Fiber yarn oiling control method and device, electronic equipment and storage medium

Abstract

The invention provides a fiber yarn oiling control method and device, electronic equipment and a storage medium, and relates to the technical field of computers. According to the implementation scheme, for an oil tanker for oiling a fiber tow within a first time period, the surface, making contact with the fiber tow, of the oil tanker is shot, and an oil tanker image sequence is obtained; identifying the oil tanker image sequence based on a first image identification model to obtain an axial movement range of the fiber tow on the oil tanker; under the condition that the axial movement range of the fiber tow on the oil tanker exceeds the preset axial movement range, the oil tanker image sequence is recognized based on a second image recognition model to obtain the thickness degree of the fiber tow, and the oil tanker image sequence is recognized based on a third image recognition model to obtain the surface roughness degree of the oil tanker; determining the target oiling quantity of the fiber tows based on the thickness degree of the fiber tows and the surface roughness degree of the oil tanker; and based on the target oiling quantity of the fiber tows, oil spraying operation of the oiling mechanism on the oil wheel is adjusted.

Description

Technical Field

[0001] The present disclosure relates to the field of computer technology, and more particularly to a method, device, electronic device and storage medium for controlling oiling of fiber filaments. Background Art

[0002] In the process of chemical fiber production, the tow needs to be oiled to improve the lubricity, cohesion, processing performance and performance of the tow, and eliminate static electricity. The type of oil and the choice of oiling method have a very important influence on the spinning process and fiber quality. Since the oiling method of the oiling wheel is more uniform in oiling the tow, the oiling wheel oiling method is generally used. Summary of the invention

[0003] The present invention provides a fiber oiling control method, device, electronic device and storage medium.

[0004] According to one aspect of the present disclosure, a method for controlling fiber filament oiling is provided, comprising:

[0005] For an oil tanker that oils a fiber tow in a first time period, photographing a surface of the oil tanker that contacts the fiber tow to obtain an image sequence of the oil tanker;

[0006] Based on the first image recognition model, the tanker image sequence is recognized to obtain the axial movement range of the fiber tow on the tanker;

[0007] In the case where the axial movement range of the fiber tow on the tanker exceeds the preset axial movement range, the tanker image sequence is recognized based on the second image recognition model to obtain the thickness of the fiber tow; and the tanker image sequence is recognized based on the third image recognition model to obtain the surface roughness of the tanker;

[0008] Determining a target oiling amount of the fiber tow based on the thickness of the fiber tow and the surface roughness of the tanker;

[0009] Based on the target oiling amount of the fiber tow, the oiling operation of the oiling mechanism on the oil tanker is adjusted.

[0010] According to another aspect of the present disclosure, a fiber filament oiling control device is provided, comprising:

[0011] an image sequence determination module, for photographing a surface of an oil tanker that oils a fiber tow in a first time period, and obtaining an image sequence of the oil tanker;

[0012] an axial range determination module, configured to identify the tanker image sequence based on a first image recognition model, and obtain an axial movement range of the fiber tow on the tanker;

[0013] a degree determination module, configured to, when the axial movement range of the fiber tow on the tanker exceeds a preset axial movement range, identify the tanker image sequence based on a second image recognition model to obtain the thickness of the fiber tow, and, based on a third image recognition model, identify the tanker image sequence to obtain the surface roughness of the tanker;

[0014] An oiling amount determination module, used for determining a target oiling amount of the fiber tow based on the thickness of the fiber tow and the surface roughness of the tanker;

[0015] The oil injection adjustment module is used to adjust the oil injection operation of the oiling mechanism on the oil tanker based on the target oiling amount of the fiber tow.

[0016] According to another aspect of the present disclosure, there is provided an electronic device, comprising:

[0017] at least one processor; and

[0018] a memory communicatively connected to the at least one processor; wherein,

[0019] The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute any fiber filament oiling control method in the embodiments of the present disclosure.

[0020] According to another aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to enable the computer to execute any fiber filament oiling control method according to the embodiments of the present disclosure.

[0021] According to the technology disclosed in the present invention, for an oil tanker that oils a fiber bundle in a first time period, the surface of the oil tanker in contact with the fiber bundle is photographed to obtain an image sequence of the oil tanker; based on a first image recognition model, the image sequence of the oil tanker is recognized to obtain the axial movement range of the fiber bundle on the oil tanker; in the case where the axial movement range of the fiber bundle on the oil tanker exceeds the preset axial movement range, the image sequence of the oil tanker is recognized based on a second image recognition model to obtain the thickness of the fiber bundle, and, based on a third image recognition model, the image sequence of the oil tanker is recognized to obtain the surface roughness of the oil tanker; based on the thickness of the fiber bundle and the surface roughness of the oil tanker, the target oiling amount of the fiber bundle is determined; based on the target oiling amount of the fiber bundle wheel, the oiling mechanism is adjusted to spray the oil on the oil tanker. Thus, the axial movement range of the fiber bundle is detected by image. If its axial movement range exceeds the preset axial movement range, it means that the oiling amount of the fiber bundle is insufficient, and the oiling amount needs to be adjusted. In addition, the thickness of the fiber tow and the surface roughness of the tanker are detected through images to determine the corresponding target oiling amount, so that the fiber tow can be evenly oiled and the quality of the fiber tow after oiling is improved.

[0022] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present disclosure, nor is it intended to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The accompanying drawings are used to better understand the present solution and do not constitute a limitation of the present disclosure.

[0024] Figure 1 is a schematic diagram of oiling a fiber tow on a tanker according to an embodiment of the present disclosure;

[0025] Figure 2 is a flow chart of a fiber filament oiling control method according to an embodiment of the present disclosure;

[0026] Figure 3 is a structural block diagram of a fiber thread oiling control device according to an embodiment of the present disclosure;

[0027] Figure 4 is a block diagram of an electronic device according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0028] The following is a description of exemplary embodiments of the present disclosure in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope of the present disclosure. Similarly, for the sake of clarity and conciseness, the description of well-known functions and structures is omitted in the following description.

[0029] Figure 1 Schematic diagram of oiling a fiber bundle on a tanker according to an embodiment of the present disclosure.

[0030] like Figure 1 As shown, the two parallel cylindrical objects in the figure are oil tankers 110, and the object in contact with the two oil tankers 110 is a fixed shaft 120. The fiber tow 130 is oiled by oiling the oil tanker so that the fiber tow 130 can be evenly oiled to improve the lubricity of the tow.

[0031] Figure 2 4 is a flow chart of an auxiliary roller control method according to an embodiment of the present invention.

[0032] like Figure 2 As shown, the fiber filament oiling control method may include:

[0033] S210, for an oil tanker that oils the fiber tow in a first time period, photographing a surface of the oil tanker that contacts the fiber tow to obtain an image sequence of the oil tanker;

[0034] S220, based on the first image recognition model, recognize the tanker image sequence to obtain the axial movement range of the fiber tow on the tanker;

[0035] S230, when the axial movement range of the fiber tow on the tanker exceeds the preset axial movement range, recognizing the tanker image sequence based on the second image recognition model to obtain the thickness of the fiber tow, and recognizing the tanker image sequence based on the third image recognition model to obtain the surface roughness of the tanker;

[0036] S240, determining a target oiling amount of the fiber tow based on the thickness of the fiber tow and the surface roughness of the tanker;

[0037] S250, adjusting the oiling operation of the oiling mechanism on the oil tanker based on the target oiling amount of the fiber tow.

[0038] Exemplarily, the first time period may be 30 seconds, 1 minute or 2 minutes, which is not limited here. It should be noted that the oiling amount within the time period is adjusted to ensure that the liquid level in the oil tank remains unchanged.

[0039] For example, the tanker image sequence may include a plurality of images taken from the same angle and arranged in time sequence.

[0040] Exemplarily, the first image recognition model may be a pre-trained model, and the first image recognition model is used to recognize position information of fiber bundles in an input image.

[0041] Exemplarily, a first training sample set is obtained, wherein the training samples in the first training sample set include an input image and axial position information of a fiber bundle in the input image on a tanker. The first training sample set is used to train a neural network to obtain a first image recognition model. For example, the neural network may be a FCN (Fully Convolutional Networks), a RefineNet (multi-path refinement network), a segnet image segmentation network, and the like.

[0042] For example, if the axial movement range of the fiber tow on the tanker exceeds the preset axial movement range, it means that the amount of oil applied to the fiber tow is insufficient, so that its position deviates from the preset axial movement range when oiled. If the axial movement range of the fiber tow on the tanker does not exceed the preset axial movement range, it means that the amount of oil applied to the fiber tow is sufficient, and there is no need to perform subsequent steps.

[0043] It can be understood that the above-mentioned axial direction refers to a direction parallel to the axial direction of the tanker.

[0044] Exemplarily, when the amount of oil applied to the fiber tow is insufficient, it is necessary to adjust the amount of oil applied to the fiber tow, or adjust the oiling method, to increase the amount of oil applied to the fiber tow.

[0045] For example, for fiber bundles of different thicknesses, the amount of oiling required is different. Generally, the thicker the fiber bundle, the greater the daily oil consumption and the greater the amount of oiling.

[0046] For example, for tankers with different surface roughness, the degree of influence on the amount of oil applied to the fiber tow is different. The rougher the surface of the tanker, the smaller the friction contact surface between the fiber tow and the tanker, and the less amount of oil applied to the fiber tow. Therefore, when the surface of the tanker becomes rougher, the amount of oil applied to the fiber tow needs to be increased.

[0047] For example, the roughness of the fiber bundles and the surface roughness of the tanker can be identified by image recognition.

[0048] Exemplarily, the second image recognition model may be a pre-trained model. The second image recognition model is used to recognize the coarseness of the fiber bundle in the input image.

[0049] Exemplarily, a second training sample set is obtained, and the training samples in the second training sample set include the input image and the thickness of the fiber bundles in the input image. The second training sample set is used to train the neural network to obtain a second image recognition model. For example, the neural network can be FCN (Fully Convolutional Networks), RefineNet (multi-path refinement network), and segnet image segmentation network, etc.

[0050] Exemplarily, a third training sample set is obtained, and the training samples in the third training sample set include the input image and the surface roughness of the oil tanker in the input image. The third training sample set is used to train the neural network to obtain a third image recognition model. For example, the neural network can be FCN (Fully Convolutional Networks), RefineNet (multi-path refinement network), and segnet image segmentation network, etc.

[0051] For example, a mapping function or a table may be used to pre-record the relationship between the thickness of the fiber tow and the surface roughness of the tanker and the amount of oil applied to the fiber tow.

[0052] Exemplarily, the thickness of the fiber tow and the surface roughness of the tanker are processed using a mapping function or a table to obtain a target oiling amount of the fiber tow.

[0053] Exemplarily, based on the target oiling amount of the fiber bundle, the oiling amount of the oiling mechanism sprayed on the tanker is directly adjusted, or the oiling angle or oiling distance of the oiling mechanism is adjusted, thereby indirectly increasing the oiling amount on the tanker.

[0054] According to the above embodiment, the axial movement range of the fiber tow is detected by image. If the axial movement range exceeds the preset axial movement range, it means that the oiling amount of the fiber tow is insufficient, and the oiling amount needs to be adjusted. In addition, the thickness of the fiber tow and the surface roughness of the oil tanker are detected by image to determine the corresponding target oiling amount, so that the fiber tow can be evenly oiled and the quality of the fiber tow after oiling is improved.

[0055] In one embodiment, a target oiling amount of the fiber tow is determined based on the thickness of the fiber tow and the surface roughness of the tanker, including: based on the thickness of the fiber tow and the surface roughness of the tanker, searching for the corresponding oiling amount in a pre-established database of relationships between the thickness of the fiber tow, the surface roughness of the tanker and the oiling amount; and determining the target oiling amount of the fiber tow based on the found oiling amount.

[0056] For example, a multivariate function can be used to fit multiple data pairs of fiber tow thickness, tanker surface roughness, and oil loading to obtain a mapping function. Thus, the fiber tow thickness and the tanker surface roughness are input into the mapping function, and the mapping function is calculated to obtain the target oil loading of the fiber tow.

[0057] Exemplarily, the least square method may be used to fit the above-mentioned multiple data pairs to obtain the above-mentioned mapping function.

[0058] Exemplarily, the oiling amount found is compared with the current oiling amount. If the oiling amount found is greater than the current oiling amount and the difference between the oiling amount found and the current oiling amount is within the first range, the sum of the product of the first coefficient and the difference and the current oiling amount is used as the target oiling amount of the fiber tow. If the difference is within the second range, the oiling amount found is used as the target oiling amount of the fiber tow. The first range is greater than the second range, and the first coefficient is greater than 1.

[0059] According to the above embodiment, based on the thickness of the fiber tow and the surface roughness of the tanker, the corresponding oiling amount is searched in a pre-established database of the relationship between the thickness of the fiber tow, the surface roughness of the tanker and the oiling amount; based on the found oiling amount, the target oiling amount of the fiber tow is determined. In this way, the required oiling amount of the fiber tow can be accurately predicted, and the oiling quality of the fiber tow can be improved.

[0060] In one embodiment, based on the first image recognition model, an oil tanker image sequence is identified to obtain the axial movement range of the fiber bundle on the oil tanker, including: based on the first image recognition model, each image in the oil tanker image sequence is identified to obtain the axial position of the fiber bundle in each image on the oil tanker; based on the axial position of the fiber bundle in each image on the oil tanker, the axial movement range of the fiber bundle on the oil tanker is determined.

[0061] It can be understood that the shooting angles of the images in the tanker image sequence are the same, so as to improve the accuracy of determining the axial movement range of the fiber tow on the tanker.

[0062] Exemplarily, the axial movement range of the fiber tow on the tanker is determined based on the farthest position and the closest position of the fiber tow in the axial positions on the tanker in each image.

[0063] According to the above embodiment, the axial movement range of the fiber tow on the tanker can be accurately determined by image recognition.

[0064] In one embodiment, based on the second image recognition model, the tanker image sequence is recognized to obtain the thickness of the fiber bundles, including: based on the second image recognition model, each image in the tanker image sequence is recognized to obtain the thickness of the fiber bundles in each image; based on the thickness of the fiber bundles in each image, the thickness of the fiber bundles is determined.

[0065] Exemplarily, the thickness of the fiber tow in each image is averaged to obtain the thickness of the fiber tow.

[0066] Exemplarily, a median is determined from the thickness of the fiber tow in each image, and the median is used as the thickness of the fiber tow.

[0067] According to the above embodiment, the thickness of the fiber bundle can be accurately determined by image recognition.

[0068] In one embodiment, based on the third image recognition model, a tanker image sequence is recognized to obtain the surface roughness of the tanker, including: based on the third image recognition model, each image in the tanker image sequence is recognized to obtain the surface roughness of the tanker in each image; based on the surface roughness of the tanker in each image, the surface roughness of the tanker is determined.

[0069] Exemplarily, the surface roughness of the oil tanker in each image is averaged to obtain the surface roughness of the oil tanker.

[0070] Exemplarily, a median is determined from the surface roughness of the oil tanker in each image, and the median is used as the surface roughness of the oil tanker.

[0071] According to the above embodiment, the surface roughness of the tanker can be accurately determined by image recognition.

[0072] Figure 3 It is a structural block diagram of a fiber filament oiling control device according to an embodiment of the present disclosure.

[0073] like Figure 3 As shown, the fiber filament oiling control device may include:

[0074] An image sequence determination module 310 is used for photographing a surface of an oil tanker that is in contact with the fiber tow during a first time period to obtain an image sequence of the oil tanker;

[0075] An axial range determination module 320, configured to identify the tanker image sequence based on a first image recognition model to obtain an axial movement range of the fiber tow on the tanker;

[0076] a degree determination module 330, configured to, when the axial movement range of the fiber tow on the tanker exceeds a preset axial movement range, identify the tanker image sequence based on a second image recognition model to obtain the thickness of the fiber tow, and, based on a third image recognition model, identify the tanker image sequence to obtain the surface roughness of the tanker;

[0077] An oiling amount determination module 340, for determining a target oiling amount of the fiber tow based on the thickness of the fiber tow and the surface roughness of the tanker;

[0078] The oil injection adjustment module 350 is used to adjust the oil injection operation of the oiling mechanism on the oil tanker based on the target oiling amount of the fiber tow.

[0079] In one embodiment, the oiling amount determination module includes:

[0080] An oiling amount searching unit is used to search for a corresponding oiling amount in a pre-established database of relationships between the thickness of the fiber tow, the roughness of the surface of the tanker and the oiling amount based on the thickness of the fiber tow and the surface roughness of the tanker;

[0081] The target amount determination unit is used to determine the target oiling amount of the fiber tow based on the found oiling amount.

[0082] In one embodiment, the axial range determination module includes:

[0083] an axial position determination unit, configured to recognize each image in the tanker image sequence based on the first image recognition model, and obtain the axial position of the fiber tow on the tanker in each image;

[0084] A range determination unit is used to determine the axial movement range of the fiber tow on the tanker based on the axial position of the fiber tow on the tanker in each of the images.

[0085] In one implementation, the degree determination module 330 includes:

[0086] A first model recognition unit is used to recognize each image in the tanker image sequence based on the second image recognition model to obtain the thickness of the fiber tow in each image;

[0087] The thickness determination unit is used to determine the thickness of the fiber tow based on the thickness of the fiber tow in each of the images.

[0088] In one implementation, the degree determination module 330 includes:

[0089] a second model recognition unit, configured to recognize each image in the tanker image sequence based on the third image recognition model, and obtain a surface roughness of the tanker in each of the images;

[0090] The roughness determination unit is used to determine the surface roughness of the oil tanker based on the surface roughness of the oil tanker in each of the images.

[0091] In the technical solution disclosed herein, the acquisition, storage and application of user personal information involved are in compliance with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0092] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium and a computer program product.

[0093] Figure 4 FIG. 1 is a structural block diagram of an electronic device according to an embodiment of the present disclosure. Figure 4 As shown, the electronic device includes: a memory 410 and a processor 420, and the memory 410 stores a computer program that can be run on the processor 420. The number of memories 410 and processors 420 can be one or more. The memory 410 can store one or more computer programs. When the one or more computer programs are executed by the electronic device, the electronic device executes the method provided by the above method embodiment. The electronic device may also include: a communication interface 430, which is used to communicate with external devices and perform data exchange transmission.

[0094] If the memory 410, the processor 420 and the communication interface 430 are implemented independently, the memory 410, the processor 420 and the communication interface 430 can be connected to each other through a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 4 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0095] Optionally, in a specific implementation, if the memory 410, the processor 420 and the communication interface 430 are integrated on a chip, the memory 410, the processor 420 and the communication interface 430 can communicate with each other through an internal interface.

[0096] It should be understood that the processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc. It is worth noting that the processor may be a processor supporting the Advanced RISC Machines (ARM) architecture.

[0097] Further, optionally, the above-mentioned memory may include a read-only memory and a random access memory, and may also include a non-volatile random access memory. The memory may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may include a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may include a random access memory (RAM), which is used as an external cache. By way of example but not limitation, many forms of RAM are available. For example, static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM) and direct memory bus random access memory (DR RAM).

[0098] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer instructions are loaded and executed on a computer, the process or function described in the embodiment of the present disclosure is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from a website site, computer, server or data center by wired (for example: coaxial cable, optical fiber, data subscriber line (Digital Subscriber Line, DSL)) or wireless (for example: infrared, Bluetooth, microwave, etc.) mode to another website site, computer, server or data center. The computer-readable storage medium can be any available medium that a computer can access, or a data storage device such as a server or data center that includes one or more available media integrations. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a magnetic tape), an optical medium (e.g., a digital versatile disc (DVD)), or a semiconductor medium (e.g., a solid state disk (SSD)), etc. It is worth noting that the computer-readable storage medium mentioned in the present disclosure may be a non-volatile storage medium, in other words, a non-transient storage medium.

[0099] A person skilled in the art will understand that all or part of the steps to implement the above embodiments may be accomplished by hardware or by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, and the above-mentioned storage medium may be a read-only memory, a disk or an optical disk, etc.

[0100] In the description of the embodiments of the present disclosure, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present disclosure. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, unless they are contradictory.

[0101] In the description of the embodiments of the present disclosure, unless otherwise specified, " / " means or, for example, A / B can mean A or B. "And / or" in this article is only a way to describe the association relationship of associated objects, indicating that there can be three relationships, for example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone.

[0102] In the description of the embodiments of the present disclosure, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of the embodiments of the present disclosure, unless otherwise specified, "plurality" means two or more.

[0103] The above description is only an exemplary embodiment of the present disclosure and is not intended to limit the present disclosure. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. A method for controlling fiber oiling, characterized in that: include: For an oil tanker that oils a fiber tow in a first time period, photographing a surface of the oil tanker that contacts the fiber tow to obtain an image sequence of the oil tanker; Based on the first image recognition model, the tanker image sequence is recognized to obtain the axial movement range of the fiber tow on the tanker; In the case where the axial movement range of the fiber tow on the tanker exceeds the preset axial movement range, the tanker image sequence is recognized based on the second image recognition model to obtain the thickness of the fiber tow; and the tanker image sequence is recognized based on the third image recognition model to obtain the surface roughness of the tanker; Determining a target oiling amount of the fiber tow based on the thickness of the fiber tow and the surface roughness of the tanker; Based on the target oiling amount of the fiber tow, the oiling operation of the oiling mechanism on the oil tanker is adjusted.

2. The method according to claim 1, characterized in that The step of determining a target oiling amount of the fiber tow based on the thickness of the fiber tow and the surface roughness of the tanker includes: Based on the thickness of the fiber tow and the surface roughness of the tanker, searching for the corresponding oiling amount in a pre-established database of relationships between the thickness of the fiber tow, the surface roughness of the tanker and the oiling amount; Based on the found oil application amount, a target oil application amount of the fiber tow is determined.

3. The method according to claim 1, characterized in that The step of recognizing the tanker image sequence based on the first image recognition model to obtain the axial movement range of the fiber tow on the tanker includes: Based on the first image recognition model, each image in the tanker image sequence is recognized to obtain the axial position of the fiber tow on the tanker in each image; Based on the axial position of the fiber tow on the tanker in each of the images, an axial movement range of the fiber tow on the tanker is determined.

4. The method according to claim 1, characterized in that: The step of identifying the tanker image sequence based on the second image recognition model to obtain the thickness of the fiber tow includes: Based on the second image recognition model, each image in the tanker image sequence is recognized to obtain the thickness of the fiber tow in each image; The thickness of the fiber tow is determined based on the thickness of the fiber tow in each of the images.

5. The method according to claim 1, characterized in that The step of recognizing the tanker image sequence based on the third image recognition model to obtain the surface roughness of the tanker includes: Based on the third image recognition model, each image in the oil tanker image sequence is recognized to obtain the surface roughness of the oil tanker in each of the images; The surface roughness of the oil tanker is determined based on the surface roughness of the oil tanker in each of the images.

6. A fiber oiling control device, characterized in that: include: an image sequence determination module, for photographing a surface of an oil tanker that oils a fiber tow in a first time period, and obtaining an image sequence of the oil tanker; an axial range determination module, configured to identify the tanker image sequence based on a first image recognition model, and obtain an axial movement range of the fiber tow on the tanker; a degree determination module, configured to, when the axial movement range of the fiber tow on the tanker exceeds a preset axial movement range, identify the tanker image sequence based on a second image recognition model to obtain the thickness of the fiber tow, and, based on a third image recognition model, identify the tanker image sequence to obtain the surface roughness of the tanker; An oiling amount determination module, used for determining a target oiling amount of the fiber tow based on the thickness of the fiber tow and the surface roughness of the tanker; The oil injection adjustment module is used to adjust the oil injection operation of the oiling mechanism on the oil tanker based on the target oiling amount of the fiber tow.

7. The device according to claim 6, characterized in that The oiling amount determination module comprises: An oiling amount searching unit is used to search for a corresponding oiling amount in a pre-established database of relationships between the thickness of the fiber tow, the roughness of the surface of the tanker and the oiling amount based on the thickness of the fiber tow and the surface roughness of the tanker; The target amount determination unit is used to determine the target oiling amount of the fiber tow based on the found oiling amount.

8. The device according to claim 6, characterized in that The axial range determination module comprises: an axial position determination unit, configured to recognize each image in the tanker image sequence based on the first image recognition model, and obtain the axial position of the fiber tow on the tanker in each image; A range determination unit is used to determine the axial movement range of the fiber tow on the tanker based on the axial position of the fiber tow on the tanker in each of the images.

9. An electronic device, comprising: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 5.

10. A non-transitory computer-readable storage medium storing computer instructions, wherein: The computer instructions are used to cause the computer to execute the method according to any one of claims 1-5.

Images