Foreign matter detection method and device for iron ore transportation, electronic equipment and storage medium

Abstract

The invention provides a foreign matter detection method and device for iron ore transportation, electronic equipment and a storage medium, and belongs to the technical field of iron ore production detection.The method comprises the steps that a target video in the iron ore transportation process is obtained, the target video is preprocessed, and a preprocessed image is obtained; and performing feature extraction on the preprocessed image based on a deep learning model to obtain a reconstructed image, calculating the reconstructed image based on an abnormal possibility algorithm to obtain a calculation result, and matching the calculation result with a preset threshold to obtain a foreign matter detection result in the iron ore transportation process. The technical problems that in the prior art, the labor cost is high, the foreign matter detection speed is low, and the accuracy rate is not high are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of iron ore production detection, and in particular to a foreign matter detection method, device, electronic equipment and storage medium for iron ore transportation. Background Art

[0002] Various types of metal production waste such as anchor rods and drill bits are easily mixed into the iron ore conveyor belt. When these metal wastes are transported to the crushing line along with the ore, they will cause equipment damage and even production safety accidents to the crushing line equipment.

[0003] In iron ore transportation, the traditional impurity removal method is to use iron removal equipment including electromagnetic iron removers and permanent magnetic iron removers to absorb metal impurities. However, the iron remover cannot intelligently identify possible metal drops and cannot effectively remove large pieces of metal passing through the belt, which can easily cause longitudinal tearing of the conveyor belt.

[0004] In view of the current iron ore transportation environment, the main metal minerals in the ore deposits are magnetite, followed by pyrite and pyrrhotite. In daily production, high-grade ores are easily mixed and adsorbed, while manganese steel parts with higher rigidity have weak magnetism and are not easy to adsorb. The current iron removal equipment cannot accurately handle various foreign objects and ironware on the transport belt. The existing iron ore production foreign matter removal process mainly relies on manual inspection, and manual inspection has time delays and cannot promptly detect major dangerous goods and transmit alarms. These dangerous goods may cause accidents such as belt tearing, deviation, damage, jamming, or even damage to the crusher during transportation. Therefore, there are technical problems in the existing technology such as high labor costs, low foreign matter detection speed, and low accuracy. Summary of the invention

[0005] In view of this, it is necessary to provide a foreign body detection method, device, electronic equipment and storage medium for iron ore transportation to solve the technical problems existing in the prior art of high labor cost, low foreign body detection speed and low accuracy.

[0006] In order to solve the above technical problems, on the one hand, the present invention provides a foreign body detection method for iron ore transportation, comprising: Get targeted video of iron ore transportation process; Preprocessing the target video to obtain a preprocessed image; Performing feature extraction on the preprocessed image based on a deep learning model to obtain a reconstructed image; Calculating the reconstructed image based on an abnormality possibility algorithm to obtain a calculation result; The calculation result is matched with a preset threshold value to obtain a foreign body detection result in the iron ore transportation process.

[0007] In a possible implementation manner, preprocessing the target video to obtain a preprocessed image includes: The target video is preprocessed by frame extraction, size adjustment and AI image illumination enhancement to obtain a preprocessed image.

[0008] In a possible implementation, the deep learning model includes: an encoder, a memory module, and a decoder connected in series in sequence; An encoder, used for extracting features from the preprocessed image to obtain a feature image; A memory module, used for obtaining overlapping features based on the feature image and the preprocessed image; The decoder is used to reconstruct the coincident features to obtain a reconstructed image.

[0009] In a possible implementation, extracting features from the preprocessed image to obtain a feature image includes: Performing spatiotemporal feature extraction on the preprocessed image to obtain contextual global information of the preprocessed image, and obtaining a feature image based on the contextual global information of the preprocessed image.

[0010] In a possible implementation manner, obtaining the coincident features based on the feature image and the preprocessed image includes: Perform feature comparison between the feature image and the preprocessed image to obtain overlapping features.

[0011] In a possible implementation, the calculating the reconstructed image based on the abnormality possibility algorithm to obtain the calculation result includes: A mean square error is calculated for the reconstructed image based on an abnormality possibility algorithm to obtain a calculation result.

[0012] In a possible implementation, matching the calculation result with a preset threshold to obtain a foreign body detection result in the iron ore transportation process includes: The calculation result is matched with a preset threshold value, and when the calculation result is greater than the preset threshold value, a detection result indicating the presence of foreign matter in the iron ore transportation process is obtained.

[0013] On the other hand, the present invention also provides a foreign body detection device for iron ore transportation, comprising: An acquisition module is used to acquire target videos during the iron ore transportation process; A preprocessing module, used for preprocessing the target video to obtain a preprocessed image; A feature extraction module, used to extract features from the preprocessed image based on a deep learning model to obtain a reconstructed image; A calculation module, used to calculate the reconstructed image based on an abnormality possibility algorithm to obtain a calculation result; The matching module is used to match the calculation result with a preset threshold value to obtain the foreign matter detection result of the iron ore transportation process.

[0014] On the other hand, the present invention also provides an electronic device, including a memory and a processor, wherein: The memory is used to store programs; The processor is coupled to the memory and is used to execute the program stored in the memory to implement the steps in the foreign body detection method for iron ore transportation described in any of the above implementations.

[0015] On the other hand, the present invention also provides a computer-readable storage medium for storing a computer-readable program or instruction, which, when executed by a processor, can implement the steps of a foreign body detection method for iron ore transportation described in any of the above-mentioned implementations.

[0016] The beneficial effect of the present invention is as follows: a foreign body detection method for iron ore transportation provided by the present invention comprises: obtaining a target video during the iron ore transportation process, the purpose of which is to obtain a video of the normal operation of the iron ore transportation belt; preprocessing the target video to obtain a preprocessed image, the purpose of which is to screen foreign bodies based on the preprocessed image to improve the speed and accuracy of foreign body detection; extracting features from the preprocessed image based on a deep learning model to obtain a reconstructed image, the purpose of which is to reduce the cost of manual screening, and extracting features from the preprocessed image based on a deep learning model to avoid losses to belts, crushers or other mechanical equipment caused by foreign bodies during transportation, thereby reducing equipment maintenance costs and improving the safety control factor of factory production lines; calculating the reconstructed image based on an abnormality probability algorithm to obtain a calculation result, matching the calculation result with a preset threshold value to obtain a foreign body detection result in the iron ore transportation process, the purpose of which is to perform calculations and matching based on the reconstructed image to obtain a foreign body detection result in the iron ore transportation process, thereby reducing manual intervention. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0018] Figure 1 A schematic flow chart of an embodiment of a method for detecting foreign matter in iron ore transportation provided by the present invention; Figure 2 An algorithm flow chart of an embodiment of a foreign body detection method for iron ore transportation provided by the present invention; Figure 3 A schematic diagram of the AI ​​image illumination enhancement auxiliary technology of an embodiment of the foreign body detection method for iron ore transportation provided by the present invention; Figure 4 A deep learning model structure diagram of an embodiment of the foreign body detection method for iron ore transportation provided by the present invention; Figure 5 An alarm principle diagram of an embodiment of a foreign body detection method for iron ore transportation provided by the present invention; Figure 6 A model prediction line chart of an embodiment of the foreign body detection method for iron ore transportation provided by the present invention; Figure 7 A schematic structural diagram of an embodiment of a foreign body detection device for iron ore transportation provided by the present invention; Figure 8 A schematic structural diagram of an embodiment of an electronic device provided by the present invention. DETAILED DESCRIPTION

[0019] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0020] In the description of the embodiments of the present application, unless otherwise specified, “plurality” means two or more than two.

[0021] The terms "including" and "having" and any variations thereof in the embodiments of the present invention are intended to cover non-exclusive inclusions. For example, a process, method, apparatus, product or equipment comprising a series of steps or modules is not necessarily limited to those steps or modules clearly listed, but may include other steps or modules not clearly listed or inherent to these processes, methods, products or equipment.

[0022] The naming or numbering of the steps in the embodiments of the present invention does not mean that the steps in the method flow must be executed in the time / logical sequence indicated by the naming or numbering. The execution order of the named or numbered process steps can be changed according to the technical purpose to be achieved, as long as the same or similar technical effects can be achieved.

[0023] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0024] The present invention provides a foreign matter detection method, device, electronic equipment and storage medium for iron ore transportation, which are respectively described below.

[0025] Figure 1 A schematic diagram of an embodiment of a foreign body detection method for iron ore transportation provided by the present invention and Figure 2 An algorithm flow chart of an embodiment of a foreign body detection method for iron ore transportation provided by the present invention includes: S101, obtaining a target video during the iron ore transportation process; S102, preprocessing the target video to obtain a preprocessed image; S103, extracting features from the preprocessed image based on a deep learning model to obtain a reconstructed image; S104, calculating the reconstructed image based on an abnormality possibility algorithm to obtain a calculation result; S105, matching the calculation result with a preset threshold value to obtain a foreign body detection result in the iron ore transportation process.

[0026] It can be understood that in step S101, a video of the iron ore transportation process in an industrial environment is obtained to establish a positive and negative sample library.

[0027] In step S102, the target video is subjected to frame extraction, size adjustment, AI (Artificial Intelligence) image illumination enhancement auxiliary technology and other operations to obtain an enhanced pre-processed image.

[0028] In step S103, a pre-trained deep learning model is used to perform foreign body recognition on the pre-processed image to obtain a reconstructed image.

[0029] In step S104, the probability value of being abnormal is obtained by an abnormality probability algorithm.

[0030] In step S105, images with a probability greater than a set threshold are defined as abnormal images.

[0031] Finally, after being processed by the software platform, the abnormal size is obtained to determine whether the size of the abnormal item meets the regulations, and different processing methods are selected accordingly.

[0032] It can be further understood that the present invention has the following technical effects: (1) Using the above technical solution, only a video of the normal operation of the iron ore conveyor belt needs to be collected as the positive sample data set, and only a small number of negative samples are needed to meet the normal training requirements of the model.

[0033] (2) Through deep learning model learning, it can realize real-time detection and identification of various foreign objects in iron ore transportation, avoid damage to belts, crushers or other mechanical equipment caused by foreign objects during transportation, reduce equipment maintenance costs and improve the safety control factor of the factory production line.

[0034] (3) The use of an automatic foreign object recognition system based on an AI model reduces the number of manual interventions and reduces personnel safety risks.

[0035] Figure 3 The AI ​​image illumination enhancement auxiliary technology principle diagram of an embodiment of the foreign body detection method for iron ore transportation provided by the present invention includes: The target video is preprocessed by frame extraction, size adjustment and AI image illumination enhancement to obtain a preprocessed image.

[0036] It can be understood that the steps of preprocessing are as follows: (1) Frame extraction: The video data is obtained as a group of 16 adjacent frames.

[0037] (2) Resizing: The input image is converted into a grayscale image using OpenCV (Open Source Computer Vision Library) and converted to a size of 65536 (256x256) pixels.

[0038] (3) AI image illumination enhancement auxiliary technology: A series of virtual images with different exposures are obtained through the camera response function, and the input single image is fused with the virtual image at multiple scales to achieve detail enhancement and noise suppression. The schematic diagram of this technology is shown in the figure. Figure 3 shown.

[0039] Figure 4 A deep learning model structure diagram of an embodiment of the foreign body detection method for iron ore transportation provided by the present invention, Figure 5 The alarm principle diagram of an embodiment of the foreign body detection method for iron ore transportation provided by the present invention and Figure 6 The model prediction line graph of an embodiment of the foreign body detection method for iron ore transportation provided by the present invention includes: An encoder, a memory module and a decoder connected in series in sequence; An encoder, used for extracting features from the preprocessed image to obtain a feature image; A memory module, used to obtain overlapping features based on the feature image and the preprocessed image; The decoder is used to reconstruct the overlapping features to obtain a reconstructed image.

[0040] It can be understood that the data processing process of the deep learning model is as follows: (1) Training: Input positive image samples through the spatiotemporal feature extraction module to obtain compressed images and original images on the time axis, and then map them to low-dimensional space through multiple convolutions. Input the memory network module to learn the feature representation set of normal images. The combination of the learned normal image feature set is used as the input of the multiple convolution decoder to reconstruct the image. The mean square error is added with the similarity loss between adjacent frames as the model optimization objective function. The objective function is defined as follows:

[0041] in, , It can be regarded as the overall parameters of the encoder and decoder. is the overall characteristic representation of the memory module, Refers to the input Frame pictures, For the reconstruction Frame pictures, The hyperparameters are set empirically. For the The frame image is expressed as a combination of features in the memory module.

[0042] (2) Prediction: After the input image has gone through the above process, only the mean square error is used as the abnormality judgment score. The abnormality probability value of the input image is obtained through the abnormality scoring algorithm. The formula is as follows: .

[0043] It should be further explained that this application uses a software platform to obtain the size data of abnormal objects based on the detection results of foreign objects in the iron ore transportation process, and judge whether an alarm is needed. If the size is compliant, the platform sends a "yellow warning" command and sends a "foreign object removal" command to the PLC joint box to deal with the foreign object. If the size is not compliant, the platform sends a "red warning" command and sends a picture and location of the foreign object to the industrial tablet, allowing on-site staff to handle large foreign objects. Specifically, Figure 5 shown.

[0044] It needs further explanation that the training process of the deep learning model is as follows: The parameters in Table 1 were used during the deep learning model training process: Table 1: Specific parameters in deep learning model training

[0045] Experiments show that the area under the curve (AUC) of the model for this dataset can reach 91.2%, which shows that the model is highly credible and proves that the model can perform prediction work well. In addition, experiments on other datasets, such as the public dataset MNIST, also have good results, proving that the model has good versatility and has the potential to be applied to foreign body detection in iron ore transportation. Figure 6 To train a complete deep learning model to predict various abnormal situations that may occur while walking on the street. Among them, walking is normal, and people riding bicycles and pushing carts are considered abnormal samples.

[0046] In some embodiments of the present invention, feature extraction is performed on the preprocessed image to obtain a feature image, including: Temporal and spatial features are extracted from the preprocessed image to obtain contextual global information of the preprocessed image, and a feature image is obtained based on the contextual global information of the preprocessed image.

[0047] It can be understood that the preprocessed image is input into the autoencoder model, and the global context information is obtained by extracting the spatiotemporal features of the preprocessed image, and the preprocessed image obtained by the comparison memory module is used to reconstruct the input image feature representation with sample features with high similarity. The image is reconstructed by the decoder, and the probability value of the abnormality is obtained through the abnormality probability algorithm.

[0048] In some embodiments of the present invention, obtaining coincident features based on the feature image and the preprocessed image includes: The feature image and the preprocessed image are compared to obtain overlapping features.

[0049] It can be understood that the preprocessed image is input into the autoencoder model, and the global context information is obtained by extracting the spatiotemporal features of the preprocessed image, and the preprocessed image obtained by the comparison memory module is used to reconstruct the input image feature representation with sample features with high similarity. The image is reconstructed by the decoder, and the probability value of the abnormality is obtained through the abnormality probability algorithm.

[0050] In some embodiments of the present invention, the reconstructed image is calculated based on the abnormality possibility algorithm to obtain a calculation result, including: The mean square error of the reconstructed image is calculated based on the abnormal possibility algorithm to obtain the calculation result.

[0051] It can be understood that after the above process, the input image only uses the mean square error as the abnormal judgment score, and the abnormal possibility value of the input image is obtained through the abnormal scoring algorithm. The formula is as follows: .

[0052] In some embodiments of the present invention, the calculation result is matched with a preset threshold value to obtain a foreign body detection result in the iron ore transportation process, including: The calculation result is matched with a preset threshold value, and when the calculation result is greater than the preset threshold value, a detection result indicating the presence of foreign matter in the iron ore transportation process is obtained.

[0053] It can be understood that an image with a probability greater than a set threshold is defined as an abnormal image.

[0054] Figure 7 A schematic structural diagram of an embodiment of a foreign body detection device for iron ore transportation provided by the present invention comprises: An acquisition module 701 is used to acquire a target video during the iron ore transportation process; A preprocessing module 702 is used to preprocess the target video to obtain a preprocessed image; A feature extraction module 703 is used to extract features from the preprocessed image based on a deep learning model to obtain a reconstructed image; A calculation module 704 is used to calculate the reconstructed image based on an abnormality possibility algorithm to obtain a calculation result; The matching module 705 is used to match the calculation result with a preset threshold value to obtain the foreign matter detection result of the iron ore transportation process.

[0055] The foreign object detection device 700 for iron ore transportation provided in the above embodiment can implement the technical solution described in the above embodiment of the foreign object detection method for iron ore transportation. The specific implementation principles of the above modules or units can refer to the corresponding contents in the above embodiment of the foreign object detection method for iron ore transportation, which will not be repeated here.

[0056] like Figure 8 As shown, the present invention also provides an electronic device 800. The electronic device 800 includes a processor 801, a memory 802 and a display 803. Figure 8 Only some components of the electronic device 800 are shown, but it should be understood that it is not required to implement all of the components shown, and more or fewer components may be implemented instead.

[0057] In some embodiments, the processor 801 may be a central processing unit (CPU), a microprocessor or other data processing chip, used to run program codes or process data stored in the memory 802, such as the foreign body detection method for iron ore transportation in the present invention.

[0058] In some embodiments, the processor 801 may be a single server or a server group. The server group may be centralized or distributed. In some embodiments, the processor 801 may be local or remote. In some embodiments, the processor 801 may be implemented in a cloud platform. In one embodiment, the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, an internal cloud, a multi-cloud, etc., or any combination thereof.

[0059] In some embodiments, the memory 802 may be an internal storage unit of the electronic device 800, such as a hard disk or memory of the electronic device 800. In other embodiments, the memory 802 may also be an external storage device of the electronic device 800, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc., equipped on the electronic device 800.

[0060] Furthermore, the memory 802 may include both an internal storage unit of the electronic device 800 and an external storage device. The memory 802 is used to store application software installed in the electronic device 800 and various data.

[0061] In some embodiments, the display 803 may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch device, etc. The display 803 is used to display information on the electronic device 800 and to display a visual user interface. The components 801-808 of the electronic device 800 communicate with each other via a system bus.

[0062] In one embodiment, when the processor 801 executes the foreign body detection program for iron ore transportation in the memory 802, the following steps may be implemented: Get targeted video of iron ore transportation process; Preprocessing the target video to obtain a preprocessed image; Perform feature extraction on the preprocessed image based on the deep learning model to obtain a reconstructed image; The reconstructed image is calculated based on an abnormal possibility algorithm to obtain a calculation result; The calculation results are matched with the preset thresholds to obtain the foreign body detection results in the iron ore transportation process.

[0063] It should be understood that: when the processor 801 executes the foreign body detection program for iron ore transportation in the memory 802, in addition to the above functions, other functions may also be implemented. For details, please refer to the description of the corresponding method embodiment above.

[0064] Furthermore, the embodiment of the present invention does not specifically limit the type of the electronic device 800 mentioned, and the electronic device 800 may be a portable electronic device such as a mobile phone, a tablet computer, a personal digital assistant (PDA), a wearable device, a laptop computer, etc. Exemplary embodiments of portable electronic devices include but are not limited to portable electronic devices equipped with IOS, Android, Microsoft or other operating systems. The above-mentioned portable electronic devices may also be other portable electronic devices, such as a laptop computer with a touch-sensitive surface (e.g., a touch panel). It should also be understood that in some other embodiments of the present invention, the electronic device 800 may not be a portable electronic device, but a desktop computer with a touch-sensitive surface (e.g., a touch panel).

[0065] Accordingly, an embodiment of the present application also provides a computer-readable storage medium, which is used to store computer-readable programs or instructions. When the program or instructions are executed by a processor, the steps or functions of the foreign body detection method for iron ore transportation provided in the above-mentioned method embodiments can be implemented.

[0066] Those skilled in the art will appreciate that all or part of the processes of the above-mentioned embodiments can be implemented by instructing related hardware (such as a processor, a controller, etc.) through a computer program, and the computer program can be stored in a computer-readable storage medium, wherein the computer-readable storage medium is a disk, an optical disk, a read-only storage memory, or a random access memory, etc.

[0067] The foreign body detection method, device, electronic device and storage medium for iron ore transportation provided by the present invention are introduced in detail above. Specific examples are used in this article to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea; at the same time, for technical personnel in this field, according to the idea of ​​the present invention, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. A method for detecting foreign matter in iron ore transportation, characterized in that: include: Get targeted video of iron ore transportation process; Preprocessing the target video to obtain a preprocessed image; Performing feature extraction on the preprocessed image based on a deep learning model to obtain a reconstructed image; Calculating the reconstructed image based on an abnormality possibility algorithm to obtain a calculation result; The calculation result is matched with a preset threshold value to obtain a foreign body detection result in the iron ore transportation process.

2. The foreign body detection method for iron ore transportation according to claim 1, characterized in that: The preprocessing of the target video to obtain a preprocessed image includes: The target video is preprocessed by frame extraction, size adjustment and AI image illumination enhancement to obtain a preprocessed image.

3. The foreign body detection method for iron ore transportation according to claim 1, characterized in that: The deep learning model includes: an encoder, a memory module and a decoder connected in series; An encoder, used for extracting features from the preprocessed image to obtain a feature image; A memory module, used for obtaining overlapping features based on the feature image and the preprocessed image; The decoder is used to reconstruct the coincident features to obtain a reconstructed image.

4. The foreign body detection method for iron ore transportation according to claim 3, characterized in that: The step of extracting features from the preprocessed image to obtain a feature image comprises: Performing spatiotemporal feature extraction on the preprocessed image to obtain contextual global information of the preprocessed image, and obtaining a feature image based on the contextual global information of the preprocessed image.

5. The foreign body detection method for iron ore transportation according to claim 3, characterized in that: The obtaining the overlapping features based on the feature image and the preprocessed image includes: Perform feature comparison between the feature image and the preprocessed image to obtain overlapping features.

6. The foreign body detection method for iron ore transportation according to claim 1, characterized in that: The calculating of the reconstructed image based on the abnormality possibility algorithm to obtain the calculation result includes: A mean square error is calculated for the reconstructed image based on an abnormality possibility algorithm to obtain a calculation result.

7. The foreign body detection method for iron ore transportation according to claim 1, characterized in that: The calculation result is matched with a preset threshold value to obtain a foreign body detection result in the iron ore transportation process, including: The calculation result is matched with a preset threshold value, and when the calculation result is greater than the preset threshold value, a detection result indicating the presence of foreign matter in the iron ore transportation process is obtained.

8. A foreign body detection device for iron ore transportation, characterized in that: include: An acquisition module is used to acquire target videos during the iron ore transportation process; A preprocessing module, used for preprocessing the target video to obtain a preprocessed image; A feature extraction module, used to extract features from the preprocessed image based on a deep learning model to obtain a reconstructed image; A calculation module, used to calculate the reconstructed image based on an abnormality possibility algorithm to obtain a calculation result; The matching module is used to match the calculation result with a preset threshold value to obtain the foreign matter detection result of the iron ore transportation process.

9. An electronic device, characterized in that: comprising a memory and a processor, wherein: The memory is used to store programs; The processor is coupled to the memory and is used to execute the program stored in the memory to implement the steps in the foreign matter detection method for iron ore transportation as described in any one of claims 1 to 7 above.

10. A computer-readable storage medium, characterized in that: Used to store computer-readable programs or instructions, which, when executed by a processor, can implement the steps of the foreign body detection method for iron ore transportation as described in any one of claims 1 to 7.

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