings mainly relied on experience, that is, they mainly relied on manual monitoring. The safety warnings in this way were mostly experience-driven and heavily dependent on human monitoring and judgment.

[0004] In current technology, with the continuous improvement of computer technology, the safety construction of workshops tends to be intelligent. The aluminum processing industry generally implements the method of human-machine joint monitoring. The staff monitors and inspects based on experience, and the monitoring equipment determines whether aluminum leakage occurs through visual processing or sound processing. Specifically,

[0005] The Chinese invention with publication number CN115423784A discloses a molten aluminum leakage monitoring alarm system and method based on liquid level fluctuation detection, which uses a camera to obtain the molten aluminum liquid level image in the diverter plate, and uses the appearance of vortices or bubbles on the molten aluminum liquid level as a basis for judgment to detect molten aluminum leakage. However, in the actual casting process, a layer of aluminum oxide will form on the surface of the molten aluminum, making the vortices or bubbles on the molten aluminum liquid level not obvious, resulting in missed detection or false detection.

[0006] The Chinese invention with publication number CN117129154A discloses a method and system for monitoring aluminum leakage in a diverter plate based on sound recognition, which discloses collecting underwater soundprint features through sensors, using deep learning methods to learn the soundprint features when aluminum is leaking, and judging the aluminum leakage situation through the soundprint features. However, the above method needs to collect a large number of aluminum leakage soundprints for algorithm training before being put into use, which is costly.

[0007] Therefore, in the current technology, although the human-machine dual monitoring method has been developed, the current intelligent monitoring method generally has certain monitoring loopholes and high usage costs. At the same time, manual monitoring by staff may fail to detect aluminum liquid leakage in time due to unauthorized absence of staff, and the overall aluminum leakage monitoring effect is poor. Summary of the invention

[0008] In order to improve the monitoring effect of aluminum leakage in human-machine monitoring, the present application provides a deep well casting aluminum leakage monitoring method, equipment and storage medium.

[0009] In the first aspect, the present application provides a method for monitoring aluminum leakage in deep well casting, which adopts the following technical solution:

[0010] A method for monitoring aluminum leakage in deep well casting, comprising the following steps:

[0011] Selecting a visual monitoring target area, and performing visual analysis on the acquired visual features based on the visual monitoring target area to obtain a visual monitoring result, wherein the objects of the visual monitoring result include workers and work objects;

[0012] Acquiring a sound signal, and preprocessing the sound signal;

[0013] Performing time-frequency processing on the pre-processed sound signal to obtain a time-frequency graph;

[0014] Calculate the intensity value corresponding to the sound signal and count the proportion of high-intensity components in the time-frequency graph based on the intensity value, and compare the proportion of high-intensity components with a preset value to obtain a sound monitoring result;

[0015] Aluminum leakage monitoring is performed based on the visual monitoring results and the sound monitoring results, and an alarm signal is generated in an abnormal situation.

[0016] In some embodiments, the workpiece includes an equipment indicator light, selecting a visual monitoring target area, and performing visual analysis based on the visual monitoring target area to obtain a visual monitoring result, including the following steps:

[0017] Set the indicator light ROI area;

[0018] Obtaining a video surveillance stream, and reading video frames from the video surveillance stream;

[0019] Cropping the video frame based on the indicator light ROI area to obtain an indicator light classification picture;

[0020] Analyze the classification results of the indicator light classification pictures, and determine the lighting status of the device indicator lights based on the classification results;

[0021] The current casting state is determined based on the lighting state, and the casting state is used as the visual monitoring result.

[0022] In some embodiments, the worker includes a worker, the work object includes a safety helmet, selecting a visual monitoring target area, and performing visual analysis based on the visual monitoring target area to obtain a visual monitoring result include the following steps:

[0023] Select the work area to be inspected;

[0024] Acquire a first detection frame for the worker and a second detection frame for the helmet in the video frame based on a visual processing algorithm;

[0025] Calculating a first center point corresponding to the first detection frame and a second center point corresponding to the second detection frame;

[0026] Determining the number of the first center points in the working area to be detected to generate an off-duty monitoring result;

[0027] Determine whether the second center point exists in the first detection frame to generate a safety monitoring result;

[0028] The off-duty monitoring results and the safety monitoring results are both included in the visual monitoring results.

[0029] In some embodiments, obtaining a sound signal and preprocessing the sound signal include the following steps:

[0030] Obtaining a preset number of the sound signals at a regular interval;

[0031] The sound signal is framed to generate a multi-frame signal including a plurality of frame signal sets, and the multi-frame signal is windowed.

[0032] In some embodiments, calculating the time-frequency processing of the pre-processed sound signal to obtain a time-frequency graph includes the following steps:

[0033] The sound signal is subjected to a fast Fourier transform to obtain a complex value matrix, and the specific formula is:

[0034] D=STFT(x,n_fft,overlap,win_lenght),

[0035] Where D represents the complex matrix of Fourier transform coefficients, n_fft represents the window size, overlap represents the window overlap length, and win_lenght represents the window length;

[0036] The time-frequency diagram is obtained based on the time-frequency analysis result of the complex-valued matrix.

[0037] In some embodiments, calculating the intensity value corresponding to the sound signal and counting the proportion of high-intensity components in the time-frequency graph based on the intensity value includes the following steps:

[0038] The absolute value of the complex value matrix is ​​calculated to obtain an amplitude matrix, and the signal strength decibel value matrix is ​​calculated according to the amplitude matrix. The specific formula is:

[0039] magnitude=|D|,

[0040] value_db=10*np.log10(magnitude+(1e-10)),

[0041] Wherein, magnitude represents the amplitude matrix, value_db represents the signal strength decibel value matrix, and np.log represents the natural logarithm operation;

[0042] The number of high-intensity signals is calculated according to the value of each element in the signal strength decibel value matrix, and the ratio of the number corresponding to the high-intensity signals to the total number of elements in the signal strength decibel value matrix is ​​calculated to obtain the high-intensity component ratio.

[0043] In some embodiments, calculating the number of high-intensity signals according to the value of each element in the signal strength decibel value includes the following steps:

[0044] Obtaining a preset normal signal strength value, and performing Fourier transform on the normal signal strength value to obtain a normal signal decibel value;

[0045] The elements in the signal strength decibel value matrix that are smaller than the normal signal decibel value are set to 0, and the number of the remaining elements in the signal strength decibel value matrix is ​​defined as the number of the high-intensity signals.

[0046] In some of the embodiments, comparing the high intensity component ratio with a preset value to obtain a sound monitoring result includes the following steps:

[0047] If the proportion of the high-strength component is greater than a preset value, the system enters a suspected aluminum leakage state and obtains the current time;

[0048] Taking the current time as a starting point, generating a determination time window including a preset time length;

[0049] When the suspected aluminum leakage state is maintained within the determination time window, the sound monitoring result is determined to be aluminum leakage.

[0050] In the second aspect, the present application provides a deep well casting aluminum leakage monitoring device, which adopts the following technical solution:

[0051] A deep well casting aluminum leakage monitoring device, comprising:

[0052] A visual feature acquisition module, used for acquiring visual features;

[0053] A visual monitoring determination module, used for selecting a visual monitoring target area, and performing visual analysis on visual features based on the visual monitoring target area to obtain a visual monitoring result, wherein the objects of the visual analysis include people and objects, and the visual monitoring result includes any one or a combination of people analysis and object analysis;

[0054] A sound signal acquisition module, used to acquire a sound signal and pre-process the sound signal;

[0055] A sound monitoring and determination module is used to calculate the time-frequency processing of the sound signal after preprocessing to obtain a time-frequency diagram, calculate the intensity value corresponding to the sound signal, and based on the intensity value, count the proportion of high-intensity components in the time-frequency diagram, and compare the proportion of high-intensity components with a preset value to obtain a sound monitoring result;

[0056] An alarm module is used to monitor aluminum leakage based on the visual monitoring results and the sound monitoring results and generate an alarm signal under abnormal circumstances.

[0057] In a third aspect, the present application provides a computer-readable storage medium, which adopts the following technical solution:

[0058] A computer-readable storage medium stores a computer program that can be run on a processor, and the computer program implements the above method when executed by the processor.

[0059] The technical solution provided by the embodiments of the present application has the following technical effects:

[0060] Through dual monitoring of vision and sound, it is possible to simultaneously monitor the aluminum leakage problems that have occurred and the potential aluminum leakage risks caused by workers' improper operations. The monitoring results are more accurate and stable. At the same time, only the time frequency and signal strength ratio of the sound signal are calculated and processed. There is no need for a large amount of aluminum leakage data, which greatly reduces costs and has higher economic value. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 It is a schematic diagram of the steps of a method for monitoring aluminum leakage in deep well casting provided in an embodiment of the present application.

[0062] Figure 2 This is a schematic diagram of module connections of a deep well casting aluminum leakage monitoring device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0063] To more clearly understand the purpose, technical solutions and advantages of the present application, the present application is described and illustrated below in conjunction with the accompanying drawings and embodiments. However, it should be understood by those of ordinary skill in the art that the present application can be implemented without these details. In some cases, in order to avoid unnecessary descriptions that make various aspects of the present application obscure, well-known methods, processes, systems, components and / or circuits that have been described at a higher level will not be described in detail. For those of ordinary skill in the art, it is obvious that various changes can be made to the embodiments disclosed in the present application, and without departing from the principles and scope of the present application, the general principles defined in the present application can be applied to other embodiments and application scenarios. Therefore, the present application is not limited to the embodiments shown, but conforms to the broadest scope consistent with the scope claimed for protection of the present application.

[0064] It should be noted that the description of these embodiments is used to help understand the present invention, but does not constitute a limitation of the present invention. In addition, the technical features involved in each embodiment of the present invention described below can be combined with each other as long as there is no conflict between them.

[0065] In the description of this application, "several" means one or more, "more" means more than two, "greater than", "less than", "exceed", etc. are understood to exclude the number itself, and "above", "below", "within", etc. are understood to include the number itself. If there is a description of "first" or "second", it is only used to distinguish the technical features, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features or implicitly indicating the order of the indicated technical features.

[0066] In the description of the present application, the description with reference to the terms "one embodiment", "some embodiments", "illustrative embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials, or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any one or more embodiments or examples.

[0067] The embodiment of the present application discloses a method for monitoring aluminum leakage in deep well casting.

[0068] like Figure 1 As shown, a method for monitoring aluminum leakage in deep well casting comprises the following steps:

[0069] S100, selecting a visual monitoring target area, and performing visual analysis on the acquired visual features based on the visual monitoring target area to obtain a visual monitoring result.

[0070] Among them, the objects of visual monitoring results include workers and work objects.

[0071] After the system is running, the visual feature acquisition module collects video information in the working environment. The visual feature acquisition module is generally a camera.

[0072] Different visual monitoring target areas are set, and the state of the aluminum processing working environment is determined according to the specific state of the workers and workpieces in the visual monitoring target areas and combined with the corresponding visual processing algorithms.

[0073] Visual monitoring is mainly used to monitor the standardization and safety of the processing process.

[0074] S200: Acquire a sound signal and pre-process the sound signal.

[0075] The soundprint in the video source collected by the visual feature collection module or the soundprint collected by an independent hydrophone is used as the sound signal.

[0076] The collected sound signals are preprocessed before aluminum leakage monitoring.

[0077] Since the current deep well casting process is divided into water casting and waterless manufacturing according to the water level, when aluminum leakage occurs during the casting process in water, it can be distinguished and judged through the sound pattern monitored by the hydrophone or video source.

[0078] S300, performing time-frequency processing on the pre-processed sound signal to obtain a time-frequency graph.

[0079] S400, calculating the intensity value corresponding to the sound signal and counting the proportion of high-intensity components in the time-frequency diagram based on the intensity value, and comparing the proportion of high-intensity components with a preset value to obtain a sound monitoring result.

[0080] The acquired sound signal is converted into a signal strength matrix through time-frequency processing, and whether aluminum leakage occurs is determined based on the change in sound signal strength during the monitoring period and its proportion in the matrix.

[0081] S500, performing aluminum leakage monitoring based on the visual monitoring results and the sound monitoring results and generating an alarm signal in an abnormal situation.

[0082] Aluminium leakage monitoring is divided into explicit aluminium leakage monitoring and implicit aluminium leakage monitoring. Explicit aluminium leakage monitoring is characterized by judging whether there is objective aluminium leakage that can be monitored through sound monitoring results; implicit aluminium leakage monitoring is characterized by judging and restricting the behaviour of the working environment through safe and standardized monitoring of workers and work objects through visual monitoring, so as to monitor potential risks of aluminium leakage that cannot be monitored.

[0083] Then, by simultaneously monitoring the visual and sound monitoring results, the aluminum leakage problems and hidden dangers in the monitored area can be effectively monitored, and an alarm signal can be generated in time to remind the relevant personnel when an abnormal situation occurs.

[0084] Through the above method, the aluminum leakage problem and the potential aluminum leakage caused by workers' improper operation can be monitored simultaneously through the dual monitoring of vision and sound. The monitoring results are more accurate and stable. At the same time, only the time frequency and signal strength ratio of the sound signal are calculated and processed. There is no need for a large amount of aluminum leakage data, which greatly reduces the cost and has higher economic value.

[0085] In other embodiments, the workpiece includes an equipment indicator light, selecting a visual monitoring target area, and performing visual analysis based on the visual monitoring target area to obtain a visual monitoring result, including the following steps:

[0086] S110, setting the indicator light ROI area.

[0087] The ROI area is also called the region of interest. In machine vision and image processing, it is an artificially set area that needs to be processed and is outlined in the image being processed in the form of a box, circle, ellipse, irregular polygon, etc.

[0088] In the embodiment of the present application, after the specific position of the visual feature acquisition module is determined, the image area collected by it is obtained, and the corresponding indicator light ROI area is set according to the area where the equipment indicator light is located in the video image. Because the casting equipment is large-scale equipment, the position will not move for a long time, so after the indicator light ROI area is set, it does not need to be revised or changed in general scenarios.

[0089] S111, obtaining a video surveillance stream, and reading video frames from the video surveillance stream.

[0090] The video frame is obtained from the video surveillance stream collected by the visual feature acquisition module.

[0091] S112, cropping the video frame based on the indicator light ROI area to obtain an indicator light classification picture.

[0092] The video frame is cropped to a size corresponding to the indicator light ROI area, and after cropping, a picture containing the indicator light is obtained.

[0093] S113, analyzing the classification results of the indicator light classification pictures, and judging the lighting status of the device indicator lights based on the classification results.

[0094] Use the MobileNetV2 algorithm to perform binary classification on the indicator light image. If the classification result is "light on", it means that the indicator light of the device is currently on. If the classification result is "light off", it means that the indicator light of the device is currently off.

[0095] S114, judging the current casting state based on the lighting state, and taking the casting state as a visual monitoring result.

[0096] The casting state is characterized by whether the current monitoring environment is in the casting working state, and the subsequent visual monitoring and sound monitoring are combined to judge the aluminum leakage based on the current casting state. If it is not in the casting state, there is no need for aluminum leakage monitoring.

[0097] In other embodiments, the worker includes a worker, the workpiece includes a safety helmet, selecting a visual monitoring target area, and performing visual analysis based on the visual monitoring target area to obtain a visual monitoring result include the following steps:

[0098] S120, selecting a working area to be inspected.

[0099] The working area to be inspected corresponding to the artificially selected working environment is generally the area around the casting plate. This range is defined as the working range of the casting staff and the monitoring range of the personnel for aluminum leakage.

[0100] S121, obtaining a first detection frame for the worker and a second detection frame for the safety helmet in the video frame based on a visual processing algorithm.

[0101] The number of categories of the YOLOv5 algorithm is set to 2 to monitor pedestrians (staff) and helmets in the video frame, so as to obtain the first detection frame person_bbox containing the staff and the second detection frame helmet_bbox containing the helmet.

[0102] S122, calculating a first center point corresponding to the first detection frame and a second center point corresponding to the second detection frame.

[0103] The center point position of each detection frame is calculated according to each first detection frame and the second detection frame.

[0104] S123, determining the number of first center points in the work area to be detected to generate an off-post monitoring result.

[0105] First, it is determined whether there is a first center point in the working area to be detected. If so, it indicates that there are workers in the working range of the casting. Then, the number of first center points in the working area to be detected is detected, which represents how many workers are around the casting plate.

[0106] Set the number of staff in the work area to be detected according to the requirements. For example, if it is set to 2, then when there are more than two first center points in the work area to be detected, it is considered that the work scope is not in an off-duty state. When the number of first center points in the work area to be detected is less than 2, it is considered that there is an off-duty state, and an alarm is required.

[0107] S124, determining whether there is a second center point in the first detection frame to generate a safety monitoring result.

[0108] Secondly, when there is a second center point in the first detection frame, it is considered that the staff is wearing a safety helmet when working. Conversely, if there is no second center point in the first detection frame, it is considered that the staff is not wearing a safety helmet when working, and an alarm signal needs to be issued.

[0109] Among them, off-duty monitoring results and safety monitoring results are both included in the visual monitoring results.

[0110] The above monitoring results are used to monitor the behavior of the staff. When no alarm signal appears in the staff's behavior, it is considered that there is no safety hazard of aluminum leakage that cannot be monitored by the staff in time, and aluminum leakage monitoring based on sound monitoring can be carried out.

[0111] In some other embodiments, obtaining a sound signal and preprocessing the sound signal include the following steps:

[0112] S210, periodically acquiring a preset number of sound signals.

[0113] 200,000 digital signals are acquired per second from a video source or hydrophone as sound signals.

[0114] S220, dividing the sound signal into frames to generate a multi-frame signal including a plurality of frame signal sets, and performing windowing on the multi-frame signal.

[0115] The digital signal is framed to divide it into multiple frame signal sets, specifically:

[0116] x→M=[x1,x2,...,x n-1 ,x n ],

[0117] n=(N-overlap) / inc,

[0118] overlap=wlen-inc.

[0119] Among them, overlap represents the overlapping part; wlen represents the frame length, which is 5000; inc represents the frame shift, which is 2500; n represents the number of frames of the entire signal; N represents the length of the entire signal, which is 200000; M represents the set of multiple frame signals.

[0120] When performing fast Fourier transform on a signal, if the selected frame of signal is not an integer multiple of the signal period, there will be discontinuity of the sample points when the period is expanded, resulting in subsequent spectrum distortion. In theory, the spectrum only contains the frequency of the signal to be measured, but when the spectrum is distorted, it will cause the spectrum to contain many frequency components, thereby producing a spectrum leakage effect.

[0121] Therefore, in order to solve the above phenomenon, it is necessary to window the multi-frame signal after framing so that the left and right ends of the windowed signal are connected to make it more periodic.

[0122] In the embodiment of the present application, the multi-frame signal is windowed by a Hamming window, and the specific formula is as follows:

[0123]

[0124] Among them, x i represents the signal of each frame input; n represents the number of frames of the entire signal; a is the coefficient, which is 0.46; W i The representation is the signal after windowing each frame.

[0125] In some other embodiments, performing time-frequency processing on the pre-processed sound signal to obtain a time-frequency graph includes the following steps:

[0126] S310, performing fast Fourier transform on the sound signal to obtain a complex value matrix.

[0127] The above frame signal is subjected to fast Fourier transform frame by frame, and the specific formula is:

[0128] D=STFT(x,n_fft,overlap,win_lenght).

[0129] Among them, D represents the complex-valued matrix of Fourier transform coefficients; n_fft represents the window size, which is 5000 in the formula; overlap represents the window overlap length, which is 2500 in the formula; win_length represents the window length, which is 5000 in the formula.

[0130] S320, obtaining a time-frequency diagram based on the time-frequency analysis result of the complex-valued matrix.

[0131] The fast Fourier transform of the sound signal is used to realize time-frequency analysis to obtain an image containing time domain and frequency domain, where the x-axis is the time series and the y-axis is the frequency range.

[0132] The amplitude and magnitude of the sound signal can be represented by complex-valued matrices and time-frequency diagrams.

[0133] In some other embodiments, calculating the intensity value corresponding to the sound signal and counting the proportion of high-intensity components in the time-frequency diagram based on the intensity value includes the following steps:

[0134] S410, calculating the absolute value of the complex value matrix to obtain an amplitude matrix, and calculating the signal strength decibel value matrix according to the amplitude matrix. The specific formula is:

[0135] magnitude=|D|,

[0136] value_db=10*np.log10(magnitude+(1e-10)).

[0137] Among them, magnitude represents the amplitude matrix, value_db represents the signal strength decibel value matrix, and np.log represents the natural logarithm operation.

[0138] The amplitude is characterized by the intensity of the frequency domain signal. After the sound signal undergoes Fourier transform, the size of each frequency component in the spectrum can be regarded as the modulus of the complex-valued spectrum. Therefore, the amplitude matrix is ​​obtained by performing an absolute value operation on the complex-valued matrix.

[0139] The signal strength decibel value matrix is ​​obtained by performing a logarithmic operation on the amplitude matrix, each element of which represents the sound intensity corresponding to each frame signal.

[0140] S420, calculating the number of high-intensity signals according to the value of each element in the signal strength decibel value matrix, and obtaining the proportion of high-intensity components according to the ratio of the number of high-intensity signals to the total number of elements in the signal strength decibel value matrix.

[0141] In the embodiment of the present application, the high intensity is set to 1000. When the signal intensity is 1000, the corresponding decibel value is 30.

[0142] When aluminum leakage occurs, the decibel value corresponding to the monitored sound will increase. When it is greater than the preset value, it is considered that there is suspected aluminum leakage. Then, a high-intensity component ratio can be obtained by the ratio of the number of high-intensity signals in the signal strength decibel value matrix.

[0143] The specific formula is:

[0144] M = positive_count / L.

[0145] L represents the numerical size of the frame signal of the complex-valued matrix, which is 2501*79.

[0146] In some other embodiments, the number of high-intensity signals is calculated according to the value of each element in the signal strength decibel value, including the following steps:

[0147] S421, obtaining a preset normal signal strength value, and performing Fourier transform on the normal signal strength value to obtain a normal signal decibel value.

[0148] S422, set the elements in the signal strength decibel value matrix that are smaller than the normal signal decibel value to 0, and define the number of remaining elements in the signal strength decibel value matrix as the number of high-intensity signals.

[0149] When judging the number of high-intensity signals, the elements in the matrix that do not belong to high-intensity signals can be set to zero first, that is, the elements with signal strength lower than 1000, whose corresponding decibel values ​​are lower than 30db, are not considered to have aluminum leakage, and the non-zero elements in the signal strength decibel value matrix are all characterized as high-intensity signals.

[0150] In some other embodiments, comparing the high intensity component ratio with a preset value to obtain a sound monitoring result includes the following steps:

[0151] S430: If the proportion of high-strength components is greater than a preset value, the system enters a suspected aluminum leakage state and obtains the current time.

[0152] S440, taking the current time as the starting point, generating a determination time window including a preset time length.

[0153] S450, when the suspected aluminum leakage state is maintained within the determination time window, the sound monitoring result is determined to be aluminum leakage.

[0154] If the proportion M of high-intensity signals in the signal strength decibel value matrix is ​​greater than 0.3, it is determined that aluminum leakage is currently suspected. At the same time, when the suspected aluminum leakage occurs, the current time is obtained, and a maintenance time of 3 seconds is selected as the determination time window. If the suspected aluminum leakage state lasts for three seconds, it is confirmed that aluminum leakage occurs, and the corresponding alarm information is output.

[0155] like Figure 2 As shown, the embodiment of the present application also discloses a deep well casting aluminum leakage monitoring device, including:

[0156] The visual feature acquisition module is used to acquire visual features.

[0157] The visual monitoring determination module is used to select a visual monitoring target area and perform visual analysis on visual features based on the visual monitoring target area to obtain a visual monitoring result, wherein the objects of visual analysis include people and objects, and the visual monitoring result includes any one or a combination of people analysis and object analysis.

[0158] The sound signal acquisition module is used to acquire the sound signal and pre-process the sound signal.

[0159] The sound monitoring and judgment module is used to calculate the time-frequency processing of the pre-processed sound signal to obtain a time-frequency diagram, calculate the intensity value corresponding to the sound signal and count the proportion of high-intensity components in the time-frequency diagram based on the intensity value, and compare the proportion of high-intensity components with the preset value to obtain the sound monitoring result.

[0160] The alarm module is used to monitor aluminum leakage based on visual monitoring results and sound monitoring results and generate alarm signals in abnormal situations.

[0161] In an embodiment of the present application, a computer-readable storage medium is also disclosed, on which a computer program that can be run on a processor is stored. When the computer program is executed by the processor, the steps of the above method are implemented.

[0162] The implementation principle is:

[0163] Through dual monitoring of vision and sound, it is possible to simultaneously monitor the aluminum leakage problems that have occurred and the potential aluminum leakage risks caused by workers' improper operations. The monitoring results are more accurate and stable. At the same time, only the time frequency and signal strength ratio of the sound signal are calculated and processed. There is no need for a large amount of aluminum leakage data, which greatly reduces costs and has higher economic value.

[0164] It should be understood that although the steps in the flowchart of the accompanying drawings are shown in sequence according to the instructions of the arrows, these steps are not necessarily performed in the order indicated by the arrows. Unless otherwise clearly stated in this document, the execution of these steps is not strictly limited in order and can be performed in other orders.

[0165] The above are all preferred embodiments of the present application, and the protection scope of the present application is not limited thereto. Therefore, any equivalent changes made according to the structure, shape, and principle of the present application should be included in the protection scope of the present application.

Claims

1. A method for monitoring aluminum leakage in deep well casting, characterized in that: The following steps are involved: Selecting a visual monitoring target area, and performing visual analysis on the acquired visual features based on the visual monitoring target area to obtain a visual monitoring result, wherein the objects of the visual monitoring result include workers and work objects; Acquiring a sound signal, and preprocessing the sound signal; Performing time-frequency processing on the pre-processed sound signal to obtain a time-frequency graph; Calculate the intensity value corresponding to the sound signal and count the proportion of high-intensity components in the time-frequency graph based on the intensity value, and compare the proportion of high-intensity components with a preset value to obtain a sound monitoring result; Aluminum leakage monitoring is performed based on the visual monitoring results and the sound monitoring results, and an alarm signal is generated in an abnormal situation.

2. The deep well casting aluminum leakage monitoring method according to claim 1 is characterized in that: The workpiece includes an equipment indicator light, a visual monitoring target area is selected, and a visual analysis is performed based on the visual monitoring target area to obtain a visual monitoring result, including the following steps: Set the indicator light ROI area; Obtaining a video surveillance stream, and reading video frames from the video surveillance stream; Cropping the video frame based on the indicator light ROI area to obtain an indicator light classification picture; Analyze the classification results of the indicator light classification pictures, and determine the lighting status of the device indicator lights based on the classification results; The current casting state is determined based on the lighting state, and the casting state is used as the visual monitoring result.

3. The deep well casting aluminum leakage monitoring method according to claim 2 is characterized in that: The worker includes a worker, the work object includes a safety helmet, a visual monitoring target area is selected, and a visual analysis is performed based on the visual monitoring target area to obtain a visual monitoring result, including the following steps: Select the work area to be inspected; Acquire a first detection frame for the worker and a second detection frame for the helmet in the video frame based on a visual processing algorithm; Calculating a first center point corresponding to the first detection frame and a second center point corresponding to the second detection frame; Determining the number of the first center points in the working area to be detected to generate an off-duty monitoring result; Determine whether the second center point exists in the first detection frame to generate a safety monitoring result; The off-duty monitoring results and the safety monitoring results are both included in the visual monitoring results.

4. The deep well casting aluminum leakage monitoring method according to claim 1 is characterized in that: Acquiring a sound signal and preprocessing the sound signal comprises the following steps: Obtaining a preset number of the sound signals at a regular interval; The sound signal is framed to generate a multi-frame signal including a plurality of frame signal sets, and the multi-frame signal is windowed.

5. The deep well casting aluminum leakage monitoring method according to claim 4 is characterized in that: Calculating and performing time-frequency processing on the pre-processed sound signal to obtain a time-frequency graph comprises the following steps: The sound signal is subjected to a fast Fourier transform to obtain a complex value matrix, and the specific formula is: D=STFT(x,n_fft,overlap,win_lenght), Where D represents the complex matrix of Fourier transform coefficients, n_fft represents the window size, overlap represents the window overlap length, and win_lenght represents the window length; The time-frequency diagram is obtained based on the time-frequency analysis result of the complex-valued matrix.

6. The deep well casting aluminum leakage monitoring method according to claim 5 is characterized in that: Calculating the intensity value corresponding to the sound signal and counting the proportion of high-intensity components in the time-frequency graph based on the intensity value, including the following steps: The absolute value of the complex value matrix is ​​calculated to obtain an amplitude matrix, and the signal strength decibel value matrix is ​​calculated according to the amplitude matrix. The specific formula is: magnitude=|D|, value_db=10*np.log10(magnitude+(1e-10)), Wherein, magnitude represents the amplitude matrix, value_db represents the signal strength decibel value matrix, and np.log represents the natural logarithm operation; The number of high-intensity signals is calculated according to the value of each element in the signal strength decibel value matrix, and the ratio of the number corresponding to the high-intensity signals to the total number of elements in the signal strength decibel value matrix is ​​calculated to obtain the high-intensity component ratio.

7. The deep well casting aluminum leakage monitoring method according to claim 6 is characterized in that: Calculating the number of high-intensity signals according to the numerical values ​​of each element in the signal intensity decibel value comprises the following steps: Obtaining a preset normal signal strength value, and performing Fourier transform on the normal signal strength value to obtain a normal signal decibel value; The elements in the signal strength decibel value matrix that are smaller than the normal signal decibel value are set to 0, and the number of the remaining elements in the signal strength decibel value matrix is ​​defined as the number of the high-intensity signals.

8. The deep well casting aluminum leakage monitoring method according to claim 6 is characterized in that: Comparing the high-intensity component ratio with a preset value to obtain a sound monitoring result includes the following steps: If the proportion of the high-strength component is greater than a preset value, the system enters a suspected aluminum leakage state and obtains the current time; Taking the current time as a starting point, generating a determination time window including a preset time length; When the suspected aluminum leakage state is maintained within the determination time window, the sound monitoring result is determined to be aluminum leakage.

9. A deep well casting aluminum leakage monitoring device, characterized in that: include: A visual feature acquisition module, used for acquiring visual features; A visual monitoring determination module, used for selecting a visual monitoring target area, and performing visual analysis on visual features based on the visual monitoring target area to obtain a visual monitoring result, wherein the objects of the visual analysis include people and objects, and the visual monitoring result includes any one or a combination of people analysis and object analysis; A sound signal acquisition module, used to acquire a sound signal and pre-process the sound signal; A sound monitoring and determination module is used to calculate the time-frequency processing of the sound signal after preprocessing to obtain a time-frequency diagram, calculate the intensity value corresponding to the sound signal, and based on the intensity value, count the proportion of high-intensity components in the time-frequency diagram, and compare the proportion of high-intensity components with a preset value to obtain a sound monitoring result; An alarm module is used to monitor aluminum leakage based on the visual monitoring results and the sound monitoring results and generate an alarm signal under abnormal circumstances.

10. A computer-readable storage medium, characterized in that: A computer program that can be run on a processor is stored thereon, and when the computer program is executed by the processor, the method according to any one of claims 1 to 8 is implemented.

Images