Intelligent cold rolling lubrication system and method

Abstract

The application discloses an intelligent cold rolling lubricating system, an intelligent cold rolling lubricating method, an intelligent cold rolling lubricating device and a storage medium, and belongs to the technical field of cold rolling.The intelligent cold rolling lubricating system comprises at least two image detection sensors arranged on a rolling mill, the shooting direction of the at least two image detection sensors is perpendicular to the moving direction of target material, a monitoring module is used for receiving target image data of the at least two image detection sensors, a calculation module is used for determining position offset data of the target material according to the target image data, and an adjusting module is used for determining the operation parameters of a lubricating device according to the position offset data, so that the quality of cold rolling is improved.

Description

Technical Field

[0001] This invention relates to the field of cold rolling, and more particularly to an intelligent cold rolling lubrication system, an intelligent cold rolling lubrication method, an intelligent cold rolling lubrication device, and a storage medium. Background Technology

[0002] Cold rolling is a process of rolling metal materials at room temperature using a cold rolling mill, causing plastic deformation. In cold rolling, it's crucial to control the amount of lubricant sprayed to ensure a smooth oil film covering the metal surface, guaranteeing the proper functioning of the process. Currently, cold rolling lubrication systems typically use fixed parameters. When the workpiece vibrates within the mill, these fixed parameters can prevent the sprayed lubricant from accurately covering the metal surface, resulting in poor lubrication quality and consequently affecting the overall quality of the cold roll.

[0003] The above content is only used to help understand the technical solution of the present invention and does not represent an admission that the above content is prior art. Summary of the Invention

[0004] The main objective of this invention is to provide an intelligent cold rolling lubrication system, an intelligent cold rolling lubrication method, an intelligent cold rolling lubrication device, and a storage medium, with the aim of improving the quality of cold rolling.

[0005] To achieve the above objectives, the present invention provides an intelligent cold rolling lubrication system, the intelligent cold rolling lubrication system comprising:

[0006] At least two image detection sensors are installed in the rolling mill, and the imaging direction of the at least two image detection sensors is perpendicular to the movement direction of the target material;

[0007] The monitoring module is used to receive target image data from at least two image detection sensors;

[0008] The calculation module is used to determine the positional offset data of the target material based on the target image data;

[0009] An adjustment module is used to determine the operating parameters of the lubrication device based on the position offset data.

[0010] Furthermore, to achieve the above objectives, the present invention also provides an intelligent cold rolling lubrication method, comprising at least two image detection sensors installed on the rolling mill, wherein the imaging direction of the at least two image detection sensors is perpendicular to the movement direction of the target material, and the intelligent cold rolling lubrication method includes the following steps:

[0011] Receive target image data from at least two image detection sensors;

[0012] Determine the position offset data of the target material based on the target image data;

[0013] The operating parameters of the lubrication device are determined based on the position offset data.

[0014] Optionally, the number of image detection sensors is two, and the two image detection sensors are perpendicular in their shooting directions. The target image data includes first target image data captured by the first image detection sensor and second target image data captured by the second image detection sensor. The step of determining the positional offset data of the target material based on the target image data includes:

[0015] Determine the first offset data of the target material in the first direction based on the first target image data;

[0016] The second offset data of the target material in a second direction is determined based on the second target image data, wherein the first direction is perpendicular to the second direction.

[0017] Optionally, the step of determining the positional offset data of the target material based on the target image data includes:

[0018] Edge information of the target material in the target image data is extracted according to an edge extraction algorithm, which includes at least one of the Laplacian algorithm, the Roberts algorithm, the Canney algorithm, and the Sobel algorithm.

[0019] The position offset data is determined based on the edge information and the preset position information.

[0020] Optionally, the at least two image detection sensors are positioned parallel to each other, and before the step of determining the positional offset data of the target material based on the target image data, the method further includes:

[0021] The depth of field of the at least two image detection sensors is controlled to a preset depth of field;

[0022] The spacing between the at least two adjacent image detection sensors in the shooting direction is adjusted to a preset distance;

[0023] The step of determining the position offset data of the target material based on the target image data further includes:

[0024] The sharpness of the target image data is determined by an image recognition algorithm, and multiple sharpness values ​​corresponding to the target image data are obtained.

[0025] The positional offset data is determined based on the target distance between the multiple resolution and image detection sensors and the target material.

[0026] Optionally, before the step of adjusting the distance between the at least two adjacent image detection sensors in the shooting direction to a preset distance, the method further includes:

[0027] The preset distance is set to half of the preset depth of field.

[0028] Optionally, the step of determining the positional offset data based on the target distance between the plurality of resolution and image detection sensors and the target material includes:

[0029] When there are adjacent target image data that meet the preset conditions, the position offset data is calculated based on the target distance corresponding to the adjacent target image data that meet the preset conditions.

[0030] When only one of the aforementioned sharpness values ​​is greater than or equal to a preset sharpness threshold, the position offset data is calculated based on the target distance corresponding to the sharpness value greater than or equal to the preset sharpness threshold.

[0031] The preset condition is that the sharpness of adjacent target image data is greater than or equal to the preset sharpness threshold.

[0032] Optionally, the step of determining the operating parameters of the lubrication device based on the position offset data includes:

[0033] Obtain the reference operating parameters of the lubrication device, including the reference lubricant flow rate, the reference lubricant injection pressure, and the reference nozzle angle;

[0034] The operating parameters are calculated based on the position offset data and the baseline operating parameters.

[0035] In addition, to achieve the above objectives, the present invention also provides an intelligent cold rolling lubrication device, the intelligent cold rolling lubrication device comprising: a memory, a processor, and an intelligent cold rolling lubrication program stored in the memory and executable on the processor, the intelligent cold rolling lubrication program being configured to implement the steps of the intelligent cold rolling lubrication method described in any of the above claims.

[0036] In addition, to achieve the above objectives, the present invention also provides a storage medium storing an intelligent cold rolling lubrication program, which, when executed by a processor, implements the steps of any of the intelligent cold rolling lubrication methods described above.

[0037] This invention proposes an intelligent cold rolling lubrication system, characterized in that the intelligent cold rolling lubrication system includes: at least two image detection sensors installed on the rolling mill, the imaging direction of the at least two image detection sensors being perpendicular to the movement direction of the target material; a monitoring module for receiving target image data from the at least two image detection sensors; a calculation module for determining the positional offset data of the target material based on the target image data; and an adjustment module for determining the operating parameters of the lubrication device based on the positional offset data. By receiving target image data, determining the positional offset data of the target material based on the target image data, and adjusting the operating parameters of the lubrication device based on the positional offset data, the lubrication device can accurately adapt and adjust when the target material shifts, thereby improving the stability of lubrication during continuous cold rolling and thus improving the quality of cold rolling. Attached Figure Description

[0038] Figure 1 This is a schematic diagram of the structure of an intelligent cold rolling lubrication equipment with hardware operating environment involved in the embodiments of the present invention;

[0039] Figure 2 This is a schematic flowchart of the first embodiment of the intelligent cold rolling lubrication method of the present invention;

[0040] Figure 3 This is a schematic flowchart of the fourth embodiment of the intelligent cold rolling lubrication method of the present invention;

[0041] Figure 4 This is a schematic flowchart of the fifth embodiment of the intelligent cold rolling lubrication method of the present invention;

[0042] Figure 5 This is a schematic flowchart of the sixth embodiment of the intelligent cold rolling lubrication method of the present invention.

[0043] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0044] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0045] Reference Figure 1 , Figure 1 This is a schematic diagram of the intelligent cold rolling lubrication equipment structure of the hardware operating environment involved in the embodiments of the present invention.

[0046] like Figure 1As shown, the intelligent cold rolling lubrication equipment may include: a processor 1001, such as a central processing unit (CPU), a communication bus 1002, a detection device 1003, a network interface 1004, and a memory 1005. The communication bus 1002 is used to enable communication between these components. The detection device 1003 may include an image detection sensor, and optionally, the detection device 1003 may also be connected to the communication bus via a standard wired interface or a wireless interface. The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface). The memory 1005 may be a high-speed random access memory (RAM) or a stable non-volatile memory (NVM), such as a disk storage device. The memory 1005 may also optionally be a storage device independent of the aforementioned processor 1001.

[0047] Those skilled in the art will understand that Figure 1 The structure shown does not constitute a limitation on intelligent cold rolling lubrication equipment, and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0048] like Figure 1 As shown, the memory 1005, which serves as a storage medium, may include an operating system, a data storage module, a network communication module, a user interface module, and an intelligent cold rolling lubrication program.

[0049] exist Figure 1 In the intelligent cold rolling lubrication equipment shown, the network interface 1004 is mainly used for data communication with other equipment; the detection device 1003 is mainly used for detecting vibration; the processor 1001 and the memory 1005 in the intelligent cold rolling lubrication equipment of the present invention can be set in the intelligent cold rolling lubrication equipment. The intelligent cold rolling lubrication equipment calls the intelligent cold rolling lubrication program stored in the memory 1005 through the processor 1001 and executes the intelligent cold rolling lubrication method provided in the embodiment of the present invention.

[0050] This invention provides an intelligent cold rolling lubrication method, referring to... Figure 2 , Figure 2 This is a schematic flowchart of the first embodiment of an intelligent cold rolling lubrication method according to the present invention.

[0051] In this embodiment, the intelligent cold rolling lubrication method includes:

[0052] Step S10: Receive target image data from at least two image detection sensors;

[0053] In this embodiment, the image detection sensor can specifically be a camera. The target image data refers to the image captured by the image detection sensor and metadata related to the shooting process. For example, this includes exposure time (shutter speed), aperture size, ISO sensitivity, focal length, and depth of field during the shooting process. Specifically, the camera can connect to the image detection sensor and receive the target image data via a wired or wireless connection.

[0054] Step S20: Determine the position offset data of the target material based on the target image data;

[0055] During cold rolling, the rolls rotate at high speed to compress and deform the workpiece. This process generates vibration, which increases when the roll speed is inappropriate. Increased vibration reduces lubrication, which in turn leads to increased vibration. The target material here refers to the workpiece being rolled in the rolling mill. Vibration causes a positional shift in the target material during the rolling process. The amount of this shift, perpendicular to the direction of movement, is determined based on the target image data.

[0056] Step S30: Determine the operating parameters of the lubrication device based on the position offset data.

[0057] The lubrication system here includes a pump system and a spray nozzle. The pump system comprises a pump, pipes, and valves. The spray nozzle has a specific divergence angle and is generally connected to a robotic arm, which controls the spray angle and position of the nozzle. The operating parameters can include lubricant flow rate, lubricant spray pressure, and nozzle angle, as well as the nozzle position. Specifically, the position offset data includes offset in a first offset direction. When the offset in the first offset direction causes some areas of the target material to be unlubricated, the robotic arm is adjusted to move in the first offset direction, thereby ensuring lubrication of all areas of the target material. The position offset data also includes offset in a second offset direction. When the offset in the second offset direction causes a pressure deviation between the target material and the emulsion sprayed by the nozzle, the pump system and the spray nozzle are dynamically adjusted based on the offset data in the second offset direction to ensure that the pressure between the target material and the emulsion sprayed by the nozzle is within a preset pressure range. For example, the distance of the nozzle along the spray direction is determined to be related to the second offset direction by the position offset data. The pump system is adjusted according to the offset data in the second offset direction so that the lubricant flow rate and lubricant pressure change dynamically with the position offset data.

[0058] In this embodiment, by receiving target image data and determining the position offset data of the target material through the target image data, and adjusting the operating parameters of the lubrication device through the position offset data, the lubrication device can accurately adapt and adjust when the target material is offset, thereby improving the stability of lubrication during continuous cold rolling and thus improving the quality of cold rolling.

[0059] Furthermore, based on the first embodiment, a second embodiment of the intelligent cold rolling lubrication method of the present invention is proposed. In this embodiment, the number of image detection sensors is two, and the two image detection sensors are perpendicular in their shooting directions. The target image data includes first target image data captured by the first image detection sensor and second target image data captured by the second image detection sensor. The step of determining the position offset data of the target material based on the target image data includes:

[0060] Determine the first offset data of the target material in the first direction based on the first target image data;

[0061] The second offset data of the target material in a second direction is determined based on the second target image data, wherein the first direction is perpendicular to the second direction.

[0062] In this embodiment, the two image detection sensors are positioned perpendicularly, and since both are perpendicular to the direction of movement of the target material, the images captured by the two sensors can determine the first offset data and the second offset data. The first offset data and the second offset data construct a complete offset profile of the target material. In other embodiments, the number of image detection sensors is not limited.

[0063] In this embodiment, by controlling the two image detection sensors to capture images perpendicularly, the captured images can fully reflect the offset of the target material.

[0064] Furthermore, based on the first or second embodiment, a third embodiment of the intelligent cold rolling lubrication method of the present invention is proposed. In this embodiment, the step of determining the position offset data of the target material based on the target image data includes:

[0065] Edge information of the target material in the target image data is extracted according to an edge extraction algorithm, which includes at least one of the Laplacian algorithm, the Roberts algorithm, the Canney algorithm, and the Sobel algorithm.

[0066] The position offset data is determined based on the edge information and the preset position information.

[0067] In this embodiment, one or more edge extraction algorithms can be selected to extract edge information of the target material in the target image data. When one or more edge extraction algorithms are selected, it is determined whether the edge information calculated by the edge extraction algorithms is consistent. When the edge information calculated by the edge extraction algorithms is consistent, the position offset data is determined based on the edge information and preset position information. When the edge information calculated by the edge extraction algorithms is inconsistent, the edge extraction algorithm is selected to calculate the corresponding edge information based on the reliability of the edge extraction algorithm. Here, the reliability is set by the user. After extracting the edge information of the target material in the target image data according to the edge extraction algorithm, the edge information is compared with the preset position information, and the difference between the edge information and the preset position information is calculated to determine the position offset data. In the second embodiment, the target image data is processed to obtain first offset data corresponding to the first direction and second offset data corresponding to the second direction. The position offset data includes first offset data and second offset data.

[0068] In this embodiment, edge information of the target material in the target image data is extracted by an edge extraction algorithm, and the position offset data is determined based on the edge information and preset position information, thereby improving the accuracy of the position offset data.

[0069] Furthermore, based on the first embodiment, a fourth embodiment of the intelligent cold rolling lubrication method of the present invention is proposed, referring to... Figure 3 In this embodiment, the at least two image detection sensors have parallel shooting directions. Before the step of determining the position offset data of the target material based on the target image data, the method further includes:

[0070] Step S201: Control the depth of field of the at least two image detection sensors to a preset depth of field;

[0071] It's important to note that depth of field is an attribute of image detection sensors, generally related to the sensor's lens. For example, a larger aperture results in a smaller depth of field. Setting the image detection sensor to a preset depth of field is crucial. Specifically, this can be achieved by adjusting the aperture to the preset depth of field and by adjusting the exposure time to avoid overexposure due to a large aperture or underexposure due to a small aperture.

[0072] Step S202: Adjust the spacing between the at least two adjacent image detection sensors in the shooting direction to a preset distance;

[0073] In this embodiment, at least two image detection sensors have parallel shooting directions and are set to the same preset depth of field. Specifically, by setting different distances between the image detection sensors and the target material, and specifically by adjusting the interval between adjacent image detection sensors in the shooting direction to a preset distance, different distances are achieved between the image detection sensors and the target material. For example, two image detection sensors are set, the preset depth of field is L, and the preset distance is half of the preset depth of field. It should be noted that the focal lengths of the different image detection sensors are the same.

[0074] The step of determining the position offset data of the target material based on the target image data further includes:

[0075] Step S21: Determine the sharpness of the target image data using an image recognition algorithm to obtain multiple sharpness values ​​corresponding to the target image data;

[0076] The image recognition algorithms used here include the Tenengrad gradient method, the Laplacian gradient method, and frequency domain analysis, etc., to identify all the target image data and determine the sharpness corresponding to each target image data. Taking the Tenengrad gradient method as an example, the target image data is preprocessed, such as reducing noise and enhancing edge information. The Sobel operator is used to calculate the gradient in the horizontal and vertical directions of the image. After obtaining the gradient value of each pixel, the gradient intensity is calculated. The gradient intensity of all pixels in the image is statistically analyzed, and statistical measures such as the average and standard deviation of the gradient intensity can be calculated. The sharpness is obtained based on the statistics.

[0077] Step S22: Determine the position offset data based on the target distance between the multiple resolution and image detection sensors and the target material.

[0078] In this embodiment, optionally, the target distances corresponding to the target image data are sorted according to the sharpness, and the difference between the target distance corresponding to the highest sharpness and the standard position distance is calculated to obtain the position offset data.

[0079] In this embodiment, by setting the interval distance of the image acquisition device in the shooting direction to a preset distance and controlling the depth of field of the at least two image detection sensors to a preset depth of field, the positions that different image detection sensors can capture in sharp focus are not exactly the same. Therefore, the sharpness of the target image data is determined by the image recognition algorithm. Since the higher the sharpness, the more of the target material is in the depth of field of the image detection sensor, the distance between the target material and the image detection sensor can be determined, thereby accurately determining the degree of offset of the target material.

[0080] Furthermore, prior to the step of adjusting the distance between the at least two adjacent image detection sensors in the shooting direction to a preset distance, the method further includes:

[0081] The preset distance is set to half of the preset depth of field.

[0082] In this embodiment, the preset distance is set to half of the preset depth of field, so that half of the clear areas captured by each pair of adjacent image detection sensors overlap, thereby further improving the accuracy of the positional offset data of the target material in the shooting direction. In other embodiments, the preset distance is set to two-thirds of the preset depth of field, so that one-third of the clear areas captured by each pair of adjacent image detection sensors overlap.

[0083] In this embodiment, by setting the preset distance to half of the preset depth of field, compared to setting the preset distance to be the same as the preset depth of field, the specific position in the depth of field can be accurately determined by the partial overlap of the depth of field intervals of adjacent image detection sensors, thereby improving the accuracy of the position offset data.

[0084] Furthermore, based on the fourth embodiment, a fifth embodiment of the intelligent cold rolling lubrication method of the present invention is proposed, referring to... Figure 4 In this embodiment, the step of determining the positional offset data based on the target distance between the plurality of sharpness and image detection sensors and the target material includes:

[0085] Step S221: When there are adjacent target image data that meet a preset condition, calculate the position offset data based on the target distance corresponding to the adjacent target image data that meet the preset condition; the preset condition is that the sharpness corresponding to the adjacent target image data is greater than or equal to the preset sharpness threshold.

[0086] It should be noted that the prerequisite for executing step S211 is that the preset distance is less than the preset depth of field. That is, there exists a region where adjacent target image data can be captured clearly at the same time. Specifically, when there are adjacent target image data that meet the preset conditions, the average distance of the target distances corresponding to the adjacent target image data that meet the preset conditions is calculated, and the difference between the average distance and the standard distance between the target material and the image detection sensor is calculated as the position offset data. It should be noted that the position offset data should include offsets in multiple directions. The position offset data calculated in step S221 only represents the offset of the image detection sensor in the shooting direction. For example, a three-dimensional coordinate system including the X-axis, Y-axis, and Z-axis is established in space. The running direction of the target material on the cold rolling mill is the X-axis direction. In this embodiment, the shooting direction of the image detection sensor is the Y-axis direction. The position offset data in the Y-axis direction can be determined through step S211. The edge information of the target material in the target image data is extracted using an edge extraction algorithm, including at least one of the Laplacian algorithm, Roberts algorithm, Canney algorithm, and Sobel algorithm. The positional offset data is determined based on the edge information and preset position information. Specifically, the positional offset data in the Z-axis direction is determined. For details on determining the positional offset data in the Z-axis direction, please refer to the steps in Embodiment 3. That is, in this embodiment, by capturing and processing data in a single direction, the positional offset data in two directions can be determined.

[0087] Step S222: When only one of the resolutions is greater than or equal to a preset resolution threshold, calculate the position offset data based on the target distance corresponding to the resolution that is greater than or equal to the preset resolution threshold.

[0088] In this embodiment, since adjacent image detection sensors are located in overlapping depth-of-field regions, when only one of the sharpness values ​​is greater than or equal to a preset sharpness threshold, the target distance of the image detection sensor corresponding to the sharpness value greater than or equal to the preset sharpness threshold is determined to calculate the position offset data. When the image detection sensor corresponding to the sharpness value greater than or equal to the preset sharpness threshold is the image detection sensor closest to the target material, the position offset data is calculated using the following formula:

[0089]

[0090] When the image detection sensor corresponding to the sharpness is greater than or equal to the preset sharpness threshold, it is the image detection sensor farthest from the target material. The calculation formula is as follows:

[0091]

[0092] P is the position offset data, k is the target distance of the image detection sensor corresponding to the sharpness that is greater than or equal to the preset sharpness threshold, s is the depth of field of the image detection sensor, l is the distance between the two image detection sensors in the shooting direction, and j 标 This is the standard distance between the image detection sensor and the target material. In addition, it should be noted that in this embodiment, the foreground depth is equal to the background depth.

[0093] When the image detection sensor corresponding to the sharpness is greater than or equal to the preset sharpness threshold is any image detection sensor other than the image detection sensor farthest from the target material and the image detection sensor closest to the target material, the position offset data calculation formula is p = j 标 -k;

[0094] In this embodiment, different methods for calculating the positional offset data are determined by the sharpness corresponding to adjacent target image data, thereby enabling more accurate calculation of the positional offset data.

[0095] Furthermore, based on any of the above embodiments, a sixth embodiment of the intelligent cold rolling lubrication method of the present invention is proposed, referring to... Figure 4 In this embodiment, the step of determining the operating parameters of the lubrication device based on the position offset data includes:

[0096] Step S31: Obtain the reference operating parameters of the lubrication device, including the reference lubricant flow rate, the reference lubricant injection pressure, and the reference nozzle angle;

[0097] Step S32: Calculate the operating parameters based on the position offset data and the reference operating parameters.

[0098] The baseline operating parameters here are set without considering the displacement of the target material caused by vibration. These baseline operating parameters are generally set by engineers and include: lubricant flow rate, lubricant injection pressure, and baseline nozzle angle, etc. Specifically, the change in attenuation distance is determined based on the position offset data, and the lubricant injection pressure is determined based on the change in attenuation distance, the velocity attenuation coefficient, and the terminal velocity; the terminal velocity is calculated based on the baseline lubricant injection pressure. The nozzle angle is determined based on the baseline nozzle angle and the position offset data.

[0099] In this embodiment, by acquiring the reference operating parameters of the lubrication device and calculating the operating parameters based on the position offset data and the reference operating parameters, the operating parameters can be accurately determined. This allows for accurate control of the lubrication device based on the operating parameters, avoiding low cold rolling quality due to poor lubrication.

[0100] Furthermore, this invention also proposes an intelligent cold rolling lubrication system, which includes:

[0101] At least two image detection sensors are installed in the rolling mill, and the imaging direction of the at least two image detection sensors is perpendicular to the movement direction of the target material;

[0102] The monitoring module is used to receive target image data from at least two image detection sensors;

[0103] The calculation module is used to determine the positional offset data of the target material based on the target image data;

[0104] An adjustment module is used to determine the operating parameters of the lubrication device based on the position offset data.

[0105] Furthermore, this invention also proposes an intelligent cold rolling lubrication device, which includes: a memory, a processor, and an intelligent cold rolling lubrication program stored in the memory and executable on the processor. The intelligent cold rolling lubrication program is configured to implement the steps of any of the embodiments of the intelligent cold rolling lubrication method described above.

[0106] Furthermore, embodiments of the present invention also propose a storage medium storing an intelligent cold rolling lubrication program, wherein when the intelligent cold rolling lubrication program is executed by a processor, it implements the steps of any of the embodiments of the intelligent cold rolling lubrication method described above.

[0107] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or system. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element.

[0108] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0109] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0110] The above are merely preferred embodiments of the present invention and do not limit the scope of the patent. Any equivalent structural or procedural transformations made based on the description and drawings of the present invention, or direct or indirect applications in other related technical fields, are similarly included within the scope of patent protection of the present invention.

Claims

1. An intelligent cold rolling lubrication method, characterized in that, The intelligent cold rolling lubrication method includes the following steps: At least two image detection sensors are installed on the rolling mill, and the image capture direction of the at least two image detection sensors is perpendicular to the movement direction of the target material. Receive target image data from at least two image detection sensors; Determine the position offset data of the target material based on the target image data; The operating parameters of the lubrication device are determined based on the position offset data; Before the step of determining the position offset data of the target material based on the target image data, wherein the at least two image detection sensors are positioned parallel to each other, the method further includes: The depth of field of the at least two image detection sensors is controlled to a preset depth of field; The spacing between at least two adjacent image detection sensors in the shooting direction is adjusted to a preset distance; The step of determining the position offset data of the target material based on the target image data further includes: The sharpness of the target image data is determined by an image recognition algorithm, and multiple sharpness values ​​corresponding to the target image data are obtained. The positional offset data is determined based on the target distance between the multiple resolution and image detection sensors and the target material; Before the step of adjusting the distance between the at least two adjacent image detection sensors in the shooting direction to a preset distance, the method further includes: The preset distance is set to half of the preset depth of field; The step of determining the positional offset data based on the target distance between the multiple resolution and image detection sensors and the target material includes: When there are adjacent target image data that meet the preset conditions, the position offset data is calculated based on the target distance corresponding to the adjacent target image data that meet the preset conditions. When only one of the aforementioned sharpness values ​​is greater than or equal to a preset sharpness threshold, the position offset data is calculated based on the target distance corresponding to the sharpness value greater than or equal to the preset sharpness threshold. The preset condition is that the sharpness of adjacent target image data is greater than or equal to the preset sharpness threshold.

2. The intelligent cold rolling lubrication method as described in claim 1, characterized in that, The step of determining the position offset data of the target material based on the target image data includes: Edge information of the target material in the target image data is extracted according to an edge extraction algorithm, which includes at least one of the Laplacian algorithm, the Roberts algorithm, the Canney algorithm, and the Sobel algorithm. The position offset data is determined based on the edge information and the preset position information.

3. The intelligent cold rolling lubrication method as described in claim 1, characterized in that, The step of determining the operating parameters of the lubrication device based on the position offset data includes: Obtain the reference operating parameters of the lubrication device, including the reference lubricant flow rate, the reference lubricant injection pressure, and the reference nozzle angle; The operating parameters are calculated based on the position offset data and the baseline operating parameters.

4. An intelligent cold rolling lubrication equipment, characterized in that, The intelligent cold rolling lubrication equipment includes: a memory, a processor, and an intelligent cold rolling lubrication program stored in the memory and executable on the processor, wherein the intelligent cold rolling lubrication program is configured to implement the steps of the intelligent cold rolling lubrication method as described in any one of claims 1 to 3.

5. A storage medium, characterized in that, The storage medium stores an intelligent cold rolling lubrication program, which, when executed by a processor, implements the steps of the intelligent cold rolling lubrication method as described in any one of claims 1 to 3.

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