CN111762692B - Intelligent crane scheduling method suitable for storage of multiple materials - Google Patents

Intelligent crane scheduling method suitable for storage of multiple materials Download PDF

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CN111762692B
CN111762692B CN202010544889.5A CN202010544889A CN111762692B CN 111762692 B CN111762692 B CN 111762692B CN 202010544889 A CN202010544889 A CN 202010544889A CN 111762692 B CN111762692 B CN 111762692B
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equipment
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crane
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CN111762692A (en
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吴庆祺
吴刚
蔡昌伟
孙秀军
张晓宇
阮日升
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DhiDcw Group Co ltd
Dalian Huarui Heavy Industry Group Co Ltd
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B66—HOISTING; LIFTING; HAULING
    • B66C—CRANES; LOAD-ENGAGING ELEMENTS OR DEVICES FOR CRANES, CAPSTANS, WINCHES, OR TACKLES
    • B66C13/00—Other constructional features or details
    • B66C13/18—Control systems or devices
    • B66C13/48—Automatic control of crane drives for producing a single or repeated working cycle; Programme control

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Abstract

The invention discloses an intelligent crane scheduling method suitable for storage of various materials, which comprises the following steps: acquiring storage related attribute information, material related attribute information and state information of crane equipment; setting the space logic when a certain designated position is filled with materials as a container tree model; generating a scheduling rule according to a scheduling strategy; carrying out optimal library position search on the container tree by adopting a scheduling rule, wherein the search mode comprises depth-first search and breadth-first search; matching the equipment container and the material container after the target container tree is determined; the equipment container is space logic description when the equipment grabs standard materials; generating a device instruction: generating an equipment resolvable task instruction from the decided optimal library position through a TCP (transmission control protocol) communication protocol according to a communication message format agreed with the crane PLC (programmable logic controller); and performing equipment task execution and state feedback.

Description

Intelligent crane scheduling method suitable for storage of multiple materials
Technical Field
The invention relates to the field of crane mechanical control, in particular to an intelligent crane scheduling method suitable for storage of multiple materials.
Background
With the advent of the intelligent era, the modern industry has higher and higher requirements on the unmanned and intelligent functions of cranes. The industrial field system also has definite five-level division from a five-level cloud platform to a four-level enterprise ERP system, to a three-level workshop finished product MES system, to a two-level equipment management system and to a one-level equipment automation system, so that a complete industrial information system architecture is formed. The intelligent crane under the control of the secondary equipment management system is evolved into an intelligent device capable of accepting production task customization operation work orders issued by a three-level workshop production system from a single mechanical device for manually operating and carrying materials in the past, and the intelligent crane can fully automatically complete a series of complex storage processes such as warehousing, ex-warehouse, and dumping of materials such as steel plates, aluminum coils, rods and wires according to the work orders.
In the prior art, an intelligent crane only focuses on solving the control problem of the equipment layer of the intelligent crane in an equipment scheduling system for optimizing the logistics process of different types of material storage, and the logistics process optimization of the whole intelligent storage area cannot be guaranteed. In order to solve the blank of the intelligent market demand, it is particularly important to provide a set of optimal overall process logistics and efficient equipment utilization scheduling system suitable for different industrial environments for the intelligent crane.
Disclosure of Invention
According to the problems in the prior art, the invention discloses an intelligent crane scheduling method suitable for storage of various materials, which specifically comprises the following steps:
acquiring storage related attribute information, material related attribute information and state information of crane equipment;
setting the space logic when a certain designated position is filled with materials as a container tree model;
generating a scheduling rule according to the scheduling policy, wherein the scheduling rule comprises a scheduling policy of a single rule and a scheduling policy of a composite rule;
initializing a warehousing scheduling scene: taking the generated container tree model, the scheduling rule and the equipment state as input information so as to construct a warehousing scheduling scene;
carrying out optimal library position search on the container tree by adopting a scheduling rule, wherein the search mode comprises depth-first search and breadth-first search;
after the target container tree is determined, matching the equipment container with the material container, and determining the optimal storage position after successful matching; the equipment container is space logic description when the equipment grabs standard materials;
generating a device instruction: generating an equipment resolvable task instruction from the decided optimal library position through a TCP (transmission control protocol) communication protocol according to a communication message format agreed with the crane PLC (programmable logic controller);
and (3) equipment task execution and state feedback: the crane equipment executes according to the task instruction, completes logistics storage action according to system requirements, and feeds back the task execution state.
Further, the containment relationships between containers form parent-child structures and thus container trees.
Furthermore, the scheduling strategy of the single rule comprises a warehousing priority strategy, a ex-warehouse priority strategy and a balance strategy; the warehousing priority policy is as follows: comparing the distances d1 between the center points of all the warehousing position containers and the center point of the target warehouse position container, and selecting the warehousing target warehouse position corresponding to the minimum distance d 1; the ex-warehouse priority strategy is as follows: comparing the distances d2 between the center points of all the ex-warehouse container and the center points of the target warehouse containers, and selecting the ex-warehouse target warehouse corresponding to the smallest distance d 2; the equilibrium strategy correspondence rule is as follows: simultaneously comparing the distance d1 with the distance d2, and selecting a library position corresponding to the minimum product of the distance d1 and the distance d 2;
the dispatching strategy of the composite rule is an in-sequence dispatching logistics strategy, namely dispatching is carried out by adopting dispatching rules from bottom to top, from left to right and from front to back.
Further, when the equipment container and the material container are matched: and setting a tolerance model of the equipment and the material container, wherein the tolerance model comprises a material bounding box model, an equipment container bounding box model and a three-dimensional tolerance model, setting a matching algorithm of the equipment container and the material container, and judging that matching is successful if each material finds a corresponding container.
Further, the related attribute information of the material comprises: the type of the material, the size of the material specification, the position of the material, the warehousing time, the planned ex-warehouse time and the information of the affiliated client.
Further, the attributes of the container are divided into: parent container, child container, AABB bounding box, container state, built-in material and evaluation value; when the container tree is generated, the AABB bounding box of the material and the container AABB bounding box are subjected to matching calculation, and if the material AABB bounding box is contained in the container, the container is judged to contain the material.
Due to the adoption of the technical scheme, the intelligent crane scheduling method applicable to storage of multiple materials can solve the problem of optimization of equipment scheduling of an intelligent crane in the process of storage logistics of multiple materials with different rules, wherein the method comprises the optimization modes of logistics selection such as material grabbing and releasing rules and storage strategies of materials with different shapes in a space in a warehouse by the crane, and the like, and a container tree mathematical model is built according to different application environment information to perform storage process optimization analysis, so that the method can be applied to equipment scheduling schemes of multiple industrial storage scenes, and has the following beneficial effects: the universality is strong: the technology is suitable for materials of different types and specifications, can be applied to the storage process of the crane in various industrial environments, and has strong universality. The flexibility is strong: the technology has strong flexibility in the warehousing process, does not limit the specific positions of logistics key nodes such as material warehousing points, ex-warehouse points and transit points, performs space mathematical calculation according to the current storage capacity condition and the material bounding box, has strong technical flexibility, and is suitable for the conditions of multilayer material placement and the like. The expansibility is strong: the technology carries out logistics process calculation by establishing a space mathematical model for the logistics process, and can carry out logistics optimization of the whole process by matching with various scheduling algorithms and strategies.
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In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments described in the present application, and other drawings can be obtained by those skilled in the art without creative efforts.
FIG. 1 is a flow chart of the method of the present invention
Fig. 2 is a schematic diagram of the AABB bounding box in the embodiment of the present invention, which is the most common spatial model in three-dimensional space geometry.
Detailed Description
In order to make the technical solutions and advantages of the present invention clearer, the following describes the technical solutions in the embodiments of the present invention clearly and completely with reference to the drawings in the embodiments of the present invention:
as shown in fig. 1, an intelligent crane scheduling method suitable for storage of multiple materials specifically adopts the following method:
s1: and reading the relational database, namely loading the library position data, loading the material data and loading the crane equipment state data.
And S2, generating a container tree model, wherein the containers refer to space logic description when a certain specified position is filled with materials, and the inclusion relationship among the containers forms a parent-child structure so as to form a container tree. The inclusion relationship:
Figure BDA0002540344640000031
Figure BDA0002540344640000032
the attributes of the container model include: parent container, child container, AABB bounding box, container status (available/removable/combinable), built-in material, evaluation value, and contained material, etc. The AABB bounding box is the most common spatial model in three-dimensional space geometry, and its structure can be expressed as 6 boundary three-dimensional coordinate values: the AABB (minX, maxX, minY, maxY, minZ, maxZ) (as shown in figure 2) enclosure box is in a cuboid space shape, the edge of the enclosure box is parallel to the coordinate axis, and the enclosure box accords with the basic storage mode of a crane. The AABB bounding box has a well-established basic algorithm architecture and related attributes, such as: center point pCenter ((minX + maxX)/2, (minY + maxY)/2, (minZ + maxZ)/2)
X long rX ═ maxX-minX
Y long rY ═ maxY-minY
Z long rZ ═ maxZ-minZ
When the container tree is generated, the AABB bounding box of the material and the container AABB bounding box are subjected to matching calculation, and if the material AABB bounding box is contained in the container, the container is judged to contain the material.
And if each material finds a corresponding container, judging that the matching is successful.
Defining:
material bounding box model AABBm(minXm,maxXm,minYm,maxYm,minZm,maxZm)
Container bounding box model AABBc(minXc,maxXc,minYc,maxYc,minZc,maxZc)
Tolerance t of material and container: material bounding box can exceed the threshold of the container bounding box size (default 0)
The matching algorithm for the material to the container is described as follows:
STEP 1: bounding box tolerance calculation
AABBc’＝(minXc-t,maxXc+t,minYc-t,maxYc+t,minZc-t,maxZc+t)
STEP 2 boundary comparison
CASE 1:AABBc' comprising AABBm，Success of matching
CASE 2 otherwise, the matching fails
S3 designing dispatching rule according to dispatching strategy
The scheduling rules are summarized as a computational comparison of the container AABB bounding box attributes according to different scheduling policies. According to the complexity of the scheduling strategy, the scheduling strategy can be expressed as a combination form of different rules, and is directly compared with the container model attributes to select a better one.
Scheduling strategy of single rule
As common aluminum logistics strategies, can be divided into: a warehousing priority strategy, a ex-warehouse priority strategy, a balancing strategy and the like. The above scheduling strategy may be translated as follows: a warehousing priority strategy: the center point of the container at the storage position is more optimal than the distance d1 between the center point of the container at the target storage position and the center point of the container at the target storage position; and (3) ex-warehouse priority strategy: and comparing the center point of the warehouse-out position container with the center point of the target warehouse position container by the distance d2, and obtaining the minimum value. The balance strategy corresponding rule is as follows: comparing d1 and d2, the product is smaller.
The scheduling strategy of the compound rule is as follows: if a common stacking type material adopts a sequential dispatching logistics strategy: the "bottom-to-top, left-to-right, front-to-back" scheduling rules may be translated into relative comparison values for the candidate container bounding box attributes minZ, minX, maxY.
And S4, initializing a warehousing scheduling scene, namely constructing the warehousing scheduling scene by taking the container tree model generated in S2, the rule generated in S3 and the equipment state as input values.
S5 best library position search based on container tree
Traversing the container tree according to the bounding box comparison rule of design 3, and applying all topology tree traversal algorithms. (algorithms include, but are not limited to, depth-first search, breadth-first search, etc.).
S6, matching the equipment container with the material container: the equipment container refers to space logic description when the equipment grabs standard materials. After the target container is determined, the equipment container, namely the theoretical grabbing space of the crane, is matched with the target container.
Defining a tolerance model of the equipment and the material container:
material bounding box model AABBm(minXm,maxXm,minYm,maxYm,minZm,maxZm)
Equipment container bounding box model AABBc(minXc,maxXc,minYc,maxYc,minZc,maxZc)
Three dimensional tolerance model tABB (plusX, minusX, plusY, minusY, plusZ, minusZ)
Tolerance model attribute meaning:
plus: a value that is larger in the coordinate axis direction on the basis of the reference point; minus: a value that may be smaller in the coordinate axis direction on the basis of the reference point.
Equipment container and material container matching algorithm:
three-dimensional size of material container: rXm,rYm,rZm
Three-dimensional dimensions of the apparatus container: rXc,rYc,rZc
STEP 1 normalization to horizontal
STEP 1.1 normalization of Material bounding Box
CASE 1:rYm>rXmHorizontally rotated by 90 degrees
CASE 2 otherwise, not changing
STEP 1.2 Equipment Container bounding Box normalization
CASE 1:rYc>r XcHorizontally rotated by 90 degrees
CASE 2 otherwise, not changing
STEP 2 three-dimensional tolerance matching
CASE 1: simultaneously satisfying
rXm-rXc<In the case of plusX/width of the material, the range can be greater than the container length
rXc-rXm<In the range of minusX// width of the material which can be smaller than the length of the container
rYm-rYc<In the range of plusY/length of material which can be greater than the length of the container
rYc-rYm<In the range of minusY// material length which can be smaller than the container length
rZm-rZc<In the range of plusZ/material height which can be greater than the container length
rZc-rZm<In the range of minusZ// material height which can be smaller than the container length
Then, the matching is successful
CASE 2 otherwise, matching fails.
And after the matching is successful, determining the optimal library position.
S7 Equipment Command Generation step
And generating the best decision-making library position into a task instruction which can be analyzed by equipment through a TCP (transmission control protocol) communication protocol according to a communication message format agreed with the PLC (programmable logic controller) of the crane.
And S8, equipment task execution and state feedback, wherein the crane equipment executes according to a task instruction, completes logistics storage actions according to system requirements, feeds back the task execution state to the system, and the system repeats S1.
Furthermore, the crane dispatching system applicable to the warehousing processes of different types of materials in the application provides and applies a container tree model to carry out spatial logic representation on multi-level warehousing positions such as warehouses, strides, stacks, layers, groups and positions. In order to adapt to storage of coiled material materials with different sizes and specifications on the storage positions, the container tree model forms a tree-shaped topological structure according to storage levels, and each node comprises attributes such as bounding boxes, levels, unique numbers and the like. Through abstraction, the scheduling core algorithm is suitable for various warehousing systems. And calculating the influence of the deviation of the actual material placement on the effectiveness of storage through a container matching algorithm with tolerance.
When the storage is scheduled, the material attributes need to be taken into consideration, and the method mainly comprises the following steps: the type of the material, the specification and the size of the material, the position of the material, the warehousing time, the planned ex-warehouse time, the affiliated customers and the like. The material properties that can be obtained vary from storage scenario to storage scenario. Therefore, in order to adapt the equipment scheduling system to various industrial scenes, a concept of a container is provided, wherein the container refers to a space logic description when a certain specified position is filled with materials. The method is an abstract concept, and can represent the space logic of the storage position and the space logic of the material grabbed on the handling equipment. The geometric space of the container can be represented by a spatial geometry, and the state of the container includes whether the container is available, whether the container contains materials, and the like.
In a warehousing scenario, the containment relationships of spaces form natural parent-child dependencies, i.e., "container trees. For example, the library sites in rod and wire warehousing scenarios have a relationship:
Figure BDA0002540344640000061
Figure BDA0002540344640000062
in addition, the container space corresponding to the equipment for grabbing a plurality of materials at one time is a 'group container', and two basic behaviors including disassembling and assembling are included. The space logic when the equipment grabs the standard materials is described as an equipment container.
The tolerance range allowed when the process equipment grabs the material is called as 'tolerance'. The concept of tolerance is particularly important in applications of electromagnetic type spreaders. The tolerance adopted by the algorithm is a bidirectional three-dimensional matching tolerance supporting 'large' and 'small', and the algorithm can be widely applied to various lifting appliance systems.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art should be considered to be within the technical scope of the present invention, and the technical solutions and the inventive concepts thereof according to the present invention should be equivalent or changed within the scope of the present invention.

Claims (4)

1. An intelligent crane scheduling method suitable for storage of multiple materials is characterized by comprising the following steps:
acquiring storage related attribute information, material related attribute information and state information of crane equipment;
setting the space logic when a certain designated position is filled with materials as a container tree model;
generating a scheduling rule according to the scheduling policy, wherein the scheduling rule comprises a scheduling policy of a single rule and a scheduling policy of a composite rule;
initializing a warehousing scheduling scene: taking the generated container tree model, the scheduling rule and the equipment state as input information so as to construct a warehousing scheduling scene;
carrying out optimal library position search on the container tree by adopting a scheduling rule, wherein the search mode comprises depth-first search and breadth-first search;
after the target container tree is determined, matching the equipment container with the material container, and determining the optimal storage position after successful matching; the equipment container is space logic description when the equipment grabs standard materials;
generating a device instruction: generating an equipment resolvable task instruction from the decided optimal library position through a TCP (transmission control protocol) communication protocol according to a communication message format agreed with the crane PLC (programmable logic controller);
and (3) equipment task execution and state feedback: the crane equipment executes according to the task instruction, completes logistics storage action according to system requirements, and feeds back the task execution state;
the containing relation among the containers forms a parent-child structure so as to form a container tree;
the scheduling strategy of the single rule comprises a warehousing priority strategy, a ex-warehouse priority strategy and a balance strategy;
the warehousing priority policy is as follows: comparing the distances d1 between the center points of all the warehousing position containers and the center point of the target warehouse position container, and selecting the warehousing target warehouse position corresponding to the minimum distance d 1; the ex-warehouse priority strategy is as follows: comparing the distances d2 between the center points of all the ex-warehouse container and the center points of the target warehouse containers, and selecting the ex-warehouse target warehouse corresponding to the smallest distance d 2; the equilibrium strategy correspondence rule is as follows: simultaneously comparing the distance d1 with the distance d2, and selecting a library position corresponding to the minimum product of the distance d1 and the distance d 2;
the dispatching strategy of the composite rule is an in-sequence dispatching logistics strategy, namely dispatching is carried out by adopting dispatching rules from bottom to top, from left to right and from front to back.
2. The intelligent crane scheduling method suitable for storage of multiple materials according to claim 1, further characterized by comprising: when the equipment container and the material container are matched:
and setting a tolerance model of the equipment and the material container, wherein the tolerance model comprises a material bounding box model, an equipment container bounding box model and a three-dimensional tolerance model, setting a matching algorithm of the equipment container and the material container, and judging that matching is successful if each material finds a corresponding container.
3. The intelligent crane scheduling method suitable for storage of multiple materials according to claim 1, further characterized by comprising: wherein the related attribute information of the material comprises: the type of the material, the size of the material specification, the position of the material, the warehousing time, the planned ex-warehouse time and the information of the affiliated client.
4. The intelligent crane scheduling method suitable for storage of multiple materials according to claim 1, further characterized by comprising: attributes of the container are divided into: parent container, child container, AABB bounding box, container state, built-in material and evaluation value; when the container tree is generated, the AABB bounding box of the material and the container AABB bounding box are subjected to matching calculation, and if the material AABB bounding box is contained in the container, the container is judged to contain the material.
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Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE2305792A1 (en) * 1973-02-07 1974-08-08 Uwe Kochanneck WAREHOUSE
NL1000168C1 (en) * 1995-04-19 1996-10-22 Jan Peeks Stacked-goods transfer system from pallets onto vehicles, vessels or storage surfaces without using pallets
FR2787771A1 (en) * 1998-12-25 2000-06-30 Hirata Spinning Automatic warehouse has handling assembly and elevator with displacement assembly and management unit controlling loading and unloading
CN107977815A (en) * 2017-12-01 2018-05-01 广东安捷供应链管理股份有限公司 Warehouse management system and method
CN109353732A (en) * 2018-03-29 2019-02-19 广州市阿思柯物流系统有限公司 A kind of large size material automated warehousing system and method
CN110498343A (en) * 2019-06-27 2019-11-26 六安远大住宅工业有限公司 Intelligently lifting is put in storage control system and method to PC component

Family Cites Families (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPS5926568B2 (en) * 1975-12-24 1984-06-28 株式会社日立製作所 butsutaiokibakanrisouchi
US5020957A (en) * 1989-12-21 1991-06-04 Eaton-Kenway, Inc. Mast to base connection for a storage and retrieval machine
US7155406B2 (en) * 2002-03-04 2006-12-26 Total Soft Bank, Ltd. Scheduling method for loading and unloading containers at the terminal and a computer readable recording medium recorded a computer programming of the same
CN102092639B (en) * 2010-12-08 2012-11-28 沈阳大学 Intelligent control equipment for coordinating operation of cranes

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE2305792A1 (en) * 1973-02-07 1974-08-08 Uwe Kochanneck WAREHOUSE
NL1000168C1 (en) * 1995-04-19 1996-10-22 Jan Peeks Stacked-goods transfer system from pallets onto vehicles, vessels or storage surfaces without using pallets
FR2787771A1 (en) * 1998-12-25 2000-06-30 Hirata Spinning Automatic warehouse has handling assembly and elevator with displacement assembly and management unit controlling loading and unloading
CN107977815A (en) * 2017-12-01 2018-05-01 广东安捷供应链管理股份有限公司 Warehouse management system and method
CN109353732A (en) * 2018-03-29 2019-02-19 广州市阿思柯物流系统有限公司 A kind of large size material automated warehousing system and method
CN110498343A (en) * 2019-06-27 2019-11-26 六安远大住宅工业有限公司 Intelligently lifting is put in storage control system and method to PC component

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
自动化立体仓库优化调度研究;刘婧峥;《中国优秀硕士学位论文全文数据库 信息科技辑》;20150630(第6期);I140-21 *

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