Information processing device, information processing method, and information processing program

Abstract

To provide an information processing device capable of more accurately determining an iron scrap group containing iron scrap of one or more grades.SOLUTION: One embodiment of the present invention is an information processing device for determining a grade ratio of each grade included in an iron scrap group to be determined, and includes: a receiving unit that receives a set of iron scrap group data obtained by imaging the iron scrap group to be determined along with the progress of unloading from a loading bed; and a grade determination unit that uses a learning model created by learning using a set of learning image data obtained by imaging the iron scrap group containing iron scrap of one or more grades along with the progress of unloading from the loading bed and a label including grade ratios of the one or more grades associated with the entire iron scrap group being a target of the set of learning data and determines a grade ratio of each grade included in the entire iron scrap group to be determined while using the set of iron scrap group data as an input to the learning model.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to an information processing device, an information processing method, and an information processing program. [Background technology]

[0002] When manufacturing steel from iron ore, a huge amount of carbon dioxide is emitted during the steelmaking process using iron ore and coal in a blast furnace. With the current demand for reducing carbon dioxide emissions, recycling of scrap steel is becoming more important.

[0003] Here, the iron scrap collected by the collector is piled up in a collection site and then transported to an electric furnace manufacturer or the like, where the price is calculated based on the measured weight, the grade of the iron scrap, etc.

[0004] Conventionally, at the scrap receiving site of an electric furnace manufacturer, the grade ratio of the iron scrap brought in was assessed visually by an inspector, which was extremely inefficient. However, a grade determination system has been proposed in which, each time a portion of the iron scrap piled up on the loading platform of a scrap-loading vehicle is lifted by a lifting magnet, the iron scrap remaining on the loading platform and the iron scrap lifted by the lifting magnet are photographed, and each time the lifting magnet lifts the iron scrap, a partial ratio determination unit averages the grade ratio estimated from the image generated by the vehicle's camera just before lifting and the grade ratio estimated from the image generated by the lifting magnet camera at the time of lifting to determine the grade ratio of the lifted iron scrap, and an overall ratio determination unit tallys up the grade ratios of the iron scrap determined by the partial ratio determination unit each time the lifting magnet lifts the iron scrap and calculates an average value. This determines the grade ratio of the entire iron scrap unloaded from the scrap-loading vehicle to the scrap yard. The partial ratio determination unit estimates the grade ratio for each image showing the iron scrap lifted by the lifting magnet from different viewpoints and determines the grade ratio for each lifted iron scrap as the average of these images, and the overall ratio judgment unit further averages the grade ratios for each lifted iron scrap to determine the grade ratio for the entire iron scrap (see Patent Document 1). [Prior art documents] [Patent documents]

[0005] [Patent Document 1] Japanese Patent Application Publication No. 2020-95709 Summary of the Invention [Problem to be solved by the invention]

[0006] However, there is room for improvement in the accuracy of determining iron scrap in the conventional technology.

[0007] The present invention has been made in consideration of these points, and its object is to provide an information processing device, an information processing method, or an information processing program that can more accurately determine a group of iron scrap containing iron scrap of one or more grades. [Means for solving the problem]

[0008] In order to solve such problems, a first aspect of the present invention is a method for determining the grade ratio of each grade contained in a group of iron scrap to be evaluated, the method including the steps of: receiving a set of iron scrap group data obtained by photographing the group of iron scrap to be evaluated as it is being unloaded from a loading platform; and using a learning model created by learning using a set of training image data obtained by photographing a group of iron scrap containing one or more grades as it is being unloaded from a loading platform and labels including grade ratios of one or more grades associated with the entire group of iron scrap that is the subject of the set of training data, and using the set of iron scrap group data as an input to the learning model to determine the grade ratio of each grade contained in the entire group of iron scrap to be evaluated.

[0009] A second aspect of the present invention is the method of the first aspect, wherein the determining step includes a step of acquiring, as intermediate features, correlation values ​​between features extracted from a plurality of image data included in the set of input iron scrap group data.

[0010] In addition, a third aspect of the present invention is the method of the first aspect, wherein the determining step includes a step of determining a grade previously associated with a specified steel material when the group of iron scrap to be determined includes the specified steel material.

[0011] In addition, a fourth aspect of the present invention is the method of the first or second aspect, further comprising a step of determining the type of iron scrap contained in the iron scrap group to be determined based on the set of iron scrap group data.

[0012] A fifth aspect of the present invention is a method according to the first or second aspect, wherein the learning model is created by further using learning sound data obtained by collecting sounds generated when dropping iron scrap of each grade, and the judgment further uses sound data obtained by collecting sounds generated when dropping the group of iron scrap to be judged as input to the learning model.

[0013] A sixth aspect of the present invention is the method according to the first or second aspect, further comprising the step of detecting foreign matter contained in the group of iron scraps to be judged.

[0014] Further, a seventh aspect of the present invention is a method according to the sixth aspect, wherein the learning model is created using synthetic data obtained by combining an image of a foreign object with an image based on at least one of the set of learning image data.

[0015] In addition, an eighth aspect of the present invention is the method of the first or second aspect, further comprising a step of calculating a value of the group of iron scraps to be evaluated according to the proportion of each grade contained in the group of iron scraps to be evaluated.

[0016] A ninth aspect of the present invention is a program for causing a computer to execute a method for determining the grade ratio of each grade contained in a group of iron scrap to be evaluated, the method including the steps of: receiving a set of iron scrap group data obtained by photographing the group of iron scrap to be evaluated as it is being unloaded from a loading platform; and using a learning model created by learning using a set of training image data obtained by photographing a group of iron scrap containing one or more grades as it is being unloaded from a loading platform and labels including grade ratios of one or more grades associated with the entire group of iron scrap that is the subject of the set of training data, and determining the grade ratio of each grade contained in the entire group of iron scrap to be evaluated by using the set of iron scrap group data as an input to the learning model.

[0017] A tenth aspect of the present invention is an apparatus for determining the grade ratio of each grade contained in a group of iron scrap to be evaluated, which receives a set of iron scrap group data obtained by photographing the group of iron scrap to be evaluated as it is being unloaded from a loading platform, and uses a learning model created by learning using a set of learning image data obtained by photographing a group of iron scrap containing one or more grades as it is being unloaded from a loading platform and labels including grade ratios of one or more grades associated with the entire group of iron scrap that is the subject of the set of learning data, and determines the grade ratio of each grade contained in the entire group of iron scrap to be evaluated using the set of iron scrap group data as an input to the learning model. [Effects of the Invention]

[0018] According to one aspect of the present invention, it is possible to provide an information processing device, an information processing method, or an information processing program that can more accurately determine the size of a group of iron scraps. [Brief explanation of the drawings]

[0019] [Figure 1] 1 is a schematic configuration diagram of an iron scrap yard according to an embodiment of the present invention. [Figure 2] 1 is a diagram illustrating an example of a schematic configuration of an information processing system according to an embodiment of the present invention. [Figure 3] FIG. 2 is a diagram illustrating an example of a hardware configuration of a server according to the present embodiment. [Figure 4] FIG. 2 is a diagram illustrating an example of a functional configuration of a server according to the present embodiment. [Figure 5] FIG. 10 is a diagram showing an example of a display screen displayed on a display device of a user terminal according to the embodiment. [Figure 6] FIG. 10 is a diagram showing an example of a display screen displayed on a display device of a user terminal according to the embodiment. [Figure 7] 10 is a flowchart illustrating an example of processing executed by the server according to the embodiment. [Figure 8] 10 is a graph showing a determination result based on a learning model according to the present embodiment. [Figure 9] 10 is a graph showing a comparative example of a determination result using a learning model according to the related art. DETAILED DESCRIPTION OF THE INVENTION

[0020] As shown in the schematic configuration diagram of Fig. 1, an iron scrap yard according to one embodiment of the present invention includes a lift magnet 30 that controls magnetic force to attract and release iron scrap piles 10 containing one or more grades of iron loaded on the loading platform of a truck 40 parked at a predetermined location, and a crane rail 50 that moves some of the iron scrap piles 10 attracted to the lift magnet 30 to an iron scrap storage area 20. The iron scrap yard may also include a measuring device (not shown) that measures the weight of the truck 40 carrying the iron scrap piles 10 on its loading platform. The weight measured by the measuring device minus the weight of the truck 40 can be input as the weight of the iron scrap piles 10 to a server 3 shown in Fig. 2.

[0021] The information processing system 1 according to this embodiment has one or more imaging devices 2 that capture images of a group of iron scraps 10 loaded on the bed of a truck 40, and as shown in Fig. 2, each imaging device 2 is connected to a server or an information processing device 3 via a network 5 so as to be able to communicate data with the server 3. The server 3 is also connected to a user terminal 4 via the network 5 so as to be able to communicate data with the server 3. The number of servers 3, the number of user terminals 4 connected to the server 3 via the network 5, and the number of imaging devices 2 are each arbitrary.

[0022] (Imaging device 2) The imaging device 2 is preferably installed in a position where it can photograph the pile of iron scraps 10 from directly above, photographs the pile of iron scraps 10, and transmits the photographed pile of iron scraps data (image data) to the server 3. As an example, the imaging device 2 photographs the surface of the pile of iron scraps 10 remaining on the bed of the truck 40 every time the lift magnet 30 lifts up some of the iron scraps from the pile of iron scraps 10 loaded on the bed of the truck 40 parked at a predetermined position. The imaging device 2 repeats this photographing of the newly appearing surface until all the iron scraps are removed from the bed of the truck 40. In this way, a plurality of piles of iron scraps are photographed as the lift magnet 30 unloads the pile of iron scraps 10 from the bed, thereby obtaining a plurality of piles of iron scraps data.

[0023] Furthermore, it is preferable that the imaging device 2 is installed in a position that overlooks directly below the pile of iron scrap 10 loaded on the bed of a truck 40 parked at a predetermined position, but if certain objects in the iron scrap yard, such as the lift magnet 30 or the crane rail 50, come within the imaging range when imaging, the imaging device 2 may be installed in a position that causes the lift magnet 30 or the crane rail 50 to be outside the imaging range or its central part.

[0024] (Server 3) Fig. 3 is a diagram showing an example of the hardware configuration and functional configuration of the server 3. As shown in Fig. 3, the server 3 includes a communication IF 300A, a storage device 300B, a CPU 300C, etc. The server 3 may also include an input device (for example, a keyboard, a touch panel, etc.) and a display device (for example, a liquid crystal monitor, an organic EL monitor, etc.).

[0025] The communication IF 300A is an interface for communicating with an external terminal (such as the user terminal 4 in this embodiment).

[0026] The storage device 300B is, for example, a hard disk drive (HDD) or a semiconductor storage device. The storage device 300B stores information processing programs and various databases used by the server 3. For example, the storage device 300B stores table data for calculating the value of the iron scrap group 10. For example, the storage device 300B stores unit prices (prices per predetermined weight) according to the grade of iron scrap for each type. In this embodiment, the information processing programs and various databases are stored in the storage device 300B of the server 3, but they may also be stored in an external storage device such as a USB memory or an external server connected via a network, and configured to be referenced or downloaded as needed.

[0027] The CPU 300C controls the server 3 and includes, for example, a read only memory (ROM) and a random access memory (RAM), neither of which is shown.

[0028] As shown in Fig. 4, the server 3 has functions such as a receiving unit 301, a transmitting unit 302, a storage device control unit 303, a variety determination unit 304, a grade determination unit 305, a foreign object detection unit 306, and a value calculation unit 307. Note that each process or operation shown in Fig. 4 is realized by a CPU 300C executing instructions contained in an information processing program stored in a storage device 300B or the like of the server 3. The program may include one or more programs, and may be recorded on a computer-readable storage medium to form a non-transitory program product.

[0029] The receiving unit 301 receives, for example, imaging data (image data) of the group of iron scraps 10 transmitted from the imaging device 2. Also, for example, the receiving unit 301 receives inspection information (see the left side of FIG. 5) from a supplier or the like that delivers the group of iron scraps 10. Also, for example, the receiving unit 301 receives information on the weight of the group of iron scraps 10.

[0030] The transmitting unit 302 transmits to the user terminal 4, for example, information such as the determination results of the type (e.g., dry, heavy, shredded, new scrap, old pig iron, etc.) and grade of the group of iron scraps 10 made by the server 3, the detection results of foreign matter contained in the group of iron scraps 10, and the calculated value of the group of iron scraps 10. The transmitting unit 302 can also transmit information that displays the detected foreign matter in a recognizable manner.

[0031] The storage device control unit 303 controls the storage device 300B. For example, the storage device control unit 303 writes and reads information to and from the storage device 300B.

[0032] The type determination unit 304 can determine the type of iron scrap contained in the iron scrap group 10 to be determined by using, for example, a learning model created by machine learning using learning image data obtained by capturing images of the iron scrap group and labels, including the type, associated with the learning image data. The iron scrap group data received by the receiving unit 301 is used as input to the learning model. The "label" indicates the type of iron scrap, such as hopper, heavy, shredded, new scrap, or old iron. The "label" may be assigned a type determined by, for example, an inspector. Note that the type may not be determined by the type determination unit 304, but may instead be input by a user, such as a worker, via the user terminal 4.

[0033] The grade determination unit 305 may use a learning model corresponding to the variety determined by the variety determination unit 304 or the variety input (for example, by a user) from the user terminal 4, or may use one learning model instead of preparing a learning model for each variety.

[0034] The grade determination unit 305 may use a learning model created by machine learning using, for example, a set of training image data obtained by capturing images of a group of iron scrap containing one or more grades of iron scrap as it is being unloaded from a loading platform, and labels corresponding to the set of training image data and including grade proportions of one or more grades associated with the entire group of iron scrap. The receiving unit 301 may receive a set of iron scrap group data obtained by capturing images of the group of iron scrap 10 to be determined as it is being unloaded from a loading platform, and use the received set of iron scrap group data as input to the learning model to determine the proportion of each grade contained in the entire group of iron scrap 10. By using the grade proportions associated with the entire group of iron scrap that is the subject of the set of training image data as labels for the set of training image data obtained by capturing images of the group of iron scrap as it is being unloaded from a loading platform, label annotation for each set of training image data is not required. In this case, it is not necessary to use all of the set of learning image data when learning for type determination, foreign object detection, or other processing or operation, and it is not necessary to use all of the set of iron scrap group data when learning for type determination, foreign object detection, or other processing or operation.

[0035] While the prior art involves determining the grade ratio of each piece of scrap iron 10 identified by training image data captured by a lift magnet 30 during unloading from the truck 40, and then using the results as annotations for each piece of training image data to determine the overall grade ratio of the scrap iron 10, this requires the use of many annotations and may at first glance appear appropriate for machine learning. However, this is a task that is not part of the traditional visual inspection work performed by inspectors. Therefore, creating a large number of sets of training image data required for machine learning is a significant burden, and this method does not allow for practical improvements in accuracy through data accumulation. Furthermore, the inventors have noted that the accuracy of trainees' assessments of the percentage of each grade contained in a group of scrap iron varies between skilled and unskilled workers, and that high accuracy cannot be expected even for skilled workers in the new task of assessing each piece of training image data. Therefore, the reliability of the annotations for each piece of training image data is low, and therefore the reliability of the learning model obtained using such annotations is also low. Based on this finding, the present invention has devised a method that does not necessarily require label annotation for each piece of training image data. As will be described later, the present invention achieves higher accuracy of judgment than conventional technology, and further improvements are expected. It will objectively verify or replace the subjective appraisal work performed by inspectors, and contribute to the recycling of iron scrap, which will be increasingly required in the future.

[0036] For example, the number of sets of learning image data is preferably between 3 and 15. If it is less than 3, there will be a lack of information necessary for class determination, and the inference accuracy of the learning model may be reduced. If it exceeds 15, the analysis time may be longer, but the analysis itself can still be performed.

[0037] The "label" includes, for example, the percentage of each grade of heavy scrap contained in the iron scrap group 10 as a grade percentage. The grades are indicated, for example, by HS, H1, H2, H3, and H4. The "label" may be assigned a value determined by an inspector, or an actually measured value. The percentage may be, for example, a weight percentage, but is not limited to this, and may also be an area percentage or a volume percentage.

[0038] The grade determination unit 305 may use a learning model created by learning using data correlating a specific steel material with the grade of the specific steel material as training data to determine the grade of the specific steel material contained in the iron scrap group 10 based on the image data. In this case, the specific steel material is detected using the learning model that has been trained on the specific steel material, and the grade is determined by referring to separate information that pre-associates the steel material with the grade. In scrap yards, specific steel materials may have a predetermined grade, which is one of the important criteria for determining the grade. Therefore, not only simply learning the grade but also learning information about the specific steel material enables more accurate determination.

[0039] The foreign object detection unit 306 can detect foreign objects contained in the group of iron scraps 10 by using, for example, learning image data obtained by capturing images of a group of iron scraps containing foreign objects (e.g., wire harnesses or motors containing large amounts of copper, tin, lead, zinc, etc.) and a learning model created by machine learning using labels indicating the types of foreign objects (e.g., motors) associated with the learning image data, and inputting the iron scrap group data received by the receiving unit 301 into the learning model. Here, the learning image data can include image data obtained by capturing images of the group of iron scraps containing foreign objects, as well as synthetic data obtained by combining images of the foreign objects with images of the group of iron scraps. While it is typically difficult to obtain images of various patterns containing foreign objects, it is possible to generate synthetic data for learning by combining images of various patterns containing foreign objects, thereby improving the efficiency of the learning model.

[0040] The value calculation unit 307 calculates, for example, the value of the group of iron scraps 10. Here, the value calculation unit 307 calculates the value of the group of iron scraps 10 based on the determination results of the type determination unit 304 and the grade determination unit 305. Here, the value calculation unit 307 calculates the value of the group of iron scraps 10 according to the proportion or weight of items other than iron scrap (for example, foreign matter, dust, moisture, etc.) contained in the group of iron scraps 10. Specifically, the value calculation unit 307 subtracts the weight of items other than iron scrap (foreign matter, dust, moisture, etc.) contained in the group of iron scraps 10 from the weight of the group of iron scraps 10 ("weight subtraction"), and then multiplies the type determined by the type determination unit 304 (the type may be input by the user) and the weight of each grade determined by the grade determination unit 305 by the unit price to calculate the value of the group of iron scraps 10. The value calculation unit 307 may automatically determine the proportion or weight of the non-iron scrap contained in the iron scrap group 10 from the imaging data received by the receiving unit 301, or may use the proportion or weight of the non-iron scrap input by the user from the user terminal 4.

[0041] (Display screen example) 5 and 6 are diagrams showing examples of screens displayed on the display device of the user terminal 4 according to this embodiment. Below, the screens displayed on the display device of the user terminal 4 will be described with reference to Fig. 5 and 6. In the following description, the same components will be assigned the same reference numerals, and duplicated description will be omitted.

[0042] 5 is a diagram showing an example of an inspection information input screen 51. As shown in FIG. 5, on the left side of the screen, inspection information, such as the supplier's trader name, trading company name, product type, and unloading location, can be entered in input boxes 52 to 55. Also, as shown in FIG. 5, on the right side of the screen, an image 56 captured by the imaging device 2 (an image looking down on the group of iron scraps 10 loaded on the bed of the truck 40) is displayed. Furthermore, when the user operates the input device of the user terminal 4 to select an inspection start button 57, analysis of the group of iron scraps (e.g., determining the grade, calculating the value, etc.) is started.

[0043] Fig. 6 is a diagram showing an example of an inspection result display screen. As shown in Fig. 6, the left side of the inspection result screen 71 displays information such as the inspection start time, inspection end time, inspection duration, supplier, responsible trading company, responsible inspector, unloading location, weight of the group of iron scrap 10 (before weight reduction), and weight of the group of iron scrap 10 (after weight reduction). As shown in Fig. 6, the right side of the inspection result screen displays information such as the type, grade, percentage of each grade, unit price, price, discount reason, and purchase price (value) of the inspected group of iron scrap 10.

[0044] (Value calculation process) 7 is a flowchart showing an example of the value calculation process executed by the server 3. The value calculation process executed by the server 3 will be described below with reference to FIG.

[0045] (Step S701) The receiving unit 301 of the server 3 receives the ratio or weight of the matter other than iron scrap (for example, dust, moisture, etc.) contained in the iron scrap group 10. The ratio or weight of the dust, moisture, etc. contained in the iron scrap group 10 may be automatically determined from the imaging data received by the receiving unit 301.

[0046] (Step S702) The value calculation unit 307 calculates the value of the group of iron scraps 10. Specifically, the value calculation unit 307 subtracts the weight of the non-iron scrap (foreign matter, dust, moisture, etc.) contained in the group of iron scraps 10 from the weight of the group of iron scraps 10 ("weight subtraction"). Next, the value calculation unit 307 references price data for the corresponding product type in the value calculation table data stored in the storage device 300B. Next, the value calculation unit 307 calculates the weight of each grade contained in the group of iron scraps 10 based on the weight of the group of iron scraps 10 after weight subtraction and the proportion of each grade contained in the group of iron scraps 10 determined by the grade determination unit 305. Next, the value calculation unit 307 references the price data and calculates a value (total value) for each grade by adding values ​​obtained by multiplying the weight by the unit price.

[0047] (Step S703) The transmitting unit 302 transmits the calculated value of the group of iron scraps 10 to the user terminal 4. As a result, the value of the group of iron scraps is displayed on the display device of the user terminal 4, as illustrated in FIG.

[0048] Comparative experiment We prepared 530 results for each truck, in which inspectors assessed the proportion of each grade contained in the total steel scrap loaded on the truck bed. We also prepared 100 results for each truck, in which inspectors assessed the surface of the steel scrap pile using five image data images taken by each truck as the scrap was being unloaded. Six grades, HS, H1, H2, H3, L1, and L2, were selected as candidates for assessment. The former data was used to generate a learning model according to this embodiment (hereinafter also referred to as a "multiple-input model"), and the latter data was used to generate a learning model according to the prior art (hereinafter also referred to as a "single-input model"). More specifically, in this embodiment, a model predicting the grade of a single truck is constructed using ResNet50, a convolutional neural network, as the backbone, and learning using correlation values ​​between features extracted from multiple image data as intermediate features.In the prior art, a model predicting the grade from a single image data is constructed using ResNet50 as the backbone, and the grade of a single truck is predicted by averaging predictions for multiple image data.In both methods, data division was performed using k-fold cross-validation (k=5).

[0049] The evaluation method was to use the inspector's assessment result for each truck as the answer (grand truth (GT)), and if the maximum error for each grade was within 10 points, the answer was evaluated as correct (true). Table 1 shows an example of an evaluation.

[0050] [Table 1]

[0051] The results of the multiple-input model are shown in Figure 8, and the results of the single-input model are shown in Figure 9. The accuracy rate for the former was 57.7%, while that for the latter was 45.0%, with the former being approximately 12.7% more accurate.

[0052] In addition to the above embodiment, the type or grade of the group of iron scrap 10 may be determined by a learning model created by learning using sound data generated when iron scrap of each type or grade is dropped. When using sound data generated when iron scrap is dropped, it is preferable to create learning sound data for each iron scrap yard.

[0053] In the case of a learning model created by further learning using sound data generated when iron scrap is dropped, a microphone is provided to pick up the sound generated when the iron scrap pile 10 is dropped into the iron scrap yard, and the server 3 receives the sound data obtained by capturing the sound generated when the iron scrap pile 10 is dropped in addition to the imaging data captured by the imaging device 2.

[0054] Furthermore, in the above embodiments, unless the word "only" is used, such as "based only on," "depending only on," or "only in the case of," it is assumed that additional information may be taken into consideration in this specification. Also, as an example, please note that the phrase "do b when a" does not necessarily mean "always do b when a" or "do b immediately after a" unless explicitly stated otherwise. Furthermore, the phrase "each a constituting A" does not necessarily mean that A is composed of multiple components, but includes the case where the component is singular.

[0055] Also, just to be clear, even if there is an aspect of a method, program, terminal, device, server, or system (hereinafter referred to as a "method, etc.") that performs an operation different from that described in this specification, each aspect of the present invention is directed to an operation that is identical to one of the operations described in this specification, and the existence of an operation different from that described in this specification does not make the method, etc. outside the scope of each aspect of the present invention.

[0056] The flowchart shown in FIG. 7 is merely an example, and does not necessarily mean that the process will necessarily start or end in the illustrated procedure.

[0057] Furthermore, the above-described embodiments and modifications are merely examples of specific embodiments of the present invention, and the technical scope of the present invention should not be construed as being limited thereby. In other words, the present invention can be embodied in various forms without departing from the gist or main characteristics thereof. [Explanation of symbols]

[0058] 1. Information Processing Systems 2. Imaging device 3 Server 4. User terminal 5. Network 10 Iron Scrap Group 20 Iron scrap storage area 30 Lift Magnet 40 tracks 50 Crane Rail 51 Information input screen 52, 53, 54, 55 Input boxes 56 Captured images 57 Start button 71 Analysis result screen 300A communication IF 300B storage device 300C CPU 301 Receiving unit 302 Transmission Unit 303 Storage device control unit 304 Variety Judgment Department 305 Grade Judgment Department 306 Foreign object detection unit 307 Value Calculation Department

Claims

1. 1. A method for calculating the value of a batch of ferrous scrap, comprising: a step of subtracting a weight of dust contained in the batch of iron scraps from a weight of the batch of iron scraps; calculating a weight of each grade of iron scrap contained in the batch based on the proportion of each grade contained in the batch of iron scrap; Includes:

2. 10. The method of claim 1, The method further includes obtaining a weight of the dust.

3. 3. The method of claim 1 or 2, The method further includes calculating the proportion of each grade contained in the group of iron scrap.

4. 1. A method for calculating a value of a batch of ferrous scrap on a computer, the method comprising: a step of subtracting a weight of dust contained in the batch of iron scraps from a weight of the batch of iron scraps; calculating a weight of each grade of iron scrap contained in the batch based on the proportion of each grade contained in the batch of iron scrap; Includes:

5. 1. An apparatus for calculating the value of a batch of iron scrap, comprising: subtracting the weight of dust contained in the group of iron scraps from the weight of the group of iron scraps; The weight of each grade contained in the group of iron scrap is calculated based on the proportion of each grade contained in the group of iron scrap.

Images