Warehouse logistics scheduling method and device based on cloud

Abstract

The invention provides a cloud-based warehouse logistics scheduling method and device, and relates to the technical field of logistics scheduling. The method comprises the steps of collecting storage real-time Internet of Things data, performing real-time storage logistics information cloud computing analysis, and forming storage real-time logistics basic data; obtaining logistics scheduling demand information, and performing scheduling planning analysis based on efficiency in combination with storage real-time logistics basic data to form initial logistics scheduling planning data; and performing scheduling interference optimization analysis according to the initial logistics scheduling planning data to form optimized logistics scheduling planning data. According to the method, logistics scheduling path planning and arrangement on the overall storage level can be realized by means of the characteristics of cloud computing, so that storage logistics scheduling is more reasonable, efficient and orderly.

Description

Technical Field

[0001] The present invention relates to the technical field of logistics scheduling, and in particular to a cloud-based warehousing logistics scheduling method and device. Background Art

[0002] With the development of Internet of Things and automation technology, warehousing logistics has gradually become more intelligent and efficient, and has undergone earth-shaking changes compared to traditional warehousing logistics. When warehousing logistics is being carried out, logistics scheduling is one of the important links. Logistics scheduling requires obtaining a lot of information, such as warehouse design information, inventory management information, basic information on logistics operations, etc., and a reasonable logistics scheduling plan is established through comprehensive analysis of these information.

[0003] At present, although logistics scheduling can be reasonably arranged to a certain extent with the help of the Internet of Things and intelligent equipment, it is difficult to reasonably and orderly implement scheduling arrangements at the entire warehousing level due to the huge amount of information. With the development of cloud technology, the amount of data processing has greatly increased, and more efficient and reasonable logistics scheduling control has become possible. On this basis, the path planning arrangement of logistics scheduling from a macro level can also be designed and controlled more reasonably and orderly.

[0004] Therefore, designing a cloud-based warehouse logistics scheduling method and device that can leverage the characteristics of cloud computing to achieve logistics scheduling path planning and arrangement at the overall warehouse level, making warehouse logistics scheduling more reasonable, efficient and orderly, is an urgent problem to be solved. Summary of the invention

[0005] The purpose of the present invention is to provide a cloud-based warehousing logistics scheduling method, which obtains real-time Internet of Things data of the warehousing, performs cloud computing processing, and analyzes and provides basic data for logistics scheduling path planning at the entire warehousing level, providing an important and accurate data basis for the reasonable implementation of logistics scheduling path planning. At the same time, in combination with the logistics scheduling needs, the real-time logistics basic data of the warehousing is fully utilized to carry out overall real-time logistics scheduling path planning, and with efficiency as the purpose, efficient logistics scheduling planning analysis and processing are achieved. In addition, on the basis of fully meeting the logistics scheduling needs, the interference analysis and optimization between different scheduling planning paths at the warehousing level are carried out on the formed preliminary plan to fully ensure that all logistics scheduling can be implemented correctly and orderly.

[0006] The purpose of the present invention is also to provide a cloud-based warehousing logistics scheduling device, which completes the logistics scheduling path planning, design and analysis from the overall warehousing level by configuring a cloud computing device capable of analyzing and processing a large amount of real-time warehousing IoT data, and at the same time performs reasonable optimization and adjustment, thereby providing an important material basis for ensuring the reasonable, efficient and orderly implementation of logistics scheduling.

[0007] In a first aspect, the present invention provides a cloud-based warehousing logistics scheduling method, including collecting real-time warehousing IoT data, performing cloud computing analysis of real-time warehousing logistics information, and forming real-time warehousing logistics basic data; obtaining logistics scheduling demand information, and combining the real-time warehousing logistics basic data, performing efficiency-based scheduling planning analysis, and forming initial logistics scheduling planning data; performing scheduling interference optimization analysis based on the initial logistics scheduling planning data, and forming optimized logistics scheduling planning data.

[0008] In the present invention, the method obtains the real-time IoT data of the warehouse, performs cloud computing processing, and analyzes and provides basic data for the logistics scheduling path planning of the entire warehouse level, which provides an important and accurate data basis for the reasonable implementation of logistics scheduling path planning. At the same time, in combination with the logistics scheduling needs, the real-time logistics basic data of the warehouse is fully utilized to carry out the overall real-time logistics scheduling path planning, and with efficiency as the purpose, efficient logistics scheduling planning analysis and processing are realized. In addition, on the basis of fully meeting the logistics scheduling needs, the interference analysis and optimization between different scheduling planning paths at the warehouse level are carried out on the formed preliminary plan to fully ensure that all logistics scheduling can be realized correctly and orderly.

[0009] As a possible implementation method, real-time warehousing IoT data is collected, and cloud computing analysis of real-time warehousing logistics information is performed to form real-time warehousing logistics basic data, including: extracting current warehousing information of different inventory objects based on real-time warehousing IoT data: determining the current in-stock storage location of different inventory objects, and calibrating the location according to the storage batch to form the current in-stock warehousing information of the inventory objects; determining the current reserved storage location of different inventory objects based on the real-time warehousing IoT data, and calibrating the location to form the current reserved warehousing information of the inventory objects; determining the current placement location of different types of material boxes based on the real-time warehousing IoT data, and calibrating the location to form the material box location warehousing information; determining the locations of different warehouse ports based on the real-time warehousing IoT data and calibrating them to form warehouse port location information; extracting the following basic scheduling data based on the real-time warehousing IoT data: determining all scheduling routes in the logistics scheduling area based on the real-time warehousing IoT data to form basic scheduling route information; determining the operating parameters of different scheduling carriers based on the real-time warehousing IoT data to form basic scheduling carrier operation information.

[0010] In the present invention, when extracting the real-time logistics basic data of warehousing by using cloud computing, it is considered that the extracted basic data is to provide a data basis for the subsequent logistics scheduling path planning, so the extracted information is directional. First of all, for logistics scheduling path planning, the purpose of planning is to complete the warehousing and outbound in the warehouse, and the warehousing and outbound involve the information of the inventory objects, such as location information, batch information, etc. Therefore, the extracted real-time logistics basic information includes the location of different inventory objects stored in the warehouse, batch number, and the information of the inventory location reserved for different inventory objects. Of course, both outbound and inbound involve the collection and transportation of materials, and the models of material boxes required for the logistics transportation of different types of materials are different, so it is also necessary to extract the location information of the material boxes. In addition, both outbound and inbound involve the warehouse port, so the warehouse port location information is also extracted. For logistics scheduling, parameter information such as the path that can be passed and the speed of the carrier itself involved in the scheduling process also needs to be extracted from the real-time logistics basic information. The amount of data to be analyzed using these data is huge. Cloud computing can solve this problem efficiently. Cloud computing can quickly obtain real-time basic logistics information, which makes it possible to realize the subsequent logistics scheduling path planning. In addition, the extraction of location information can be accurately calibrated by establishing a spatial coordinate system for the entire warehouse. The batch number, inventory object, and type of material box information can be extracted from the inventory management system, and the scheduling route can be calibrated and extracted based on the spatial coordinate system.

[0011] As a possible implementation method, logistics scheduling demand information is obtained, and combined with the real-time basic logistics data of the warehousing, efficiency-based scheduling planning analysis is performed to form initial logistics scheduling planning data, including: based on the logistics scheduling demand information and combined with the current warehousing information, the logistics scheduling points are determined to form logistics scheduling point information; based on the logistics scheduling point information and combined with the scheduling basic data, efficiency-based route planning selection is performed to form initial logistics scheduling planning data.

[0012] In the present invention, after extracting the real-time basic logistics information, reasonable logistics scheduling path planning can be carried out based on the acquisition of logistics scheduling requirements and in combination with these extracted real-time basic logistics information. There are two main aspects to consider for logistics scheduling path planning. On the one hand, in order to achieve logistics scheduling that meets scheduling requirements, it is first necessary to determine the inventory object information for completing logistics scheduling, and then lock these inventory objects for reasonable path planning. On the other hand, it is how to plan the route when the inventory object information is determined, so that the planned path is more reasonable, efficient and safe.

[0013] As a possible implementation method, the logistics scheduling point is determined according to the logistics scheduling demand information and combined with the current warehousing information to form the logistics scheduling point information, including: determining the scheduling point in the following manner according to the logistics scheduling demand information and combined with the current warehousing information: determining the type of material box required according to the inventory object required by the logistics scheduling demand information to form the material box type information; determining the scheduling material box according to the warehouse opening position required by the logistics scheduling demand information and combined with the material box type information: determining all material box positions that match the material box type information according to the material box position storage information; determining the material box position closest to the warehouse opening position among all material box positions, and marking it as the scheduling material box; determining the storage location points of different types of inventory objects based on the logistics scheduling direction according to the inventory object type required by the logistics scheduling demand information to form the inventory object storage location point information; and combining the inventory object storage location point information, the logistics scheduling direction, the warehouse opening position and the location of the scheduling material box to form the logistics scheduling point information.

[0014] In the present invention, the determination of the location information of the inventory object is the determination of the reasonable logistics scheduling point to complete the scheduling requirements. The present application determines the logistics scheduling point, first of all, considering that the material box will be used whether it is out of the warehouse or in the warehouse, so it is necessary to determine the material box used first. For the location of the material box, the material box must pass through the warehouse entrance, so the distance from the material box position to the warehouse entrance position is used as a reference for determining the optimal material box position. It can be understood that the sequential logistics scheduling may involve a one-time out of the warehouse or in the warehouse of multiple materials of the same or different types, so the determination of the material box type is based on the number and size of the materials involved in the scheduling requirements. After determining the scheduling of the material box, considering whether it is out of the warehouse or in the warehouse, in the case of scheduling multiple materials at one time, the material box will inevitably pass through all the loading or picking points, that is, all the scheduling points, so for the material box, its logistics scheduling still has a certain order, so by setting a reasonable scheduling direction for analysis and processing, the best scheduling point information can be obtained.

[0015] As a possible implementation method, according to the inventory object type required by the logistics scheduling demand information, the storage location points of different types of inventory objects based on the logistics scheduling direction are determined to form the inventory object storage location point information, including: when the logistics scheduling is an outbound scheduling demand: the direction of the scheduling material box toward the warehouse entrance position is determined as the logistics scheduling direction; for each different inventory object type, the position of the earliest batch of inventory objects is determined to form the early batch inventory object position information; with the position of the scheduling material box as the origin and the logistics scheduling direction as the first coordinate axis, an outbound scheduling planning coordinate system is established; in the outbound scheduling planning coordinate system, the sum of the distances of each inventory object position in the early batch inventory object position information under different inventory objects relative to the first coordinate axis and the warehouse entrance position respectively is determined; along the logistics scheduling direction, the inventory object positions with the smallest sum of distances in the early batch inventory object position information of different inventory objects are determined respectively, and are marked as the nth inventory object scheduling point in turn, where n represents the number of different inventory objects of the inventory object type required by the logistics scheduling demand information.

[0016] In the present invention, there are certain differences in different situations in the use of material boxes for directional logistics scheduling. For the logistics scheduling requirements of the nature of completing the outbound delivery, it is critical to first determine the optimal position for the different types of materials to be delivered. However, for the materials to be delivered, it is necessary to select according to the batch number to ensure the reasonable consumption and use of the materials previously stored in the inventory. Therefore, when the corresponding materials have outbound delivery requirements, the early batches are first screened out, and then the shortest position from the warehouse port is confirmed in these early batches of inventory materials, so that the scheduling point can be reasonably determined. Of course, for the situation where both inventory positions are short in the inventory, it is considered that the closer the material box is to the warehouse port in the direction of the warehouse port position, the better it is to make the selection. After all, it can avoid walking in the opposite direction during line planning and concentrate the density of different types of materials in the scheduling direction to a certain extent, so as to improve the efficiency of scheduling.

[0017] As a possible implementation method, according to the logistics scheduling point information and combined with the scheduling basic data, the route planning selection based on efficiency is carried out to form the initial logistics scheduling planning data, including: in the outbound scheduling planning coordinate system, taking the location of the scheduling material box as the starting point and the warehouse port location as the end point, the connection lines of the adjacent scheduling points including the location of the scheduling material box, the warehouse port location and n inventory object scheduling points are successively established along the logistics scheduling direction to form m scheduling relationship lines, where m=n+1; for each scheduling relationship line, the following analysis is carried out according to the scheduling line basic information to determine the shortest scheduling path: starting from the previous scheduling point, a route with the smallest area formed by the vertical projection of both ends on the corresponding scheduling relationship line is successively determined, where: if the route that meets the conditions needs to pass through a previous adjacent route If there is no line connected to a vertical scheduling relationship line, and no line is directly connected to the preceding adjacent line, the line of the vertical scheduling relationship line is determined to be the planned line; if a line that meets the conditions has both a line connection with the preceding adjacent line that needs to be connected through a vertical scheduling relationship line and a line directly connected to the preceding adjacent line, the line directly connected to the preceding adjacent line is determined to be the planned line; connect all the determined lines to form the shortest scheduling path corresponding to the scheduling relationship line; connect the shortest scheduling paths corresponding to all scheduling relationship lines to form an initial logistics scheduling path; determine the planned time and location information for scheduling operations on the initial logistics scheduling path based on the basic information of the scheduling carrier operation; combine the initial logistics scheduling path and the planned time and location information to form initial logistics scheduling planning data.

[0018] In the present invention, after the optimal position of different types of materials to be shipped out is determined, reasonable path planning can be carried out. Path planning should consider the entire process from the dispatching material box to the delivery to the warehouse. Therefore, when the path planning analysis is performed, the connection between adjacent scheduling points is established in the order of the reference scheduling direction. This is conducive to the planning of the path between adjacent scheduling points. For the planning of the scheduling path under the scheduling relationship line, on the one hand, the path line planning close to the scheduling relationship line is carried out based on the scheduling relationship line, which greatly improves the guidance of selecting the optimal path, and also provides reasonable direction guidance to avoid the situation of reverse scheduling wasting resources and reducing efficiency. On the other hand, through the guidance of the scheduling relationship line, it is avoided that the selected path cannot be concentrated for this scheduling point, so that the path planning has the clustering characteristics of the local location at the overall level, and to a certain extent, the workload of data processing and analysis is reduced macroscopically. After all, the selected path has no reference, and the path will appear to radiate radially to the entire storage area and affect more operation content and operation paths, making the analysis and processing work more complicated. Here, the path selection between adjacent scheduling points is based on the projection lines of the two ends of the path on the scheduling relationship line and the comparison of the size of the closed area formed by the scheduling relationship line and the path. There is a special case here, that is, the path of the vertical scheduling relationship line cannot be reasonably evaluated. However, for the path of the vertical scheduling relationship line, although it provides a choice of paths that are not necessarily directly connected in the direction of the scheduling relationship line, the line of the vertical scheduling relationship will actually increase the length of the entire path and increase the time consumption of scheduling. Therefore, it will only be determined as a planned route when this choice is unique. In addition, the selected path will eventually form the initial planned path. For the planned path, the logistics information on the path is the basis for determining whether it is efficiently scheduled. Therefore, it is necessary to determine the reasonable time and position relationship of the initial planned path, that is, from the starting point of the planned path, after too little time, the logistics will reach where on the path. This information is also the basis for the subsequent analysis of whether the paths with intersections interfere with each other.

[0019] As a possible implementation method, according to the inventory object type required by the logistics scheduling demand information, the storage location points of different types of inventory objects based on the logistics scheduling direction are determined to form the inventory object storage location point information, including: when the logistics scheduling is the inbound scheduling demand: according to the warehouse entrance position, the current reserved storage position closest to the warehouse entrance position is determined among the different inventory object types, and marked as the object scheduling point; among the different object scheduling points, the object scheduling point farthest from the warehouse entrance position is determined, and marked as the scheduling end point; with the warehouse entrance position as the origin and the logistics scheduling direction from the warehouse entrance position to the scheduling end point as the first coordinate axis, an inbound scheduling planning coordinate system is established; the position of the scheduling material box is recorded as the first scheduling point, and the warehouse entrance position is recorded as the second scheduling point, and the positions including the warehouse entrance position and the object scheduling point are calibrated in turn along the inbound logistics scheduling direction, and recorded as the kth scheduling point.

[0020] In the present invention, similarly, for warehousing, it is also necessary to load the material box and complete the warehousing through logistics scheduling. However, compared with outbound warehousing, the difference is that when warehousing, the path direction from the material box to the warehouse entrance and the direction from the warehouse entrance to the material storage location may be different, or may intersect or be opposite. Therefore, the scheduling direction is not considered for the scheduling of the material box warehouse entrance. However, the scheduling direction needs to be considered from the warehouse entrance to the material storage location. The determination of the scheduling direction is achieved by establishing a connection between the shortest storage location corresponding to the material type farthest from the warehouse entrance and the warehouse entrance.

[0021] As a possible implementation method, according to the logistics scheduling point information and combined with the basic scheduling data, the efficiency-based route planning selection is carried out to form the initial logistics scheduling planning data, including: in the warehousing scheduling planning coordinate system, taking the first scheduling point as the starting point and the kth scheduling point as the end point, successively establishing the connection lines of the adjacent scheduling points to form p scheduling relationship lines, where p=k-1; for each scheduling relationship line, the following analysis is carried out according to the basic information of the scheduling line to determine the shortest scheduling path: starting from the previous scheduling point, successively determining a route with the smallest area formed by the vertical projections of both ends on the corresponding scheduling relationship line, where: if the route that meets the conditions needs to be connected to the previous adjacent route through a route perpendicular to the scheduling relationship line, and there is no If any line can be directly connected with the preceding adjacent line, the line of the vertical scheduling relationship line is determined to be the planned line; if the line that meets the conditions has both a line connection with the preceding adjacent line through a vertical scheduling relationship line and a line directly connected with the preceding adjacent line, the line directly connected with the preceding adjacent line is determined to be the planned line; connect all the determined lines to form the shortest scheduling path corresponding to the scheduling relationship line; connect the shortest scheduling paths corresponding to all scheduling relationship lines to form an initial logistics scheduling path; determine the planned time and location information for scheduling operations on the initial logistics scheduling path based on the basic information of the scheduling carrier operation; combine the initial logistics scheduling path and the planned time and location information to form initial logistics scheduling planning data.

[0022] In the present invention, similarly, the logistics scheduling path planning for warehousing is also realized by selecting the optimal path between adjacent scheduling points based on the scheduling relationship line. Of course, when selecting a path, there is also a situation where a path perpendicular to the scheduling relationship line may be selected, so only a unique and certain choice can make the path perpendicular to the scheduling relationship line as part of the planned path.

[0023] As a possible implementation method, a scheduling interference optimization analysis is performed based on the initial logistics scheduling planning data to form optimized logistics scheduling planning data, including: determining the path intersection point between different initial logistics scheduling paths; determining the time difference t between different initial logistics scheduling paths reaching the path intersection point based on the planned time position information corresponding to the initial logistics scheduling paths related to the path intersection point; obtaining the maximum size value L of the scheduling material box corresponding to the initial logistics scheduling paths related to the path intersection point in the direction perpendicular to the running direction; obtaining the minimum speed value v of the scheduling material box running speed corresponding to the initial logistics scheduling paths related to the path intersection point; setting the intersection interval limit value q, and performing the following optimization adjustment based on the time difference t, the maximum size value L and the minimum speed value v: if L<t*v+q, then keep the initial logistics scheduling paths related to the path intersection point unchanged; if L≥t*v+q, then accelerate the running speed of the initial logistics scheduling paths with shorter time consumption in the initial logistics scheduling paths related to the path intersection point at the path intersection point until L<t*v+q is satisfied.

[0024] In the present invention, after completing the path planning, many scheduling paths will be formed at the storage level. These paths may intersect. If they arrive at the intersection at the same time, they will definitely cause a collision, so a reasonable interference optimization analysis is required. It can be understood that the limit case where no collision occurs is that the material box that arrives first with a fast speed can pass the entire body in the time difference of the intersection. Therefore, the analysis is based on the size of the material box and the time difference between the speed and the intersection. Of course, a reasonable margin is necessary, so an interval limit value is provided. For the case of interference, two aspects are considered. One is that there are multiple intersections in the planned path. Changing the running speed on the entire path may cause interference with other intersections. On the other hand, if the running speed of the material box that arrives at the intersection later is reduced, it may cause the storage time to be untimely. Therefore, when analyzing the existence of interference, the application performs a processing to improve the running speed of the material box that arrives first within a reasonable area near the intersection. By optimizing the small area of ​​interference with the interaction point, the influence of optimization on other and subsequent routes of this line is avoided. Even if the route in the entire storage is complex, it can be guaranteed that there will be no interference after optimization.

[0025] In a second aspect, the present invention provides a cloud-based warehousing logistics scheduling device, which is configured to: collect real-time warehousing IoT data, perform real-time warehousing logistics information analysis, and form real-time warehousing logistics basic data; obtain logistics scheduling demand information, and combine the real-time warehousing logistics basic data to perform efficiency-based scheduling planning analysis to form initial logistics scheduling planning data; perform scheduling interference optimization analysis based on the initial logistics scheduling planning data to form optimized logistics scheduling planning data.

[0026] In the present invention, the device is configured as a cloud computing device capable of analyzing and processing a large amount of real-time warehouse IoT data to complete the logistics scheduling path planning design analysis from the overall warehouse level, and at the same time perform reasonable optimization and adjustment, thereby providing an important material basis for ensuring the reasonable, efficient and orderly implementation of logistics scheduling.

[0027] The beneficial effects of a cloud-based warehousing logistics scheduling method and device provided by the present invention are as follows:

[0028] This method obtains real-time IoT data of the warehouse, performs cloud computing processing, and analyzes and provides basic data for logistics scheduling path planning at the entire warehouse level, providing an important and accurate data basis for the reasonable implementation of logistics scheduling path planning. At the same time, in combination with the logistics scheduling needs, the real-time logistics basic data of the warehouse is fully utilized to carry out overall real-time logistics scheduling path planning, and with efficiency as the purpose, efficient logistics scheduling planning analysis and processing are achieved. In addition, on the basis of fully meeting the logistics scheduling needs, the interference analysis and optimization between different scheduling planning paths at the warehouse level are carried out on the preliminary plan formed, so as to fully ensure that all logistics scheduling can be implemented correctly and orderly.

[0029] The device is configured as a cloud computing device capable of analyzing and processing a large amount of real-time warehouse IoT data, completing the logistics scheduling path planning, design and analysis from the overall warehouse level, while making reasonable optimization and adjustments, providing an important material basis for ensuring the reasonable, efficient and orderly progress of logistics scheduling. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments of the present invention are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0031] Figure 1 A step diagram of a cloud-based warehousing logistics scheduling method provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0032] The technical solutions in the embodiments of the present invention will be described below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0033] With the development of Internet of Things and automation technology, warehousing logistics has gradually become more intelligent and efficient, and has undergone earth-shaking changes compared to traditional warehousing logistics. When warehousing logistics is being carried out, logistics scheduling is one of the important links. Logistics scheduling requires obtaining a lot of information, such as warehouse design information, inventory management information, basic information on logistics operations, etc., and a reasonable logistics scheduling plan is established through comprehensive analysis of these information.

[0034] At present, although logistics scheduling can be reasonably arranged to a certain extent with the help of the Internet of Things and intelligent equipment, it is difficult to reasonably and orderly implement scheduling arrangements at the entire warehousing level due to the huge amount of information. With the development of cloud technology, the amount of data processing has greatly increased, and more efficient and reasonable logistics scheduling control has become possible. On this basis, the path planning arrangement of logistics scheduling from a macro level can also be designed and controlled more reasonably and orderly.

[0035] refer to Figure 1 The embodiment of the present invention provides a cloud-based warehouse logistics scheduling method, which obtains real-time IoT data of the warehouse, performs cloud computing processing, and analyzes and provides basic data for logistics scheduling path planning at the entire warehouse level, providing an important and accurate data basis for the reasonable implementation of logistics scheduling path planning. At the same time, in combination with the logistics scheduling needs, the real-time logistics basic data of the warehouse is fully utilized to carry out overall real-time logistics scheduling path planning, and with efficiency as the purpose, efficient logistics scheduling planning analysis and processing is achieved. In addition, on the basis of fully meeting the logistics scheduling needs, the interference analysis and optimization between different scheduling planning paths at the warehouse level are performed on the formed preliminary plan to fully ensure that all logistics scheduling can be implemented correctly and orderly.

[0036] The cloud-based warehousing logistics scheduling method specifically includes the following steps:

[0037] S1: Collect real-time IoT data of warehouses, conduct cloud computing analysis of real-time warehouse logistics information, and form basic data of real-time warehouse logistics.

[0038] Collect real-time warehousing IoT data, conduct cloud computing analysis of real-time warehousing logistics information, and form real-time warehousing logistics basic data, including: extracting current warehousing information of different inventory objects based on real-time warehousing IoT data: determine the current in-stock storage location of different inventory objects, and perform location calibration based on storage batches to form the current in-stock warehousing information of inventory objects; determine the current reserved storage location of different inventory objects based on real-time warehousing IoT data, and perform location calibration to form the current reserved warehousing information of inventory objects; determine the current placement location of different types of material boxes based on real-time warehousing IoT data, and perform location calibration to form material box location warehousing information; determine the locations of different warehouse ports based on real-time warehousing IoT data and perform calibration to form warehouse port location information; extract the following basic scheduling data based on real-time warehousing IoT data: determine all scheduling routes in the logistics scheduling area based on real-time warehousing IoT data to form basic scheduling route information; determine the operating parameters of different scheduling carriers based on real-time warehousing IoT data to form basic scheduling carrier operation information.

[0039] When using cloud computing to extract real-time logistics basic data for warehousing, it is considered that the extracted basic data provides a data basis for subsequent logistics scheduling path planning, so the extracted information is directional. First of all, for logistics scheduling path planning, the purpose of planning is to complete the warehousing and outbound in the warehouse, and the warehousing and outbound involve the information of inventory objects, such as location information, batch information, etc. Therefore, the extracted real-time logistics basic information includes the location of different inventory objects, batch numbers, and information on the inventory locations reserved for different inventory objects. Of course, both outbound and inbound involve the collection and transportation of materials, and the types of material boxes required for the logistics transportation of different types of materials are different, so the location information of the material boxes also needs to be extracted. In addition, both outbound and inbound involve the warehouse port, so the warehouse port location information is also extracted. For logistics scheduling, parameter information such as the path that can be passed and the speed of the carrier itself involved in the scheduling process also needs to be extracted from the real-time logistics basic information. The amount of data to be analyzed using these data is huge. Cloud computing can solve this problem efficiently. Cloud computing can quickly obtain real-time basic logistics information, which makes it possible to realize the subsequent logistics scheduling path planning. In addition, the extraction of location information can be accurately calibrated by establishing a spatial coordinate system for the entire warehouse. The batch number, inventory object, and type of material box information can be extracted from the inventory management system, and the scheduling route can be calibrated and extracted based on the spatial coordinate system.

[0040] S2: Obtain logistics scheduling demand information, and combine it with the real-time logistics basic data of the warehouse to conduct efficiency-based scheduling planning analysis to form initial logistics scheduling planning data.

[0041] Obtain logistics scheduling demand information, and combine it with the real-time basic logistics data of the warehouse to conduct efficiency-based scheduling planning analysis to form initial logistics scheduling planning data, including: based on the logistics scheduling demand information and combined with the current warehouse information, determine the logistics scheduling point to form logistics scheduling point information; based on the logistics scheduling point information and combined with the basic scheduling data, conduct efficiency-based route planning selection to form initial logistics scheduling planning data.

[0042] After extracting the real-time basic logistics information, we can combine the extracted real-time basic logistics information to carry out reasonable logistics scheduling path planning based on the logistics scheduling requirements. There are two main aspects to consider for logistics scheduling path planning. On the one hand, in order to achieve logistics scheduling that meets the scheduling requirements, we first need to determine the inventory object information to complete the logistics scheduling, and then lock these inventory objects for reasonable path planning. On the other hand, how to plan the route after determining the inventory object information makes the planned path more reasonable, efficient and safe.

[0043] According to the logistics scheduling demand information and in combination with the current warehousing information, the logistics scheduling point is determined to form the logistics scheduling point information, including: according to the logistics scheduling demand information and in combination with the current warehousing information, the scheduling point is determined in the following manner: according to the inventory object required by the logistics scheduling demand information, the type of material box required is determined to form the material box type information; according to the warehouse opening position required by the logistics scheduling demand information and in combination with the material box type information, the following analysis is performed to determine the scheduling material box: according to the material box position storage information, all material box positions that match the material box type information are determined; the material box position closest to the warehouse opening position among all material box positions is determined, and marked as the scheduling material box; according to the inventory object type required by the logistics scheduling demand information, the storage location points of different types of inventory objects based on the logistics scheduling direction are determined to form the inventory object storage location point information; the inventory object storage location point information, the logistics scheduling direction, the warehouse opening position and the location of the scheduling material box are combined to form the logistics scheduling point information.

[0044] Determining the location information of inventory objects is to determine the reasonable logistics scheduling point to complete the scheduling requirements. The present application determines the logistics scheduling point by first considering that the material box will be used whether it is out of the warehouse or in the warehouse, so it is necessary to determine the material box used first. For the location of the material box, the material box must pass through the warehouse entrance, so the distance from the material box position to the warehouse entrance position is used as a reference for determining the optimal material box position. It is understandable that the sequential logistics scheduling may involve one-time out of the warehouse or in the warehouse of multiple materials of the same or different types, so the determination of the material box type is based on the number and size of the materials involved in the scheduling requirements. After determining the scheduling of the material box, considering whether it is out of the warehouse or in the warehouse, in the case of one-time scheduling of multiple materials, the material box will inevitably pass through all the loading or picking points, that is, all the scheduling points, so for the material box, its logistics scheduling still has a certain order, so by setting a reasonable scheduling direction for analysis and processing, the best scheduling point information can be obtained.

[0045] According to the inventory object type required by the logistics scheduling demand information, the storage location points of different types of inventory objects are determined based on the logistics scheduling direction to form the inventory object storage location point information, including: when the logistics scheduling is an outbound scheduling demand: the direction of the scheduling material box toward the warehouse entrance position is determined as the logistics scheduling direction; for each different inventory object type, the position of the earliest batch of inventory objects is determined to form the early batch inventory object position information; with the position of the scheduling material box as the origin and the logistics scheduling direction as the first coordinate axis, an outbound scheduling planning coordinate system is established; in the outbound scheduling planning coordinate system, the sum of the distances of each inventory object position in the early batch inventory object position information under different inventory objects relative to the first coordinate axis and the warehouse entrance position respectively is determined; along the logistics scheduling direction, the inventory object positions with the smallest sum of distances in the early batch inventory object position information of different inventory objects are determined respectively, and are marked as the nth inventory object scheduling point in turn, where n represents the number of different inventory objects of the inventory object type required by the logistics scheduling demand information.

[0046] There are certain differences in different situations when using bins for directional logistics scheduling. For the logistics scheduling needs of completing outbound delivery, it is critical to first determine the best location for different types of materials to be shipped out. However, for materials to be shipped out, they need to be selected according to the batch number to ensure the reasonable consumption and use of the previously stored materials in the inventory. Therefore, when the corresponding materials have outbound delivery requirements, the early batches are first screened out, and then the shortest position from the warehouse port is confirmed among the inventory materials in these early batches, so that the scheduling point can be reasonably determined. Of course, for the situation where both inventory positions are short, it is considered that the closer the bin is to the warehouse port, the better it is to select. After all, it can avoid walking in the opposite direction during line planning and concentrate the density of different types of materials in the scheduling direction to a certain extent, so as to improve the efficiency of scheduling.

[0047] According to the logistics dispatch point information and combined with the dispatch basic data, the efficiency-based route planning selection is carried out to form the initial logistics dispatch planning data, including: in the outbound dispatch planning coordinate system, taking the location of the dispatch material box as the starting point and the warehouse port location as the end point, the connection lines of the adjacent dispatch points including the location of the dispatch material box, the warehouse port location and n inventory object dispatch points are successively established along the logistics dispatch direction to form m dispatch relationship lines, where m=n+1; for each dispatch relationship line, the following analysis is carried out according to the dispatch line basic information to determine the shortest dispatch path: starting from the previous dispatch point, successively determine a route with the smallest area formed by the vertical projections of both ends on the corresponding dispatch relationship line, where: if the route that meets the conditions needs to pass through a vertical dispatch relationship with the previous adjacent route If the lines of the line are connected and there is no line that is directly connected to the adjacent line in front, the line of the vertical scheduling relationship line is determined to be the planned line; if the line that meets the conditions has both a line connection with the adjacent line in front through a vertical scheduling relationship line and a line directly connected to the adjacent line in front, the line directly connected to the adjacent line in front is determined as the planned line; connect all the determined lines to form the shortest scheduling path corresponding to the scheduling relationship line; connect the shortest scheduling paths corresponding to all scheduling relationship lines to form an initial logistics scheduling path; determine the planned time and location information for scheduling operations on the initial logistics scheduling path based on the basic information of the scheduling carrier operation; combine the initial logistics scheduling path and the planned time and location information to form the initial logistics scheduling planning data.

[0048] After the optimal location of different types of materials to be shipped out is determined, reasonable path planning can be carried out. Path planning should consider the entire process from the dispatching of the material box to the delivery to the warehouse. Therefore, when performing path planning analysis, the connection between adjacent scheduling points is established in the order of the reference scheduling direction. This is conducive to the planning of paths between adjacent scheduling points. For the planning of scheduling paths under the scheduling relationship line, on the one hand, the path line planning close to the scheduling relationship line is carried out based on the scheduling relationship line, which greatly improves the guidance of selecting the optimal path, and also provides reasonable direction guidance to avoid the situation of reverse scheduling wasting resources and reducing efficiency. On the other hand, through the guidance of the scheduling relationship line, it is avoided that the selected path cannot be concentrated for this scheduling point, so that the path planning has the clustering characteristics of the local location at the overall level, which reduces the workload of data processing and analysis to a certain extent. After all, if the selected path has no reference, the path will radiate to the entire storage area and affect more operation content and operation paths, making the analysis and processing work more complicated. Here, the path selection between adjacent scheduling points is based on the projection lines of the two ends of the path on the scheduling relationship line and the comparison of the size of the closed area formed by the scheduling relationship line and the path. There is a special case here, that is, the path of the vertical scheduling relationship line cannot be reasonably evaluated. However, for the path of the vertical scheduling relationship line, although it provides a choice of paths that are not necessarily directly connected in the direction of the scheduling relationship line, the line of the vertical scheduling relationship will actually increase the length of the entire path and increase the time consumption of scheduling. Therefore, it will only be determined as a planned route when this choice is unique. In addition, the selected path will eventually form the initial planned path. For the planned path, the logistics information on the path is the basis for determining whether it is efficiently scheduled. Therefore, it is necessary to determine the reasonable time and position relationship of the initial planned path, that is, from the starting point of the planned path, after too little time, the logistics will reach where on the path. This information is also the basis for the subsequent analysis of whether the paths with intersections interfere with each other.

[0049] In addition, according to the inventory object type required by the logistics scheduling demand information, the storage location points of different types of inventory objects based on the logistics scheduling direction are determined to form the inventory object storage location point information, including: when the logistics scheduling is an incoming scheduling demand: according to the warehouse entrance position, the current reserved storage location closest to the warehouse entrance position is determined among the different inventory object types, and marked as the object scheduling point; among the different object scheduling points, the object scheduling point farthest from the warehouse entrance position is determined, and marked as the scheduling end point; with the warehouse entrance position as the origin and the logistics scheduling direction from the warehouse entrance position to the scheduling end point as the first coordinate axis, an incoming scheduling planning coordinate system is established; the position of the scheduling material box is recorded as the first scheduling point, and the warehouse entrance position is recorded as the second scheduling point, and the positions including the warehouse entrance position and the object scheduling point are calibrated in turn along the incoming logistics scheduling direction, and recorded as the kth scheduling point.

[0050] Similarly, for warehousing, the material boxes need to be loaded and completed through logistics scheduling. However, compared with outbound, the difference is that when entering the warehouse, the path direction from the material box to the warehouse entrance and the direction from the warehouse entrance to the material storage location may be different, or they may intersect or be opposite. Therefore, the scheduling direction is not considered for the scheduling of the material box warehouse entrance. However, the scheduling direction needs to be considered from the warehouse entrance to the material storage location. The determination of the scheduling direction is realized by the connection between the shortest storage location corresponding to the material type farthest from the warehouse entrance and the warehouse entrance.

[0051] According to the logistics dispatch point information and combined with the dispatch basic data, the efficiency-based route planning selection is carried out to form the initial logistics dispatch planning data, including: in the warehousing dispatch planning coordinate system, taking the first dispatch point as the starting point and the kth dispatch point as the end point, successively establishing the connection lines of the adjacent dispatch points to form p dispatch relationship lines, where p=k-1; for each dispatch relationship line, the following analysis is carried out according to the dispatch line basic information to determine the shortest dispatch path: starting from the previous dispatch point, successively determining a route with the smallest area formed by the vertical projections of both ends on the corresponding dispatch relationship line, where: if the route that meets the conditions needs to be connected to the previous adjacent route through a route perpendicular to the dispatch relationship line, and no route is connected to the previous adjacent route, If the preceding adjacent line is directly connected, the line of the vertical scheduling relationship line is determined to be the planned line; if the line that meets the conditions has both a line connection with the preceding adjacent line through a vertical scheduling relationship line and a line directly connected to the preceding adjacent line, the line directly connected to the preceding adjacent line is determined to be the planned line; connect all the determined lines to form the shortest scheduling path corresponding to the scheduling relationship line; connect the shortest scheduling paths corresponding to all scheduling relationship lines to form the initial logistics scheduling path; determine the planned time and location information for scheduling operations on the initial logistics scheduling path based on the basic information of the scheduling carrier operation; combine the initial logistics scheduling path and the planned time and location information to form the initial logistics scheduling planning data.

[0052] Similarly, the logistics scheduling path planning for warehousing is also realized by selecting the optimal path between adjacent scheduling points based on the scheduling relationship line. Of course, when selecting a path, there is also the possibility of choosing a path that is perpendicular to the scheduling relationship line. Therefore, only the unique choice can make the path perpendicular to the scheduling relationship line as part of the planning path.

[0053] S3: Based on the initial logistics scheduling planning data, perform scheduling interference optimization analysis to form optimized logistics scheduling planning data.

[0054] According to the initial logistics scheduling planning data, scheduling interference optimization analysis is performed to form optimized logistics scheduling planning data, including: determining the path intersection point between different initial logistics scheduling paths; determining the time difference t between different initial logistics scheduling paths reaching the path intersection point according to the planned time position information corresponding to the initial logistics scheduling paths related to the path intersection point; obtaining the maximum size value L of the scheduling material box corresponding to the initial logistics scheduling paths related to the path intersection point in the direction perpendicular to the running direction; obtaining the minimum speed value v of the scheduling material box running speed corresponding to the initial logistics scheduling paths related to the path intersection point; setting the intersection interval limit value q, and according to the time difference t, the maximum size value L and the minimum speed value v, performing the following optimization adjustment: if L<t*v+q, then keeping the initial logistics scheduling paths related to the path intersection point unchanged; if L≥t*v+q, then accelerating the running speed of the initial logistics scheduling paths with shorter time consumption in the initial logistics scheduling paths related to the path intersection point at the path intersection point until L<t*v+q is satisfied.

[0055] After completing the path planning, many scheduling paths will be formed at the warehouse level. These paths may intersect. If they arrive at the intersection at the same time, they will definitely cause a collision, so a reasonable interference optimization analysis is needed. It can be understood that the limit case where no collision occurs is that the material box that arrives first with a fast speed can pass the entire body in the time difference of the intersection. Therefore, the analysis is based on the size of the material box and the time difference between the speed and the intersection. Of course, a reasonable margin is necessary, so an interval limit value is provided. For the case of interference, two aspects are considered. One is that there are multiple intersections in the planned path. Changing the running speed on the entire path may cause interference with other intersections. On the other hand, if the running speed of the material box that arrives at the intersection later is reduced, it may cause the storage time to be untimely. Therefore, when analyzing the existence of interference, the application improves the running speed of the material box that arrives first within a reasonable area near the intersection. By optimizing the small area of ​​interference at the interaction point, the impact of optimization on other and subsequent routes of this line is avoided. Even if the route in the entire warehouse is complex, it can be guaranteed that there will be no interference after optimization.

[0056] The present invention also provides a cloud-based warehousing logistics scheduling device, which is configured to: collect real-time warehousing IoT data, perform real-time warehousing logistics information analysis, and form real-time warehousing logistics basic data; obtain logistics scheduling demand information, and combine the real-time warehousing logistics basic data to perform efficiency-based scheduling planning analysis to form initial logistics scheduling planning data; perform scheduling interference optimization analysis based on the initial logistics scheduling planning data to form optimized logistics scheduling planning data.

[0057] The device is configured as a cloud computing device capable of analyzing and processing a large amount of real-time warehouse IoT data, completing the logistics scheduling path planning, design and analysis from the overall warehouse level, while making reasonable optimization and adjustments, providing an important material basis for ensuring the reasonable, efficient and orderly progress of logistics scheduling.

[0058] In summary, the cloud-based warehousing logistics scheduling method and device provided by the embodiments of the present invention have the following beneficial effects:

[0059] This method obtains the real-time IoT data of the warehouse, performs cloud computing processing, and analyzes and provides basic data for logistics scheduling path planning at the entire warehouse level, providing an important and accurate data basis for the reasonable implementation of logistics scheduling path planning. At the same time, in combination with the logistics scheduling needs, the real-time logistics basic data of the warehouse is fully utilized to carry out overall real-time logistics scheduling path planning, and with efficiency as the purpose, efficient logistics scheduling planning analysis and processing are achieved. In addition, on the basis of fully meeting the logistics scheduling needs, the interference analysis and optimization between different scheduling planning paths at the warehouse level are carried out on the preliminary plan formed, so as to fully ensure that all logistics scheduling can be implemented correctly and orderly.

[0060] The device is configured as a cloud computing device capable of analyzing and processing a large amount of real-time warehouse IoT data, completing the logistics scheduling path planning, design and analysis from the overall warehouse level, while making reasonable optimization and adjustments, providing an important material basis for ensuring the reasonable, efficient and orderly progress of logistics scheduling.

[0061] In the embodiments of the present application, "indication" may include direct indication and indirect indication, and may also include explicit indication and implicit indication. The information indicated by a certain information is called information to be indicated. In the specific implementation process, there are many ways to indicate the information to be indicated, such as but not limited to, the information to be indicated can be directly indicated, such as the information to be indicated itself or the index of the information to be indicated. The information to be indicated can also be indirectly indicated by indicating other information, wherein there is an association between the other information and the information to be indicated. It is also possible to indicate only a part of the information to be indicated, while the other parts of the information to be indicated are known or agreed in advance. For example, the indication of specific information can also be achieved by means of the arrangement order of each piece of information agreed in advance (for example, specified by the protocol), thereby reducing the indication overhead to a certain extent. At the same time, the common parts of each piece of information can also be identified and indicated uniformly to reduce the indication overhead caused by indicating the same information separately.

[0062] In addition, the specific indication method may also be various existing indication methods, such as but not limited to the above-mentioned indication methods and various combinations thereof. The specific details of the various indication methods can refer to the prior art and will not be repeated herein. As can be seen from the above, for example, when it is necessary to indicate multiple information of the same type, different indication methods may be used for different information. In the specific implementation process, the desired indication method can be selected according to specific needs. The embodiment of the present application does not limit the selected indication method. In this way, the indication method involved in the embodiment of the present application should be understood to cover various methods that can enable the party to be indicated to obtain the information to be indicated.

[0063] It should be understood that the information to be indicated can be sent as a whole, or divided into multiple sub-information and sent separately, and the sending period and / or sending time of these sub-information can be the same or different. The specific sending method is not limited in the embodiment of the present application. Among them, the sending period and / or sending time of these sub-information can be pre-defined, for example, pre-defined according to a protocol, or can be configured by the sending end device by sending configuration information to the receiving end device.

[0064] "Pre-definition" or "pre-configuration" can be implemented by pre-saving corresponding codes, tables or other methods that can be used to indicate relevant information in the device, and the embodiments of the present application do not limit the specific implementation method. Among them, "saving" can mean saving in one or more memories. The one or more memories can be set separately or integrated in an encoder or decoder, a processor, or a communication device. The one or more memories can also be partially set separately and partially integrated in a decoder, a processor, or a communication device. The type of memory can be any form of storage medium, which is not limited by the embodiments of the present application.

[0065] The "protocol" involved in the embodiments of the present application may refer to a protocol family in the communication field, a standard protocol with a similar protocol family frame structure, or a related protocol used in future communication systems, and the embodiments of the present application do not make specific limitations on this.

[0066] In the embodiments of the present application, descriptions such as "when...", "in the case of...", "if" and "if" all mean that the device will make corresponding processing under certain objective circumstances. It does not limit the time, nor does it require the device to have a judgment action when implementing it, nor does it mean that there are other limitations.

[0067] In the description of the embodiments of the present application, unless otherwise specified, " / " indicates that the objects associated before and after are in an "or" relationship, for example, A / B can represent A or B; "and / or" in the embodiments of the present application is only a description of the association relationship of the associated objects, indicating that there can be three relationships, for example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone, where A and B can be singular or plural. In addition, in the description of the embodiments of the present application, unless otherwise specified, "multiple" refers to two or more than two. "At least one of the following" or its similar expression refers to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, c can be single or multiple. In addition, in order to facilitate the clear description of the technical solution of the embodiments of the present application, in the embodiments of the present application, the words "first" and "second" are used to distinguish the same or similar items with basically the same functions and effects. Those skilled in the art will appreciate that words such as "first" and "second" do not limit the quantity and execution order, and words such as "first" and "second" do not necessarily limit the difference. At the same time, in the embodiments of the present application, words such as "exemplary" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "exemplary" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a concrete way for easy understanding.

[0068] It should be understood that the processor in the embodiment of the present application may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0069] It should also be understood that the memory in the embodiments of the present application may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic random access memory (DRAM), synchronous DRAM (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link DRAM (SLDRAM), and direct rambus RAM (DRRAM).

[0070] The above embodiments can be implemented in whole or in part by software, hardware (such as circuits), firmware or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0071] It should be understood that the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. A and B can be singular or plural. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship, but it may also indicate an "and / or" relationship. Please refer to the context for specific understanding.

[0072] In this application, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can be represented by: a, b, c, ab, ac, bc, or abc, where a, b, c can be single or multiple.

[0073] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0074] Those of ordinary skill 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 depends on the specific application and design constraints of the technical solution. Professional and technical personnel 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 this application.

[0075] Those skilled in the art 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.

[0076] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that 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.

[0077] 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 distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0078] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0079] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage media include: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM), random access memories (RAM), magnetic disks or optical disks.

[0080] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A cloud-based warehousing logistics scheduling method, characterized in that: include: Collect real-time warehousing IoT data, conduct cloud computing analysis of real-time warehousing logistics information, and form real-time warehousing logistics basic data; Obtaining logistics scheduling demand information, and combining the real-time warehouse logistics basic data, performing efficiency-based scheduling planning analysis to form initial logistics scheduling planning data; According to the initial logistics scheduling planning data, scheduling interference optimization analysis is performed to form optimized logistics scheduling planning data.

2. The cloud-based warehousing logistics scheduling method according to claim 1 is characterized in that: The real-time storage IoT data is collected, and real-time storage logistics information cloud computing analysis is performed to form real-time storage logistics basic data, including: According to the real-time warehouse IoT data, current warehouse information is extracted for different inventory objects: Determine the current storage location of different inventory objects, and calibrate the location according to the storage batch to form the current storage information of the inventory objects; According to the real-time IoT data of the warehouse, the current reserved storage locations of different inventory objects are determined, and the locations are calibrated to form the current reserved storage information of the inventory objects; According to the real-time IoT data of the warehouse, the current placement positions of different types of material boxes are determined, and the positions are calibrated to form the material box position storage information; According to the real-time IoT data of the warehouse, the locations of different warehouse openings are determined and calibrated to form warehouse opening location information; Based on the real-time warehouse IoT data, the following basic scheduling data is extracted: According to the real-time IoT data of the warehouse, all dispatch routes in the logistics dispatch area are determined to form basic dispatch route information; According to the real-time IoT data of the warehouse, the operating parameters of different dispatching carriers are determined to form the basic information of the dispatching carrier operation.

3. The cloud-based warehousing logistics scheduling method according to claim 2 is characterized in that: The acquisition of logistics scheduling demand information, combined with the real-time warehouse logistics basic data, performs efficiency-based scheduling planning analysis to form initial logistics scheduling planning data, including: Determine the logistics dispatch point according to the logistics dispatch demand information and in combination with the current storage information to form logistics dispatch point information; According to the logistics scheduling point information and in combination with the scheduling basic data, efficiency-based route planning selection is performed to form the initial logistics scheduling planning data.

4. The cloud-based warehousing logistics scheduling method according to claim 3 is characterized in that: The determining of the logistics scheduling point according to the logistics scheduling demand information and in combination with the current warehousing information to form the logistics scheduling point information includes: According to the logistics scheduling demand information, combined with the current warehousing information, the scheduling point is determined in the following manner: Determine the required material box type according to the inventory object required by the logistics scheduling demand information, and form material box type information; According to the warehouse port location required by the logistics scheduling demand information, the following analysis is performed in combination with the material box type information to determine the scheduling material box: According to the material box location storage information, determine all material box locations that match the material box type information; Determine the material box position closest to the warehouse entrance position among all material box positions and mark it as the scheduling material box; According to the inventory object type required by the logistics scheduling demand information, the storage location points of different types of inventory objects based on the logistics scheduling direction are determined to form the storage location point information of the inventory object; The storage location point information of the inventory object, the logistics scheduling direction, the warehouse entrance position and the position of the scheduling material box are collected to form the logistics scheduling point information.

5. The cloud-based warehousing logistics scheduling method according to claim 4 is characterized in that: The method of determining storage locations of different types of inventory objects based on the logistics scheduling direction according to the inventory object type required by the logistics scheduling demand information to form the inventory object storage location information includes: When logistics scheduling is for outbound scheduling requirements: Determine the direction of the dispatching material box toward the warehouse opening as the logistics dispatching direction; For each different inventory object type, determine the location of the earliest batch of inventory objects to form the location information of the earliest batch of inventory objects; Taking the position of the dispatching material box as the origin and the logistics dispatching direction as the first coordinate axis, a dispatching dispatching planning coordinate system is established; In the outbound dispatch planning coordinate system, determine the sum of the distances of each inventory object position in the early batch inventory object position information under different inventory objects relative to the first coordinate axis and the warehouse entrance position; The distance and the minimum inventory object position of different inventory objects in the early batch inventory object position information are determined respectively along the logistics scheduling direction, and are marked as the nth inventory object scheduling point in turn, where n represents the number of different inventory objects of the inventory object type required by the logistics scheduling demand information.

6. The cloud-based warehousing logistics scheduling method according to claim 5 is characterized in that: The method of performing efficiency-based route planning and selection based on the logistics scheduling point information and combining the scheduling basic data to form the initial logistics scheduling planning data includes: In the outbound dispatch planning coordinate system, with the location of the dispatch material box as the starting point and the warehouse port location as the end point, lines connecting adjacent dispatch points including the location of the dispatch material box, the warehouse port location and n inventory object dispatch points are sequentially established along the logistics dispatch direction to form m dispatch relationship lines, where m=n+1; For each of the scheduling relationship lines, the following analysis is performed based on the basic information of the scheduling line to determine the shortest scheduling path: Starting from the previous scheduling point, a line with the smallest area formed by the vertical projections of both ends on the corresponding scheduling relationship line is determined one by one, wherein: If the line that meets the conditions needs to be connected to the preceding adjacent line through a line perpendicular to the scheduling relationship line, and no line can be directly connected to the preceding adjacent line, then the line perpendicular to the scheduling relationship line is determined to be the planned line; If a route that meets the conditions has both a route that needs to be connected to the preceding adjacent route through a route that is perpendicular to the scheduling relationship line and a route that is directly connected to the preceding adjacent route, the route that is directly connected to the preceding adjacent route is determined as the planned route; Connecting all the determined routes to form the shortest scheduling path corresponding to the scheduling relationship line; Connecting the shortest scheduling paths corresponding to all the scheduling relationship lines to form an initial logistics scheduling path; Determine the planned time and location information of the scheduling operation on the initial logistics scheduling path according to the basic operation information of the scheduling carrier; The initial logistics scheduling path and the planned time and location information are combined to form the initial logistics scheduling planning data.

7. The cloud-based warehousing logistics scheduling method according to claim 4 is characterized in that: The method of determining storage locations of different types of inventory objects based on the logistics scheduling direction according to the inventory object type required by the logistics scheduling demand information to form the inventory object storage location information includes: When logistics scheduling is for warehousing scheduling requirements: According to the warehouse entrance position, determine the current reserved storage position closest to the warehouse entrance position among the different inventory object types, and mark it as the object scheduling point; Among the different object scheduling points, determine the object scheduling point farthest from the warehouse entrance position and mark it as the scheduling end point; Taking the warehouse entrance position as the origin and the logistics scheduling direction from the warehouse entrance position to the scheduling end point as the first coordinate axis, a warehouse entry scheduling planning coordinate system is established; The position of the scheduling material box is recorded as the first scheduling point, and the warehouse entrance position is recorded as the second scheduling point. The positions including the warehouse entrance position and the object scheduling point are calibrated in sequence along the inbound logistics scheduling direction and recorded as the kth scheduling point.

8. The cloud-based warehousing logistics scheduling method according to claim 7 is characterized in that: The method of performing efficiency-based route planning and selection based on the logistics scheduling point information and combining the scheduling basic data to form the initial logistics scheduling planning data includes: In the warehousing scheduling planning coordinate system, taking the first scheduling point as the starting point and the kth scheduling point as the end point, connecting lines of adjacent scheduling points are established in sequence to form p scheduling relationship lines, where p=k-1; For each of the scheduling relationship lines, the following analysis is performed based on the basic information of the scheduling line to determine the shortest scheduling path: Starting from the previous scheduling point, a line with the smallest area formed by the vertical projections of both ends on the corresponding scheduling relationship line is determined one by one, wherein: If the line that meets the conditions needs to be connected to the preceding adjacent line through a line perpendicular to the scheduling relationship line, and no line can be directly connected to the preceding adjacent line, then the line perpendicular to the scheduling relationship line is determined to be the planned line; If a route that meets the conditions has both a route that needs to be connected to the preceding adjacent route through a route that is perpendicular to the scheduling relationship line and a route that is directly connected to the preceding adjacent route, the route that is directly connected to the preceding adjacent route is determined as the planned route; Connecting all the determined routes to form the shortest scheduling path corresponding to the scheduling relationship line; Connecting the shortest scheduling paths corresponding to all the scheduling relationship lines to form an initial logistics scheduling path; Determine the planned time and location information of the scheduling operation on the initial logistics scheduling path according to the basic operation information of the scheduling carrier; The initial logistics scheduling path and the planned time and location information are combined to form the initial logistics scheduling planning data.

9. The cloud-based warehousing logistics scheduling method according to claim 8 or 6, characterized in that: The step of performing scheduling interference optimization analysis based on the initial logistics scheduling planning data to form optimized logistics scheduling planning data includes: Determining a path intersection point between different initial logistics scheduling paths; Determine the time difference t of different initial logistics scheduling paths arriving at the path intersection point according to the planned time position information corresponding to the initial logistics scheduling path related to the path intersection point; Obtaining a maximum dimension value L of the scheduling material box corresponding to the initial logistics scheduling path related to the path intersection point in a direction perpendicular to the running direction; Obtaining a minimum speed value v of the scheduling material box running speed corresponding to the initial logistics scheduling path related to the path intersection point; The intersection interval limit value q is set, and the following optimization adjustment is performed according to the time difference t, the maximum size value L and the minimum speed value v: If L<t*v+q, the initial logistics scheduling path related to the path intersection point remains unchanged; If L≥t*v+q, the running speed of the initial logistics scheduling path with shorter time in the initial logistics scheduling path related to the path intersection is accelerated and adjusted at the path intersection until L<t*v+q is satisfied.

10. A cloud-based warehousing logistics scheduling device, using the cloud-based warehousing logistics scheduling method according to any one of claims 1 to 9, characterized in that: The cloud-based warehousing logistics scheduling system is configured as follows: Collect real-time warehousing IoT data, conduct real-time warehousing logistics information analysis, and form real-time warehousing logistics basic data; Obtaining logistics scheduling demand information, and combining the real-time warehouse logistics basic data, performing efficiency-based scheduling planning analysis to form initial logistics scheduling planning data; According to the initial logistics scheduling planning data, scheduling interference optimization analysis is performed to form optimized logistics scheduling planning data.

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