Monitoring apparatus and operating method thereof

Abstract

A monitoring apparatus, according to an embodiment disclosed in the present document, may comprise: an image obtainment unit that obtains a captured image of at least one process treatment apparatus related to the manufacture of a battery cell; a state determination unit that inputs the obtained image into an artificial intelligence model for determining the state of a top insulator of the battery cell, and generates a first determination result by determining whether the battery cell is defective; and a determination unit that determines, on the basis of the first determination result, whether the battery cell is defective.

Description

Monitoring device and method of operation thereof

[0001] Cross-citation with related applications

[0002] The embodiments disclosed in this document claim the benefit of priority based on Korean Patent Application No. 10-2023-0043688, filed on April 3, 2023, which is incorporated herein by reference in its entirety.

[0003] Technology field

[0004] The embodiments disclosed in this document relate to a monitoring device and a method of operating the same.

[0005] Battery cells are classified into cylindrical, square, and pouch types depending on the type of battery case, and a cylindrical battery cell includes an electrode assembly, a battery case of a cylindrical metal can that accommodates the electrode assembly and electrolyte, and a cap assembly assembled on the top of the cylindrical can.

[0006] An electrode assembly is formed by interposing a separator between a positive electrode plate formed by coating a positive electrode current collector with a positive active material and a negative electrode plate formed by coating a negative electrode current collector with a negative active material. Depending on the type of battery case, the electrode assembly can be manufactured in a jelly roll type, stack type, etc., and accommodated inside the battery case.

[0007] Battery cells can be manufactured through a series of manufacturing processes, including an electrode manufacturing process, an assembly process, and a formation process. Here, the assembly process may include a process of assembling positive and negative electrode plates manufactured through the electrode manufacturing process and injecting an electrolyte, and may include a notching process, a winding process, an assembly process, and a packaging process.

[0008] The packaging process can be defined as the process of injecting and sealing the electrode assembly and electrolyte into the battery case. For cylindrical battery cells, the electrode assembly is mounted in a cylindrical metal can, the negative tab extending from the negative electrode of the electrode assembly is welded to the bottom of the can, and the positive tab extending from the positive electrode of the electrode assembly, with the electrode assembly and electrolyte embedded, is welded to the top cap of the cap assembly.

[0009] A top insulator is mounted on the upper surface of the electrode assembly to electrically insulate the electrode assembly and the cap assembly, and the electrode terminal is connected to an electrode lead wire so that it can be connected to an external terminal.

[0010] One purpose of the embodiments disclosed in this document is to provide a monitoring device and its operating method capable of accurately analyzing the state of a top insulator of a battery cell to detect a defective battery cell.

[0011] The technical problems of the embodiments disclosed in this document are not limited to the technical problems mentioned above, and other technical problems not mentioned will be clearly understood by those skilled in the art from the descriptions below.

[0012] A monitoring device according to an embodiment disclosed in the present document may include an image acquisition unit that acquires an image of at least one process processing device related to the manufacture of a battery cell; a status judgment unit that inputs the acquired image into an artificial intelligence model for judging the status of a top insulator of a battery cell to generate a first judgment result for judging whether the battery cell is defective; and a judgment unit that determines whether the battery cell is defective based on the first judgment result.

[0013] A method of operating a monitoring device according to an embodiment disclosed in this document may include an operation of acquiring an image of at least one process processing device related to the manufacture of a battery cell, an operation of inputting the acquired image into an artificial intelligence model for determining a state of a top insulator of a battery cell to generate a first judgment result for determining whether the battery cell is defective, and an operation of determining whether the battery cell is defective based on the first judgment result.

[0014] According to one embodiment of the monitoring device and its operating method disclosed in this document, it is possible to accurately analyze the state of a top insulator of a battery cell to detect a defective battery cell.

[0015] The effects of the monitoring device and its operating method according to the disclosure of this document are not limited to the effects mentioned above, and other effects not mentioned will be clearly understood by those skilled in the art according to the disclosure of this document.

[0016] FIG. 1 is a block diagram of a monitoring device according to an embodiment of the present disclosure.

[0017] FIG. 2 illustrates a can assembly according to one embodiment of the present disclosure.

[0018] FIG. 3 is a diagram illustrating a battery unit with a top insulator and a battery unit without a TI according to one embodiment disclosed in the present document.

[0019] FIG. 4 illustrates an image showing foreign matter present inside a top insulator of a battery unit according to one embodiment disclosed in this document.

[0020] FIG. 5 illustrates an image of a top insulator of a battery unit according to an embodiment disclosed in this document.

[0021] FIG. 6 is a drawing illustrating a state in which a crack exists in a beading area of ​​a battery unit according to one embodiment disclosed in this document.

[0022] FIG. 7 is a drawing showing an operation method of a monitoring device according to an embodiment of the present disclosure.

[0023] In connection with the description of the drawings, the same or similar reference numerals may be used for identical or similar components.

[0024] Hereinafter, embodiments of the present invention will be described with reference to the attached drawings. However, this is not intended to limit the present invention to specific embodiments, and it should be understood that the present invention encompasses various modifications, equivalents, and / or alternatives of the embodiments.

[0025] The embodiments and terminology used in this document are not intended to limit the technical features described in this document to a specific embodiment, but should be understood to encompass various modifications, equivalents, or alternatives of the embodiment. In connection with the description of the drawings, similar reference numerals may be used to refer to similar or related components. The singular form of a noun corresponding to an item may include one or more of the item, unless the relevant context clearly indicates otherwise.

[0026] In this document, the phrases "A or B", "at least one of A and B", "at least one of A or B", "A, B, or C", "at least one of A, B, and C", and "at least one of A, B, or C" can each include any one of the items listed together in that phrase, or all possible combinations thereof. Terms such as "first", "second", "first", "second", "A", "B", "(a)", or "(b)" may be used merely to distinguish the corresponding component from other corresponding components, and do not limit the corresponding components in any other respect (e.g., importance or order) unless specifically stated otherwise.

[0027] In this document, when a component (e.g., a first component) is referred to as being “connected,” “coupled,” or “connected,” with or without the terms “functionally” or “communicatively,” or “coupled” or “connected,” it means that the component can be connected to the other component directly (e.g., wired or wirelessly), or indirectly (e.g., via a third component).

[0028] The methods according to various embodiments disclosed in this document may be provided as included in a computer program product. The computer program product may be traded as a commodity between a seller and a buyer. The computer program product may be distributed in the form of a machine-readable storage medium (e.g., compact disc read-only memory, CD-ROM), or may be distributed online (e.g., downloaded or uploaded) through an application store or directly between two user devices. In the case of online distribution, at least a portion of the computer program product may be temporarily stored or temporarily generated in a machine-readable storage medium, such as the memory of a manufacturer's server, an application store's server, or an intermediary server.

[0029] According to the embodiments disclosed in this document, each component (e.g., a module or a program) of the above-described components may include one or more entities, and some of the entities may be separated and placed in other components. According to the embodiments disclosed in this document, one or more components or operations of the aforementioned components may be omitted, or one or more other components or operations may be added. Alternatively or additionally, a plurality of components (e.g., a module or a program) may be integrated into a single component. In this case, the integrated component may perform one or more functions of each of the plurality of components identically or similarly to those performed by the corresponding component among the plurality of components prior to the integration. According to the embodiments disclosed in this document, the operations performed by a module, program, or other component may be executed sequentially, in parallel, iteratively, or heuristically, or one or more of the operations may be executed in a different order, omitted, or one or more other operations may be added.

[0030] FIG. 1 is a block diagram showing the configuration of a monitoring device (100) according to one embodiment disclosed in this document. FIG. 2 illustrates a can assembly (200) according to one embodiment disclosed in this document.

[0031] In one embodiment, the monitoring device (100) can determine the status of the battery unit (105) based on an image of the battery unit (105) acquired by the image acquisition device (103). Here, the image acquisition device (103) can include a camera module. Here, the battery unit (105) can be a battery cell or a can assembly (200) included in the battery cell. Here, the can assembly (200) can include a cylindrical can (210), a top insulator (TI, top insulator) 220, a hole (230) inside the TI (220), and a tab (240) exposed through the hole (230).

[0032] In one embodiment, the monitoring device (100) can determine the condition of the battery unit (105) based on the rule-based inspection results and / or the artificial intelligence model-based inspection results for the battery unit (105). In one embodiment, the rule-based inspection can include an inspection for at least one of the presence or absence of a TI (220) in the battery unit (105), the presence or absence of an internal foreign substance in the TI (220), the presence or absence of an external foreign substance in the TI (220), or the presence or absence of a lifting of the TI (220). In one embodiment, the artificial intelligence model-based inspection can include an inspection for at least one of the presence or absence of a TI (220), the presence or absence of an internal foreign substance in the TI (220), the presence or absence of an external foreign substance in the TI (220), the presence or absence of a lifting of the TI (220), or the presence or absence of a beading crack. Depending on the embodiment, the number of items that can be inspected in the artificial intelligence model-based inspection can be more than the number of items that can be inspected in the rule-based inspection. Here, the outside of the TI (220) may be an area between the outer edge of the TI (220) and the cylindrical can (210), which may be an area where the TI (220) is beaded to the cylindrical can (210). And, the inside of the TI (220) may be an area within the outer edge of the TI (220), which may be an area within the beading area.

[0033] In one embodiment, the monitoring device (100) may determine that the state of the battery unit (105) is good if both the rule-based inspection result and the artificial intelligence model-based inspection result for the battery unit (105) indicate normal. In one embodiment, the monitoring device (100) may determine that the state of the battery unit (105) is defective if at least one of the rule-based inspection result and the artificial intelligence model-based inspection result for the battery unit (105) indicates a defect.

[0034] Below, the components of the monitoring device (100) are briefly described, and then a specific operating method of the monitoring device (100) is described.

[0035] Referring to FIG. 1, the monitoring device (100) may include a communication circuit (110), a memory (120), and a processor (130). According to an embodiment, the monitoring device (100) illustrated in FIG. 1 may further include at least one component (e.g., a display, an input device, or an output device) other than the components illustrated in FIG. 1.

[0036] In one embodiment, the communication circuit (110) can establish a wired communication channel and / or a wireless communication channel between the monitoring device (100) and the image acquisition device (103), and transmit and receive data to and from the image acquisition device (103) through the established communication channel.

[0037] In one embodiment, the memory (120) may include volatile memory and / or non-volatile memory.

[0038] In one embodiment, the memory (120) may store data used by at least one component (e.g., processor (130)) of the monitoring device (100). For example, the data may include a program (125) (or instructions related thereto), input data, or output data. In one embodiment, the instructions, when executed by the processor (130), may cause the monitoring device (100) to perform operations defined by the instructions.

[0039] In one embodiment, the memory (120) may include a program (125) (e.g., an artificial intelligence model learning unit (141), artificial intelligence models (145, 146, 147, 148), an image acquisition unit (150), a state determination unit (160), an analysis unit (170), and / or a judgment unit (180)).

[0040] In one embodiment, the processor (130) may include a central processing unit, an application processor, a graphics processing unit, a neural processing unit (NPU), an image signal processor, a sensor hub processor, or a communication processor.

[0041] In one embodiment, the processor (130) may execute a program (125) (e.g., an artificial intelligence model learning unit (141), artificial intelligence models (145, 146, 147, 148), an image acquisition unit (150), a state judgment unit (160), an analysis unit (170), and / or a judgment unit (180)) to control at least one other component (e.g., a hardware or software component) of a monitoring device (100) connected to the processor (130) and perform various data processing or calculations.

[0042] In one embodiment, the artificial intelligence model learning unit (141) may learn artificial intelligence models (145, 146, 147, 148) based on learning data. In one embodiment, the artificial intelligence models (145, 146, 147, 148) may be models learned to generate output results for different purposes based on different learning data.

[0043] In one embodiment, the image acquisition unit (150) can acquire an image of the battery unit (105) from the image acquisition device (103). In one embodiment, the status determination unit (160) can determine the status of the TI (210) in the can assembly (200) based on the image of the battery unit (105). In one embodiment, the status determination unit (160) can determine the status of the TI (210) in the can assembly (200) by inputting the image of the battery unit (105) into the artificial intelligence models (145, 146, 147, 148). In one embodiment, the analysis unit (170) can analyze the status of the TI (210) in the can assembly (200) by performing a rule-based inspection on the image of the battery unit (105). In one embodiment, the judgment unit (180) can determine whether the battery unit (105) is of good quality based on the judgment results of the status judgment unit (160) and / or the analysis unit (170).

[0044] Hereinafter, with reference to FIGS. 3 to 6, a method for the monitoring device (100) to determine the status of the battery unit (105) through the artificial intelligence model learning unit (141), artificial intelligence models (145, 146, 147, 148), image acquisition unit (150), status determination unit (160), analysis unit (170), and / or determination unit (180) will be specifically described.

[0045] AI model training

[0046] FIG. 3 is a diagram illustrating a battery unit with and without TI according to an embodiment disclosed in this document.

[0047] In one embodiment, the artificial intelligence model learning unit (141) may input an image of a battery unit into the artificial intelligence model (145). In one embodiment, the artificial intelligence model (145) may be a model capable of multinomial classification (e.g., a model based on a convolutional neural network (CNN)). In one embodiment, the artificial intelligence model (145) may be trained to classify the presence or absence of a TI based on images (310, 320) of the battery unit.

[0048] In one embodiment, data for training the artificial intelligence model (145) may include an image (310) of a battery unit in which TI is normally present, and an image (320) in which TI is absent. In one embodiment, the images (310, 320) used for training the artificial intelligence model (145) may be prepared in advance.

[0049] In one embodiment, the artificial intelligence model (145) may be trained to classify an input image into at least two states based on training data. Here, the at least two states may include a state in which a TI is normally present in the can assembly and a state in which a TI is not present in the can assembly.

[0050] FIG. 4 illustrates an image showing a foreign substance inside a TI of a battery unit according to one embodiment disclosed in this document. FIG. 4 includes an image (410) showing a foreign substance (415) inside a TI and an image (420) that enlarges a portion of the image (410) containing the foreign substance (415).

[0051] In one embodiment, the artificial intelligence model learning unit (141) may input an image of a battery unit into the artificial intelligence model (146). In one embodiment, the artificial intelligence model (146) may be a model capable of multinomial classification. In one embodiment, the artificial intelligence model (146) may be trained to identify a foreign object (415) based on an image (410) of a battery unit. Here, although FIG. 4 only illustrates an image (410) in which a foreign object (415) exists, this is merely an example. In one embodiment, the artificial intelligence model (146) may be trained to perform multinomial classification through an image (410) in which a foreign object (415) exists and an image (not shown) in which a foreign object (415) does not exist.

[0052] In one embodiment, the artificial intelligence model (146) may be trained to classify an input image into at least two states based on training data. Here, the at least two states may include a state in which a foreign object is present and a state in which a foreign object is not present.

[0053] FIG. 5 illustrates an image of a TI of a battery unit according to an embodiment disclosed in this document. FIG. 5 includes an image (510) showing an excitation within the TI and an enlarged image (520) of an area (515) in the image (510) where the excitation exists.

[0054] In one embodiment, the artificial intelligence model learning unit (141) may input an image of a battery unit into the artificial intelligence model (147). In one embodiment, the artificial intelligence model (147) may be a model capable of multinomial classification. In one embodiment, the artificial intelligence model (147) may be trained to identify whether a TI is lifted based on an image (510) of the battery unit. Here, the TI lift may occur in an area adjacent to a cylindrical can among the internal areas of the TI.

[0055] In one embodiment, data for training the artificial intelligence model (147) may include an image (510) with TI excitation and an image (not shown) without TI excitation. In one embodiment, the images used for training the artificial intelligence model (147) may be prepared in advance.

[0056] In one embodiment, the artificial intelligence model (147) can be trained to identify a raised region (515) in an input image based on training data.

[0057] FIG. 6 is a diagram illustrating a state in which a crack exists in a beading area of ​​a battery unit according to an embodiment disclosed in this document. FIG. 6 includes an image (610) showing a crack in a beading area outside a TI and an image (620) that enlarges an area (615) in which a crack exists in the image (610). Referring to the image (620), it can be confirmed that a crack exists in the area (625).

[0058] In one embodiment, the artificial intelligence model learning unit (141) may input an image of a battery unit into the artificial intelligence model (148). In one embodiment, the artificial intelligence model learning unit (141) may train the artificial intelligence model (148) to classify a beading state in a can assembly of the battery unit based on the image (610). In one embodiment, the artificial intelligence model (148) may be a model capable of multinomial classification.

[0059] In one embodiment, data for training the artificial intelligence model (148) may include an image (610) in which a crack exists in the bidding area, and an image (not shown) in which a crack does not exist in the bidding area. In one embodiment, the images used for training the artificial intelligence model (148) may be prepared in advance.

[0060] In one embodiment, the artificial intelligence model (148) may be trained to classify an input image into at least two states based on training data. Here, the at least two states may include a state in which a crack exists in the bidding region and a state in which a crack does not exist in the bidding region.

[0061] Condition assessment using artificial intelligence models

[0062] In one embodiment, the state determination unit (160) can determine the state of the can assembly based on an image of the battery unit (105). In one embodiment, the state determination unit (160) can determine the state of the can assembly by inputting an image of the battery unit (105) into the artificial intelligence model (145). In one embodiment, the state determination unit (160) can determine the state of the can assembly of the battery unit (105) based on the output of the artificial intelligence model (145). For example, the state determination unit (160) can determine the state of the can assembly of the battery unit (105) based on the state of the battery unit (105) classified by the artificial intelligence models (145 to 148) (e.g., presence or absence of TI, presence or absence of foreign matter inside the TI, presence or absence of foreign matter outside the TI, presence or absence of lifting of the TI, or presence or absence of beading crack).

[0063] In one embodiment, if TI is normally present in the battery unit (105), the status determination unit (160) may determine that the status of the can assembly is normal. In one embodiment, if TI is absent, the status determination unit (160) may determine that the status of the can assembly is defective.

[0064] In one embodiment, if there is no foreign matter inside and / or outside the TI of the battery unit (105), the status determination unit (160) may determine that the status of the can assembly is normal. In one embodiment, if there is a foreign matter inside and / or outside the TI, the status determination unit (160) may determine that the status of the can assembly is defective.

[0065] In one embodiment, if the TI in the battery unit (105) is not excited, the status determination unit (160) may determine that the status of the can assembly is normal. In one embodiment, if the TI is excited, the status determination unit (160) may determine that the status of the can assembly is defective.

[0066] In one embodiment, if there is no crack in the beading of the battery unit (105), the condition determination unit (160) may determine that the condition of the can assembly is normal. In one embodiment, if there is a crack in the beading, the condition determination unit (160) may determine that the condition of the can assembly is defective.

[0067] In one embodiment, the status judgment unit (160) can transmit the status judgment result of the battery unit (105) to the judgment unit (180).

[0068] In one embodiment, the analysis unit (170) can analyze the condition of the TI (210) in the can assembly (200) by performing a rule-based inspection on an image of the battery unit (105). Here, the rule-based inspection may be an inspection that determines whether the battery unit (105) is defective based on gray level information of the image of the battery unit (105).

[0069] For example, the analysis unit (170) may determine at least one of the presence or absence of a TI (220) of the battery unit (105), the presence or absence of an internal foreign substance in the TI (220), the presence or absence of an external foreign substance in the TI (220), or the presence or absence of a lifting of the TI (220) based on the gray level information of the image of the battery unit (105), and generate a determination result. According to one embodiment, the analysis unit (170) may sequentially determine the presence or absence of a TI (220), the presence or absence of an internal foreign substance in the TI (220), the presence or absence of an external foreign substance in the TI (220), or the presence or absence of a lifting of the TI (220) based on the gray level information of the image of the battery unit (105).

[0070] In one embodiment, the analysis unit (170) can transmit the status determination result of the battery unit (105) to the determination unit (180).

[0071] In one embodiment, the number of inspection items that can be inspected by the status determination unit (160) may be greater than the number of inspection items that can be inspected by the analysis unit (170). In other words, the number of inspection items based on the artificial intelligence model may be greater than the number of inspection items based on gray level information. For example, while the presence of a bidding crack may be inspected by the status determination unit (160), it may not be inspected by the analysis unit (170).

[0072] In one embodiment, the judgment unit (180) may determine whether the battery unit (105) is a good product based on the judgment results of the status judgment unit (160) and / or the analysis unit (170). For example, if the judgment results of the status judgment unit (160) and the analysis unit (170) both indicate normal, the judgment unit (180) may determine that the battery unit (105) is a good product. For example, if at least one judgment result of the status judgment unit (160) or the analysis unit (170) indicates a defect, the judgment unit (180) may determine that the battery unit (105) is not a good product.

[0073] As described above, according to the monitoring device (100) according to one embodiment disclosed in this document, a defective battery unit can be detected by accurately analyzing the TI status of the battery unit.

[0074] In FIG. 1, the artificial intelligence models of the program (125) are depicted as separate, but this is merely an example. In one embodiment, the artificial intelligence models (145, 146, 147, 148) may be implemented as a single artificial intelligence model.

[0075] FIG. 7 is a diagram illustrating an operation method of a monitoring device (100) according to one embodiment disclosed in this document. FIG. 7 may be described with reference to FIGS. 1 to 6.

[0076] Referring to FIG. 7, in operation 710, the monitoring device (100) can acquire an image. Here, the image may be an image of a battery unit (105) acquired by the image acquisition device (103).

[0077] In operation 720, the monitoring device (100) can perform a rule-based inspection. In one embodiment, the monitoring device (100) can perform a rule-based inspection based on gray level information of the image.

[0078] For example, the monitoring device (100) may determine at least one of the presence or absence of a TI (220) of the battery unit (105), the presence or absence of an internal foreign substance in the TI (220), the presence or absence of an external foreign substance in the TI (220), or the presence or absence of a lifting of the TI (220) based on the gray level information of the image of the battery unit (105), and generate a determination result. According to one embodiment, the monitoring device (100) may sequentially determine the presence or absence of a TI (220) of the battery unit, the presence or absence of an internal foreign substance in the TI (220), the presence or absence of an external foreign substance in the TI (220), or the presence or absence of a lifting of the TI (220) based on the gray level information of the image of the battery unit (105).

[0079] In operation 730, the monitoring device (100) can perform an artificial intelligence model-based inspection. For example, the monitoring device (100) can perform an artificial intelligence model-based inspection on an image based on pre-learned artificial intelligence models (145, 146, 147, 148).

[0080] In one embodiment, the monitoring device (100) can input an image of the battery unit (105) into the artificial intelligence model (145) to determine the state of the can assembly. In one embodiment, the monitoring device (100) can determine the state of the can assembly of the battery unit (105) based on the output of the artificial intelligence model (145). For example, the monitoring device (100) can determine the state of the can assembly of the battery unit (105) based on the state of the battery unit (105) classified by the artificial intelligence models (145 to 148) (e.g., presence or absence of TI, presence or absence of internal foreign matter in TI, presence or absence of external foreign matter in TI, presence or absence of lifting of TI, or presence or absence of beading crack).

[0081] In one embodiment, if TI is normally present in the battery unit (105), the monitoring device (100) can determine that the condition of the can assembly is normal. In one embodiment, if TI is absent, the monitoring device (100) can determine that the condition of the can assembly is defective.

[0082] In one embodiment, if there is no foreign matter inside and / or outside the TI of the battery unit (105), the monitoring device (100) can determine that the condition of the can assembly is normal. In one embodiment, if there is a foreign matter inside and / or outside the TI, the monitoring device (100) can determine that the condition of the can assembly is defective.

[0083] In one embodiment, if the TI in the battery unit (105) is not excited, the monitoring device (100) can determine that the condition of the can assembly is normal. In one embodiment, if the TI is excited, the monitoring device (100) can determine that the condition of the can assembly is defective.

[0084] In one embodiment, if there is no crack in the beading of the battery unit (105), the monitoring device (100) can determine that the condition of the can assembly is normal. In one embodiment, if there is a crack in the beading, the monitoring device (100) can determine that the condition of the can assembly is defective.

[0085] In operation 740, the monitoring device (100) can determine whether there is a defect based on the inspection result.

[0086] In one embodiment, the monitoring device (100) may determine that the battery unit (105) is good if both the results of the rule-based inspection and the results of the artificial intelligence model-based inspection indicate normality.

Claims

1. An image acquisition unit for acquiring an image of at least one process processing device related to the manufacturing of a battery cell; A status judgment unit that inputs the acquired image into an artificial intelligence model for judging the status of the top insulator of the battery cell and generates a first judgment result for judging whether the battery cell is defective; and A monitoring device including a judgment unit that judges whether the battery cell is defective based on the first judgment result.

2. In claim 1, Further comprising an analysis unit that generates a second judgment result for determining whether the battery cell is defective based on the gray level information of the acquired image, The above judgment unit determines whether the battery cell is defective based on the first judgment result and the second judgment result. Monitoring device.

3. In claim 2, The number of inspection items based on the above artificial intelligence model is greater than the number of inspection items based on the above gray level information. Monitoring device.

4. In claim 1, The artificial intelligence model includes an artificial intelligence model for detecting the presence or absence of the top insulator of the battery cell, the presence or absence of foreign matter inside the top insulator, the presence or absence of foreign matter outside the top insulator, and the presence or absence of lifting of the top insulator based on the image. Monitoring device.

5. In claim 1, The above artificial intelligence model includes an artificial intelligence model for detecting whether the battery cell has a beading crack based on the image. Monitoring device.

6. An operation of acquiring an image of at least one process processing device associated with the manufacturing of a battery cell; An operation of inputting the acquired image into an artificial intelligence model for determining the state of the top insulator of the battery cell to generate a first judgment result for determining whether the battery cell is defective, and An operation for determining whether the battery cell is defective based on the first judgment result is included. How the monitoring device operates.

7. In claim 6, Further comprising an operation of generating a second judgment result for determining whether the battery cell is defective based on the gray level information of the acquired image, The operation of determining whether the battery cell is defective is to determine whether the battery cell is defective based on the first determination result and the second determination result. How it works.

8. In claim 7, The number of inspection items based on the above artificial intelligence model is greater than the number of inspection items based on the above gray level information. How it works.

9. In claim 6, The artificial intelligence model includes an artificial intelligence model for detecting the presence or absence of the top insulator of the battery cell, the presence or absence of foreign matter inside the top insulator, the presence or absence of foreign matter outside the top insulator, and the presence or absence of lifting of the top insulator based on the image. How it works.

10. In claim 6, The above artificial intelligence model includes an artificial intelligence model for detecting whether the battery cell has a beading crack based on the image. How it works.

Images