Production line optimization method, system and electronic equipment

Abstract

The present invention relates to the technical field of production management, and discloses a production line optimization method, system and electronic equipment. The method assigns a node identifier to each process node from multiple preset identifiers based on a node relationship matrix, and ensures that adjacent process nodes do not have the same node identifier, thereby obtaining multiple identifier allocation schemes. A target scheme is determined from each identifier allocation scheme according to the number of identifiers adopted, and then a set label of each process node is determined according to the node identifier of the target scheme. A linear model is established based on the set label, and the production sequence between each process node in the original production line is converted from a mixed programming problem to a linear programming problem for solution, thereby providing linear reference and linear constraints for the linear theory of the automated production line, improving the analysis accuracy of the linear theory on the automated production line, and improving the optimization effect of the automated production line.

Description

Technical Field

[0001] The present invention relates to the technical field of production management, and in particular to a production line optimization method, system and electronic equipment. Background Art

[0002] As the degree of automation in the manufacturing industry becomes increasingly higher, automated production lines have become the core technology of the manufacturing industry. Based on relevant experience, technicians have summarized a complete set of linear theories for the design, integration, and debugging processes of automated production lines, which are used to solve the allocation planning of the production capacity paths of automated production lines, summarize the advantages and disadvantages of automated production lines, and thus provide improvement plans and capacity optimization for the production lines.

[0003] However, due to the relatively complex manufacturing technology in industries such as automobiles and aircraft, the automated production lines within the industry are relatively complex. The existing production line topology diagrams are not good at expressing the event relationship between the processes, and cannot provide linear references and linear constraints for the linear theory of automated production lines. Analyzing automated production lines through linear theory is relatively complex, and the analysis results obtained are inaccurate, resulting in the optimization effect of automated production lines failing to meet industry requirements. Summary of the Invention

[0004] In order to provide a basic understanding of some aspects of the disclosed embodiments, a brief summary is given below. The summary is not an extensive review, nor is it intended to identify key / critical elements or delineate the scope of protection of these embodiments, but rather serves as a prelude to the detailed description that follows.

[0005] In view of the above-mentioned shortcomings of the prior art, the present invention discloses a production line optimization method, system and electronic equipment to improve the optimization effect of the automated production line.

[0006] The present invention provides a production line optimization method, comprising: obtaining an original production line, wherein the original production line includes multiple process nodes and a production sequence between the process nodes; establishing a node relationship matrix according to the production sequence between the process nodes, wherein the node relationship matrix is used to characterize the node adjacency relationship between the process nodes; obtaining multiple identifier allocation schemes that meet preset constraints according to the node relationship matrix, and determining a target scheme from each identifier allocation scheme according to the number of identifiers adopted, wherein the identifier allocation scheme is obtained by respectively assigning a node identifier to each process node from multiple preset identifiers, the preset constraints include that adjacent process nodes do not have the same node identifier, and the number of identifiers adopted is the number of preset identifiers adopted by the identifier allocation scheme; determining the set label of each process node according to the node identifier of the target scheme, and establishing a linear model according to the set label to optimize each process node in the original production line according to the linear model.

[0007] Optionally, a node relationship matrix is established according to the production sequence between the process nodes, including: generating edge data for characterizing the current node to the child node according to the production sequence, wherein the current node and the child node are both process nodes; and establishing a node relationship matrix according to the edge data, wherein the matrix elements in the node relationship matrix are determined according to whether edge data exists between the process nodes.

[0008] Optionally, a node relationship matrix is established based on the edge data using the following formula: Where A is the node relationship matrix, a ij is the matrix element in the i-th row and j-th column of the node relationship matrix, e ij is the edge data from the i-th process node to the j-th process node, and N is the number of process nodes.

[0009] Optionally, multiple identification allocation schemes that meet preset restrictions are obtained according to the node relationship matrix, including: the preset identification is different types of colors; any process node is colored with a color from the preset identification as a node identification, and the process node is determined as the current node; in response to the current node, the adjacent nodes of the current node are determined from the process nodes according to the node relationship matrix, and the node identifications of the adjacent nodes are determined from the preset identification according to the preset restrictions to color the adjacent nodes; if all adjacent nodes of the current node have been colored, a new current node is determined from the adjacent nodes; if all process nodes have been colored, an identification allocation scheme is generated according to the node identifications of each process node.

[0010] Optionally, the preset constraint condition is represented by the following formula: Where M is the number of preset markers, x i is the node identifier of the i-th process node, x p is the node identifier of the p-th process node, x q is the node identifier of the qth process node, a pi is the matrix element in the pth row and ith column of the node relationship matrix, a iq is the matrix element in the i-th row and q-th column of the node relationship matrix, N is the number of process nodes, Z + Represents a positive integer.

[0011] Optionally, a target scheme is determined from each of the identifier allocation schemes based on the number of identifiers adopted, including: if the number of identifiers adopted in the identifier allocation scheme is equal to 2, then the identifier allocation scheme is determined as the target scheme; if the number of identifiers adopted in the identifier allocation scheme is not equal to 2, then the identifier allocation scheme is discarded.

[0012] Optionally, a linear model is established based on the set label to optimize each process node in the original production line based on the linear model, including: obtaining the original capacity threshold corresponding to each process node, and obtaining the capacity plan of the original production line, wherein the capacity plan includes raw material data, capacity target and capacity constraint conditions; establishing a linear model based on the original capacity threshold, the set label, the capacity target and at least one of the capacity constraint conditions to obtain the capacity model corresponding to the original production line; inputting the raw material data into the capacity model to simulate the original production line based on the linear constraint through the capacity model to obtain a simulation result, and adjusting the original capacity threshold and / or the production sequence according to the simulation result until the simulation result meets the preset first capacity requirement.

[0013] Optionally, a linear model is established based on the set label to optimize each process node in the original production line based on the linear model, including: the process nodes include production line input nodes and production line output nodes; obtaining raw material data and a capacity model of the original production line; stratifying each process node in the original production line according to the set label and the production sequence to obtain multiple production layers arranged according to the production sequence, and establishing linear constraints based on the process nodes in the production layers; inputting the raw material data into the production line input node of the capacity model to simulate the original production line based on the linear constraints through the capacity model and determine the capacity data of each process node; adjusting the production sequence between associated nodes based on the capacity data until the capacity data of the production line output node meets the preset second capacity requirement, wherein the associated nodes are two different process nodes, and the associated nodes exist in adjacent node layers respectively.

[0014] The present invention provides a production line optimization system, comprising: an acquisition module for acquiring an original production line, wherein the original production line includes multiple process nodes and a production sequence between the process nodes; an establishment module for establishing a node relationship matrix according to the production sequence between the process nodes, wherein the node relationship matrix is used to characterize the node adjacency relationship between the process nodes; a determination module for acquiring multiple identifier allocation schemes that meet preset constraints according to the node relationship matrix, and determining a target scheme from each identifier allocation scheme according to the number of identifiers adopted, wherein the identifier allocation scheme is obtained by respectively assigning a node identifier to each process node from multiple preset identifiers, the preset constraints include that adjacent process nodes do not have the same node identifier, and the number of identifiers adopted is the number of preset identifiers adopted by the identifier allocation scheme; an optimization module for respectively determining the set label of each process node according to the node identifier of the target scheme, and establishing a linear model according to the set label, so as to optimize each process node in the original production line according to the linear model.

[0015] The present invention provides an electronic device, comprising: a processor and a memory; the memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory, so that the electronic device performs the above method.

[0016] Beneficial effects of the present invention:

[0017] A node relationship matrix is established based on the production sequence between each process node in the original production line. Based on the node relationship matrix, a node identifier is assigned to each process node from multiple preset identifiers, and adjacent process nodes are not assigned the same node identifier. Multiple identifier allocation schemes are obtained. A target scheme is determined from each identifier allocation scheme based on the number of identifiers adopted. The set labels of each process node are then determined based on the node identifiers of the target scheme. A linear model is then established based on the set labels to optimize each process node in the original production line according to the linear model. In this way, the production sequence between each process node in the original production line is converted from a hybrid programming problem to a linear programming problem for solution, thereby providing linear references and linear constraints for the linear theory of the automated production line, improving the accuracy of the linear theory's analysis of the automated production line, and improving the optimization effect of the automated production line. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 is a flow chart of a production line optimization method according to an embodiment of the present invention;

[0019] Figure 2 is a topological diagram of an original production line in an embodiment of the present invention;

[0020] Figure 3 This is a flow chart of a method for obtaining an identification allocation scheme in an embodiment of the present invention;

[0021] Figure 4 is a structural diagram of a production line optimization system according to an embodiment of the present invention;

[0022] Figure 5 is a schematic diagram of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0023] The following describes the embodiments of the present invention through specific examples. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through different specific embodiments. The details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the following embodiments and sub-samples in the embodiments can be combined with each other unless there is a conflict.

[0024] It should be noted that the illustrations provided in the following embodiments are merely schematic illustrations of the basic concept of the present invention. Therefore, the illustrations only show components related to the present invention and are not drawn according to the number, shape, and size of components in actual implementation. In actual implementation, the type, quantity, and proportion of each component may be changed arbitrarily, and the component layout may also be more complex.

[0025] In the following description, numerous details are discussed to provide a more thorough explanation of the embodiments of the present invention. However, it will be apparent to those skilled in the art that the embodiments of the present invention may be practiced without these specific details. In other embodiments, well-known structures and devices are shown in block diagram form rather than in detail to avoid obscuring the embodiments of the present invention.

[0026] In the description and claims of the embodiments of the present disclosure, as well as in the accompanying drawings, the terms "first," "second," and the like are used to distinguish similar items and are not necessarily used to describe a particular order or precedence. It should be understood that the terms used in this manner are interchangeable where appropriate to describe the embodiments of the present disclosure herein. In addition, the terms "including," "having," and any variations thereof are intended to cover non-exclusive inclusions.

[0027] Unless otherwise stated, the term "plurality" means two or more.

[0028] In the embodiment of the present disclosure, the character " / " indicates that the preceding and following objects are in an "or" relationship. For example, A / B means: A or B.

[0029] The term "and / or" describes an association between objects, indicating that three relationships can exist. For example, A and / or B means: A or B, or A and B.

[0030] Combine Figure 1 As shown, the embodiment of the present disclosure provides a production line optimization method, including:

[0031] Step S101, obtaining the original production line;

[0032] The original production line includes multiple process nodes and the production sequence between the process nodes;

[0033] Step S102, establishing a node relationship matrix according to the production sequence between process nodes;

[0034] Among them, the node relationship matrix is used to characterize the node adjacency relationship between process nodes;

[0035] Step S103: obtaining multiple identifier allocation schemes that meet preset constraints based on the node relationship matrix, and determining a target scheme from each identifier allocation scheme based on the number of adopted identifiers;

[0036] The identifier allocation scheme is obtained by assigning a node identifier to each process node from a plurality of preset identifiers;

[0037] The preset restriction condition includes that adjacent process nodes do not have the same node identifier;

[0038] The number of adopted identifiers is the number of preset identifiers adopted by the identifier allocation scheme;

[0039] Step S104 , determining the set labels of each process node according to the node identifier of the target solution, and establishing a linear model according to the set labels, so as to optimize each process node in the original production line according to the linear model.

[0040] The production line optimization method provided by the embodiment of the present disclosure is adopted. A node relationship matrix is established through the production sequence between each process node in the original production line. Based on the node relationship matrix, a node identifier is assigned to each process node from multiple preset identifiers, and adjacent process nodes do not have the same node identifier, thereby obtaining multiple identifier allocation schemes. A target scheme is determined from each identifier allocation scheme based on the number of identifiers adopted, thereby determining the set label of each process node based on the node identifier of the target scheme, and establishing a linear model based on the set label to optimize each process node in the original production line according to the linear model. In this way, the production sequence between each process node in the original production line is converted from a mixed programming problem to a linear programming problem for solution, thereby providing a linear reference and linear constraint for the linear theory of the automated production line, improving the accuracy of the linear theory's analysis of the automated production line, and improving the optimization effect of the automated production line.

[0041] In some embodiments, the original production line is abstracted into a two-part logical structure, wherein the process nodes are stored through the V set, and the process nodes are used to represent the machines, process flows or production islands; the edge data are stored through the E set, and the edge data are used to represent the upstream and downstream relationships between the process nodes.

[0042] Optionally, a node relationship matrix is established according to the production sequence, including: generating edge data for representing the current node to the child node according to the production sequence, wherein the current node and the child node are both process nodes; establishing a node relationship matrix according to the edge data, wherein the matrix elements in the node relationship matrix are determined according to whether edge data exists between the process nodes.

[0043] Optionally, a node relationship matrix is established based on edge data using the following formula:

[0044]

[0045] Where A is the node relationship matrix, a ij is the matrix element in the i-th row and j-th column of the node relationship matrix, e ij is the edge data from the i-th process node to the j-th process node, and N is the number of process nodes.

[0046] In some embodiments, the topology of the original production line is as follows: Figure 2As shown, the original production line includes node A, node B, node C, node D and node E, the child node of node A is node C, the child nodes of node B are node C and node D, and the child node of node C is node E; according to the production sequence of the original production line, edge data for characterizing the current node to the child node is generated, and the matrix elements in the node relationship matrix are determined according to whether there is edge data between the process nodes to obtain the node relationship matrix, wherein the node relationship matrix is shown in Table 1; through the node relationship matrix, the order relationship between the process nodes, the parent node of any process node, the number of parent nodes of any process node, the child node of any process node and the number of child nodes of any process node can be obtained.

[0047] Table 1

[0048] Node A Node B Node C Node D Node E Node A 0 0 1 0 0 Node B 0 0 1 1 0 Node C 0 0 0 0 1 Node D 0 0 0 0 0 Node E 0 0 0 0 0

[0049] Optionally, multiple identification allocation schemes that meet preset restrictions are obtained based on the node relationship matrix, including: presetting identifications as different types of colors; coloring any process node with a color from the preset identification as the node identification, and determining the process node as the current node; in response to the current node, determining the adjacent nodes of the current node from the process nodes based on the node relationship matrix, and determining the node identifications of the adjacent nodes from the preset identifications based on the preset restrictions to color the adjacent nodes; if all adjacent nodes of the current node have been colored, determining a new current node from the adjacent nodes; if all process nodes have been colored, generating an identification allocation scheme based on the node identifications of each process node.

[0050] Combine Figure 3 As shown, the embodiment of the present disclosure provides a method for obtaining an identification allocation scheme, including:

[0051] Step S301, pre-setting a plurality of preset identifiers;

[0052] Among them, the preset logos are different kinds of colors;

[0053] Step S302, obtaining the original production line;

[0054] The original production line includes multiple process nodes and the production sequence between the process nodes;

[0055] Step S303, establishing a node relationship matrix according to the production sequence;

[0056] Among them, the node relationship matrix is used to characterize the node adjacency relationship between process nodes;

[0057] Step S304 , coloring any process node using a color from the preset identifiers as a node identifier, and determining the process node as the current node;

[0058] Step S305, determining the adjacent nodes of the current node according to the node relationship matrix;

[0059] Step S306: determine whether all adjacent nodes have node identifiers. If so, jump to step S308; if not, jump to step S307;

[0060] Step S307: Determine the node identifiers of the adjacent nodes from the preset identifiers according to the preset restriction conditions to color the adjacent nodes, and then jump to step S306;

[0061] Among them, the current node and the adjacent nodes do not have the same color;

[0062] Step S308 , determining whether all process nodes have node identifiers, if so, skipping to step S310 , if not, skipping to step S309 ;

[0063] Step S309, determine a new current node from the adjacent nodes and jump to step S305;

[0064] Step S310 : generating an identifier allocation scheme according to the node identifiers corresponding to the process nodes.

[0065] The identification allocation scheme acquisition method provided by the embodiment of the present disclosure is adopted. A node relationship matrix is established through the production sequence between each process node in the original production line. Based on the node relationship matrix, a node identifier is assigned to each process node from multiple preset identifiers, and adjacent process nodes do not have the same node identifier, thereby obtaining multiple identification allocation schemes. A target scheme is determined from each identification allocation scheme according to the number of identifications adopted, thereby determining the set label of each process node according to the node identifier of the target scheme, and establishing a linear model based on the set label to optimize each process node in the original production line according to the linear model. In this way, the production sequence between each process node in the original production line is converted from a mixed programming problem to a linear programming problem for solution, thereby providing a linear reference and linear constraint for the linear theory of the automated production line, improving the accuracy of the linear theory's analysis of the automated production line, and improving the optimization effect of the automated production line.

[0066] Optionally, the preset constraint condition is represented by the following formula:

[0067]

[0068] Where M is the number of preset markers, x i is the node identifier of the i-th process node, x p is the node identifier of the p-th process node, x q is the node identifier of the qth process node, a piis the matrix element in the pth row and ith column of the node relationship matrix, a iq is the matrix element in the i-th row and q-th column of the node relationship matrix, N is the number of process nodes, Z + Represents a positive integer.

[0069] In some embodiments, in order to improve computational efficiency, the variable types in the preset constraints are all determined to be integers; a one-dimensional array is established to obtain array X, and the node identifiers of the process nodes are stored in array X; M is the number of preset identifiers, wherein the initial value of M is set to 4 based on the four-color principle, and the process nodes are colored; if the four-color principle is satisfied, the value of M is reduced; in the process of traversing each process node for coloring, the parent node and child node of the current node are determined according to the node relationship matrix, so as to determine the node identifier of the current node according to the node identifier of the parent node and the node identifier of the child node.

[0070] Optionally, a target scheme is determined from each identifier allocation scheme based on the number of identifiers adopted, including: if the number of identifiers adopted in the identifier allocation scheme is equal to 2, then the identifier allocation scheme is determined as the target scheme; if the number of identifiers adopted in the identifier allocation scheme is not equal to 2, then the identifier allocation scheme is discarded.

[0071] In some embodiments, the target solution is determined by solving an objective function, where the objective function is represented by the following formula:

[0072] min(z)=M,

[0073] Wherein, z is the optimization target, and the optimization target z is set to the minimum number of preset identifiers, that is, min(z)=2.

[0074] In some embodiments, a node identifier is assigned to each process node based on the principle that adjacent process nodes do not have the same node identifier, and then a target scheme is determined from the identifier assignment scheme, and the topology graph of the original production line is divided into two parts through the target scheme, which means that the linear problem of the original production line has a solution or can be decomposed.

[0075] In some embodiments, the topology of the original production line is as follows: Figure 2 As shown, the original production line includes node A, node B, node C, node D and node E, the child node of node A is node C, the child nodes of node B are node C and node D, and the child node of node C is node E; according to the node relationship matrix, multiple identification allocation schemes that meet the preset restrictions are obtained, and a target scheme is determined from each identification allocation scheme according to the number of identifications adopted, and the target scheme Result of the original production line is obtained: [2,2,1,1,2]; based on the target scheme, it can be seen that nodes A, node B and node E belong to a set, and nodes C and node D belong to a set.

[0076] In this way, through the linear decomposition of the original production line, there is no need to consider the upstream and downstream issues between nodes in the topology diagram. The result of the linear decomposition can determine the dependency and independence between the process flows, and the mixed planning problem is converted into a linear programming problem for solution, providing a reference for constructing an efficient production capacity path and a reasonable production capacity quantity.

[0077] Optionally, a linear model is established based on the set label to optimize each process node in the original production line according to the linear model, including: obtaining the original capacity threshold corresponding to each process node, and obtaining the capacity plan of the original production line, wherein the capacity plan includes raw material data, capacity target and capacity constraint conditions; establishing a linear model based on at least one of the original capacity threshold, set label, capacity target and capacity constraint conditions to obtain the capacity model corresponding to the original production line; inputting the raw material data into the capacity model to simulate the original production line based on the linear constraint through the capacity model to obtain a simulation result, and adjusting the original capacity threshold and / or production sequence according to the simulation result until the simulation result meets the preset first capacity requirement.

[0078] In some embodiments, the topology of the original production line is as follows: Figure 2 As shown in FIG, the original production line includes nodes A, B, C, D, and E, the child node of node A is node C, the child nodes of node B are nodes C and D, and the child node of node C is node E. Due to the large scale of production line capacity data, it is necessary to consider the selection of the initial node when traversing the process nodes. The connectivity of all process nodes in the graph is often checked through multiple loops or recursive calls. However, the above algorithm requires algorithmic backtracking logic coding, which is not conducive to engineering coding. By assigning set labels to the process nodes, when the number of sets is 2, a full single-mode matrix is established according to the set labels to obtain the linear matrix model corresponding to the production line. The linear evidence model is shown in Table 2.

[0079] In this way, even if the production line topology has multiple connected components, there is no need to check the connectivity between process nodes, making the production line directed. Figure 2 The linear problem of points has a solution.

[0080] Table 2

[0081] Edge 1 Edge 2 Side 3 Side 4 Node A 1 0 0 0 Node B 0 1 1 0 Node C -1 -1 0 1 Node D 0 0 -1 0 Node E 0 0 0 -1

[0082] Optionally, a linear model is established based on the set labels to optimize each process node in the original production line based on the linear model, including: the process nodes include production line input nodes and production line output nodes; obtaining raw material data and a capacity model of the original production line; stratifying each process node in the original production line according to the set labels and the production sequence to obtain multiple production layers arranged according to the production sequence, and establishing linear constraints based on the process nodes in the production layer; inputting the raw material data into the production line input node of the capacity model to simulate the original production line based on the linear constraints through the capacity model and determine the capacity data of each process node; adjusting the production sequence between associated nodes based on the capacity data until the capacity data of the production line output node meets the preset second capacity requirement, wherein the associated nodes are two different process nodes, and the associated nodes exist in adjacent node layers respectively.

[0083] In some embodiments, the topology of the original production line is as follows: Figure 2 As shown, the original production line includes node A, node B, node C, node D and node E, the child node of node A is node C, the child nodes of node B are node C and node D, and the child node of node C is node E; based on the target solution, it can be seen that nodes A, node B and node E belong to a set, and nodes C and node D belong to a set; the process nodes in the original production line are layered according to the set label and production sequence, and three production layers arranged in production sequence are obtained, among which the first production layer includes nodes A and node B, the second production layer includes nodes C and node D, and the third production layer includes node E.

[0084] In some embodiments, the production line topology is used as a decision variable to represent the production sequence between process nodes, which often has both integer variables and continuous variables, and is a mixed overall planning problem. Even if the mathematical model of the integer programming problem is not complex, since the feasible domain of the integer programming problem contains feasible points of multiple production lines, the method of exhaustively enumerating all feasible points is not suitable for the automotive industry with large data dimensions. The computational complexity of problem solving increases exponentially with the expansion of data dimensions, so direct calculation cannot be selected. In the production line capacity planning, the capacity data is an integer, and the objective function and constraints are both represented by linear functions. A linear model is established based on the set labels. Through the full single-mode matrix established based on the set labels, the mixed integer programming problem is converted into a linear programming problem for solution. Through other mature methods such as calculus, optimality conditions for capacity optimization can often be established, thereby reducing the complexity of the problem calculation. According to the linear programming solution, the following problems of the production line can be solved: first, the overall solution efficiency of the algorithm is improved; second, a feasible solution that can satisfy the linear constraints is obtained; third, if the linear programming problem is a differentiable convex optimization problem, the optimal necessary and sufficient conditions can be provided to the production line globally.

[0085] Combine Figure 4 As shown, an embodiment of the present disclosure provides a production line optimization system, including an acquisition module 401, an establishment module 402, a determination module 403, and an optimization module 404. The acquisition module 401 is used to acquire an original production line, wherein the original production line includes multiple process nodes and a production sequence between the process nodes; the establishment module 402 is used to establish a node relationship matrix according to the production sequence, wherein the node relationship matrix is used to characterize the node adjacency relationship between the process nodes; the determination module 403 is used to obtain multiple identifier allocation schemes that meet preset constraints according to the node relationship matrix, and determine a target scheme from each identifier allocation scheme according to the number of identifiers adopted, wherein the identifier allocation scheme is obtained by assigning a node identifier to each process node from multiple preset identifiers, the preset constraint includes that adjacent process nodes do not have the same node identifier, and the number of identifiers adopted is the number of preset identifiers adopted by the identifier allocation scheme; the optimization module 404 is used to determine the set label of each process node according to the node identifier of the target scheme, and establish a linear model based on the set label to optimize each process node in the original production line according to the linear model.

[0086] The identification allocation scheme acquisition system provided by the embodiment of the present disclosure is adopted. A node relationship matrix is established through the production sequence between each process node in the original production line. Based on the node relationship matrix, a node identifier is assigned to each process node from multiple preset identifiers, and adjacent process nodes do not have the same node identifier, thereby obtaining multiple identification allocation schemes. A target scheme is determined from each identification allocation scheme according to the number of identifications adopted, and then the set label of each process node is determined according to the node identifier of the target scheme. A linear model is established based on the set label to optimize each process node in the original production line according to the linear model. In this way, the production sequence between each process node in the original production line is converted from a mixed programming problem to a linear programming problem for solution, thereby providing a linear reference and linear constraint for the linear theory of the automated production line, improving the accuracy of the linear theory's analysis of the automated production line, and improving the optimization effect of the automated production line.

[0087] Figure 5 The following is a schematic diagram showing the structure of a computer system suitable for implementing an electronic device according to an embodiment of the present application. Figure 5 The computer system 500 of the electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0088] like Figure 5As shown, the computer system 500 includes a central processing unit (CPU) 501, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 502 or the program loaded from the storage part 508 into the random access memory (RAM) 503, such as executing the method in the above embodiment. Various programs and data required for system operation are also stored in the RAM 503. The CPU 501, ROM 502 and RAM 503 are connected to each other via a bus 504. An input / output (I / O) interface 505 is also connected to the bus 504.

[0089] The following components are connected to the I / O interface 505: an input section 506 including a keyboard, a mouse, and the like; an output section 507 including devices such as a cathode ray tube (CRT), a liquid crystal display (LCD), and a speaker; a storage section 508 including a hard disk; and a communication section 509 including a network interface card such as a LAN (Local Area Network) card or a modem. The communication section 509 performs communication processing via a network such as the Internet. A drive 510 is also connected to the I / O interface 505 as needed. Removable media 511, such as a magnetic disk, an optical disk, a magneto-optical disk, or a semiconductor memory, is installed in the drive 510 as needed, so that computer programs read therefrom can be installed into the storage section 508 as needed.

[0090] In particular, according to an embodiment of the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a computer program for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via the communication section 509, and / or installed from a removable medium 511. When the computer program is executed by the central processing unit (CPU) 501, the various functions defined in the system of the present application are executed.

[0091] It should be noted that the computer-readable medium shown in the embodiments of the present application can be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium can be, for example, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media can include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, which carries a computer-readable computer program. This propagated data signal can take a variety of forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. A computer program embodied on a computer-readable medium may be transmitted using any suitable medium, including but not limited to wireless, wired, or any suitable combination thereof.

[0092] The electronic device disclosed in this embodiment includes a processor, a memory, a transceiver, and a communication interface. The memory and the communication interface are connected to the processor and the transceiver and complete communication with each other. The memory is used to store computer programs, the communication interface is used to communicate, and the processor and the transceiver are used to run the computer program, so that the electronic device executes each step of the above method.

[0093] In this embodiment, the memory may include a random access memory (RAM), and may also include a non-volatile memory (non-volatile memory), such as at least one disk storage.

[0094] The above-mentioned processor can be a general-purpose processor, including a central processing unit (CPU), a graphics processing unit (GPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0095] The above description and the accompanying drawings fully illustrate the embodiments of the present disclosure to enable those skilled in the art to practice them. Other embodiments may include structural, logical, electrical, process and other changes. The embodiments represent only possible variations. Unless expressly required, individual components and functions are optional, and the order of operations may vary. Parts and subsamples of some embodiments may be included in or replace parts and subsamples of other embodiments. Moreover, the terms used in this application are only used to describe the embodiments and are not used to limit the claims. As used in the description of the embodiments and claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to also include the plural forms. Similarly, the term "and / or" as used in this application refers to any and all possible combinations of one or more associated listings. In addition, when used in this application, the term "comprise" and its variations "comprises" and / or comprising refer to the presence of a stated subsample, whole, step, operation, element, and / or component, but do not exclude the presence or addition of one or more other subsamples, wholes, steps, operations, elements, components and / or groups of these. In the absence of further restrictions, an element defined by the statement "comprises a..." does not exclude the presence of other identical elements in the process, method or device that includes the element. In this article, each embodiment may focus on the differences from other embodiments, and the same and similar parts between the various embodiments can be referenced to each other. For the methods, products, etc. disclosed in the embodiments, if they correspond to the method part disclosed in the embodiments, then the relevant parts can be found in the description of the method part.

[0096] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software may depend on the specific application and design constraints of the technical solution. Technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the embodiments of the present disclosure. Technicians can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0097] In the embodiments disclosed herein, the disclosed methods and products (including but not limited to devices, equipment, etc.) can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of units can be merely a logical functional division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some sub-samples can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the units can be selected according to actual needs to implement this embodiment. In addition, the functional units in the embodiments of the present disclosure can be integrated into a processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0098] The flowcharts and block diagrams in the accompanying drawings show the possible implementation architectures, functions and operations of the systems, methods and computer program products according to the embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, program segment or part of the code, and the module, program segment or part of the code contains one or more executable instructions for implementing the specified logical functions. In some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, or they can sometimes be executed in the opposite order, which can depend on the functions involved. In the descriptions corresponding to the flowcharts and block diagrams in the accompanying drawings, the operations or steps corresponding to different boxes can also occur in an order different from that disclosed in the description, and sometimes there is no specific order between different operations or steps. For example, two consecutive operations or steps can actually be executed substantially in parallel, or they can sometimes be executed in the opposite order, which can depend on the functions involved. Each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented by a dedicated hardware-based system that performs the specified function or action, or may be implemented by a combination of dedicated hardware and computer instructions.

Claims

1. A production line optimization method, characterized in that: include: Acquire an original production line, wherein the original production line includes a plurality of process nodes and a production sequence between the process nodes; Establishing a node relationship matrix according to the production sequence between the process nodes, wherein the node relationship matrix is used to represent the node adjacency relationship between the process nodes; Acquire multiple identifier allocation schemes that meet preset constraints according to the node relationship matrix, and determine a target scheme from each identifier allocation scheme according to the number of adopted identifiers, wherein the identifier allocation scheme is obtained by respectively assigning a node identifier to each process node from a plurality of preset identifiers, the preset constraint includes that adjacent process nodes do not have the same node identifier, and the number of adopted identifiers is the number of preset identifiers adopted by the identifier allocation scheme; The set labels of the process nodes are determined respectively according to the node identifiers of the target solution, and a linear model is established according to the set labels, so as to optimize the process nodes in the original production line according to the linear model.

2. The method according to claim 1, characterized in that A node relationship matrix is established according to the production sequence between the process nodes, including: Generate edge data for representing a current node to a child node according to the production sequence, wherein the current node and the child node are both process nodes; A node relationship matrix is established according to the edge data, wherein matrix elements in the node relationship matrix are determined according to whether edge data exists between the process nodes.

3. The method according to claim 2, characterized in that The node relationship matrix is established based on the edge data using the following formula: Where A is the node relationship matrix, a ij is the matrix element in the i-th row and j-th column of the node relationship matrix, e ij is the edge data from the i-th process node to the j-th process node, and N is the number of process nodes.

4. The method according to claim 2, characterized in that Acquire multiple identifier allocation schemes that meet preset constraints based on the node relationship matrix, including: The preset logos are different types of colors; Coloring any process node using a color from the preset identifiers as a node identifier, and determining the process node as a current node; In response to a current node, determining adjacent nodes of the current node from the process nodes according to the node relationship matrix, and determining node identifiers of the adjacent nodes from the preset identifiers according to the preset restriction condition, so as to color the adjacent nodes; If all adjacent nodes of the current node have been colored, a new current node is determined from the adjacent nodes; If all process nodes have been colored, an identifier allocation scheme is generated according to the node identifiers of the process nodes.

5. The method according to claim 4, characterized in that The preset constraint conditions are expressed by the following formula: Where M is the number of preset markers, x i is the node identifier of the i-th process node, x p is the node identifier of the p-th process node, x q is the node identifier of the qth process node, a pi is the matrix element in the pth row and ith column of the node relationship matrix, a iq is the matrix element in the i-th row and q-th column of the node relationship matrix, N is the number of process nodes, Z + Represents a positive integer.

6. The method according to any one of claims 1 to 5, characterized in that Determining a target solution from each of the identification allocation solutions according to the number of identification adoptions includes: If the number of identifiers adopted in the identifier allocation scheme is equal to 2, the identifier allocation scheme is determined as the target scheme; If the number of identifiers used in the identifier allocation scheme is not equal to 2, the identifier allocation scheme is discarded.

7. The method according to any one of claims 1 to 5, characterized in that Establishing a linear model according to the set labels to optimize each process node in the original production line according to the linear model includes: The process nodes include production line input nodes and production line output nodes; Obtaining raw material data and production capacity model of the original production line; Layering each process node in the original production line according to the set label and the production sequence to obtain multiple production layers arranged according to the production sequence, and establishing linear constraints based on the process nodes in the production layers; Inputting the raw material data into the production line input node of the capacity model, so as to simulate the original production line based on the linear constraints through the capacity model and determine the capacity data of each process node; The production sequence between the associated nodes is adjusted according to the capacity data until the capacity data of the output node of the production line meets the preset second capacity requirement, wherein the associated nodes are two different process nodes and the associated nodes exist in adjacent node layers respectively.

8. The method according to claim 7, characterized in that Establishing a linear model according to the set labels to optimize each process node in the original production line according to the linear model includes: Obtaining the original capacity threshold corresponding to each process node, and obtaining the capacity plan of the original production line, wherein the capacity plan includes raw material data, capacity targets, and capacity constraints; Establishing a linear model based on at least one of the original production capacity threshold, the set label, the production capacity target, and the production capacity constraint to obtain a production capacity model corresponding to the original production line; The raw material data is input into the capacity model to simulate the original production line based on the linear constraints through the capacity model to obtain a simulation result, and the original capacity threshold and / or the production sequence are adjusted according to the simulation result until the simulation result meets the preset first capacity requirement.

9. A production line optimization system, characterized in that: include: An acquisition module, configured to acquire an original production line, wherein the original production line includes a plurality of process nodes and a production sequence between the process nodes; An establishing module, configured to establish a node relationship matrix according to the production sequence between the process nodes, wherein the node relationship matrix is used to represent the node adjacency relationship between the process nodes; a determination module, configured to obtain, based on the node relationship matrix, a plurality of identifier allocation schemes that satisfy preset constraints, and determine a target scheme from each of the identifier allocation schemes according to a number of adopted identifiers, wherein the identifier allocation scheme is obtained by respectively assigning a node identifier to each of the process nodes from a plurality of preset identifiers, the preset constraints include that adjacent process nodes do not have the same node identifier, and the number of adopted identifiers is the number of preset identifiers adopted by the identifier allocation scheme; An optimization module is used to determine the set labels of each process node according to the node identification of the target solution, and establish a linear model according to the set labels to optimize each process node in the original production line according to the linear model.

10. An electronic device, characterized in that: include: processor and memory; The memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory, so that the electronic device performs the method according to any one of claims 1 to 8.

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