Plate cutting matching method and system based on big data

Abstract

The present invention relates to the field of sheet material cutting, and in particular to a sheet material cutting matching method and system based on big data. The method comprises: collecting order information of at least three orders, wherein the order information comprises the workpiece size, the total number of workpieces and the number of workpieces of each size of each order; calculating the probability distribution of workpieces of each size in each order; calculating the similarity of every two orders according to the workpiece size of each order and the probability distribution of workpieces of each size; calculating the ratio of the total number of workpieces of every two orders, and matching the at least three orders using the Hungarian algorithm to obtain pairwise order pairs, wherein the weight between every two orders in the Hungarian algorithm is the product of the ratio of the total number of workpieces and the similarity. The present invention effectively solves the problem of low cutting efficiency caused by asynchronous production of dual production lines.

Description

Technical Field

[0001] The present invention relates to the field of plate cutting, and more specifically, to a plate cutting matching method and system based on big data. Background Art

[0002] As people's living standards continue to improve, the requirements for living environment and furniture are also increasing. Many developers' newly built residential buildings are delivered as fully furnished houses, and almost all of them use customized panel furniture with unified standards. Therefore, the customized panel furniture industry has flourished.

[0003] In order to improve production efficiency when producing furniture workpieces, there are differences in materials, structures and sizes between furniture of multiple orders, so it is often necessary to use multiple gantries on multiple production lines to cut the plates of different orders. Multi-gantry equipment can cut multiple orders on different production lines at the same time. However, when cutting, it may happen that one production line has completed the cutting of orders, while another production line still has many unfinished orders, resulting in one production line being busy and the other idle, and production being out of sync, which ultimately makes the efficiency of the entire processing and production process low.

[0004] The patent document with the authorization announcement number "CN113689094B" and the name "A method for grouping and processing orders for personalized customized furniture" discloses a method for achieving the purpose of efficient overall processing and production of multiple orders of personalized customized furniture by sequentially classifying orders into sets, marking parts, clustering parts into groups, sorting parts by orders, and packaging parts by orders.

[0005] The above method does not take into account the plate size and similarity required by different orders, and cannot effectively solve the problem of low cutting efficiency caused by asynchronous production of dual production lines. Summary of the invention

[0006] In order to solve the problem of low cutting efficiency caused by the asynchronous production of the two production lines, the present invention provides solutions in the following aspects.

[0007] In a first aspect, the present invention provides a plate material matching method based on big data, comprising: collecting order information of at least three orders, wherein the order information includes the workpiece size, the total number of workpieces and the number of workpieces of each size for each order; calculating the probability distribution of workpieces of each size in each order; calculating the similarity between every two orders based on the workpiece size of each order and the probability distribution of workpieces of each size. ,in, e represents a natural constant, L represents the number of types of workpiece lengths of two orders, W represents the number of types of workpiece widths of two orders, and p is the sum of the cumulative probability ratios of two orders within a plurality of different set size ranges, wherein the set size range is ( , ), represents the length variable, represents the width variable, ∈l, ; l is a set of different lengths in two orders, and w is a set of different lengths in two orders; calculate the ratio of the total number of workpieces between every two orders, and use the Hungarian algorithm to match the at least three orders to obtain pairwise order pairs, wherein the weight between every two orders in the Hungarian algorithm is the product of the ratio of the total number of workpieces and the similarity.

[0008] By calculating the probability of two orders and deriving the similarity, we can indirectly determine the similarity of the cutting paths of the two orders, thereby reducing the impact of the cutting paths on the asynchronous production of the two production lines. We then use the total number of workpieces ratio to avoid the problem of a large difference in cutting time on the two production lines due to a large difference in the total number of workpieces of the two orders. Finally, we use the Hungarian algorithm, combined with the total number of workpieces ratio and similarity, to pair all orders at the global level to obtain the pairing solution with the highest synchronization rate for the dual production lines, solving the problem of low cutting efficiency caused by the asynchronous production of the dual production lines.

[0009] Preferably, after calculating the ratio of the total number of workpieces and the similarity between every two orders, the method further comprises: using a sliding window method to calculate the similarity fluctuation value between every two orders.

[0010] By calculating the similarity fluctuation value of every two orders using the sliding window method, we can avoid the problem that two orders are very similar, but the sizes of some workpieces are actually very different, which leads to large differences in cutting paths and cutting times.

[0011] Preferably, the Hungarian algorithm is used to match the at least three orders to obtain pairwise order pairs, wherein the weight between each two orders in the Hungarian algorithm is the product of the ratio of their total number of workpieces and their similarity, including: when the Hungarian algorithm is used to match the at least three orders, if the similarity fluctuation value of the two orders is greater than a preset threshold, the weight between the two orders is set to negative infinity.

[0012] Preferably, the sliding window method is used to calculate the similarity fluctuation value of every two orders, including: listing L×W types of workpiece sizes and arranging them according to the number of length types L and the number of width types W of the two orders; dividing the arranged L×W types of workpiece sizes according to a preset window size; calculating the similarity of the two orders in each window; and calculating the similarity fluctuation value of the two orders based on the similarity of the two orders in each window.

[0013] Preferably, the similarity fluctuation value b of the two orders calculated according to the similarity of the two orders in each window is: ,in, Indicates the number of windows, represents the similarity between two orders in the kth window, Represents the mean similarity of all windows of two orders.

[0014] Summing the square of the difference between the similarity of different windows and the average similarity can amplify larger errors and make the parts of the two orders with larger size differences more significant, thereby better reflecting the similarity fluctuations of the two orders.

[0015] Preferably, the number of windows for: , Indicates the window size.

[0016] The specific division method for L×W workpiece sizes can be determined by calculating the number of windows.

[0017] Preferably, the ratio of the total number of workpieces between every two orders is less than or equal to 1.

[0018] By limiting the ratio of the total number of workpieces of two orders to less than or equal to 1, the weights of the two orders in the final Hungarian algorithm can be limited to the same level, avoiding the problem of unreasonable pairing caused by excessive ratio and excessive weight.

[0019] In a second aspect, the present invention also provides a plate material matching system based on big data, comprising: a memory and a processor, wherein a computer program is stored in the memory, and the processor executes the computer program to implement the above-mentioned plate material matching method based on big data.

[0020] The beneficial effect of the present invention is that by calculating the probability of two orders and deriving the similarity, the similarity of the cutting paths of the two orders can be indirectly determined, thereby avoiding the influence of the cutting paths on the asynchronous production of the two production lines, and then using the total number of workpieces ratio to avoid the problem of a large difference in cutting time on the two production lines due to a large difference in the total number of workpieces of the two orders. Finally, the Hungarian algorithm is used, and the total number of workpieces ratio and similarity are combined to pair all orders at the global level to obtain the pairing solution with the highest synchronization rate of the dual production lines, thereby solving the problem of low cutting efficiency caused by the asynchronous production of the dual production lines. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] By reading the following detailed description with reference to the accompanying drawings, the above and other objects, features and advantages of the exemplary embodiments of the present invention will become readily understood. In the accompanying drawings, several embodiments of the present invention are shown in an exemplary and non-limiting manner, and the same or corresponding reference numerals represent the same or corresponding parts, wherein:

[0022] Figure 1 This is a flow chart of a plate material matching method based on big data provided by an embodiment of the present invention;

[0023] Figure 2 is a schematic diagram of a bipartite graph provided by an embodiment of the present invention;

[0024] Figure 3 It is a block diagram of a plate material matching system based on big data provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0025] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0026] The specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0027] Figure 1 The present invention provides a flow chart of a plate material matching method based on big data, which includes the following steps:

[0028] S101, collecting order information of at least three orders, wherein the order information includes the workpiece size, the total number of workpieces, and the number of workpieces of each size for each order;

[0029] In the field of furniture production, the initial material is called a plate, and the object that is processed or partially processed during the processing is called a workpiece. In the present invention, the workpiece can be obtained by cutting the plate.

[0030] The size of the workpieces in an order will affect the cutting path of the gantry head on the production line. If the size difference of the workpieces of two orders is too large, the cutting paths of the two production lines will generally be quite different, resulting in a large difference in the cutting time of the two orders on the two production lines. On the other hand, the total number of workpieces will generally have a great impact on the cutting time of the two orders on the two production lines. For example, the first order requires 100 30cm×40cm workpieces, and the second order requires 1,000 30cm×40cm workpieces. Obviously, the two orders on the two production lines will have a large difference. In addition, the number of workpieces of different sizes will also have a great impact on the cutting time. For example, the first order requires 100 30cm×40cm workpieces and 100 50cm×80cm workpieces, and the second order requires 30 30cm×50cm workpieces and 170 55cm×80cm workpieces. Obviously, although the total number of workpieces in the two orders is the same and the workpiece sizes are very close, the number of workpieces of different sizes is very different, so the cutting time of the second order will be much longer than that of the first order. Therefore, it is necessary to collect the workpiece size, total number of workpieces, and number of workpieces of each size for each order.

[0031] S102, calculating the probability distribution of workpieces of different sizes in each order;

[0032] S103: Calculate the similarity between every two orders based on the size of the workpieces in each order and the probability distribution of the workpieces of each size. ;

[0033] in, e represents a natural constant, L represents the number of types of workpiece lengths of two orders, W represents the number of types of workpiece widths of two orders, and p is the sum of the cumulative probability ratios of two orders within a plurality of different set size ranges, wherein the set size range is ( , ), represents the length variable, represents the width variable, ∈l, ; l is the set of different lengths in the two orders, w is the set of different lengths in the two orders;

[0034] The calculation of the similarity between each two orders The specific formula can be ≠0, where represents the similarity between the i-th order and the j-th order, Indicates that the length of the i-th order is less than or equal to and the width is less than or equal to The cumulative probability of the artifacts, Indicates that the length of the j-th order is less than or equal to and the width is less than or equal to The cumulative probability of the artifact.

[0035] For example, calculate the similarity between orders a and b , order a requires 30 7cm×3cm, 40 4cm×6cm, and 30 5cm×5cm workpieces, and order b requires 40 6cm×3cm, 30 4cm×5cm, and 30 5cm×6cm workpieces. The workpiece size is length in front and width in the back. There are four lengths of workpieces in orders a and b, namely 7cm, 4cm, 5cm, and 6cm, and three widths, namely 3cm, 6cm, and 5cm. That is, the set l={7cm, 4cm, 5cm, 6cm}, w={3cm, 6cm, 5cm}, so L=4, W=3, and the four lengths are also the length independent variables The range of values, three widths, namely the width independent variables From the above conditions, we can see that = , , , , , , = , , , = , , ; , , , , , , = , , , = , , ; thus obtaining , , , because So it doesn't exist. , , does not exist, , , , , , and we get = , and then the similarity between orders a and b is .

[0036] Since the above method of calculating similarity is to calculate the overall similarity of two orders and does not focus on the local similarity, it may happen that although the two orders are very similar, the sizes of some workpieces are actually very different, which leads to a large difference in cutting paths and cutting times, and they are not suitable for matching together. Therefore, after calculating the ratio and similarity of the total number of workpieces of every two orders, the similarity fluctuation value can be introduced, and the sliding window method can be used to calculate the similarity fluctuation value of every two orders.

[0037] In some embodiments, the sliding window method is used to calculate the similarity fluctuation value of every two orders, including: listing L×W types of workpiece sizes and arranging them according to the number of length types L and the number of width types W of the two orders; dividing the arranged L×W types of workpiece sizes according to a preset window size; calculating the similarity of the two orders in each window; and calculating the similarity fluctuation value of the two orders based on the similarity of the two orders in each window.

[0038] In some embodiments, the similarity fluctuation value b of the two orders is calculated based on the similarity of the two orders in each window as follows: ,in, Indicates the number of windows, represents the similarity between two orders in the kth window, Represents the mean of the similarity of all windows of two orders. for: , Indicates the window size.

[0039] As shown in the previous example, the lengths of the workpieces for the two orders a and b are 7cm, 4cm, 5cm, and 6cm, and the widths are 3cm, 6cm, and 5cm, that is, L=4, W=3. The lengths can be arranged in order as 4cm, 5cm, 6cm, and 7cm, and the widths can be arranged in order as 3cm, 5cm, and 6cm. After combining the 12 workpiece sizes, they can be sorted by length first and then width, or by width first and then length. For example, the first sorting scheme is: 4cm×3cm, 4cm×5cm, 4c m×6cm, 5cm×3cm, 5cm×5cm, 5cm×6cm, 6cm×3cm, 6cm×5cm, 6cm×6cm, 7cm×3cm, 7cm×5cm, 7cm×6cm, the second sorting scheme: 4cm×3cm, 5cm×3cm, 6cm×3cm, 7cm×3cm, 4cm×5cm, 5cm×5cm, 6cm×5cm, 7cm×5cm, 4cm×6cm, 5cm×6cm, 6cm×6cm, 7cm×6cm. Then divide the arranged L×W workpiece sizes according to the preset window size. For example, if the window size is 3, the first sorting scheme can be divided into There are three windows, namely (4cm×3cm, 4cm×5cm, 4cm×6cm), (4cm×5cm, 4cm×6cm, 5cm×3cm), (4cm×6cm, 5cm×3cm, 5cm×5cm), (5cm×3cm, 5cm×5cm, 5cm×6cm), (5cm×5cm, 5cm×6cm, 6cm×3cm), (5cm×6cm, 6cm×3cm, 6cm×5cm), (6cm×3cm, 6cm×5cm, 6cm×6cm), (6cm×5cm, 6cm×6cm, 7cm×3cm), (6cm×6cm, 7cm×3cm, 7cm×5cm), (7cm×3cm, 7cm×5cm, 7cm×6cm), calculate the similarity of orders a and b under the first group of windows: = = =0.574, and similarly we can calculate — , then — By taking the average, we can get the average of the similarities of all windows of the two orders, from which we can calculate the similarity fluctuation value of the two orders. .

[0040] S104, calculating the ratio of the total number of workpieces between every two orders, and matching the at least three orders using the Hungarian algorithm to obtain pairwise order pairs, wherein the weight between every two orders in the Hungarian algorithm is the product of the ratio of the total number of workpieces and the similarity.

[0041] The ratio of the total number of workpieces of two orders can reflect the difference in the total number of workpieces of the two orders. The closer it is to 1, the closer the total number of workpieces of the two orders is. Introducing this ratio can avoid the problem of a large difference in the cutting time of two orders on two production lines due to a large difference in the total number of workpieces. Generally speaking, the ratio of the total number of workpieces of each two orders is less than or equal to 1. For example, order c has 100 workpieces and order d has 120 workpieces. The ratio is calculated as , if c orders 120 workpieces and d orders 100 workpieces, the ratio is calculated as .

[0042] When using the Hungarian algorithm to match orders, you need to first create a bipartite graph, such as Figure 2 The figure is a schematic diagram of a bipartite graph of an embodiment of the present invention. In the figure, 1 / 2 / 3 / 4 represent orders, and the line between orders represents the matching weight of two orders, which is the product of the ratio of the total number of workpieces and the similarity. For example, the total number of workpieces in order 1 and order 2 is 100 and 120 respectively, and the similarity is 0.8, then the weight is , no connection between the same orders means that they cannot be matched, and the same pairing scheme has only one connection, that is, after the left order 1 and the right order 2 are connected, the left order 2 can no longer be connected with the right order 1. According to the above conditions, the Hungarian algorithm can be used to obtain the optimal pairwise pairing scheme. It should be noted that the specific Hungarian algorithm is a well-known technology and will not be described in detail here.

[0043] After introducing similarity fluctuation, the above pairing method can be improved by setting the weight between the two orders to negative infinity when the similarity fluctuation value of the two orders is greater than a preset threshold. Setting the weight between the two orders to negative infinity is equivalent to the two orders being unable to be paired, that is, having no connection.

[0044] The above-mentioned plate cutting matching method based on big data provided by an embodiment of the present invention can indirectly determine the similarity of the cutting paths of the two orders by calculating the probability of two orders and deriving the similarity, thereby avoiding the influence of the cutting paths on the asynchronous production of the two production lines, and then using the total number of workpieces ratio to avoid the problem of a large difference in cutting time on the two production lines due to the large difference in the total number of workpieces of the two orders. Finally, the Hungarian algorithm is used, and the total number of workpieces ratio and similarity are combined to pair all orders at the global level to obtain the pairing solution with the highest synchronization rate of the dual production lines, thereby solving the problem of low cutting efficiency caused by the asynchronous production of the dual production lines.

[0045] The present invention also provides a plate material matching system based on big data. Figure 3As shown, the system includes a processor and a memory, wherein the memory stores computer program instructions. When the computer program instructions are executed by the processor, a plate material matching method based on big data described in the present invention is implemented.

[0046] The system also includes other components familiar to those skilled in the art, such as a communication bus and a communication interface, whose configuration and functions are known in the art and thus will not be described in detail here.

[0047] In the present invention, the aforementioned memory may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, apparatus or device. For example, a computer-readable storage medium may be any appropriate magnetic storage medium or magneto-optical storage medium, such as a resistive random access memory RRAM (Resistive Random Access Memory), a dynamic random access memory DRAM (Dynamic Random Access Memory), a static random access memory SRAM (Static Random-Access Memory), an enhanced dynamic random access memory EDRAM (Enhanced Dynamic Random Access Memory), a high-bandwidth memory HBM (High-Bandwidth Memory), a hybrid memory cube HMC (Hybrid Memory Cube), etc., or any other medium that can be used to store the required information and can be accessed by an application, a module, or both. Any such computer storage medium may be part of a device or accessible or connectable to a device. Any application or module described in the present invention may be implemented using computer-readable / executable instructions that may be stored or otherwise maintained by such a computer-readable medium.

[0048] In the description of this specification, "plurality" or "several" means at least two, such as two, three or more, etc., unless otherwise clearly and specifically defined.

[0049] Although this specification has shown and described a number of embodiments of the present invention, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Those skilled in the art will conceive of many modifications, changes and alternatives without departing from the ideas and spirit of the present invention. It should be understood that in the practice of the present invention, various alternatives to the embodiments of the present invention described herein may be employed.

Claims

1. A plate material matching method based on big data, characterized in that: include: Collecting order information of at least three orders, wherein the order information includes the workpiece size, the total number of workpieces, and the number of workpieces of each size for each order; Calculate the probability distribution of workpieces of each size in each order; Calculate the similarity between every two orders based on the size of the workpieces in each order and the probability distribution of the workpieces of each size ,in, e represents a natural constant, L represents the number of types of workpiece lengths of two orders, W represents the number of types of workpiece widths of two orders, and the setting size range is ( , ), represents the length variable, represents the width variable, ∈l, ; l is a set of different lengths in two orders, w is a set of different lengths in two orders, Indicates that the length of the i-th order is less than or equal to and the width is less than or equal to The cumulative probability of the artifacts, Indicates that the length of the j-th order is less than or equal to and the width is less than or equal to The cumulative probability of the artifacts; Calculate the ratio of the total number of workpieces between every two orders, and use the Hungarian algorithm to match the at least three orders to obtain pairing order pairs, wherein the weight between every two orders in the Hungarian algorithm is the product of the ratio of the total number of workpieces and the similarity; After calculating the ratio and similarity of the total number of workpieces of every two orders, the method includes: The sliding window method is used to calculate the similarity fluctuation value between every two orders.

2. The plate material matching method based on big data according to claim 1 is characterized in that: The at least three orders are matched by using the Hungarian algorithm to obtain pairwise order pairs, wherein the weight between each two orders in the Hungarian algorithm is the product of the ratio of the total number of workpieces and the similarity, including: When the Hungarian algorithm is used to match the at least three orders, if the similarity fluctuation value between the two orders is greater than a preset threshold, the weight between the two orders is set to negative infinity.

3. The plate material matching method based on big data according to claim 1 is characterized in that: The sliding window method is used to calculate the similarity fluctuation value of every two orders, including: According to the number of length types L and width types W of the two orders, list L×W types of workpiece sizes and arrange them; Divide and arrange L×W workpiece sizes according to the preset window size; Calculate the similarity between two orders in each window; The similarity fluctuation value of the two orders is calculated according to the similarity of the two orders in each window.

4. The plate material matching method based on big data according to claim 3 is characterized in that: The similarity fluctuation value b of the two orders is calculated based on the similarity of the two orders in each window as follows: ,in, Indicates the number of windows, represents the similarity between two orders in the kth window, Represents the mean similarity of all windows of two orders.

5. The plate material matching method based on big data according to claim 4 is characterized in that: The number of windows for: , Indicates the window size.

6. The plate material matching method based on big data according to claim 1, characterized in that: The ratio of the total number of workpieces between every two orders is less than or equal to 1.

7. A plate material matching system based on big data, characterized in that: include: A memory and a processor, wherein a computer program is stored in the memory, and the processor executes the computer program to implement the plate material matching method based on big data as described in any one of claims 1 to 6.

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