A method and system for controlling the cutting speed of an aluminum plate cutting device

Abstract

A cutting speed control method and system for an aluminum plate cutting device, relating to the field of laser cutting, is disclosed. This method, applied to a cutting speed control system, includes: acquiring and analyzing multiple monitorable data points of the target aluminum plate to obtain multiple analytical data types, including material hardness, ambient temperature, and equipment vibration frequency; acquiring the numerical distribution of the multiple analytical data types along the cutting path and generating multiple parameter curves, where each analytical data type corresponds to one parameter curve; generating multiple feasible cutting speed curves based on the multiple parameter curves; calculating the optimal solution for the multiple feasible cutting speed curves to obtain the target cutting speed curve; and controlling the aluminum plate cutting device to cut the target aluminum plate according to the target cutting speed curve. Implementing the technical solution provided in this application solves the problem of unstable aluminum plate cutting quality caused by the complex and variable cutting conditions during laser cutting of aluminum plates.

Description

Technical Field

[0001] This application relates to the technical field of laser cutting, specifically to a cutting speed control method and system for an aluminum plate cutting device. Background Technology

[0002] Laser cutting is a non-contact processing method. The laser beam can complete the cutting without directly contacting the material surface. It is particularly suitable for processing thin plates or fragile materials, thus easily achieving the cutting of complex shapes and has strong adaptability.

[0003] Currently, when using laser cutting to cut aluminum plates, it is generally necessary to consider the compatibility between the cutting speed and the thickness of the aluminum plate, so as to ensure that the aluminum plate can be cut through while the cutting surface is continuous and uniform. The common method is to use sensors to detect the thickness of the aluminum plate on the cutting path in advance, and then adjust the cutting speed accordingly during the cutting process.

[0004] However, the above methods mostly rely on historical experience to make judgments when adjusting the cutting speed, while the working conditions of aluminum plate cutting are mostly complex and changeable, which leads to unstable cutting quality of aluminum plate. Summary of the Invention

[0005] To address the problem of unstable cutting quality caused by complex and variable cutting conditions during laser cutting of aluminum plates, this application provides a cutting speed control method and system for an aluminum plate cutting device.

[0006] In a first aspect, this application provides a cutting speed control method for an aluminum plate cutting device, applied in a cutting speed control system, the method comprising:

[0007] Multiple monitorable data of the target aluminum plate are acquired and analyzed to obtain multiple analytical data types, including material hardness, ambient temperature and equipment vibration frequency;

[0008] Obtain the numerical distribution of multiple parsed data types on the cutting path and generate multiple parameter curves, wherein one parsed data type corresponds to one parameter curve;

[0009] Based on the multiple parameter curves, generate multiple feasible cutting speed curves;

[0010] The optimal solution is calculated for multiple feasible cutting speed curves to obtain the target cutting speed curve.

[0011] Based on the target cutting speed curve, the aluminum plate cutting device is controlled to cut the target aluminum plate.

[0012] Optionally, based on the numerical distribution of aluminum plate thickness along the cutting path, an initial cutting speed curve is matched and generated from a preset cutting speed library, which stores the correspondence between cutting speed and aluminum plate thickness.

[0013] Calculate the entropy values ​​of multiple parameter curves;

[0014] Based on the numerical distribution of the multiple parsed data types, calculate the correlation coefficient between the multiple parsed data types;

[0015] The weights of the multiple parsed data types are calculated based on the correlation coefficients between the entropy values ​​of the multiple parameter curves and the multiple parsed data types.

[0016] Multiple feasible cutting speed curves are generated based on the weights of the multiple parsed data types and the initial cutting speed curve.

[0017] Optionally, the weights of the multiple parsed data are calculated based on the correlation coefficients between the entropy values ​​of the multiple parameter curves and the multiple parsed data types, using the following formula:

[0018]

[0019]

[0020] in, The information content of the j-th parsed data type conflicts with other parsed data. Let be the entropy value of the parameter curve corresponding to the j-th parsed data type. Let be the correlation coefficient between the j-th parsed data type and the k-th parsed data type. Let be the weight of the j-th parsed data type, m be the total number of parsed data types, and k be the total number of parsed data types minus 1.

[0021] Optionally, cluster analysis can be performed on the correlation coefficients among multiple parsed data types to obtain multiple cluster centers;

[0022] Based on the multiple cluster centers and the number of the multiple cluster centers, construct multiple parsed data type weight storage sets;

[0023] Based on preset storage conditions, the weights of multiple parsed data types are stored in multiple parsed data type weight storage sets;

[0024] The initial cutting speed curve is adjusted using multiple weighted storage sets of the parsed data types that have been stored to obtain multiple feasible cutting speed curves.

[0025] Optionally, the preset storage conditions include that the storable quantity of the parsed data type weight storage set is equal to the number of the multiple cluster centers, and that the correlation coefficient between any two parsed data types is less than the mean of the multiple cluster centers.

[0026] Optionally, the step of calculating the optimal solution for multiple feasible cutting speed curves to obtain the target cutting speed curve specifically involves:

[0027] Extract curve feature sets from multiple feasible cutting speed curves, the curve feature sets including curve variance, mean curve slope, and cutting time;

[0028] Based on the curve feature sets of multiple feasible cutting speed curves, an optimal curve feature set is constructed, which includes the minimum variance, the minimum mean curve slope, and the shortest cutting time.

[0029] Based on the optimal curve feature set and the initial cutting speed curve, an ideal cutting speed curve is constructed.

[0030] The feasible cutting speed curve with the highest similarity to the ideal cutting curve among the multiple feasible cutting speed curves is taken as the target cutting speed curve.

[0031] Secondly, this application provides a cutting speed control system for an aluminum plate cutting device. The system is a cutting speed control system, comprising an acquisition module, a processing module, and a control module, wherein:

[0032] The acquisition module is used to acquire and parse multiple monitorable data of the target aluminum plate to obtain multiple parsed data types, including material hardness, ambient temperature and equipment vibration frequency; acquire the numerical distribution of multiple parsed data types on the cutting path and generate multiple parameter curves, wherein one parsed data type corresponds to one parameter curve;

[0033] The processing module is used to generate multiple feasible cutting speed curves based on multiple parameter curves; and to calculate the optimal solution of the multiple feasible cutting speed curves to obtain the target cutting speed curve.

[0034] The control module is used to control the aluminum plate cutting device to cut the target aluminum plate according to the target cutting speed curve.

[0035] Thirdly, this application provides an electronic device including a processor, a memory, a user interface, and a network interface. The memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory to cause the electronic device to perform the method as described in any one of the first aspects.

[0036] Fourthly, this application provides a computer-readable storage medium storing instructions that, when executed, perform the method described in any one of the first aspects.

[0037] In summary, one or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:

[0038] This application acquires multiple measurable data points of the target aluminum plate based on actual monitoring conditions. These data points may not be quantitative, such as material properties or processing environment. Therefore, this application analyzes these multiple measurable data points to obtain multiple quantifiable analytical data types (e.g., material properties are analyzed as hardness and surface roughness). These measurable data points reflect the real-time cutting conditions of the aluminum plate. Then, the numerical distribution of these analytical data types along the cutting path is acquired, and multiple parameter curves are generated. Based on these parameter curves, multiple feasible cutting speed curves are generated. These feasible cutting speed curves characterize the possible changes in cutting speed under the influence of the current cutting conditions. The optimal solution for the cutting speed is then obtained from these feasible cutting speed curves, resulting in the target cutting speed curve, which adapts to the current cutting conditions. This solution avoids the problem of relying on historical experience for judgment, achieving automatic adjustment of the cutting speed through a data-driven approach, thereby improving cutting efficiency and cutting quality. Attached Figure Description

[0039] Figure 1 This is a flowchart illustrating a cutting speed control method for an aluminum plate cutting device provided in an embodiment of this application.

[0040] Figure 2 This is a schematic diagram of the cutting speed control system of an aluminum plate cutting device provided in an embodiment of this application.

[0041] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0042] Explanation of reference numerals in the attached drawings: 1. Acquisition module; 2. Processing module; 3. Control module; 300. Electronic device; 301. Processor; 302. Communication bus; 303. User interface; 304. Network interface; 305. Memory. Detailed Implementation

[0043] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0044] In the description of the embodiments of this application, the words "for example" or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design that is described as "for example" or "for instance" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design options. Rather, the use of the words "for example" or "for instance" is intended to present the relevant concepts in a specific manner.

[0045] In the description of the embodiments of this application, the term "multiple" means two or more. For example, multiple systems means two or more systems, and multiple screen terminals means two or more screen terminals. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the indicated technical features. Thus, a feature defined with "first" or "second" may explicitly or implicitly include one or more of that feature. The terms "comprising," "including," "having," and variations thereof all mean "including but not limited to," unless otherwise specifically emphasized.

[0046] For aluminum sheet cutting, there are two main methods: contact cutting and non-contact cutting. Contact cutting refers to blade cutting, which utilizes stress to cut materials of varying thicknesses, making it highly adaptable and producing high-quality surfaces without the need for post-processing. It is a commonly used cutting method among manufacturers. However, blade cutting is difficult to handle complex shapes and often consumes a large amount of sheet material. To save costs, non-contact laser cutting is generally used for complex shapes. Laser cutting can complete the cutting without direct contact with the material surface, making it particularly suitable for thin sheets or fragile materials, thus easily achieving the cutting of complex shapes and demonstrating strong adaptability.

[0047] Currently, when using laser cutting to cut aluminum plates, it is generally necessary to consider the compatibility between the cutting speed and the thickness of the aluminum plate, so as to ensure that the aluminum plate can be cut through while the cutting surface is continuous and uniform. The common method is to use sensors to detect the thickness of the aluminum plate on the cutting path in advance, and then adjust the cutting speed accordingly during the cutting process.

[0048] However, the above methods mostly rely on historical experience to make judgments when adjusting the cutting speed, while the working conditions of aluminum plate cutting are mostly complex and changeable, which leads to unstable cutting quality of aluminum plate.

[0049] To address the aforementioned problems, this application provides a cutting speed control method for an aluminum plate cutting device. This method is applied in a cutting speed control system, such as... Figure 1 As shown, the method includes steps S101 to S105, which are as follows:

[0050] S101. Acquire and analyze multiple monitorable data of the target aluminum plate to obtain multiple analytical data types, including material hardness, ambient temperature and equipment vibration frequency.

[0051] In the above steps, before starting to cut the target aluminum plate, it is necessary to determine multiple monitorable data of the aluminum plate. These monitorable data characterize the cutting conditions during the aluminum plate cutting process. The monitorable data are determined by the factory's monitoring conditions, which include, but are not limited to, the processing material, the processing environment, and the equipment status. However, these monitorable data are some non-quantitative data, which need to be converted into quantitative parameters that can characterize these cutting conditions. Therefore, multiple monitorable data are analyzed to obtain multiple analytical data types. For example, after analyzing the processing material, the data becomes the material hardness and the material surface roughness; after analyzing the processing environment, the data becomes the ambient temperature and humidity; and after analyzing the equipment status, the data becomes the equipment vibration frequency and the laser beam focusing deviation.

[0052] S102. Obtain the numerical distribution of multiple parsed data types on the cutting path and generate multiple parameter curves, where one parsed data type corresponds to one parameter curve.

[0053] In the above steps, during the aluminum plate cutting process, various analytical data types will have a certain impact on the quality of the cutting path. For example, if the hardness dispersion on the cutting path is high, it will cause delamination of the cut surface and an increase in burrs, thereby leading to a decrease in cutting quality. Therefore, this application needs to know the numerical distribution of multiple analytical data types on the cutting path in advance and convert the numerical distribution into a parameter curve. Specifically, taking material hardness as an example, since the cutting path consists of multiple continuous cutting points, the hardness value of each cutting point can be detected sequentially along multiple continuous cutting points. Then, the hardness value of each cutting point is constructed in a two-dimensional coordinate system of hardness-cutting path points to obtain the parameter curve corresponding to the material hardness. This provides more comprehensive data support for the subsequent adjustment of the cutting speed, thereby improving the cutting quality.

[0054] S103. Generate multiple feasible cutting speed curves based on multiple parameter curves.

[0055] In the above steps, the parameter curves reflect the changes in processing conditions along the cutting path. If the cutting speed is set solely based on the aluminum plate thickness along the cutting path, it can easily lead to unstable cutting quality. Furthermore, under these changing conditions, the cutting speed is difficult to adjust based on historical experience, resulting in inaccurate cutting speed adjustment strategies and further increasing the instability of cutting quality. To address this issue, this application adjusts the initial cutting speed curve corresponding to the aluminum plate thickness based on the changing trends of multiple parameter curves and their correlation coefficients, obtaining multiple feasible cutting speed curves that better match the current processing conditions. It should be noted that the more drastic the fluctuation of a parameter curve for a certain analytical data type, the greater the impact of that analytical data type on cutting quality, and the more necessary it is to adjust the cutting speed to compensate for this impact. Additionally, there are also influence relationships between analytical data types; for example, higher material hardness can cause a rapid increase in ambient temperature, thereby increasing the impact of ambient temperature on the cutting speed. Therefore, to obtain multiple feasible cutting speed curves that better match the current conditions, the specific steps are as follows:

[0056] First, based on the numerical distribution of aluminum plate thickness along the cutting path, an initial cutting speed curve is generated by matching from a preset cutting speed library, which stores the correspondence between cutting speed and aluminum plate thickness. Then, the entropy values ​​of multiple parameter curves are calculated to understand the fluctuations of various parsing data types. Next, the correlation coefficients between multiple parsing data types are calculated. For example, given parsing data types A / B / C, the correlation coefficients to be calculated include those between parsing data type A and parsing data type B, between parsing data type B and parsing data type C, and between parsing data type A and parsing data type C. The correlation coefficient can be understood as the degree of influence between parsing data. Specifically, taking parsing data type A / B as an example, the parameter curves of parsing data type A / B are first normalized to obtain the normalized curves of parsing data type A and parsing data type B. Then, using the Pearson correlation coefficient formula, the correlation coefficient between the normalized curves of parsing data type A and parsing data type B is calculated, thus obtaining the correlation coefficient between parsing data type A and parsing data type B.

[0057] Then, based on the entropy values ​​of multiple parametric curves and the correlation coefficients between multiple parsed data types, the weights of the multiple parsed data types are calculated. In this step, the objective weighting method is used for weight calculation, and the formula is as follows:

[0058]

[0059]

[0060] in, The information content of the j-th parsed data type conflicts with other parsed data. Let be the entropy value of the parameter curve corresponding to the j-th parsed data type. Let be the correlation coefficient between the j-th parsed data type and the k-th parsed data type. Let be the weight of the j-th parsed data type, m be the total number of parsed data types, and k be the total number of parsed data types minus 1.

[0061] In the above formula, the standard deviation reflects not only the magnitude of data variation but also the amount of information contained in the data. A larger standard deviation for the parameter curve of a certain parsing data type indicates that the parameter curve corresponding to that data type contains more information and has a greater impact on the cutting speed. Conversely, a higher conflict value between the information content of the parameter curve corresponding to a parsing data type and other parsing data types indicates higher independence and the ability to provide information that other parsing data type parameter curves cannot replace. In this case, its influence on the cutting speed is also greater. Therefore, for the j-th parsing data type, the conflict values ​​between the j-th parsing data type and other parsing data types are calculated one by one, and then the calculated conflict values ​​are summed to determine the degree of influence of the j-th parsing data type on the cutting speed compared to other parsing data types. It should be noted that since the correlation coefficient between the j-th parsing data type and itself is 1, the conflict value between the j-th parsing data type and itself is 0, i.e., when j=k. =0; After calculating the information content of all parsed data types and the conflict value with other parsed data, normalization is performed to obtain the weights corresponding to each parsed data type.

[0062] However, the standard deviation of the parsed data type in the above formula is sensitive to outliers. If outliers exist in the data, the standard deviation may be exaggerated, leading to inaccurate weight allocation. Furthermore, the standard deviation only reflects the dispersion of the data and cannot directly measure the information content of the indicators or their contribution to the results. To improve the accuracy of weight allocation for each parsed data type, this application replaces the standard deviation in the objective weighting method formula with entropy values. Since entropy values ​​are calculated based on probability distributions, they are less sensitive to extreme values ​​(outliers). Moreover, entropy values ​​can measure the uniformity of data distribution; a larger entropy value indicates a more uniform data distribution, richer information content, and a higher assigned weight; a smaller entropy value indicates a more concentrated data distribution, less information content, and a lower assigned weight. The entropy value is calculated using the Shannon entropy formula, specifically:

[0063]

[0064] in, Let j be the entropy value of the j-th parameter curve. The total number of data points. Let represent the proportion of the i-th data point in the parametric curve among all data points.

[0065] The final improved weight calculation formula is as follows:

[0066]

[0067]

[0068] in, This is the entropy value of the parameter curve corresponding to the j-th parsed data class.

[0069] After determining the weights of multiple parsing data types on the cutting speed, the initial cutting speed curve cannot be directly adjusted using the weights of multiple parsing data types. This is because there is a high correlation between some parsing data types. If their weights are used to adjust the initial cutting speed curve at the same time, their influence on the result will be calculated repeatedly in the weight allocation, resulting in weight redundancy and affecting the accuracy of the adjustment result.

[0070] Therefore, in order to improve the adjusted cutting speed curve to be more ideal, this application first performs cluster analysis on the correlation coefficients between multiple parsed data types to obtain multiple cluster centers. The clustering method adopts split hierarchical clustering, and the number of splits is less than or equal to the number of multiple parsed data types. This step is to identify the parsed data types that are highly correlated among the multiple parsed data types. Then, based on the multiple cluster centers and the number of multiple cluster centers, multiple parsed data type weight storage sets are constructed. At this time, the parsed data type weight storage set can be understood as a blank storage unit used to store the weight values ​​of the parsed data types. Its storage quantity is the number of multiple cluster centers. For example, if there are 3 cluster centers, then the parsed data type weight storage set can only store the weight values ​​of 3 parsed data types, thereby reflecting the multiple main feature dimensions of multiple parsed data types.

[0071] In addition, to avoid storing highly correlated parsed data type weights simultaneously, preset storage conditions are set for the parsed data type weight storage set. On the one hand, the number of parsed data type weights that can be stored in the aforementioned parsed data type weight storage set is equal to the number of multiple cluster centers. On the other hand, the correlation coefficient between any two parsed data types stored in the parsed data type weight storage set must be less than the mean of multiple cluster centers, thereby avoiding the influence of weight redundancy on the cutting curve adjustment results.

[0072] Then, according to the preset storage conditions, the weights of multiple parsed data types are stored in multiple parsed data type weight storage sets; finally, the initial cutting speed curve is adjusted using the stored multiple parsed data type weight storage sets to obtain multiple feasible cutting speed curves, wherein the adjustment method is to perform a weighted average of the initial cutting speed curve.

[0073] S104. Calculate the optimal solution for multiple feasible cutting speed curves to obtain the target cutting speed curve.

[0074] In the above steps, after obtaining multiple feasible cutting speed curves, these feasible cutting speed curves reflect the direction in which the cutting speed can be adjusted under the influence of the current cutting conditions. However, each of these feasible cutting speed curves has its own advantages and disadvantages. For example, some feasible cutting speed curves have high cutting quality but low cutting efficiency. In order to find the optimal solution of feasible cutting speed curve with high cutting quality and high cutting efficiency from multiple feasible cutting speed curves, we need to further explore these options.

[0075] This application first extracts curve feature sets from multiple feasible cutting speed curves. These feature sets include, but are not limited to, curve variance, mean curve slope, and cutting time. Curve variance and mean curve slope both characterize cutting quality; for example, a smaller variance or mean curve slope indicates a more stable cutting process and thus higher cutting quality. Cutting time characterizes cutting efficiency; with a constant cutting path length, a shorter cutting time indicates higher cutting efficiency. Then, based on the curve feature sets of multiple feasible cutting speed curves, an optimal curve feature set is constructed. The parameter set in the line feature set contains the curve parameters of the optimal solution cutting speed curve, including minimum variance, minimum mean curve slope, and shortest cutting time. The parameters in the optimal curve feature set are selected from the variance, mean curve slope, and cutting time of multiple feasible cutting speed curves to ensure the reliability of the optimal solution. Then, based on the optimal curve feature set, spline interpolation or curve fitting is used to fit the initial cutting speed curve to construct the ideal cutting speed curve. The curve parameters of the ideal cutting speed curve are minimum variance, minimum mean curve slope, and shortest cutting time.

[0076] However, achieving an ideal cutting speed curve can be technically challenging. For example, maintaining both high cutting efficiency and high cutting quality requires high precision and control latency from the laser cutting equipment, which may be impossible for processing manufacturers. In other words, the ideal cutting speed curve may not meet the actual needs of processing manufacturers. However, the ideal cutting speed curve can serve as an evaluation standard for feasible cutting speed curves. This application calculates the similarity between multiple feasible cutting speed curves and the ideal cutting speed curve, and then selects the feasible cutting speed curve with the highest similarity as the optimal solution to obtain the target cutting speed curve.

[0077] S105. Based on the target cutting speed curve, control the aluminum plate cutting device to cut the target aluminum plate.

[0078] In the above steps, after obtaining the target cutting speed curve, while controlling the aluminum plate cutting device to cut the target aluminum plate, the quality of the cutting path during the cutting process is monitored in real time. Then, based on the quality, the target cutting speed curve is optimized and stored in the cutting curve library. When encountering the same or similar cutting path and cutting conditions again, the cutting speed curve in the cutting curve library can be directly called, thereby improving processing efficiency.

[0079] Reference Figure 2 This application also provides a cutting speed control system for an aluminum plate cutting device. The system is a cutting speed control system, which includes an acquisition module 1, a processing module 2, and a control module 3, wherein:

[0080] Module 1 is used to acquire and parse multiple monitorable data of the target aluminum plate to obtain multiple parsed data types, including material hardness, ambient temperature and equipment vibration frequency; acquire the numerical distribution of multiple parsed data types on the cutting path and generate multiple parameter curves, where one parsed data type corresponds to one parameter curve;

[0081] Processing module 2 is used to generate multiple feasible cutting speed curves based on multiple parameter curves; and to calculate the optimal solution of multiple feasible cutting speed curves to obtain the target cutting speed curve.

[0082] Control module 3 is used to control the aluminum plate cutting device to cut the target aluminum plate according to the target cutting speed curve.

[0083] In one possible implementation, an initial cutting speed curve is generated by matching the numerical distribution of aluminum plate thickness along the cutting path from a preset cutting speed library, which stores the correspondence between cutting speed and aluminum plate thickness.

[0084] Calculate the entropy values ​​of multiple parametric curves;

[0085] Calculate the correlation coefficient between multiple parsed data types based on their numerical distributions.

[0086] The weights of multiple parsed data types are calculated based on the correlation coefficients between the entropy values ​​of multiple parametric curves and multiple parsed data types.

[0087] Based on the weights of multiple parsed data types and the initial cutting speed curve, multiple feasible cutting speed curves are generated.

[0088] In one possible implementation, the weights of the multiple parsed data are calculated based on the correlation coefficients between the entropy values ​​of multiple parametric curves and the multiple parsed data types, specifically using the following formula:

[0089]

[0090]

[0091] in, The information content of the j-th parsed data type conflicts with other parsed data. Let be the entropy value of the parameter curve corresponding to the j-th parsed data type. Let be the correlation coefficient between the j-th parsed data type and the k-th parsed data type. Let be the weight of the j-th parsed data type, m be the total number of parsed data types, and k be the total number of parsed data types minus 1.

[0092] In one possible implementation, cluster analysis is performed on the correlation coefficients between multiple parsed data types to obtain multiple cluster centers;

[0093] Based on multiple cluster centers and the number of cluster centers, construct multiple parsed data type weight storage sets;

[0094] Based on preset storage conditions, the weights of multiple parsed data types are stored in multiple parsed data type weight storage sets;

[0095] The initial cutting speed curve is adjusted using multiple weighted storage sets of parsed data types that have been stored, resulting in multiple feasible cutting speed curves.

[0096] In one possible implementation, the preset storage conditions include that the storable size of the parsed data type weight storage set is equal to the number of multiple cluster centers, and that the correlation coefficient between any two parsed data types is less than the mean of the multiple cluster centers.

[0097] In one possible implementation, the optimal solution is calculated for multiple feasible cutting speed curves to obtain the target cutting speed curve, specifically:

[0098] Extract the curve feature set of multiple feasible cutting speed curves. The curve feature set includes curve variance, mean curve slope, and cutting time.

[0099] Based on the curve feature sets of multiple feasible cutting speed curves, an optimal curve feature set is constructed. The optimal curve feature set includes the minimum variance, the minimum mean curve slope, and the shortest cutting time.

[0100] Construct an ideal cutting speed curve based on the optimal curve feature set and the initial cutting speed curve;

[0101] The feasible cutting speed curve with the highest similarity to the ideal cutting curve among multiple feasible cutting speed curves is taken as the target cutting speed curve.

[0102] It should be noted that the above embodiments of the apparatus are only illustrated by the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the apparatus and method embodiments provided in the above embodiments belong to the same concept, and the specific implementation process can be found in the method embodiments, which will not be repeated here.

[0103] This application also discloses an electronic device. (See reference...) Figure 3 , Figure 3 This is a schematic diagram of the structure of an electronic device disclosed in an embodiment of this application. The electronic device 300 may include: at least one processor 301, at least one network interface 304, a user interface 303, a memory 305, and at least one communication bus 302.

[0104] The communication bus 302 is used to enable communication between these components.

[0105] The user interface 303 may include a display screen and a camera. Optionally, the user interface 303 may also include a standard wired interface and a wireless interface.

[0106] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface).

[0107] The processor 301 may include one or more processing cores. The processor 301 connects to various parts of the server using various interfaces and lines, and performs various server functions and processes data by running or executing instructions, programs, code sets, or instruction sets stored in memory 305, and by calling data stored in memory 305. Optionally, the processor 301 may be implemented using at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor 301 may integrate one or a combination of several of the following: Central Processing Unit (CPU), Graphics Processing Unit (GPU), and modem. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the content required for display; and the modem handles wireless communication. It is understood that the modem may also not be integrated into the processor 301 and may be implemented as a separate chip.

[0108] The memory 305 may include random access memory (RAM) or read-only memory. Optionally, the memory 305 may include a non-transitory computer-readable storage medium. The memory 305 may be used to store instructions, programs, code, code sets, or instruction sets. The memory 305 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-described method embodiments, etc.; the data storage area may store data involved in the above-described method embodiments, etc. Optionally, the memory 305 may also be at least one storage device located remotely from the aforementioned processor 301. (Refer to...) Figure 3 The memory 305, which serves as a computer storage medium, may include an operating system, a network communication module, a user interface module, and an application program for a cutting speed control method for an aluminum plate cutting device.

[0109] exist Figure 3In the illustrated electronic device 300, the user interface 303 is mainly used to provide an input interface for the user and acquire user input data; while the processor 301 can be used to call an application program stored in the memory 305 for controlling the cutting speed of an aluminum plate cutting device. When executed by one or more processors 301, the electronic device 300 performs one or more of the methods described in the above embodiments. It should be noted that, for the foregoing method embodiments, for the sake of simplicity, they are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, because according to this application, some steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also understand that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0110] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0111] In the various embodiments provided in this application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some service interface; the indirect coupling or communication connection between apparatuses or units may be electrical or other forms.

[0112] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0113] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0114] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as USB flash drives, portable hard drives, magnetic disks, or optical disks.

[0115] The above description is merely an exemplary embodiment of this disclosure and should not be construed as limiting the scope of this disclosure. Any equivalent changes and modifications made in accordance with the teachings of this disclosure shall still fall within the scope of this disclosure. Other embodiments of this disclosure will be readily apparent to those skilled in the art upon consideration of the specification and the disclosure of practical truths.

[0116] This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not described in this disclosure. The specification and embodiments are to be considered exemplary only, and the scope and spirit of this disclosure are defined by the claims.

Claims

1. A method for controlling the cutting speed of an aluminum plate cutting device, characterized in that, The method, applied in a cutting speed control system, includes: Multiple monitorable data of the target aluminum plate are acquired and analyzed to obtain multiple analytical data types, including material hardness, ambient temperature and equipment vibration frequency; Obtain the numerical distribution of multiple parsed data types on the cutting path and generate multiple parameter curves, wherein one parsed data type corresponds to one parameter curve; Based on the multiple parameter curves, generate multiple feasible cutting speed curves; The optimal solution is calculated for multiple feasible cutting speed curves to obtain the target cutting speed curve. Based on the target cutting speed curve, the aluminum plate cutting device is controlled to cut the target aluminum plate. Specifically, generating multiple feasible cutting speed curves based on multiple parameter curves involves: Based on the numerical distribution of aluminum plate thickness along the cutting path, an initial cutting speed curve is generated by matching from a preset cutting speed library, which stores the correspondence between cutting speed and aluminum plate thickness. Calculate the entropy values ​​of multiple parameter curves; Based on the numerical distribution of the multiple parsed data types, calculate the correlation coefficient between the multiple parsed data types; The weights of the multiple parsed data types are calculated based on the correlation coefficients between the entropy values ​​of the multiple parameter curves and the multiple parsed data types. Based on the weights of the various parsed data types and the initial cutting speed curve, multiple feasible cutting speed curves are generated, specifically including: Cluster analysis was performed on the correlation coefficients among the multiple parsed data types to obtain multiple cluster centers; Based on the multiple cluster centers and the number of the multiple cluster centers, construct multiple parsed data type weight storage sets; Based on preset storage conditions, the weights of multiple parsed data types are stored in multiple parsed data type weight storage sets; The initial cutting speed curve is adjusted using multiple weighted storage sets of the parsed data types that have been stored to obtain multiple feasible cutting speed curves.

2. The method according to claim 1, characterized in that, The weights of the multiple parsed data are calculated based on the correlation coefficients between the entropy values ​​of the multiple parameter curves and the multiple parsed data types, using the following formula: ； ； in, The information content of the j-th parsed data type conflicts with other parsed data. Let be the entropy value of the parameter curve corresponding to the j-th parsed data type. Let be the correlation coefficient between the j-th parsed data type and the k-th parsed data type. Let be the weight of the j-th parsed data type, m be the total number of parsed data types, and k be the total number of parsed data types minus 1.

3. The method according to claim 1, characterized in that, The preset storage conditions include that the storable quantity of the parsed data type weight storage set is equal to the number of the multiple cluster centers, and that the correlation coefficient between any two parsed data types is less than the mean of the multiple cluster centers.

4. The method according to claim 1, characterized in that, The optimal solution calculation for multiple feasible cutting speed curves is used to obtain the target cutting speed curve, specifically as follows: Extract curve feature sets from multiple feasible cutting speed curves, the curve feature sets including curve variance, mean curve slope, and cutting time; Based on the curve feature sets of multiple feasible cutting speed curves, an optimal curve feature set is constructed, which includes the minimum variance, the minimum mean curve slope, and the shortest cutting time. Based on the optimal curve feature set and the initial cutting speed curve, an ideal cutting speed curve is constructed. The feasible cutting speed curve with the highest similarity to the ideal cutting curve among the multiple feasible cutting speed curves is taken as the target cutting speed curve.

5. A cutting speed control system for an aluminum plate cutting device, characterized in that, The system is a cutting speed control system, which includes an acquisition module (1), a processing module (2), and a control module (3), wherein: The acquisition module (1) is used to acquire and parse multiple monitorable data of the target aluminum plate to obtain multiple parsing data types, including material hardness, ambient temperature and equipment vibration frequency; acquire the numerical distribution of multiple parsing data types on the cutting path and generate multiple parameter curves, wherein one parsing data type corresponds to one parameter curve; The processing module (2) is used to generate multiple feasible cutting speed curves based on multiple parameter curves; and to calculate the optimal solution of the multiple feasible cutting speed curves to obtain the target cutting speed curve. Specifically, generating multiple feasible cutting speed curves based on multiple parameter curves involves: Based on the numerical distribution of aluminum plate thickness along the cutting path, an initial cutting speed curve is generated by matching from a preset cutting speed library, which stores the correspondence between cutting speed and aluminum plate thickness. Calculate the entropy values ​​of multiple parameter curves; Based on the numerical distribution of the multiple parsed data types, calculate the correlation coefficient between the multiple parsed data types; The weights of the multiple parsed data types are calculated based on the correlation coefficients between the entropy values ​​of the multiple parameter curves and the multiple parsed data types. Based on the weights of the various parsed data types and the initial cutting speed curve, multiple feasible cutting speed curves are generated, specifically including: Cluster analysis was performed on the correlation coefficients among the multiple parsed data types to obtain multiple cluster centers; Based on the multiple cluster centers and the number of the multiple cluster centers, construct multiple parsed data type weight storage sets; Based on preset storage conditions, the weights of multiple parsed data types are stored in multiple parsed data type weight storage sets; The initial cutting speed curve is adjusted using multiple stored weighted sets of the parsed data types to obtain multiple feasible cutting speed curves. The control module (3) is used to control the aluminum plate cutting device to cut the target aluminum plate according to the target cutting speed curve.

6. An electronic device, characterized in that, The device includes a processor (301), a memory (305), a user interface (303), and a network interface (304). The memory (305) is used to store instructions. The user interface (303) and the network interface (304) are used to communicate with other devices. The processor (301) is used to execute the instructions stored in the memory (305) to cause the electronic device (300) to perform the method as described in any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions that, when executed, perform the method as described in any one of claims 1 to 4.

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