CN111652463A

CN111652463A - APS (active pixel System) recursive system, method and equipment based on fractal self-similarity principle

Info

Publication number: CN111652463A
Application number: CN202010308205.1A
Authority: CN
Inventors: 谭培波; 刘伟华; 柳晶晶; 王哓鸣
Original assignee: Beijing Zhitong Yunlian Technology Co Ltd
Current assignee: Beijing Zhitong Yunlian Technology Co Ltd
Priority date: 2020-04-18
Filing date: 2020-04-18
Publication date: 2020-09-11

Abstract

The invention discloses an APS recursive system, a method and equipment based on fractal self-similarity principle, wherein the system comprises: the data layer is used for acquiring processing data transmitted from different systems, arranging the processing data into a format meeting an APS algorithm according to the processing data, and storing a rule table containing all rules, wherein the rule table is used for switching APS results under different rules; the construction layer is used for integrating three flows of a computer processing sequence, an equipment processing sequence and a workpiece flow based on a rule table, determining an optimal sequencing method and constructing a scheme tree, wherein nodes on the scheme tree are all process states of the workpiece, each node stores the processing state of the whole equipment, and each node makes a decision for selecting a next process according to the overall state; and the display layer is used for providing a human-computer interaction interface, finishing the input of processing data and finishing the drawing and display of the APS Gantt chart according to the scheme tree.

Description

APS (active pixel System) recursive system, method and equipment based on fractal self-similarity principle

Technical Field

The invention relates to the technical field of computers, in particular to an APS (active pixel system) recursive system, method and device based on a fractal self-similarity principle.

Background

Advanced Planning and Scheduling (APS) is the core algorithm of smart manufacturing, and is a problem that must be solved when a factory is upgraded from mechanization to intelligence. APS have been mathematically proven to be the "worst" and "hardest" problems, and thus the theory and technique of APS is not fully developed. The conventional APS has the following problems:

1. the result obtained by the algorithm based on the rule is usually one-sided, for example, First In First Out (FIFO), Shortest Processing Time (SPT), etc. are all based on a single local rule, and the cooperation of the overall performance is difficult to be considered;

2. because APS relates to sequencing, the traditional optimization method based on continuous data is not applicable, the efficiency based on genetic algorithm is very low, the tens of thousands of generations of evolution process cannot adapt to the occasions with strong requirements on real-time performance of factories, especially the genetic algorithm cannot ensure that the optimal value of a system can be obtained, the heuristic algorithm can only be used as a guessing and probing method, the result is difficult to explain, and the identity of a first-line operator is difficult to obtain;

3. the practical conditions are variable, and many practical factors are associated with each other, such as the connection with warehousing, the minimum number of times of changing color plates in practice, the change of the process with time, and the like, that is, the influence of high-order association between the front and the back of multiple sequences is often considered in one process, and the model of the APS in the reality is not a simple process sequence but a complex network.

Disclosure of Invention

The present invention provides an APS recursive system, method and device based on fractal self-similarity principle, so as to solve the above problems in the prior art.

The invention provides an APS recursive system based on fractal self-similarity principle, which comprises:

the data layer is used for receiving the processing data transmitted from different systems, arranging the processing data into a format meeting an APS algorithm according to the processing data, and storing a rule table containing all rules, wherein the rule table is used for switching APS results under different rules;

the system comprises a construction layer, a data processing layer and a data processing layer, wherein the construction layer is used for integrating three streams of a computer processing sequence, an equipment processing sequence and a workpiece flow, determining an optimal sequencing method and constructing a scheme tree, nodes on the scheme tree are all process states of a workpiece, each node stores the processing state of the whole equipment, and each node makes a decision for selecting a next process according to the overall state;

and the display layer is used for providing a human-computer interaction interface and finishing drawing and displaying the APS Gantt chart according to the scheme tree.

The invention provides an APS (active pixel system) recursion method based on a fractal self-similarity principle, which comprises the following steps:

receiving processing data transmitted from different systems through a data layer, arranging the processing data into a format meeting an APS algorithm according to the processing data, and storing a rule table containing all rules, wherein the rule table is used for switching APS results under different rules;

integrating three streams of a computer processing sequence, an equipment processing sequence and a workpiece flow through a construction layer, determining an optimal sequencing method, and constructing a scheme tree, wherein nodes on the scheme tree are all process states of the workpiece, each node stores the processing state of the whole equipment, and each node makes a decision for selecting a next process according to the overall state;

and completing drawing and displaying of the APS Gantt chart according to the scheme tree through a human-computer interaction interface provided by the display layer.

The embodiment of the invention also provides an APS recursive device based on the fractal self-similarity principle, which comprises: a memory, a processor and a computer program stored on the memory and executable on the processor, the computer program, when executed by the processor, implementing the steps of the above-mentioned fractal self-similarity based APS recursive method.

The embodiment of the present invention further provides a computer-readable storage medium, where an implementation program for information transfer is stored on the computer-readable storage medium, and when the implementation program is executed by a processor, the implementation program implements the steps of the APS recursive method based on the fractal self-similarity principle.

By adopting the embodiment of the invention, the contradiction between the rule and the optimization is coordinated, all the sequencing schemes including the optimal solution are successfully obtained, the distrust problem of the first-line personnel on the optimization is solved, the fractal tree structure also solves the theoretical basic problem of the method, and a good practical effect is obtained.

The foregoing description is only an overview of the technical solutions of the present invention, and the embodiments of the present invention are described below in order to make the technical means of the present invention more clearly understood and to make the above and other objects, features, and advantages of the present invention more clearly understandable.

Drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.

FIG. 1 is a schematic diagram of an APS recursive system based on fractal self-similarity principles according to an embodiment of the present invention;

FIG. 2 is a tree diagram of an ordering of an embodiment of the present invention;

FIG. 3 is a schematic illustration of a Gantt chart of the present invention;

FIG. 4 is a flow chart of an APS recursive method based on fractal self-similarity principles according to an embodiment of the present invention;

FIG. 5 is a detailed schematic diagram of an APS recursive method based on a fractal self-similarity principle according to an embodiment of the present invention;

fig. 6 is a schematic diagram of an APS recursive device based on the principle of fractal self-similarity according to an embodiment of the present invention.

Detailed Description

The technical solutions of the present invention will be described clearly and completely with reference to the following embodiments, and it should be understood that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

In the description of the present invention, it is to be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", and the like, indicate orientations and positional relationships based on those shown in the drawings, and are used only for convenience of description and simplicity of description, and do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus, should not be considered as limiting the present invention.

Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, features defined as "first", "second", may explicitly or implicitly include one or more of the described features. In the description of the present invention, "a plurality" means two or more unless specifically defined otherwise. Furthermore, the terms "mounted," "connected," and "connected" are to be construed broadly and may, for example, be fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.

System embodiment

In carrying out the present invention, the applicant has found that:

(1) any system has a set of operation rules, namely a set of rules, but any rule can only determine details but cannot determine the final overall performance, so that the algorithm needs to realize the optimal overall performance on the premise of meeting local rules;

(2) the ideal optimal sequence is that all machines are continuously processing without wasted neutral time and all machines are finished simultaneously without underworking of the first machine. The rules that can be derived from this optimal case are for machines that are globally prioritized as idle; for each machine, selecting the machine according to the principle of minimum interval left by the previous procedure;

(3) performing local sequencing according to rules, forming a sequence tree by sequencing all sequences meeting the rules according to a depth priority principle, and selecting an optimal sequence according to a final index, so that the cooperation between the local rules and the overall performance is realized to the maximum extent;

(4) the sequencing tree reflects the relation between 3 sequences, the overall workpiece sequence selected by the computer and the same processing equipment form a processing sequence and a processing sequence of the same workpiece, and the 3 sequences can be traced back;

(5) the APS is a typical information flow idea, which is different from the material flow and the energy flow which satisfy the conservation, the information flow continuously flows according to the principle of maximum entropy, the traditional analysis method of mathematical physical equation cannot be used, and the APS is more like the ordering of electrons in the orbit of atoms which must satisfy some rules, therefore, in the algorithm of the APS, the rules have more essential significance in the APS;

(6) according to the fractal theory, any practical system can be equivalent to a complex system, the complex system has a fractal form, and the fractal characteristic is self-similarity, so that the method for constructing the whole tree-shaped process sequence by adopting a recursive method is the method which can embody the fractal idea most. The self-similarity of a fractal system is reflected by recursion, and the tree is a typical fractal structure, so that the recursion fractal has a local and integral system and can be used for closely expressing the physical mechanism of a sequencing scheme.

Based on the above analysis, according to the embodiment of the present invention, an APS recursive system based on a fractal self-similarity principle is provided, fig. 1 is a schematic diagram of the APS recursive system based on the fractal self-similarity principle according to the embodiment of the present invention, as shown in fig. 1, the APS recursive system based on the fractal self-similarity principle specifically includes:

a data layer 10, configured to receive processing data transmitted from different systems, arrange the processing data into a format meeting an APS algorithm according to the processing data, and store a rule table including all rules, where the rule table is used to switch APS results under different rules;

that is, the data layer 10 arranges the process data, order data, equipment data, etc. transmitted from the systems such as MES, ERP, etc. into the format required by the APS algorithm, and stores a rule table for switching the APS results under different rules;

the data layer 10 specifically includes:

the constraint table module 1 is used for receiving processing data transmitted from different systems, and arranging the processing data into a product of a workpiece and an operation sequence, wherein the product forms all processing procedures, and parameters of the processing procedures comprise operation time and the operation sequence; the structure is shown in table 1, wherein the product of the work piece and the operation sequence constitutes all processing procedures, and is also all objects considered by the computer, and constitutes all tree nodes of the APS sequencing tree; the operation time is the time of machine operation of each node, the time is only one of all machine selectable indexes, and all indexes can be converted into time in the sequencing research; the sequence of operations represents the machine used for the machining, the same machine being able to machine different workpieces, particularly in discrete manufacturing.

TABLE 1 processing time and Process constraints Table

A rule table module 2, configured to store a rule table, where the rule table includes the following parameters: minimum wait, first-in-first-out, last-in-first-out, work piece priority value, delivery earliest priority, shortest machining time, and/or longest machining time. The rule table is shown in table 2, where the minimum waiting SW means that it is established according to the situation where the machine has no stopping time and is therefore optimal.

TABLE 2 rules Table

The construction layer 12 is used for integrating three streams, namely a computer processing sequence, an equipment processing sequence and a workpiece flow, determining an optimal sequencing method and constructing a scheme tree, wherein nodes on the scheme tree are all process states of the workpiece, each node stores the processing state of the whole equipment, and each node makes a decision for selecting a next process according to the overall state;

that is, the building layer 12 completes the building of the plan tree, and integrates 3 streams of the computer processing sequence, the equipment processing sequence, and the workpiece flow into one number, nodes on the tree are all process states of the workpiece, and each node stores the processing state of the whole equipment, so that each node can make a decision for selecting a next process according to the overall state;

the build layer 12 specifically includes:

the data preprocessing module 3 is used for initializing tree nodes and the whole machine state of the APS sequencing tree, specifically, initializing tree nodes in the APS sequencing tree for parameters such as node serial numbers, node names, time, subsequent processes and previous processes of the nodes, and initializing the whole machine state for parameters such as machine numbers, work serial numbers, process candidates and final positions;

the data preprocessing module completes initialization of the node and the entire machine state. The serial number of the node consists of 3 parts, and the workpiece number # is the operation serial number # machine number, so that each node can be conveniently disassembled to obtain a corresponding serial number. The result of node initialization is shown in table 3, which includes the time attribute of the node, its previous node and the next node sequence number. Among the nodes, a starting point number 0#0#0 is added, which serves as a starting node of a preceding process and an ending node of a subsequent process, and is used in a program for judging a program boundary, similarly to a common zero point in circuit design. The result of initializing the whole machine state by the module 3 is shown in table 4, wherein the work order number refers to the sequence of machining and is stored by a sequence, and the sequence of the sequence represents the sequence of machining; the process candidates refer to process nodes which can be selected next, the initial state is composed of nodes with the processing sequence of 1, and then the nodes are distributed to corresponding machine numbers, for example, 2 candidates exist on a machine with the machine number of 1, that is, the first process of the workpiece 1 and the first process of the workpiece 2 are processed by the machine 1; the final position is the time coordinate of the last process of the machine, the granularity of the time coordinate is determined by the minimum processing time of the processing process, generally minutes.

TABLE 3 nodes and their associated tables

Node sequence number	Node name	Time of day	Thick track procedure	Previous procedure
					Index	No	Time	Suf	Pre
0	0#0#0	0	0#0#0	0#0#0
					1	1#1#1	3	1#2#2	0#0#0
2	1#2#2	3	1#3#3	1#1#1
					3	1#3#3	2	0#0#0	1#2#2
4	2#1#1	1	2#2#3	0#0#0
					5	2#2#3	5	2#3#2	2#1#1
6	2#3#2	3	0#0#0	2#2#3
					7	3#1#2	3	3#2#1	0#0#0
8	3#2#1	2	3#3#3	3#1#2
					9	3#3#3	3	0#0#0	3#2#1

TABLE 4 Overall State Table of all machines

	Machine number	Work order number	Procedure candidates	Last position
					Index	M_No	seq	Houxuan_No	dis
0	1	[]	[2#1#1,1#1#1]	0
					1	2	[]	[3#1#2]	0
2	3	[]	[]	0

The building node module 4 is configured to define attributes of the tree nodes, where the attributes specifically include: a single character c stored by a node, a word stored by the node, child nodes of the node, parent nodes of the node, the condition M of the whole equipment stored by the node, and the updated node and the thisPoint stored by the node;

the building node module defines node attributes, as shown in table 5, where children complete the width and depth expansion of the tree; the parent finishes the retrieval of the indexes of the previous process; m represents the intermediate process state of the entire machine as shown in Table 4, from which the system can make a decision to select the first step; word is used for recording the sequence of all processes from the starting node to the node; c is the name of the node.

TABLE 5 node field description Table

A process tree construction module 5 for constructing a process tree: firstly, defining a recursive exit condition, namely when all candidate sets are empty; then constructing branches of the node, wherein the branch condition is that a plurality of minimum waiting time are selected, updating an M matrix in the branches, and keeping the M matrix of the parent node unchanged; finally, a self-calling method is adopted, the pointer points to the branch node which is just established, and a depth-first strategy is realized, wherein the M matrix is a matrix for displaying the initial state of the global machine;

the procedure tree building module completes the construction of the whole procedure tree, firstly, a recursion exit condition is defined, and when all candidate sets are empty; then constructing branches of the node, wherein the branch condition is that a plurality of minimum waiting times are selected, updating an M matrix in the branches, and keeping the M matrix of the parent node unchanged; and finally, a self-calling method is adopted to point the pointer to the branch node which is just established, so that the depth-first strategy is realized.

And selecting an optimal procedure module 6 for listing all tree structures meeting the rule sorting, finding the maximum time value of each branch, and then taking the minimum branch as the final optimal sorting.

The select best process module first lists all tree structures that satisfy the regular ordering, finds the maximum time value of each branch, then takes the smallest branch as the last best ordering, and completes the function of min (max (per machine processing time)), as shown in fig. 2, where each node is added with a different @ id to distinguish each same process, because if the node is not distinct, it will be regarded as a node on the graph, and the graph is not readable. The last column in fig. 2 is the time of each permutation, and it is apparent that the smallest time permutation is the last permutation, and the time is 11; the time of the first 2 permutations is 14, the longest time is 18, so it can also be seen that, in the permutation tree according to the SW rule, the best and worst results are included, and compared with 216 total permutation schemes, only 5 are needed to determine the optimal solution, which greatly reduces the solution space containing the optimal solution and greatly improves the problem-solving efficiency. If other auxiliary rules facing the overall optimization are added, for example, according to the principle of the longest machine or the longest workpiece priority, the decoding space can be compressed, but because other rules except SW are very meaningful in scenes and are not universal rules, only candidate rules are made.

And the display layer 14 is used for providing a human-computer interaction interface and finishing drawing and displaying the APS Gantt chart according to the scheme tree.

That is, the presentation layer 14 completes the interaction with the input and output, and for the APS, it is necessary to complete the rendering of the gantt chart as shown in fig. 3.

The display layer 14 specifically includes:

the data input module 7 is used for completing the input of processing data and the selection of corresponding rules;

and the Gantt chart module 8 is used for displaying the Gantt chart of the optimal solution so as to meet the reading habit of front-line personnel.

In conclusion, by means of the technical scheme of the embodiment of the invention, the contradiction between the rules and the optimization is coordinated, all the sequencing schemes including the optimal solution are successfully obtained, the distrust problem of a front-line worker on the optimization is solved, the fractal tree structure also solves the theoretical basic problem of the method, and a good practical effect is achieved.

Method embodiment

According to an embodiment of the present invention, an APS recursion method based on a fractal self-similarity principle is provided, fig. 4 is a flowchart of the APS recursion method based on the fractal self-similarity principle according to the embodiment of the present invention, as shown in fig. 4, the APS recursion method based on the fractal self-similarity principle according to the embodiment of the present invention specifically includes:

step 401, acquiring processing data transmitted from different systems through a data layer, arranging the processing data into a format meeting an APS algorithm according to the processing data, and storing a rule table containing all rules, wherein the rule table is used for switching APS results under different rules;

it should be noted that the processing data transmitted from different systems is input through the human-computer interaction interface provided by the display layer; and corresponding rule selection is performed.

Step 401 specifically includes:

1. receiving processing data transmitted from different systems, and arranging the processing data into a product of a workpiece and an operation sequence, wherein the product forms all processing procedures, and parameters of the processing procedures comprise operation time and the operation sequence;

2. storing a rule table, wherein the rule table comprises the following parameters: minimum wait, first-in-first-out, last-in-first-out, work piece priority value, delivery earliest priority, shortest machining time, and/or longest machining time.

Step 402, integrating three streams of a computer processing sequence, an equipment processing sequence and a workpiece flow on the basis of a rule table through a construction layer, determining an optimal sequencing method, and constructing a scheme tree, wherein nodes on the scheme tree are all process states of a workpiece, each node stores the processing state of the whole equipment, and each node makes a decision for selecting a next process according to the whole state;

step 402 specifically includes:

1. initializing tree nodes and the whole machine state of the APS sequencing tree, specifically, initializing the tree nodes in the APS sequencing tree for parameters such as node serial numbers, node names, time, subsequent procedures and previous procedures of the nodes, and initializing the whole machine state for parameters such as machine numbers, work serial numbers, procedure candidates and final positions;

2. defining attributes of the tree nodes, wherein the attributes specifically comprise: a single character c stored by a node, a word stored by the node, child nodes of the node, parent nodes of the node, the condition M of the whole equipment stored by the node, and the updated node and the thisPoint stored by the node;

3. constructing a process tree: firstly, defining a recursive exit condition, namely when all candidate sets are empty; then constructing branches of the node, wherein the branch condition is that a plurality of minimum waiting time are selected, updating an M matrix in the branches, and keeping the M matrix of the parent node unchanged; finally, a self-calling method is adopted, the pointer points to the branch node which is just established, and a depth-first strategy is realized, wherein the M matrix is a matrix for displaying the initial state of the global machine;

4. listing all tree structures satisfying the regular ordering, finding the maximum time value of each branch, and then taking the minimum branch as the final optimal ordering.

And step 403, completing drawing and displaying of the APS Gantt chart according to the scheme tree through a human-computer interaction interface provided by the display layer.

The above technical solutions of the embodiments of the present invention are described in detail below with reference to the accompanying drawings.

As shown in fig. 5, the APS algorithm process based on the fractal theory mainly includes 3 steps, wherein step 1 is to establish a process sequence tree satisfying a rule; step 2, searching the tree, finding all sorting schemes and selecting the best sorting; and step 3, performing Gantt chart display on the sequencing result to meet the actual production requirement, wherein the specific steps are described as follows:

step 1: method for establishing process tree

Establishing a process tree according to the SW rule, determining leaf nodes on each node according to the known integral condition, and forming the bifurcation of the tree if a plurality of candidate processes exist according to the rule, wherein the bifurcation is the width of the tree; and then pointing the pointer to each leaf node, which is a necessary method constructed according to a fractal self-similar structure, and is realized by self-adaptation of the method, so that a tree depth-first construction strategy is realized.

Step 1-1: root node initialization

This is the root node of the tree, and the root node is also a node, so the construction method of the node is also called; meanwhile, introducing an M matrix for displaying the initial state of the global machine on the root node so that the system can select the next process node according to the M matrix;

step 1-2: determining a processing machine number M

Selecting the machine number to satisfy two conditions, one is that the positions dis are arranged in descending order to ensure that the first value obtained each time is always minimum to satisfy the minimum waiting condition of SW; secondly, selecting a machine number M _ No according to whether the M _ No is empty or not, wherein the first machine number selected under the combined action of the two conditions is the machine to be considered firstly, and the existence of process nodes in a candidate set is ensured;

step 1-3: using whether the candidate set is empty as an exit condition

In recursive invocation, an exit condition is necessary and this condition must be fulfilled, otherwise a dead loop is formed. Referring to fig. 4, the first procedure is put into the candidate sequence of the corresponding machine, and it can be determined that the system has not been sorted, and at this time, the first of the candidate sequences of the machine is taken as the machine to be sorted; if all the candidate sequences are empty, the system is judged to be finished in sequencing, and the condition is the exit condition of the system;

step 1-4: previous process node for taking out candidate set

Taking out the candidate set of the selected machine, then finding out the corresponding previous working procedure from the previous working procedure pre shown in the table 3, completing the association between the working procedures, and establishing a basic member of the tree, namely a line segment;

step 1-5 updating selected machine states

The importance of this step is that for some variable types, after the value of the child node changes, the value of the reference variable of the child parent node also changes, which is not true for the parent node, so the initial state is isolated by the copy variable first; selecting a candidate node, searching the thisPoint of the previous procedure of the node in the parent, wherein the searching method is to continuously point a pointer to a father node, namely the previous node, and searching the position of the previous node of the selected node back to the father node, because the tree is established according to the previous node, the parent reflects the front-back incidence relation of the same workpiece; the most important thing is to update the dis of the machine, firstly, a distance space list corresponding to each candidate is constructed, and the initial position of each candidate, namely the maximum value of the current machine position and the previous process position, is recorded, so that the SW principle is embodied;

step 1-6 node initialization

Selecting a candidate node, wherein if the node is 0#0#0, the node is either a starting node or an ending node and is the boundary of the program, so that direct connection does not perform the rest of processing, and the candidate node is empty and is also realized by the step; initializing by taking the selected node as a name, namely establishing an object with a structure shown in a table 5;

step 1-7: assign value to the node position

Assigning a value to the thisPoint of the node, wherein the starting point is the position of the corresponding space, and the end point is a processing time length moved forward, so that the positions of the obtained machines are different after a candidate process is selected due to the time length, and the next new machine selection is caused; searching the next node of the selected node as a process node added into the candidate node set;

step 1-8: take out the next process node of the node

Searching for the next procedure node in suf columns in the corresponding O matrix;

step 1-9: machine parameter assignment to nodes

Firstly, removing corresponding nodes in a candidate set of a local machine, then, taking out corresponding machine numbers from the nodes firstly because the machines corresponding to the nodes in the next process are different, and then adding new candidate nodes in the candidate nodes of the machine numbers; meanwhile, assigning a value to a workpiece sequence seq of a machine where the current node is located, and adding a new process;

step 1-10: assigning a value to the child node attribute

The word of the sub-node is the accumulation of the whole intermediate process, and the number of nodes with processes in the last process is determined by the number of nodes in all O; the overall intermediate state M of the node is assigned, as only the past sequencing can be determined, and the future sequencing is uncertain, a plurality of rules about overall optimization are difficult to obtain the optimal solution, SW comprises the intrinsic optimal solution, so the node is an algorithm with a representative meaning, for the rule considering the overall performance, the remaining candidate set can be added, certain indexes of the remaining workpieces or machines are selected and added into the selected rule, and the selected path is adjusted; adding the node into child nodes, and setting parent of the node; then returning to the second candidate node, and performing the same node attribute operation;

step 1-11: construction method for self-adaptation pointing to each node

Each node in the sub-nodes continuously circulates, and the function of pointing the pointer to the next node is realized by calling the self, so that the depth-first strategy is realized;

step 2: method for determining optimal ordering

Judging whether the process is finished or not by the number of the nodes of the word by adopting a recursive calling method, thereby determining the total sequencing number meeting the rule and preparing a candidate set for selecting the optimization result; firstly, defining an empty set for placing the final sequence;

step 2-1: determining exit conditions

The exit condition is that the number of nodes satisfying the word field is the total number of points in O, and 1 more 0#0#0 starting nodes than the real process nodes are noticed; when the condition is met, the total number of the schemes is increased by one;

step 2-2: ordering method for self-adjusting pointing to next node

Circulating the leaf nodes childs, and pointing the pointer to the next node through recursive calling to realize the search of the depth-first strategy; adding the result of the recursive call into the total scheme;

step 2-3: sorting result arrangement

Sorting the sorting result into line segments, then removing duplication, and preparing data for drawing the sorted tree graph;

step 2-4: drawing the whole sequencing tree diagram

Drawing a whole ordered tree diagram by using drawing software grapeviz, as shown in FIG. 2;

step 2-5: selecting the best ranking

According to the fact that the sort with the shortest time is selected from all sorts to be the whole sort

And step 3: gantt chart demonstration method

Performing Gantt chart display on the optimal sequence according to a machine;

step 3-1: taking out the position of each machine step

Taking out the last node according to the last word value, and taking out the sequencing seq of the corresponding machine from the M matrix of the nodes; searching ideal coordinates of each procedure on a tree according to nodes in the seq; arranging the longitudinal coordinates of each procedure from low to high according to the machine number;

step 3-2: dispensing different workpiece colors

Automatically distributing colors to different procedures to facilitate the visual distinguishing effect;

step 3-3. filling the process locations with a bar function

In python, the selected process can be colored with the bar function;

step 3-4: display

The display data in the memory is displayed on the screen through the show () function, and the final effect is as shown in fig. 3.

Apparatus embodiment one

An embodiment of the present invention provides an APS recursive device based on a fractal self-similarity principle, as shown in fig. 6, including: a memory 60, a processor 62 and a computer program stored on the memory 60 and executable on the processor 62, which computer program, when executed by the processor 62, carries out the following method steps:

Step 401 specifically includes:

step 402 specifically includes:

Device embodiment II

The embodiment of the present invention provides a computer-readable storage medium, on which an implementation program for information transmission is stored, and when being executed by a processor 62, the implementation program implements the following method steps:

Step 401 specifically includes:

step 402 specifically includes:

The computer-readable storage medium of this embodiment includes, but is not limited to: ROM, RAM, magnetic or optical disks, and the like.

It will be apparent to those skilled in the art that the modules or steps of the present invention described above may be implemented by a general purpose computing device, they may be centralized on a single computing device or distributed across a network of multiple computing devices, and alternatively, they may be implemented by program code executable by a computing device, such that they may be stored in a storage device and executed by a computing device, and in some cases, the steps shown or described may be performed in an order different than that described herein, or they may be separately fabricated into individual integrated circuit modules, or multiple ones of them may be fabricated into a single integrated circuit module. Thus, the present invention is not limited to any specific combination of hardware and software.

Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. An advanced planning and scheduling APS recursion system based on fractal self-similarity principles, comprising.

The data layer is used for acquiring processing data transmitted from different systems, arranging the processing data into a format meeting an APS algorithm according to the processing data, and storing a rule table containing all rules, wherein the rule table is used for switching APS results under different rules;

the construction layer is used for integrating three flows of a computer processing sequence, an equipment processing sequence and a workpiece flow based on a rule table, determining an optimal sequencing method and constructing a scheme tree, wherein nodes on the scheme tree are all process states of the workpiece, each node stores the processing state of the whole equipment, and each node makes a decision for selecting a next process according to the overall state;

and the display layer is used for providing a human-computer interaction interface, finishing the input of processing data and finishing the drawing and display of the APS Gantt chart according to the scheme tree.

2. The system according to claim 1, wherein the data layer specifically comprises:

the constraint table module is used for receiving the machining data transmitted from different systems and arranging the machining data into a product of a workpiece and an operation sequence, wherein the product forms all machining processes, and parameters of the machining processes comprise operation time and the operation sequence;

a rule table module for storing a rule table, wherein the rule table comprises the following parameters: minimum wait, first-in-first-out, last-in-first-out, work piece priority value, delivery earliest priority, shortest machining time, and/or longest machining time.

3. The system according to claim 1, characterized in that said building layers comprise in particular:

the data preprocessing module is used for initializing tree nodes and the whole machine state of the APS sequencing tree, specifically, initializing the tree nodes in the APS sequencing tree for parameters such as node serial numbers, node names, time, subsequent procedures and previous procedures of the nodes, and initializing the whole machine state for parameters such as machine numbers, work serial numbers, procedure candidates and final positions;

a node building module, configured to define attributes of tree nodes, where the attributes specifically include: a single character c stored by a node, a word stored by the node, child nodes of the node, parent nodes of the node, the condition M of the whole equipment stored by the node, and the updated node and the thisPoint stored by the node;

the procedure tree construction module is used for constructing a procedure tree: firstly, defining a recursive exit condition, namely when all candidate sets are empty; then constructing branches of the node, wherein the branch condition is that a plurality of minimum waiting time are selected, updating an M matrix in the branches, and keeping the M matrix of the parent node unchanged; finally, a self-calling method is adopted, the pointer points to the branch node which is just established, and a depth-first strategy is realized, wherein the M matrix is a matrix for displaying the initial state of the global machine;

and selecting an optimal procedure module for listing all tree structures meeting the rule sorting, finding the maximum time value of each branch, and then taking the minimum branch as the final optimal sorting.

4. The system of claim 1, wherein the presentation layer specifically comprises:

the data input module is used for completing the input of processing data and the selection of corresponding rules;

and the Gantt chart module is used for displaying the Gantt chart of the optimal solution.

5. An advanced planning and scheduling APS recursion method based on fractal self-similarity principle, comprising:

processing data transmitted from different systems are obtained through a data layer, the processing data are arranged into a format meeting an APS algorithm according to the processing data, and a rule table containing all rules is stored, wherein the rule table is used for switching APS results under different rules;

integrating three streams of a computer processing sequence, an equipment processing sequence and a workpiece flow on the basis of a rule table through a construction layer, determining an optimal sequencing method, and constructing a scheme tree, wherein nodes on the scheme tree are all process states of a workpiece, each node stores the processing state of the whole equipment, and each node makes a decision for selecting a next process according to the whole state;

and completing the drawing and displaying of the APS Gantt chart according to the scheme tree through a human-computer interaction interface provided by the display layer.

6. The method of claim 5, wherein receiving the processed data from different systems through a data layer, arranging the processed data into a format satisfying an APS algorithm according to the processed data, and storing a rule table containing all rules specifically comprises:

receiving processing data transmitted from different systems, and arranging the processing data into a product of a workpiece and an operation sequence, wherein the product forms all processing procedures, and parameters of the processing procedures comprise operation time and the operation sequence;

storing a rule table, wherein the rule table comprises the following parameters: minimum wait, first-in-first-out, last-in-first-out, work piece priority value, delivery earliest priority, shortest machining time, and/or longest machining time.

7. The method of claim 5, wherein the optimal ordering method is determined by integrating three streams of a computer processing sequence, an equipment processing sequence and a workpiece flow through a building layer, and the building of the plan tree specifically comprises:

initializing tree nodes and the whole machine state of the APS sequencing tree, specifically, initializing the tree nodes in the APS sequencing tree for parameters such as node serial numbers, node names, time, subsequent procedures and previous procedures of the nodes, and initializing the whole machine state for parameters such as machine numbers, work serial numbers, procedure candidates and final positions;

defining attributes of tree nodes, wherein the attributes specifically include: a single character c stored by a node, a word stored by the node, child nodes of the node, parent nodes of the node, the condition M of the whole equipment stored by the node, and the updated node and the thisPoint stored by the node;

constructing a process tree: firstly, defining a recursive exit condition, namely when all candidate sets are empty; then constructing branches of the node, wherein the branch condition is that a plurality of minimum waiting time are selected, updating an M matrix in the branches, and keeping the M matrix of the parent node unchanged; finally, a self-calling method is adopted, the pointer points to the branch node which is just established, and a depth-first strategy is realized, wherein the M matrix is a matrix for displaying the initial state of the global machine;

listing all tree structures satisfying the regular ordering, finding the maximum time value of each branch, and then taking the minimum branch as the final optimal ordering.

8. The method of claim 5, further comprising:

inputting processing data transmitted from different systems through a human-computer interaction interface provided by a display layer; and corresponding rule selection is performed.

9. An advanced planning and scheduling APS recursive device based on fractal self-similarity principles, comprising: memory, processor and computer program stored on the memory and executable on the processor, which computer program, when being executed by the processor, carries out the steps of the signal measurement method according to any one of claims 5 to 8.

10. A computer-readable storage medium, characterized in that the computer-readable storage medium has stored thereon an implementation program of information transfer, which when executed by a processor implements the steps of the signal measurement method according to any one of claims 5 to 8.