Method and device for determining zero time of detonator initiation, and electronic equipment

Abstract

The application provides a method and device for determining the zero moment of detonator initiation and electronic equipment. The method comprises the following steps: sending a blasting instruction to an initiating bomb in a target area to make the initiating bomb initiate a detonator; acquiring a plurality of images in the initiation process; determining the Hamming distance between a first image taken at a first time and a second image taken at a second time according to a perceptual hashing algorithm, first pixel data of the first image and second pixel data of the second image, the first time occurring before the second time; determining the Euclidean norm between the first image and the second image according to the first pixel data, the second pixel data and a first preset relationship; determining a target image according to the Hamming distance, the Euclidean norm and a preset threshold value, and acquiring a target shooting time corresponding to the target image, the target shooting time representing the zero moment of detonator initiation. Through the application, the technical problem of the existing technology that the zero moment of detonator initiation is ahead of or behind is solved.

Description

Technical Field

[0001] This application relates to the field of blasting engineering technology, and more specifically, to a method for determining the zero moment of detonation of a detonator, a device for determining the zero moment of detonation of a detonator, a computer-readable storage medium, and an electronic device. Background Technology

[0002] Sensing blasting vibration information is fundamental to studying the propagation, attenuation, and energy release patterns of blasting vibrations, and is also key to accurately understanding the blasting vibration response characteristics around open-pit coal mines. However, the vibration monitoring equipment around the mining area is located at different distances from the blast zone, and the baseline time for acquiring vibration data varies for each device, which can affect subsequent analysis.

[0003] Furthermore, in joint data analysis, the standardization of the baseline time for vibration data and the accurate determination of the zero moment of blasting initiation are crucial, with the zero moment of blasting initiation being the detonation time of the first detonator. Current technologies commonly use the following two methods to record the initiation time of explosives in blasting engineering:

[0004] Method 1: Determine and record changes in surface bulges;

[0005] Method 2: Determine and record the moment of detonation by the detonator.

[0006] However, in the process of determining and recording the time of blasting initiation by observing changes in surface bulges, the detonating bombs and explosives inside the blast holes have already detonated, resulting in a lag in the recording of the zero moment of blasting initiation. In the process of determining and recording the time of blasting initiation by observing the moment of detonation of the detonator, the time interval between the opening of the detonator's detonation switch and the detonation of the first detonating bomb may be considered, resulting in a possibility that the recording of the zero moment of blasting initiation may be premature.

[0007] Therefore, there is an urgent need for a method to determine the zero moment of blasting initiation in order to accurately record the zero moment of blasting initiation. Summary of the Invention

[0008] The main objective of this application is to provide a method, device, storage medium, processor, and electronic device for determining the zero moment of detonation, so as to at least solve the problem that the zero moment of detonation determined in the prior art is either too early or too late.

[0009] To achieve the above objectives, according to one aspect of this application, a method for determining the zero moment of detonation of a detonator is provided. The method includes: sending a detonation command to a detonating bomb in a target area to detonate the detonating bomb, the detonating bomb being used to detonate the detonator; acquiring multiple images during the detonation process of the detonator, the multiple images having multiple corresponding first target pixel data, the multiple images being obtained by photographing the target area within a first time period, the start time of the first time period being the moment the detonation command is issued, the end time of the first time period being the moment when the detonator explodes and produces a light of a preset brightness, each image including a detonator image, the detonator image being obtained by photographing the detonator, the first target pixel data including detonator pixel data corresponding to the detonator image in the image; and determining the zero moment of detonation of the first image according to a perceptual hash algorithm. The first image and the second image are used to determine the Hamming distance between the first image and the second image. The first image and the second image are any two of a plurality of images, and the first image is the image captured at the first moment in the first time period, and the second image is the image captured at the second moment in the first time period. The first moment occurs before the second moment. The plurality of first target pixel data includes the first pixel data and the second pixel data. Based on the first pixel data, the second pixel data and the first preset relation, the Euclidean norm between the first image and the second image is determined. Based on the Hamming distance, the Euclidean norm and the preset threshold, the target image is determined, and the target shooting time corresponding to the target image is obtained. The target shooting time is used to characterize the zero moment of detonation of the detonator.

[0010] Optionally, the determination method further includes: acquiring third pixel data of a first detonator deployment area in a first image and fourth pixel data of a second detonator deployment area in a second image, wherein the first detonator deployment area includes a detonator image in the first image, the second detonator deployment area includes a detonator image in the second image, the first pixel data includes third pixel data, and the second pixel data includes fourth pixel data; updating the first pixel data to the third pixel data, and updating the second pixel data to the fourth pixel data.

[0011] Optionally, acquiring multiple images during the detonator initiation process includes: acquiring multiple explosion images, each explosion image having an initial filename and an initial file extension; updating the initial file extension of the explosion images to a target file extension to give the explosion images a target storage format; updating the initial filename of the explosion images to the target filename to sort the multiple explosion images according to a preset rule; and performing image grayscale processing on the multiple explosion images with the target file extension and target filename to obtain multiple images.

[0012] Optionally, multiple pixel coordinates constitute first pixel data and second pixel data. Obtaining third pixel data of the first detonator deployment area in the first image and fourth pixel data of the second detonator deployment area in the second image includes: obtaining first detonator pixel coordinates and second detonator pixel coordinates, where the first detonator pixel coordinates are the pixel coordinates corresponding to the detonator image in the first image, and the second detonator pixel coordinates are the pixel coordinates corresponding to the detonator image in the second image; determining multiple first target horizontal coordinate values ​​and multiple first target vertical coordinate values ​​based on the first detonator pixel coordinates, a preset pixel width, and a preset pixel height, where the multiple first target horizontal coordinate values ​​are multiple horizontal coordinate values ​​corresponding one-to-one with multiple pixel coordinates of the preset pixel width, and the multiple first target vertical coordinate values ​​are multiple horizontal coordinate values ​​corresponding one-to-one with multiple pixel coordinates of the preset pixel width. Multiple vertical coordinates corresponding to multiple pixel coordinates of the pixel height are defined. Multiple horizontal coordinates of the first target include the horizontal coordinates of the first detonator pixel coordinates, and multiple vertical coordinates of the first target include the vertical coordinates of the first detonator pixel coordinates. Based on the second detonator pixel coordinates, a preset pixel width, and a preset pixel height, multiple horizontal coordinates and multiple vertical coordinates of the second target are determined. The multiple horizontal coordinates of the second target are multiple horizontal coordinates corresponding to multiple pixel coordinates of the preset pixel width, and the multiple vertical coordinates of the second target are multiple vertical coordinates corresponding to multiple pixel coordinates of the preset pixel height. The multiple horizontal coordinates of the second target include the horizontal coordinates of the second detonator pixel coordinates, and the multiple vertical coordinates of the second target include the vertical coordinates of the second detonator pixel coordinates.

[0013] Optionally, determining the Hamming distance between the first image and the second image based on the perceptual hash algorithm, the first pixel data of the first image, and the second pixel data of the second image includes: performing a discrete cosine transform on the first image based on the first pixel data to obtain a first coefficient matrix, the first coefficient matrix having multiple first coefficients; compressing the first coefficient matrix to obtain a first target coefficient matrix, the first target coefficient matrix including multiple first target coefficients, the first target coefficient matrix being a part of the first coefficient matrix; obtaining a first average value of the multiple first target coefficients in the first target coefficient matrix; determining a first target coefficient in the first target coefficient matrix that is less than the first average value as a first set value, determining a first target coefficient in the first target coefficient matrix that is greater than or equal to the first average value as a second set value, the first set value and the second set value being different, the first set value and the second set value constituting a first hash string of the first image; obtaining a second hash string of the second image; performing an XOR operation on the first hash string and the second hash string to obtain a target string, and determining the number of second set values ​​in the target string as the Hamming distance.

[0014] Optionally, determining the Euclidean norm between the first image and the second image based on the first pixel data, the second pixel data, and the first preset relation includes: generating a first pixel value of the first image based on the first pixel data; generating a second pixel value of the second image based on the second pixel data; and determining the Euclidean norm based on the first pixel value, the second pixel value, and the first preset relation.

[0015] Optionally, the preset threshold includes a first threshold and a second threshold. Determining the target image based on the Hamming distance, the Euclidean norm, and the preset threshold includes: determining whether the Hamming distance is greater than the first threshold to obtain a first determination result; determining whether the Euclidean norm is greater than the second threshold to obtain a second determination result; and determining the second image as the target image if both the first determination result and the second determination result indicate yes.

[0016] According to another aspect of this application, a device for determining the zero moment of detonation of a detonator is provided. The device includes: a sending module for sending a detonation command to a detonating bomb in a target area to detonate the detonating bomb, the detonating bomb being used to detonate a detonator; a first acquisition module for acquiring multiple images during the detonation process of the detonator, the multiple images having multiple corresponding first target pixel data, the multiple images being obtained by photographing the target area within a first time period, the start time of the first time period being the time when the detonation command is issued, the end time of the first time period being the time when the detonator explodes and produces a light of a preset brightness, each image including a detonator image obtained by photographing the detonator, the first target pixel data including detonator pixel data corresponding to the detonator image in the image; and a first determination module for determining the zero moment of detonation of a detonator based on a perceptual hash algorithm and the first target pixel data of the first image. The first target pixel data includes the first pixel data and the second pixel data of the second image. The first image and the second image are any two of a plurality of images. The first image is the image captured at the first moment in the first time period, and the second image is the image captured at the second moment in the first time period. The first moment occurs before the second moment. The plurality of first target pixel data includes the first pixel data and the second pixel data. The second determining module is used to determine the Euclidean norm between the first image and the second image based on the first pixel data, the second pixel data and the first preset relation. The second acquiring module is used to determine the target image based on the Hamming distance, the Euclidean norm and the preset threshold, and to acquire the target shooting time corresponding to the target image. The target shooting time is used to characterize the zero moment of detonation of the detonator.

[0017] According to another aspect of this application, a computer-readable storage medium is provided, the computer-readable storage medium including a stored program, wherein, when the program is executed, it controls the device where the computer-readable storage medium is located to execute the above-described method for determining the zero moment of detonation of a detonator.

[0018] According to another aspect of this application, an electronic device is provided, comprising: one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include a method for performing the above-described method for determining the zero moment of detonation of a detonator.

[0019] By applying the technical solution of this application, a detonating bomb is first detonated by sending a detonation command to the target area. Since the detonating bomb is used to detonate a detonator, the detonator will also detonate. Then, by photographing the target area within a first time period (the start time of the first time period is the time when the detonation command is issued, and the end time of the first time period is the time when the detonator explodes and produces a light of a preset brightness), multiple images of the detonator detonation process can be obtained. Since the detonator is located in the target area during the photographing process, each image includes a detonator image. Each detonator image is obtained by photographing the detonator in the target area. The multiple images have multiple corresponding first target pixel data, which include detonator pixel data corresponding to the detonator image in the image. Furthermore, based on the perceptual hash algorithm, the first pixel data of the first image, and the second pixel data of the second image, the Hamming distance between the first image and the second image can be determined. The first image and the second image are any two of a plurality of images, where the first image was captured at the first moment within the first time period, and the second image was captured at the second moment within the first time period. The first moment occurs before the second moment. The plurality of first target pixel data includes both first pixel data and second pixel data. Additionally, the Euclidean norm between the first image and the second image can be determined based on the first pixel data of the first image, the second pixel data of the second image, and a first preset formula. Finally, based on the aforementioned Hamming distance, Euclidean norm range, and preset threshold, the target image can be determined. The target capture time corresponding to the target image is used to characterize the zero moment of detonation of the detonator. In other words, after setting the detonating bomb to detonate the detonator, this application obtains the Hamming distance between the first and second images taken at different times and the Euclidean norm between the first and second images taken at different times. This achieves the purpose of analyzing the image similarity between the first and second images using an image similarity analysis method that combines the Hamming distance and the Euclidean norm. As a result, the error range of the analysis results can be controlled within the millisecond level, which greatly improves the accuracy of determining the zero moment of detonator detonation and solves the technical problem of the zero moment of detonator detonation being ahead or behind in the prior art. Attached Figure Description

[0020] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. In the drawings:

[0021] Figure 1 A hardware structure block diagram of a mobile terminal for performing a method for determining the zero moment of detonation of a detonator, according to an embodiment of this application, is shown.

[0022] Figure 2 A flowchart illustrating a method for determining the zero moment of detonation of a detonator according to an embodiment of this application is shown.

[0023] Figure 3 The diagram illustrates a ground detonator deployment scheme in a method for determining the zero moment of detonator detonation according to an embodiment of this application.

[0024] Figure 4 A flowchart illustrating a specific method for determining the zero moment of detonation of a detonator according to an embodiment of this application is shown.

[0025] Figure 5 A structural block diagram of a device for determining the zero moment of detonation of a detonator according to an embodiment of this application is shown;

[0026] The above figures include the following reference numerals:

[0027] 102. Processor; 104. Memory; 106. Transmission device; 108. Input / output device; 1. Detonator; 2. Surface detonator; 3. Direction of throwing. Detailed Implementation

[0028] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0029] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0030] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate for the embodiments of this application described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0031] As described in the background section, the detonation time of explosives commonly used in existing blasting engineering is generally determined and recorded by changes in surface bulges and by the instant of detonation of the detonator. However, in the process of determining and recording by changes in surface bulges, the detonating shell and explosive inside the borehole have already detonated successively, resulting in a lag in the recording of the zero-time of blasting detonation. In the process of determining and recording by the instant of detonation of the detonator, considering the time interval between the opening of the detonator's detonation switch and the detonation of the first detonating shell, the recording of the zero-time of blasting detonation may be premature. To solve the problem of premature or delayed determination of the zero-time of blasting detonation in the prior art, embodiments of this application provide a method for determining the zero-time of detonator detonation, a device for determining the zero-time of detonator detonation, a computer-readable storage medium, and an electronic device.

[0032] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0033] The methods and embodiments provided in this application can be executed on a mobile terminal, a computer terminal, or a similar computing device. Taking running on a mobile terminal as an example, Figure 1 This is a hardware structure block diagram of a mobile terminal for a method of determining the zero moment of detonator detonation according to an embodiment of the present invention. Figure 1 As shown, a mobile terminal may include one or more ( Figure 1 Only one is shown in the diagram. A processor 102 (which may include, but is not limited to, a microprocessor MCU or a programmable logic device FPGA, etc.) and a memory 104 for storing data are also shown. The mobile terminal may further include a transmission device 106 for communication functions and an input / output device 108. Those skilled in the art will understand that... Figure 1 The structure shown is for illustrative purposes only and does not limit the structure of the mobile terminal described above. For example, the mobile terminal may also include components that are more... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown.

[0034] The memory 104 can be used to store computer programs, such as application software programs and modules, like the computer program corresponding to the method for determining the zero moment of detonation of a detonator in this embodiment of the invention. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, thereby implementing the above-described method. The memory 104 may include high-speed random access memory and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include memory remotely located relative to the processor 102, and these remote memories can be connected to the mobile terminal via a network. Examples of the aforementioned networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof. The transmission device 106 is used to receive or send data via a network. Specific examples of the aforementioned networks may include wireless networks provided by the mobile terminal's communication provider. In one example, the transmission device 106 includes a network interface controller (NIC), which can be connected to other network devices via a base station to communicate with the Internet. In one example, the transmission device 106 may be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0035] This embodiment provides a method for determining the zero moment of detonation of a detonator, which runs on a mobile terminal, computer terminal, or similar computing device. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0036] Figure 2 This is a flowchart of a method for determining the zero moment of detonation of a detonator according to an embodiment of this application. Figure 2 As shown, the method includes the following steps:

[0037] Step S201: Send a blasting command to the detonating bomb in the target area to detonate the detonating bomb, which is used to detonate the detonator;

[0038] Specifically, this application applies to detonator deployment schemes where detonators and detonating shells are connected in parallel. For example, one detonator deployment scheme of this application is as follows: a surface detonating detonator (hereinafter referred to as detonator) is added outside the pre-splitting blast hole, and the detonator and the explosive detonating shell inside the blast hole (hereinafter referred to as detonating shell) are connected in parallel. When a blasting command is sent to the detonating shell, the explosion of the detonating shell will trigger the detonator connected in parallel to detonate and produce a flash.

[0039] The detonator contains very little explosive. During the detonation process, the detonator only serves to detonate the explosive. Moreover, personnel and equipment are kept at a safe blasting distance. Therefore, the detonator detonation meets the blasting safety requirements.

[0040] Step S202: Acquire multiple images during the detonation process of the detonator. The multiple images have multiple first target pixel data that correspond one-to-one. The multiple images are obtained by shooting the target area within a first time period. The start time of the first time period is the time when the detonation command is issued, and the end time of the first time period is the time when the detonator explodes and produces a light of preset brightness. Each image includes a detonator image, which is obtained by shooting the detonator. The first target pixel data includes the detonator pixel data corresponding to the detonator image in the image.

[0041] Specifically, an image acquisition system can be used to photograph the target area where the aforementioned detonators are deployed. The first time period for the image acquisition system to capture images is from the moment the detonation command is issued until the moment the detonator explodes and produces a large area of ​​bright light; this large area of ​​bright light can be a preset brightness set according to operational needs. Optionally, the image acquisition system can capture one image every millisecond, meaning that the time interval between any two adjacent images captured within the first time period can be 1 ms.

[0042] Optionally, to accurately capture the instant of blasting initiation, the image acquisition system can mainly consist of four parts: an information sensing end, a device mounting end, a device power supply end, and a parameter setting end (information output end). For example, the information sensing end can be a high-speed camera, the mounting end of the device acquisition end can be a tripod, the power supply end of the acquisition end can be a portable power bank, and the parameter setting end (information output end) of the acquisition end can be a laptop.

[0043] Step S203: Determine the Hamming distance between the first image and the second image based on the perceptual hash algorithm, the first pixel data of the first image, and the second pixel data of the second image. The first image and the second image are any two of a plurality of images, and the first image is the image captured at the first moment in the first time period, and the second image is the image captured at the second moment in the first time period. The first moment occurs before the second moment, and the plurality of first target pixel data includes the first pixel data and the second pixel data.

[0044] For multiple images, the first image taken at a first moment and the second image taken at a second moment, the first moment occurs before the second moment. The perceptual hash algorithm is an algorithm used to determine image similarity; its function is to assess the similarity of images by comparing their information.

[0045] Therefore, in order to perceive the image similarity between the first image and the second image, this application uses a perceptual hash algorithm to calculate the Hamming distance between the first image and the second image, which is used to characterize the image similarity between the first image and the second image.

[0046] Step S204: Determine the Euclidean norm between the first image and the second image based on the first pixel data, the second pixel data, and the first preset relational formula;

[0047] On the other hand, in order to perceive the image similarity between the first image and the second image, this application also employs a first preset relational formula for calculating the Euclidean norm between the first image and the second image. This Euclidean norm is used to characterize the image similarity between the first image and the second image. The smaller the Euclidean norm, the more similar the two images are; the larger the Euclidean norm, the greater the difference between the two images.

[0048] Step S205: Determine the target image based on Hamming distance, Euclidean norm and preset threshold, and obtain the target shooting time corresponding to the target image. The target shooting time is used to characterize the zero moment of detonation of the detonator.

[0049] Specifically, Hamming distance and Euclidean norm can both be used to analyze the image similarity between two images. In order to control the error range of the analysis results of the image similarity between two images within the millisecond level, this application sets a preset threshold, thereby controlling the error range of the analysis results of the image similarity between two images within the millisecond level.

[0050] In this embodiment, by sending a detonation command to the target area, the detonating bomb is first detonated. Since the detonating bomb is used to detonate a detonator, the detonator will also detonate. Then, by photographing the target area within a first time period (the start time of the first time period is the time when the detonation command is issued, and the end time of the first time period is the time when the detonator explodes and produces a light of a preset brightness), multiple images of the detonator detonation process can be obtained. Since the detonator is located in the target area during the photographing process, each image includes a detonator image. Each detonator image is obtained by photographing the detonator in the target area. The multiple images have multiple corresponding first target pixel data, which include the detonator pixel data corresponding to the detonator image in the image. Furthermore, based on the perceptual hash algorithm, the first pixel data of the first image, and the second pixel data of the second image, the Hamming distance between the first image and the second image can be determined. The first image and the second image are any two of a plurality of images, where the first image was captured at the first moment within the first time period, and the second image was captured at the second moment within the first time period. The first moment occurs before the second moment. The plurality of first target pixel data includes both first pixel data and second pixel data. Additionally, the Euclidean norm between the first image and the second image can be determined based on the first pixel data of the first image, the second pixel data of the second image, and a first preset formula. Finally, based on the aforementioned Hamming distance, Euclidean norm range, and preset threshold, the target image can be determined. The target capture time corresponding to the target image is used to characterize the zero moment of detonation of the detonator. In other words, after setting the detonating bomb to detonate the detonator, this application obtains the Hamming distance between the first and second images taken at different times and the Euclidean norm between the first and second images taken at different times. This achieves the purpose of analyzing the image similarity between the first and second images using an image similarity analysis method that combines the Hamming distance and the Euclidean norm. As a result, the error range of the analysis results can be controlled within the millisecond level, which greatly improves the accuracy of determining the zero moment of detonator detonation and solves the technical problem of the zero moment of detonator detonation being ahead or behind in the prior art.

[0051] In some optional implementations, the method further includes: acquiring third pixel data of a first detonator deployment area in a first image and fourth pixel data of a second detonator deployment area in a second image, wherein the first detonator deployment area includes a detonator image in the first image, the second detonator deployment area includes a detonator image in the second image, the first pixel data includes third pixel data, and the second pixel data includes fourth pixel data; updating the first pixel data to the third pixel data, and updating the second pixel data to the fourth pixel data.

[0052] In the above embodiments, since the range of the multiple images obtained by capturing the target area is relatively large, including the detonator deployment area and the background area other than the detonator deployment area, in order to effectively preserve the core image information corresponding to the detonator deployment area and improve the calculation accuracy and effectiveness of the image similarity algorithm (calculating Hamming distance and Euclidean norm), this application reasonably calibrates the detonator deployment area (image similarity calculation processing area) in the image. Therefore, after obtaining the pixel data of the calibrated area, since the pixel data of the calibrated area eliminates the interference factors of the background area, the algorithm does not process the part outside the calibrated area during the calculation of the Hamming distance and Euclidean norm between different images based on the pixel data of the calibrated area. Thus, after calculation according to the above algorithm, the calculation accuracy and effectiveness of the image similarity algorithm can be improved. Specifically, the calibrated area is the first detonator deployment area and the second detonator deployment area, the pixel data of the first detonator deployment area is the third pixel data, and the pixel data of the second detonator deployment area is the fourth pixel data.

[0053] To improve the computational efficiency of image similarity algorithms, in some optional implementations, acquiring multiple images during the detonation process includes: acquiring multiple explosion images, each with an initial filename and an initial file extension; to improve subsequent image recognition accuracy, the images need to retain as many original pixel values ​​as possible, thus updating the initial file extension of the explosion images to a target file extension, giving the explosion images a target storage format; to ensure that the multiple images captured by the image acquisition system (high-speed camera) have a specific filename format, enabling rapid image indexing and recognition, the initial filename of the explosion images can be updated to the target filename, so that the multiple explosion images are sorted according to a preset rule; to reduce the comparison parameters of the image similarity algorithm, the multiple explosion images with target file extensions and target filenames undergo image grayscale processing to obtain multiple images.

[0054] Optionally, the target storage format can be a lossless, uncompressed BMP file storage format.

[0055] Optionally, updating the initial filename of the explosion image to the target filename is equivalent to renaming the explosion image, thereby sorting multiple images according to a preset rule.

[0056] In some optional implementations, multiple pixel coordinates constitute first pixel data and second pixel data. Obtaining third pixel data of the first detonator deployment area in the first image and fourth pixel data of the second detonator deployment area in the second image includes: obtaining first detonator pixel coordinates and second detonator pixel coordinates, where the first detonator pixel coordinates are the pixel coordinates corresponding to the detonator image in the first image, and the second detonator pixel coordinates are the pixel coordinates corresponding to the detonator image in the second image; determining multiple first target horizontal coordinate values ​​and multiple first target vertical coordinate values ​​based on the first detonator pixel coordinates, a preset pixel width, and a preset pixel height, where the multiple first target horizontal coordinate values ​​are multiple horizontal coordinate values ​​that correspond one-to-one with the multiple pixel coordinates of the preset pixel width, and the multiple first target vertical coordinate values ​​are... Multiple ordinate values ​​are defined as follows: each ordinate value corresponds one-to-one with a set pixel coordinate of a preset pixel height; the multiple first target ordinate values ​​include the ordinate values ​​of the first detonator pixel coordinates; the multiple first target ordinate values ​​include the ordinate values ​​of the first detonator pixel coordinates; based on the second detonator pixel coordinates, the preset pixel width, and the preset pixel height, multiple second target ordinate values ​​are determined; the multiple second target ordinate values ​​are defined as follows: each ordinate value corresponds one-to-one with a set pixel coordinate of the preset pixel width; the multiple second target ordinate values ​​are defined as follows: each ordinate value corresponds one-to-one with a set pixel coordinate of the preset pixel height; the multiple second target ordinate values ​​include the ordinate values ​​of the second detonator pixel coordinates; the multiple second target ordinate values ​​include the ordinate values ​​of the second detonator pixel coordinates.

[0057] In the above embodiments, the first detonator placement area and the second detonator placement area each include multiple pixel coordinates, each pixel coordinate corresponding to an abscissa value and a ordinate value. The first and second detonator placement areas are calibration areas (with preset pixel height and preset pixel width) calibrated according to actual needs. The third pixel data of the first detonator placement area includes multiple pixel coordinates, and the fourth pixel data of the second detonator placement area includes multiple pixel coordinates. Furthermore, when the first and second detonator placement areas each include a detonator image, the multiple pixel coordinates of the first detonator placement area include first detonator pixel coordinates corresponding to the detonator image, and the multiple pixel coordinates of the second detonator placement area include second detonator pixel coordinates corresponding to the detonator image. Therefore, it is easily understood that the multiple abscissa values ​​of the multiple pixel coordinates in the first (and second) detonator placement areas constitute the preset pixel width, and the multiple ordinate values ​​of the multiple pixel coordinates in the second (and second) detonator placement areas constitute the preset pixel height.

[0058] Optionally, the first detonator placement area and the second detonator placement area can each be a square area with a preset pixel width * preset pixel height of 200 * 200.

[0059] The perceptual hash algorithm can be used to calculate hash strings within a square region of an image. Therefore, when the first image and the second image are square regions, in some optional implementations, the Hamming distance between the first image and the second image is determined based on the perceptual hash algorithm, the first pixel data of the first image, and the second pixel data of the second image. This includes: performing a discrete cosine transform (DCT) on the first image based on the first pixel data to obtain a first coefficient matrix, the first coefficient matrix having multiple first coefficients; compressing the first coefficient matrix to obtain a first target coefficient matrix, the first target coefficient matrix including multiple first target coefficients, the first target coefficient matrix being a portion of the first coefficient matrix; and so on. The first target coefficient matrix undergoes a binary threshold operation, namely, obtaining the first average value of multiple first target coefficients in the first target coefficient matrix; determining the first target coefficients in the first target coefficient matrix that are less than the first average value as first set values, and determining the first target coefficients in the first target coefficient matrix that are greater than or equal to the first average value as second set values. Since the first set values ​​and the second set values ​​are different, after expanding the first target coefficient matrix, the first set values ​​and the second set values ​​constitute the first hash string of the first image; obtaining the second hash string of the second image; performing an XOR operation on the first hash string and the second hash string to obtain the target string, and determining the number of second set values ​​in the target string as the Hamming distance.

[0060] Similarly, the step of obtaining the second hash string of the second image may include: performing a discrete cosine transform on the second image based on the second pixel data to obtain a second coefficient matrix, the second coefficient matrix having multiple second coefficients; compressing the second coefficient matrix to obtain a second target coefficient matrix, the second target coefficient matrix including multiple second target coefficients, the second target coefficient matrix being a part of the second coefficient matrix; performing a binary threshold operation on the second target coefficient matrix, i.e. obtaining the second average value of the multiple second target coefficients in the second target coefficient matrix; determining the second target coefficients in the second target coefficient matrix that are less than the second average value as the first set value, determining the second target coefficients in the second target coefficient matrix that are greater than or equal to the second average value as the second set value, and then, after expanding the second target coefficient matrix, the first set value and the second set value corresponding to the multiple second target coefficients in the second target coefficient matrix respectively constitute the second hash string of the second image.

[0061] In some alternative implementations, when updating the first pixel data to the third pixel data and updating the second pixel data to the fourth pixel data, determining the Hamming distance between the first image and the second image based on the perceptual hash algorithm, the first pixel data of the first image, and the second pixel data of the second image includes: performing a discrete cosine transform on the first detonator deployment area based on the third pixel data to obtain a first coefficient matrix, the first coefficient matrix having multiple first coefficients; compressing the first coefficient matrix to obtain a first target coefficient matrix, the first target coefficient matrix including multiple first target coefficients, the first target coefficient matrix being a part of the first coefficient matrix; obtaining a first average value of the multiple first target coefficients in the first target coefficient matrix; determining a first target coefficient in the first target coefficient matrix that is less than the first average value as a first set value, determining a first target coefficient in the first target coefficient matrix that is greater than or equal to the first average value as a second set value, the first set value and the second set value being different, the first set value and the second set value constituting a first hash string of the first detonator deployment area; obtaining a second hash string of the second detonator deployment area; performing an XOR operation on the first hash string and the second hash string to obtain a target string, and determining the number of second set values ​​in the target string as the Hamming distance.

[0062] The step of obtaining the second hash string of the second detonator deployment area includes: performing a discrete cosine transform on the second detonator deployment area based on the fourth pixel data to obtain a second coefficient matrix, which contains multiple second coefficients; compressing the second coefficient matrix to obtain a second target coefficient matrix, which includes multiple second target coefficients, and the second target coefficient matrix is ​​a part of the second coefficient matrix; obtaining the second average value of the multiple second target coefficients in the second target coefficient matrix; determining the second target coefficients in the second target coefficient matrix that are less than the second average value as first set values, and determining the second target coefficients in the second target coefficient matrix that are greater than or equal to the second average value as second set values; the first set values ​​and second set values ​​in the second target coefficient matrix that correspond to the multiple second target coefficients respectively constitute the second hash string of the second detonator deployment area.

[0063] Among them, the DCT transform is the discrete cosine transform, which is a special Fourier transform used for lossless data or image compression. It can convert spatial domain signals to frequency domain signals. The lossless compression operation of the DCT transform creates conditions for image quantization and encoding operations.

[0064] The one-dimensional DCT transformation formulas are shown in formulas (1)-(2):

[0065]

[0066]

[0067] Where f(i) is the original signal, namely the first pixel data of the first image, the second pixel data of the second image, the third pixel data of the first detonator deployment area or the fourth pixel data of the second detonator deployment area, F(u) is the coefficient after DCT transformation, namely the first coefficient matrix or the second coefficient matrix, N is the number of points of the original signal, and c(u) is the compensation coefficient.

[0068] The two-dimensional DCT transformation formulas are shown in equations (3)-(5):

[0069]

[0070] Where F(u, v) represents the frequency coefficients obtained after the two-dimensional DCT transformation, with coordinates u and v in the frequency domain, f(i, j) represents the gray values ​​of the original image (the image before the DCT transformation) with coordinates i and j in the spatial domain, N is the side length of the image before the DCT transformation, and C(u) and C(v) are normalization factors used to normalize the coefficients on the u-axis and v-axis.

[0071] The Euclidean norm is used to measure the magnitude of a vector in a linear space. It is a method for measuring the distance between two vectors and can be used to compare the similarity of two images. In comparing the similarity of two images, the vectors are the pixel values ​​of the two images. Therefore, in some optional implementations, determining the Euclidean norm between the first image and the second image based on first pixel data, second pixel data, and a first preset relation includes: generating a first pixel value for the first image based on the first pixel data; generating a second pixel value for the second image based on the second pixel data; and determining the Euclidean norm based on the first pixel value, the second pixel value, and the first preset relation.

[0072] The first preset formula mentioned above is the Euclidean norm definition formula. Optionally, the Euclidean norm definition formula (6) can be:

[0073]

[0074] Where L2 is the Euclidean norm, x i x is the value of the first pixel in the first image. i ' is the second pixel value of the second image.

[0075] Considering that open-pit mines are open blasting environments, the image acquisition system (high-speed camera) is highly susceptible to changes in image pixels due to factors such as light, dust, and wind speed. Therefore, the preset thresholds (thresholds applicable to the perceptual hash algorithm and the Euclidean norm) need to be adjusted and corrected according to the on-site acquisition conditions. In some optional implementations, the preset thresholds include a first threshold and a second threshold. The target image is determined based on the Hamming distance, the Euclidean norm, and the preset thresholds, including: determining whether the Hamming distance is greater than the first threshold to obtain a first determination result; determining whether the Euclidean norm is greater than the second threshold to obtain a second determination result; and determining the second image as the target image if both the first and second determination results indicate yes.

[0076] To enable those skilled in the art to better understand the technical solution of this application, the implementation process of the method for determining the zero moment of detonator detonation in this application will be described in detail below with reference to specific embodiments.

[0077] This embodiment relates to a specific method for determining the zero moment of detonation of a detonator. This method is applicable to the design of surface detonator deployment schemes (such as...). Figure 3 As shown below: The surface detonator deployment scheme controls the detonation of the initiating bomb 1, which is then simultaneously detonated by the surface detonator 2 to produce a flash. The types of blast holes for the blasting bench are divided into pre-splitting holes and blasting holes. During the blasting operation according to blasting direction 3, to minimize the impact of the blasting seismic effect on the adjacent high bench rock structure and to maximize the optimal slope structure and shape of the adjacent high bench after the blasting, the pre-detonated blast holes are pre-splitting holes. The surface detonator deployment scheme involves adding a surface detonator 2 outside the first pre-splitting blast hole. The surface detonator 2 is connected in parallel with the initiating bomb 1 inside the blast hole. That is, the blasting technician triggers the initiating bomb 1 inside the blast hole using the detonator, simultaneously triggering the surface detonator 2. During the explosive detonation process, the detonator only acts as an initiator; the amount of explosive inside the detonator is minimal, and personnel and equipment are outside the blasting safety distance. Therefore, the surface detonator detonation meets the blasting safety requirements. Figure 4 As shown, the image similarity analysis and processing at the zero moment of blast initiation are as follows:

[0078] S1: Images of the moment a surface detonator detonates, captured using a high-speed camera:

[0079] A high-speed camera was used to record images of the moment the detonator detonated. The high-speed camera recorded images from the moment the blasting command was issued until the moment the detonator exploded on the ground, producing a large area of ​​bright light. The high-speed camera needed to capture one image every millisecond within this time range, with each image having a uniform time interval of 1ms.

[0080] In this embodiment, the high-speed camera is installed and deployed in a target area of ​​an open-pit coal mine (considering that during this blasting operation, the target area is beyond the personnel safety warning range from the blasting zone, thus meeting safety warning requirements). Following the high-speed camera installation rules, the tripod's horizontal adjustment strategy, the mobile power supply mode, and the debugging strategies for equipment exposure time, focal length, frame rate, resolution, and image window, the entire acquisition system is installed, debugged, and verified. The information sensing device in this embodiment can be a Thousand-Eye Wolf X150, with a resolution of 2560×1920, a frame rate of 1600FPS, a color ISO7000 sensitivity, and a trigger delay accuracy of <1µm.

[0081] S2: Standardize the images acquired in S1 (modify storage format, rename images, and perform grayscale processing):

[0082] S201: Use the Thousand-Eyed Wolf X150 high-speed camera to capture photos of the initial blasting process and save the blasting images as Windows bitmap files (BMP).

[0083] In this embodiment, the image saving format is BMP file format because BMP file format is a lossless and uncompressed image storage method. Each pixel in the image represents the color information of the image through a pixel value. This storage format supports both indexed color and direct color computer image formats, providing support for image similarity analysis algorithms.

[0084] S202: Image renaming refers to the fact that the images acquired by high-speed cameras have specific file name formats. In order to achieve the fastest possible image indexing and recognition, the images need to be renamed before image similarity comparison.

[0085] S203: Image grayscale processing refers to the grayscale processing operation performed on all images captured by a high-speed camera in order to reduce the comparison parameters of the image similarity algorithm and improve the algorithm's computational efficiency.

[0086] S3: Mark the locations of detonators on the ground in the image and set the square area for image processing;

[0087] The high-speed camera captures a large area, including the detonator deployment area on the ground and the background area. To effectively preserve the core information of the image and improve the calculation accuracy and effectiveness of the image similarity algorithm, the detonator deployment area in the image, i.e., the image similarity algorithm processing area, is reasonably labeled. In this image set, the pixel position of the detonator explosion emission is manually found and marked. A 200×200 pixel square area centered on this pixel is designated as the algorithm processing area. The format of the center position coordinates is (number 1, number 2), where number 1 ranges from 1 to 2560, and number 2 ranges from 1 to 1920. In this embodiment, the center coordinates of the square area can be (215, 1100), thus determining the image similarity algorithm calculation area as a 200×200 pixel square area centered on the (215, 1100) pixel in the image.

[0088] S4: Calculate the Hamming distance between images using the perceptual hashing algorithm (perceptual hashing algorithm processing);

[0089] The perceptual hash algorithm is an algorithm used to determine image similarity. Its function is to generate a "fingerprint" string for each image of a bomb-prone area. The more similar the images, the smaller the difference in their fingerprint strings. Therefore, the similarity of images is determined by comparing the "fingerprint" information of different bomb-prone area images. In this embodiment, the perceptual hash algorithm is used to calculate the "fingerprint" string for a square region in each image recorded by the high-speed camera. The square region is the 200×200 pixel square region centered at pixel (215, 1100) defined in S3. The specific steps are as follows:

[0090] S401: Two-dimensional Discrete Cosine Transform (DCT) yields a 200×200 DCT transform;

[0091] S402: Compress the DCT result obtained in S401 (DCT compression);

[0092] After the image undergoes Discrete Cosine Transform (DCT), the energy of the DCT coefficients is mainly concentrated in the upper left corner. The DCT matrix represents coefficients with increasingly higher frequencies from the upper left to the lower right corner. In this embodiment, the 200×200 DCT coefficient matrix in S401 is replaced by the 32×32 DCT coefficient matrix in the upper left corner, which is the result of the compressed DCT transform.

[0093] S403: Perform binary threshold operation on DCT (average DCT coefficients and DCT threshold operation);

[0094] Calculate the average value of multiple DCT coefficients in the 32×32 compressed DCT coefficient matrix in S402, and then perform a threshold operation on the 32×32 DCT coefficient matrix. In this embodiment, DCT coefficients less than the average value are assigned a value of 0, and DCT coefficients greater than or equal to the average value are assigned a value of 1.

[0095] S404: Obtain the hash string for each image. This hash string is a one-dimensional binary sequence of length 1024 obtained by flattening the 32×32 coefficient matrix into a one-dimensional array in the order from left to right and from top to bottom.

[0096] The perceptual hashing algorithm process diagram includes the original image (first image and second image), frequency domain diagram, 200×200 square region, 200×200 DCT image and 32×32 DCT image.

[0097] S405: Calculate the Hamming distance between the hash strings of the images. Perform an XOR operation on each bit of the two hash strings to obtain the number of 1s in the string, which is the Hamming distance between the images.

[0098] S5: Calculate the L2 norm between two images (L2 norm algorithm processing);

[0099] L p The norm is also called the Euclidean norm. The L2 norm is the square root of the sum of the squares of all elements in a vector. In comparing the similarity of two images, the vector represents the pixel values ​​of the two images.

[0100] The L2 norm is the square root of the sum of squares of the pixel differences between two images, and is a standard Euclidean metric. When the norm p = 2, the L2 norm is defined as shown in formula (6) above.

[0101] Right now

[0102] In this embodiment, the similarity between two images can be obtained by calculating the L2 norm (the L2 norm value between images). The smaller the norm difference, the more similar the two images are; the larger the norm difference, the greater the difference between the two images.

[0103] S6: Set the perceptual hashing algorithm and L2 norm threshold respectively, and compare whether the Hamming distance between images exceeds the threshold? And compare whether the L2 norm value between images exceeds the threshold?

[0104] The perceptual hash algorithm and the L2 norm thresholds are set to differentiate between two images (comparing the previous and next images). Considering that open-pit mines are open blasting environments, they are easily affected by light, dust, and wind speed during the high-speed camera acquisition phase, leading to pixel changes in the images. Therefore, the perceptual hash algorithm threshold (first threshold) and the L2 norm threshold (second threshold) settings need to be adjusted and corrected according to the on-site acquisition conditions. In this embodiment, the perceptual hash algorithm threshold (first threshold) is set to 100, and the L2 norm threshold (second threshold) is set to 750.

[0105] S7: Obtain the image at the zero moment of the blast initiation;

[0106] Each image is compared with the previous image, and the Hamming distance between the hash strings of the two images is obtained using a perceptual hashing algorithm; using L... p The norm formula calculates the L2 norm difference between two images (p = 2). If either the perceptual hash algorithm or the L2 norm difference between the two images does not exceed a set threshold, then the brute-force attack is considered to have not occurred.

[0107] Wherein, norm L p Calculate the pixel-level difference between two images ||xx′|| p L p Norm formula (7) is: Here, p represents the norm, and p∈{0,1,2,∞}. When p=1, it is the L1 norm, also known as the Manhattan distance; when p=2, it is the L2 norm, also known as the Euclidean distance; when p=∞, it is the L1 norm. ∞ Norm, also known as Chebyshev distance.

[0108] In other words, the following three cases are considered as the explosion instant not occurring: the Hamming distance between two images exceeds the threshold of the perceptual hash algorithm but the L2 norm difference does not exceed the L2 norm threshold; the Hamming distance between two images does not exceed the threshold of the perceptual hash algorithm but the L2 norm difference exceeds the L2 norm threshold; and the Hamming distance between two images does not exceed the threshold of the perceptual hash algorithm and the L2 norm difference does not exceed the L2 norm threshold.

[0109] If the results of the perceptual hash algorithm and the L2 norm comparison of the two images both exceed the set threshold, then the image that occurred earlier in the current two images is considered to be the image at the moment the explosion occurred (i.e., the image at the zero moment of the explosion initiation is determined).

[0110] This application also provides a device for determining the zero moment of detonation. It should be noted that this device can be used to execute the method for determining the zero moment of detonation provided in this application. This device is used to implement the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0111] The following describes the device for determining the zero moment of detonation of a detonator provided in the embodiments of this application.

[0112] Figure 5 This is a schematic diagram of a device for determining the zero moment of detonation of a detonator according to an embodiment of this application. Figure 5 As shown, the device includes:

[0113] The sending module 10 is used to send a blasting command to the detonating bomb in the target area so that the detonating bomb can be detonated. The detonating bomb is used to detonate the detonator.

[0114] Specifically, this application applies to detonator deployment schemes where detonators and detonating shells are connected in parallel. For example, one detonator deployment scheme of this application is as follows: a surface detonating detonator (hereinafter referred to as detonator) is added outside the pre-splitting blast hole, and the detonator and the explosive detonating shell inside the blast hole (hereinafter referred to as detonating shell) are connected in parallel. When a blasting command is sent to the detonating shell, the explosion of the detonating shell will trigger the detonator connected in parallel to detonate and produce a flash.

[0115] The detonator contains very little explosive. During the detonation process, the detonator only serves to detonate the explosive. Moreover, personnel and equipment are kept at a safe blasting distance. Therefore, the detonator detonation meets the blasting safety requirements.

[0116] The first acquisition module 20 is used to acquire multiple images during the detonation process of the detonator. The multiple images have multiple first target pixel data that correspond one-to-one. The multiple images are obtained by shooting the target area within a first time period. The start time of the first time period is the time when the detonation command is issued, and the end time of the first time period is the time when the detonator explodes and produces a light of preset brightness. Each image includes a detonator image, which is obtained by shooting the detonator. The first target pixel data includes the detonator pixel data corresponding to the detonator image in the image.

[0117] Specifically, an image acquisition system can be used to photograph the target area where the aforementioned detonators are deployed. The first time period for the image acquisition system to capture images is from the moment the detonation command is issued until the moment the detonator explodes and produces a large area of ​​bright light; this large area of ​​bright light can be a preset brightness set according to operational needs. Optionally, the image acquisition system can capture one image every millisecond, meaning that the time interval between any two adjacent images captured within the first time period can be 1 ms.

[0118] Optionally, to accurately capture the instant of blasting initiation, the image acquisition system can mainly consist of four parts: an information sensing end, a device mounting end, a device power supply end, and a parameter setting end (information output end). For example, the information sensing end can be a high-speed camera, the mounting end of the device acquisition end can be a tripod, the power supply end of the acquisition end can be a portable power bank, and the parameter setting end (information output end) of the acquisition end can be a laptop.

[0119] The first determining module 30 is used to determine the Hamming distance between the first image and the second image based on the perceptual hash algorithm, the first pixel data of the first image, and the second pixel data of the second image. The first image and the second image are any two of a plurality of images, and the first image is an image captured at the first moment in the first time period, and the second image is an image captured at the second moment in the first time period. The first moment occurs before the second moment, and the plurality of first target pixel data includes the first pixel data and the second pixel data.

[0120] For multiple images, the first image captured at a first time moment and the second image captured at a second time moment, the first time moment occurs before the second time moment. The perceptual hash algorithm is an algorithm used to determine image similarity; its function is to assess the similarity of images by comparing image information from different images. Therefore, in the first aspect, to perceive the image similarity between the first image and the second image, this application uses the perceptual hash algorithm to calculate the Hamming distance between the first image and the second image. This Hamming distance is used to characterize the image similarity between the first image and the second image.

[0121] The second determining module 40 is used to determine the Euclidean norm between the first image and the second image based on the first pixel data, the second pixel data and the first preset relational formula;

[0122] On the other hand, in order to perceive the image similarity between the first image and the second image, this application also employs a first preset relational formula for calculating the Euclidean norm between the first image and the second image. This Euclidean norm is used to characterize the image similarity between the first image and the second image. The smaller the Euclidean norm, the more similar the two images are; the larger the Euclidean norm, the greater the difference between the two images.

[0123] The second acquisition module 50 is used to determine the target image based on the Hamming distance, Euclidean norm and preset threshold, and to acquire the target shooting time corresponding to the target image. The target shooting time is used to characterize the zero moment of detonation of the detonator.

[0124] Specifically, Hamming distance and Euclidean norm can both be used to analyze the image similarity between two images. To control the error range of the analysis results for analyzing the image similarity between two images to the millisecond level, this application sets a preset threshold, thereby controlling the error range of the analysis results for analyzing the image similarity between two images to the millisecond level. Optionally, the device for determining the zero moment of detonation further includes: a third acquisition module, used to acquire the third pixel data of the first detonator deployment area in the first image and the fourth pixel data of the second detonator deployment area in the second image, wherein the first detonator deployment area includes the detonator image in the first image, the second detonator deployment area includes the detonator image in the second image, the first pixel data includes the third pixel data, and the second pixel data includes the fourth pixel data; and an update module, used to update the first pixel data to the third pixel data and update the second pixel data to the fourth pixel data.

[0125] Optionally, the first acquisition module includes: a first acquisition unit for acquiring multiple blasting images, each blasting image having an initial filename and an initial file extension; a first update unit for updating the initial file extension of the blasting images to a target file extension, so that the blasting images have a target storage format; a second update unit for updating the initial filename of the blasting images to the target filename, so that the multiple blasting images are sorted according to a preset rule; and a first processing unit for performing image grayscale processing on the multiple blasting images having target file extensions and target filenames to obtain multiple images.

[0126] Optionally, multiple pixel coordinates constitute the first pixel data and the second pixel data. The third acquisition module includes: a second acquisition unit, used to acquire the first detonator pixel coordinates and the second detonator pixel coordinates, wherein the first detonator pixel coordinates are the pixel coordinates corresponding to the detonator image in the first image, and the second detonator pixel coordinates are the pixel coordinates corresponding to the detonator image in the second image; and a first determination unit, used to determine multiple first target horizontal coordinate values ​​and multiple first target vertical coordinate values ​​based on the first detonator pixel coordinates, a preset pixel width, and a preset pixel height, wherein the multiple first target horizontal coordinate values ​​are multiple horizontal coordinate values ​​corresponding one-to-one with the multiple pixel coordinates of the preset pixel width, and the multiple first target vertical coordinate values ​​are multiple pixel coordinates corresponding one-to-one with the multiple pixel coordinates of the preset pixel height. The first target has multiple ordinate values, and the multiple first target abscissa values ​​include the abscissa value of the first detonator pixel coordinate, and the multiple first target ordinate values ​​include the ordinate value of the first detonator pixel coordinate; the second determining unit is used to determine multiple second target abscissa values ​​and multiple second target ordinate values ​​based on the second detonator pixel coordinate, the preset pixel width, and the preset pixel height, wherein the multiple second target abscissa values ​​are multiple abscissa values ​​corresponding one-to-one with the multiple pixel coordinates of the preset pixel width, and the multiple second target ordinate values ​​are multiple ordinate values ​​corresponding one-to-one with the multiple pixel coordinates of the preset pixel height, wherein the multiple second target abscissa values ​​include the abscissa value of the second detonator pixel coordinate, and the multiple second target ordinate values ​​include the ordinate value of the second detonator pixel coordinate.

[0127] Optionally, the first determining module includes: a second processing unit, configured to perform a discrete cosine transform on the first image based on the first pixel data to obtain a first coefficient matrix, wherein the first coefficient matrix has multiple first coefficients; a third processing unit, configured to compress the first coefficient matrix to obtain a first target coefficient matrix, wherein the first target coefficient matrix includes multiple first target coefficients, and the first target coefficient matrix is ​​a part of the first coefficient matrix; a third obtaining unit, configured to obtain a first average value of the multiple first target coefficients in the first target coefficient matrix; a third determining unit, configured to determine a first target coefficient in the first target coefficient matrix that is less than the first average value as a first set value, and determine a first target coefficient in the first target coefficient matrix that is greater than or equal to the first average value as a second set value, wherein the first set value and the second set value are different, and the first set value and the second set value constitute a first hash string of the first image; and obtain a second hash string of the second image; and a fourth processing unit, configured to perform an XOR operation on the first hash string and the second hash string to obtain a target string, and determine the number of second set values ​​in the target string as the Hamming distance.

[0128] Optionally, the second determining module includes: a first generating unit, configured to generate a first pixel value of a first image based on the first pixel data; a second generating unit, configured to generate a second pixel value of a second image based on the second pixel data; and a fourth determining unit, configured to determine the Euclidean norm based on the first pixel value, the second pixel value, and a first preset relational expression.

[0129] Optionally, the preset threshold includes a first threshold and a second threshold. The second acquisition module includes: a first judgment unit, used to judge whether the Hamming distance is greater than the first threshold and obtain a first judgment result; a second judgment unit, used to judge whether the Euclidean norm is greater than the second threshold and obtain a second judgment result; and a fifth determination unit, used to determine the second image as the target image when both the first judgment result and the second judgment result indicate yes.

[0130] The device for determining the zero moment of detonation includes a processor and a memory. The aforementioned sending module 10, first acquisition module 20, first determination module 30, second determination module 40, and second acquisition module 50 are all stored as program units in the memory. The processor executes these program units stored in the memory to achieve their respective functions. All of the above modules are located in the same processor; alternatively, the modules may be located in different processors in any combination.

[0131] The processor contains a kernel, which retrieves the corresponding program units from memory. One or more kernels can be configured, and adjusting kernel parameters can at least address the issue of premature or delayed detonation at the zero moment of explosive detonation, a problem already identified in existing technologies.

[0132] The memory may include non-permanent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0133] This invention provides a computer-readable storage medium including a stored program, wherein the program, when running, controls the device containing the computer-readable storage medium to execute the method for determining the zero moment of detonation of a detonator.

[0134] This invention provides an electronic device, which includes a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, it implements the steps of a method for determining the zero moment of detonation of a detonator according to at least any of the above embodiments. The device described herein may be a server, PC, PAD, mobile phone, etc.

[0135] This application also provides a computer program product, which, when executed on a data processing device, is adapted to execute a program that initializes the method steps for determining the zero moment of detonation of a detonator according to any of the above embodiments:

[0136] It is obvious to those skilled in the art that the modules or steps of the present invention described above can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. They can be implemented using computer-executable program code, and thus can be stored in a storage device for execution by a computing device. In some cases, the steps shown or described can be performed in a different order than those described herein, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, the present invention is not limited to any particular combination of hardware and software.

[0137] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0138] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0139] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0140] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0141] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0142] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0143] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0144] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0145] As can be seen from the above description, the embodiments of this application achieve the following technical effects:

[0146] This application, after setting the detonating bomb to detonate the detonator, obtains the Hamming distance between the first and second images taken at different times and the Euclidean norm between the first and second images taken at different times. It achieves the purpose of analyzing the image similarity between the first and second images based on the image similarity analysis method combining the Hamming distance and the Euclidean norm, thereby making the error range of the analysis results controllable at the millisecond level. This greatly improves the accuracy of determining the zero moment of detonator detonation and solves the technical problem of the zero moment of detonator detonation being ahead or behind in the prior art.

[0147] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A method for determining the zero moment of detonation of a detonator, characterized in that, The determination method includes: A detonation command is sent to a detonating bomb in the target area to detonate the detonating bomb, which is used to detonate the detonator. Multiple images are acquired during the detonation process of the detonator. Each image has a one-to-one correspondence with multiple first target pixel data. The multiple images are obtained by photographing the target area within a first time period. The start time of the first time period is the time when the detonation command is issued, and the end time of the first time period is the time when the detonator explodes and produces a light of a preset brightness. Each image includes a detonator image, which is obtained by photographing the detonator. The first target pixel data includes the detonator pixel data corresponding to the detonator image in the image. Based on the perceptual hash algorithm, the first pixel data of the first image, and the second pixel data of the second image, the Hamming distance between the first image and the second image is determined. The first image and the second image are any two of the plurality of images. The first image is an image captured at a first moment in the first time period, and the second image is an image captured at a second moment in the first time period. The first moment occurs before the second moment. The plurality of first target pixel data includes the first pixel data and the second pixel data. Based on the first pixel data, the second pixel data, and the first preset relation, determine the Euclidean norm between the first image and the second image; The target image is determined based on the Hamming distance, the Euclidean norm, and the preset threshold. The target shooting time corresponding to the target image is used to characterize the zero moment of detonation of the detonator.

2. The determination method according to claim 1, characterized in that, Also includes: Obtain the third pixel data of the first detonator deployment area in the first image and the fourth pixel data of the second detonator deployment area in the second image. The first detonator deployment area includes the detonator image in the first image, and the second detonator deployment area includes the detonator image in the second image. The first pixel data includes the third pixel data, and the second pixel data includes the fourth pixel data. The first pixel data is updated to the third pixel data, and the second pixel data is updated to the fourth pixel data.

3. The determination method according to claim 1, characterized in that, Acquiring multiple images during the detonator initiation process includes: Acquire multiple explosion images, each image having an initial filename and an initial file extension; Update the initial suffix of the blasting image to the target suffix so that the blasting image has the target storage format; The initial filename of the explosion image is updated to the target filename so that the multiple explosion images are sorted according to a preset rule; Multiple explosion images having the target suffix and the target file name are subjected to image grayscale processing to obtain multiple images.

4. The determination method according to claim 2, characterized in that, Multiple pixel coordinates constitute the first pixel data and the second pixel data. Obtaining the third pixel data of the first detonator deployment area in the first image and the fourth pixel data of the second detonator deployment area in the second image includes: Obtain the pixel coordinates of the first detonator and the pixel coordinates of the second detonator, wherein the first detonator pixel coordinates are the pixel coordinates corresponding to the detonator image in the first image, and the second detonator pixel coordinates are the pixel coordinates corresponding to the detonator image in the second image; Based on the first detonator pixel coordinates, the preset pixel width, and the preset pixel height, multiple first target abscissa values ​​and multiple first target ordinate values ​​are determined. The multiple first target abscissa values ​​are multiple abscissa values ​​that correspond one-to-one with the multiple pixel coordinates of the preset pixel width. The multiple first target ordinate values ​​are multiple ordinate values ​​that correspond one-to-one with the multiple pixel coordinates of the preset pixel height. The multiple first target abscissa values ​​include the abscissa values ​​of the first detonator pixel coordinates. The multiple first target ordinate values ​​include the ordinate values ​​of the first detonator pixel coordinates. Based on the second detonator pixel coordinates, the preset pixel width, and the preset pixel height, multiple second target abscissa values ​​and multiple second target ordinate values ​​are determined. The multiple second target abscissa values ​​are multiple abscissa values ​​that correspond one-to-one with the multiple pixel coordinates of the preset pixel width, and the multiple second target ordinate values ​​are multiple ordinate values ​​that correspond one-to-one with the multiple pixel coordinates of the preset pixel height. The multiple second target abscissa values ​​include the abscissa values ​​of the second detonator pixel coordinates, and the multiple second target ordinate values ​​include the ordinate values ​​of the second detonator pixel coordinates.

5. The determination method according to any one of claims 1 to 4, characterized in that, Based on the perceptual hash algorithm, the first pixel data of the first image, and the second pixel data of the second image, the Hamming distance between the first image and the second image is determined, including: The first image is subjected to discrete cosine transform based on the first pixel data to obtain a first coefficient matrix, wherein the first coefficient matrix has multiple first coefficients; The first coefficient matrix is ​​compressed to obtain a first target coefficient matrix, which includes multiple first target coefficients and is a part of the first coefficient matrix. Obtain the first average value of multiple first target coefficients in the first target coefficient matrix; The first target coefficients in the first target coefficient matrix that are less than the first average value are determined as first set values, and the first target coefficients in the first target coefficient matrix that are greater than or equal to the first average value are determined as second set values. The first set values ​​and the second set values ​​are different, and the first set values ​​and the second set values ​​constitute the first hash string of the first image. Obtain the second hash string of the second image; Perform an XOR operation on the first hash string and the second hash string to obtain the target string, and determine the number of the second set values ​​in the target string as the Hamming distance.

6. The determining method according to any one of claims 1 to 4, characterized in that, Based on the first pixel data, the second pixel data, and the first preset relation, the Euclidean norm between the first image and the second image is determined, including: Generate the first pixel value of the first image based on the first pixel data; Based on the second pixel data, generate the second pixel value of the second image; The Euclidean norm is determined based on the first pixel value, the second pixel value, and the first preset relation.

7. The determining method according to any one of claims 1 to 4, characterized in that, The preset threshold includes a first threshold and a second threshold. The target image is determined based on the Hamming distance, the Euclidean norm, and the preset threshold, including: Determine whether the Hamming distance is greater than the first threshold to obtain a first determination result; Determine whether the Euclidean norm is greater than the second threshold to obtain a second determination result; If both the first and second judgment results indicate yes, the second image is determined to be the target image.

8. A device for determining the zero moment of detonation of a detonator, characterized in that, The determining device includes: A sending module is used to send a blasting command to a detonating bomb in a target area to detonate the detonating bomb, which is used to detonate the detonator. The first acquisition module is used to acquire multiple images during the detonation process of the detonator. The multiple images have multiple first target pixel data that correspond one-to-one. The multiple images are obtained by shooting the target area within a first time period. The start time of the first time period is the time when the detonation command is issued, and the end time of the first time period is the time when the detonator explodes and produces a light of a preset brightness. Each image includes a detonator image, which is obtained by shooting the detonator. The first target pixel data includes detonator pixel data corresponding to the detonator image in the image. The first determining module is used to determine the Hamming distance between the first image and the second image based on the perceptual hash algorithm, the first pixel data of the first image, and the second pixel data of the second image. The first image and the second image are any two of the plurality of images, and the first image is an image captured at a first moment in the first time period, and the second image is an image captured at a second moment in the first time period. The first moment occurs before the second moment, and the plurality of first target pixel data includes the first pixel data and the second pixel data. The second determining module is used to determine the Euclidean norm between the first image and the second image based on the first pixel data, the second pixel data and the first preset relational formula; The second acquisition module is used to determine the target image based on the Hamming distance, the Euclidean norm, and a preset threshold. The target shooting time corresponding to the target image is used to characterize the zero moment of detonation of the detonator.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored program, wherein, when the program is executed, it controls the device containing the computer-readable storage medium to perform the method for determining the zero moment of detonation of a detonator as described in any one of claims 1 to 7.

10. An electronic device, characterized in that, include: One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including a method for determining the zero moment of detonation of a detonator as described in any one of claims 1 to 7.

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