CN113554397B - Logistics planning method, device, electronic equipment and computer readable medium - Google Patents

Logistics planning method, device, electronic equipment and computer readable medium Download PDF

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CN113554397B
CN113554397B CN202110877479.7A CN202110877479A CN113554397B CN 113554397 B CN113554397 B CN 113554397B CN 202110877479 A CN202110877479 A CN 202110877479A CN 113554397 B CN113554397 B CN 113554397B
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store
cone
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CN113554397A (en
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张俊
钱娱
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Hangzhou Pinjie Network Technology Co Ltd
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Hangzhou Pinjie Network Technology Co Ltd
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    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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    • G06Q10/0835—Relationships between shipper or supplier and carriers
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
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    • G06Q10/067—Enterprise or organisation modelling

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Abstract

The application discloses a logistics planning method, a logistics planning device, electronic equipment and a computer readable medium, wherein the method comprises the following steps: constructing a three-dimensional simulation model corresponding to a commodity SKU; generating a simulated fluid corresponding to a commodity SKU in the purchase order according to the purchase order of the store; simulating the flowing process of the simulated fluid from the store cone table corresponding to the store which initiates the purchase order to the store cone tables corresponding to all stores in the preset range in the three-dimensional simulation model corresponding to the commodity SKU; recording all flowing processes corresponding to one commodity SKU in one purchase order and extracting the completion time of the flowing processes; and inquiring a warehouse cone platform corresponding to the flowing process with the shortest finishing time, and taking the warehouse position corresponding to the warehouse cone platform as a goods taking address of the commodity SKU in the purchase order. The logistics configuration method has the advantage that logistics configuration is realized in a reverse logistics process simulation mode by constructing a three-dimensional simulation model.

Description

Logistics planning method, device, electronic equipment and computer readable medium
Technical Field
The present application relates to the field of computers, and in particular, to a method, an apparatus, an electronic device, and a computer readable medium for logistics planning.
Background
The existing small and medium-sized convenience stores often carry out online commodity purchasing at suppliers through own channels, and cannot purchase goods in large quantities due to the characteristics of the small and medium-sized convenience stores, so that effective bargained price cannot be carried out with the suppliers, and meanwhile, due to the requirement of wholesale of the suppliers on purchasing quantity, the small and medium-sized convenience stores need to guarantee a certain scale every time of purchasing, so that inventory problems are caused.
From the perspective of suppliers, the scattered purchasing mode of small and medium-sized convenience stores leads to the increase of the warehouse cost of the suppliers, thereby leading to the high supply price.
In the related art, as shown in fig. 1, orders of a plurality of stores are collected by means of an internet platform, unified purchase and logistics goods taking are performed to suppliers, and then the goods are distributed to corresponding stores by carrier vehicles according to the purchase orders, so that storage cost of stores such as convenience stores is reduced, and the flexibility of purchase is improved.
In another related art, in order to implement optimal distribution of technical logistics, as disclosed in chinese patent publication No. CN112016876B, a method for automatically configuring goods through a computer program is provided, which is capable of configuring a path and an assembly scheme of a vehicle according to a location of a provider, a merchant, and an order situation.
Other related art approaches provide for configuring a logistics route based on pick-up and delivery addresses and vehicle location.
However, these schemes often rely on more complex algorithms and they only consider the way in which the two-dimensional route is optimal, and not the order itself and the warehousing situation.
Disclosure of Invention
The content of the present application is intended to introduce concepts in a simplified form that are further described below in the detailed description. The section of this application is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter.
Some embodiments of the present application provide a logistics planning method, apparatus, electronic device and computer readable medium to solve the technical problems mentioned in the background section above.
As a first aspect of the present application, some embodiments of the present application provide a method of logistics planning, the method comprising the steps of: constructing a three-dimensional simulation model corresponding to a commodity SKU, wherein a shop cone table, a warehouse cone table and a diversion trench are respectively constructed in the three-dimensional model to represent a shop, a warehouse and a road, and the shop cone table and the warehouse cone table have opposite generation directions; generating a simulated fluid corresponding to a commodity SKU in the purchase order according to the purchase order of the store; simulating a flowing process of the simulated fluid from the store cone frustum corresponding to the store which initiates the purchase order to the store cone frustum corresponding to all stores in a preset range in the three-dimensional simulation model corresponding to the commodity SKU; recording all the flowing processes corresponding to one commodity SKU in one purchase order and extracting the completion time of the flowing processes; inquiring a warehouse cone frustum corresponding to the flowing process with the shortest finishing time, and taking the warehouse cone frustum corresponding to the warehouse position as a goods taking address of the commodity SKU in the purchase order.
Further, the logistics planning method based on dynamic data updating further comprises the following steps: and simulating the flowing process and acquiring the goods taking addresses for all the goods SKUs in the purchase order of one store, so as to generate a goods taking address list containing the goods taking addresses of all the goods SKUs in the purchase order of the store.
Further, the logistics planning method based on dynamic data updating further comprises the following steps: and summarizing the goods taking address lists of all stores into a purchasing relation list, wherein the purchasing relation list comprises a delivery address, a goods taking address, a purchasing order number and a commodity SKU with corresponding relations.
Further, the logistics planning method based on dynamic data updating further comprises the following steps: and combining the goods taking addresses according to the same goods taking addresses in the purchase relation table to generate a supply relation table, wherein the supply relation table comprises the goods taking addresses, the goods delivering address sets, the goods taking SKU sets, the purchase order number sets and the total transport volume amount which have corresponding relations.
Further, the logistics planning method based on dynamic data updating further comprises the following steps: judging whether the transport volume corresponding to one pick-up address in the supply relation table is larger than a preset maximum transport capacity volume threshold value or not; if the maximum capacity volume threshold is greater, splitting the total transport volume.
Further, the logistics planning method based on dynamic data updating further comprises the following steps: if the transport volume is smaller than the maximum transport volume threshold, judging whether the transport volume corresponding to one pick-up address in the supply relation table is larger than the minimum transport volume threshold; if the minimum capacity volume threshold is greater than the minimum capacity volume threshold, matching a virtual carrier for a pickup address in the supply relationship table; and if the total transport volume is smaller than the minimum transport capacity volume threshold, merging the total transport volume with the transport volumes corresponding to other pick addresses.
Further, the logistics planning method based on dynamic data updating further comprises the following steps: generating a logistics order according to the original transportation volume, the split transportation volume and the combined transportation volume; matching carrier vehicles for each of the logistics orders.
As a second aspect of the present application, some embodiments of the present application provide a logistics planning method apparatus based on dynamic data update, including: the model construction module is used for constructing a three-dimensional simulation model corresponding to a commodity SKU, wherein a shop cone table, a warehouse cone table and a diversion trench are respectively constructed in the three-dimensional model to represent shops, warehouses and roads, and the shop cone table and the warehouse cone table have opposite generation directions; the fluid generation module is used for generating a simulation fluid corresponding to one commodity SKU in the purchase order according to the purchase order of the store; the fluid simulation module is used for simulating the flowing process of the simulated fluid from the store cone station corresponding to the store which initiates the purchase order to all the store cone stations corresponding to the stores in a preset range in the three-dimensional simulation model corresponding to the commodity SKU; the time recording module is used for recording all the flowing processes corresponding to one commodity SKU in one purchase order and extracting the completion time of the flowing processes; and the address inquiry module is used for inquiring the warehouse cone frustum corresponding to the flowing process with the shortest finishing time and taking the warehouse cone frustum corresponding to the warehouse position as the goods taking address of the commodity SKU in the purchase order.
As a third aspect of the present application, some embodiments of the present application provide an electronic device, including: one or more processors; a storage device having one or more programs stored thereon, which when executed by one or more processors causes the one or more processors to implement the method described in any of the implementations of the first aspect above.
As a fourth aspect of the present application, some embodiments of the present application provide a computer readable medium having a computer program stored thereon, wherein the program, when executed by a processor, implements the method described in any of the implementations of the first aspect above.
The beneficial effects of this application lie in: provided are a logistics planning method, device, electronic equipment and computer readable medium for realizing logistics configuration in a reverse logistics process simulation manner by constructing a three-dimensional simulation model based on electronic commerce data and map data.
More specifically, some embodiments of the present application may produce the following specific benefits: helping stores find a warehouse of the fastest route by constructing a three-dimensional simulation you model and simulating fluid; the supply and demand conditions are reflected by the construction of store and warehouse virtual bodies, and factors brought by the supply and demand conditions are reflected to the flow process of the simulated fluid; the efficiency of logistics bill is improved through combining the goods taking addresses.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, are included to provide a further understanding of the application and to provide a further understanding of the application with regard to the other features, objects and advantages of the application. The drawings of the illustrative embodiments of the present application and their descriptions are for the purpose of illustrating the present application and are not to be construed as unduly limiting the present application.
In addition, the same or similar reference numerals denote the same or similar elements throughout the drawings. It should be understood that the figures are schematic and that elements and components are not necessarily drawn to scale.
In the drawings:
FIG. 1 is a schematic diagram of a store in the related art making "order" purchases;
FIG. 2 is a flow chart of a method of logistics planning in accordance with one embodiment of the present application;
FIG. 3 is a schematic top view of a three-dimensional simulation model according to one embodiment of the present application;
FIG. 4 is a schematic top view of a three-dimensional simulation model according to one embodiment of the present application;
FIG. 5 is a schematic diagram of a pick address list generated after a purchase order of a store is simulated through a flow process in accordance with one embodiment of the present application;
FIG. 6 is a schematic diagram of a procurement relationship table according to an embodiment of the application;
FIG. 7 is a schematic diagram of a provisioning relationship table according to an embodiment of the present application;
Fig. 8 is a schematic structural view of an electronic device according to an embodiment of the present application.
Meaning of reference numerals in the drawings:
100 three-dimensional visual model, 101 warehouse cone frustum and 102 shop cone frustum.
Description of the embodiments
Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While certain embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete. It should be understood that the drawings and embodiments of the present disclosure are for illustration purposes only and are not intended to limit the scope of the present disclosure.
It should be noted that, for convenience of description, only the portions related to the present invention are shown in the drawings. Embodiments of the present disclosure and features of embodiments may be combined with each other without conflict.
It should be noted that the terms "first," "second," and the like in this disclosure are merely used to distinguish between different devices, modules, or units and are not used to define an order or interdependence of functions performed by the devices, modules, or units.
It should be noted that references to "one", "a plurality" and "a plurality" in this disclosure are intended to be illustrative rather than limiting, and those of ordinary skill in the art will appreciate that "one or more" is intended to be understood as "one or more" unless the context clearly indicates otherwise.
The names of messages or information interacted between the various devices in the embodiments of the present disclosure are for illustrative purposes only and are not intended to limit the scope of such messages or information.
The present disclosure will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
As shown in fig. 2, the method for planning the e-commerce logistics based on the visual model specifically includes the following steps:
s101: constructing a three-dimensional simulation model corresponding to a commodity SKU, and respectively constructing a shop cone table, a warehouse cone table and a diversion trench in the three-dimensional model to represent shops, warehouses and roads, wherein the shop cone table and the warehouse cone table have opposite generation directions;
s102: a simulated fluid corresponding to a commodity SKU in the purchase order is generated based on the purchase order of the store.
S103: in the three-dimensional simulation model corresponding to the commodity SKU, the flow process of the simulation fluid from the store cone station corresponding to the store which initiates the purchase order to the store cone stations corresponding to all stores in the preset range is simulated.
S104: recording all the flowing processes corresponding to one commodity SKU in one purchase order and extracting the completion time of the flowing processes.
S105: and inquiring a warehouse cone platform corresponding to the flowing process with the shortest finishing time, and taking the warehouse position corresponding to the warehouse cone platform as a goods taking address of the commodity SKU in the purchase order.
The specific steps of step S101 include:
acquiring two-dimensional coordinate data of a warehouse and a store in the two-dimensional map data;
acquiring two-dimensional coordinate data of a road in the two-dimensional map data;
generating height data of the warehouse according to commodity inventory data of the warehouse;
generating height data of the store according to commodity order data of the store;
a three-dimensional simulation model reflecting a warehouse, a store, and a road is constructed based on the two-dimensional coordinate data and the height data.
More specifically, as shown in fig. 3 and 4, two-dimensional coordinate data of the warehouse and the store in the two-dimensional map data is acquired in order to acquire position data of the warehouse and the store. In particular, the two-dimensional coordinates are obtained from a two-dimensional map data because, although the shops and warehouses are altitude differences in actual geographic locations due to the topography and the earth curvature, these altitude differences are not decisive factors for logistics planning in terms of logistics planning, and since most shops and warehouses are located in cities and even limited to the business circles considered to be divided in terms of the "menu" mode of the shops to which the method of the present application is applied, from this point of view, it is sufficient to obtain only the two-dimensional coordinate data from the two-dimensional map data to represent the locations when constructing the three-dimensional simulation model of the present application.
Alternatively, the source of the two-dimensional map data may be public map data such as a Goldmap, a Baidu map, and a Google map.
In addition, the generation of the height data of the warehouse from the commodity inventory data of the warehouse specifically refers to: the method comprises the steps of acquiring current inventory data of a certain commodity in a certain warehouse, wherein the larger the inventory data is, the larger the height data of the warehouse is, and in contrast, the higher the height of the warehouse in a three-dimensional simulation model is.
As a preferred scheme, the inventory data corresponding to the height data of the warehouse may be the quantity corresponding to a certain commodity SKU, and as a further preferred scheme, the inventory data corresponding to the height data of the warehouse is the volume value corresponding to a certain commodity SKU, and the volume value may be calculated according to the package specification and commodity data. The advantage of using a volumetric value is that it is more straightforward to react to space occupancy by inventory.
Similarly, generating height data of the store from merchandise order data of the store specifically refers to: the purchasing data of a certain commodity in the store order data is acquired, the greater the purchasing data is, the greater the height data of the store is, and in contrast, the higher the height of the store in the three-dimensional simulation model is.
Preferably, the purchase data corresponding to the height data of the store may be quantity and volume data, and preferably, the volume data corresponding to the height data of the store is a volume value corresponding to a certain commodity SKU.
As a further preferred embodiment, the ratio of the volume value V to the height data is 1:1, and the volume data of 1L in terms of dimension is equal to the height data of 1m, calculated according to this dimension criterion.
As a more specific aspect, constructing a three-dimensional simulation model reflecting a warehouse, a store, and a road based on two-dimensional coordinate data and altitude data includes the steps of: generating a two-dimensional coordinate center point of the warehouse according to the two-dimensional coordinate data of the warehouse; constructing a warehouse cone table to represent a warehouse, wherein the central axis of the warehouse cone table is vertical to the horizontal plane of the three-dimensional simulation model and passes through the two-dimensional coordinate center point of the warehouse, and the height of the warehouse cone table is in direct proportion to the height data of the warehouse; collecting maximum inventory data of commodities in a warehouse; generating the bottom radius of the warehouse cone frustum according to the maximum inventory capacity data; collecting the last ex-warehouse data of commodities in a warehouse; and generating the upper bottom radius of the warehouse cone frustum according to the last ex-warehouse data.
The two-dimensional coordinate center point may be taken as the midpoint of the two-dimensional coordinate center by the midpoint of the diagonal of the largest rectangular frame of the warehouse building or footprint.
The maximum stock data refers to the maximum capacity data of the commodity or the highest value reflected in the historical data stored in the system registration warehouse, and the last delivery data is the data of the delivery quantity of the product on the current day of the last delivery. Preferably, the maximum stock data and the last stock data are produced by using the volume data described above, the data ratio is 1:0.1, and the volume data of 1L corresponds to the length data of 0.1m in dimension. That is, for the maximum inventory data and the last inventory data, the 1L of the maximum inventory data and the last inventory data corresponds to a production radius of 0.1m.
The corresponding proportion of the height and the radius is different (ten times of the difference), firstly, in order to prevent the warehouse cone platform from being too large and the shop cone platform from weakening the influence of the road distance, and secondly, the real-time data (the reaction in height) is more hopeful to play a decisive role in terms of the attribute of the reaction of the cone platform.
By means of the scheme, the warehouse is built into a cone-shaped three-dimensional object in the visual three-dimensional model, the three-dimensional object can reflect the warehouse attribute of a certain product, such as the height representing the residual inventory, the bottom representing the warehouse capability and the top representing the last day shipment capability.
Similarly, constructing a three-dimensional simulation model reflecting a warehouse, a store, and a road based on two-dimensional coordinate data and altitude data includes the steps of: generating a two-dimensional coordinate center point of the warehouse according to the two-dimensional coordinate data of the warehouse; constructing a store cone table representing warehouse, wherein the central axis of the store cone table is vertical to the horizontal plane of the three-dimensional simulation model and passes through the two-dimensional coordinate center point of the warehouse, and the height of the store cone table is in direct proportion to the height data of the store; collecting historical maximum purchase quantity data of commodities of a store; generating the bottom radius of the cone frustum of the store according to the historical maximum purchase quantity data; collecting historical minimum purchase quantity data of commodities of a store; and generating the radius of the upper bottom of the cone frustum of the store according to the historical minimum purchase quantity data.
The values of the height and the radius can refer to the scheme of converting the commodity volume into the length size according to the scheme.
After the above-described warehouse cone and store cone are constructed, in order to realize the logistic simulation based on the hydrodynamic simulation, the height directions of the warehouse cone and the store cone are made different, that is, if the warehouse cone is formed above the horizontal plane with respect to the horizontal plane of the three-dimensional simulation model, the store cone is formed below the horizontal plane. In popular terms, if the warehouse cone is a mountain, the store cone is a basin. Of course, the warehouse cone and the store cone are only relatively different, and can be inverted when the analysis purpose is different, that is to say, the whole three-dimensional simulation model can be horizontally inverted. This depends on the purpose of logistics planning, if the store is taken as the initiating end for observing and policing the store to the warehouse, the store cone is made "mountain" and the warehouse is made "basin", and vice versa. As shown in fig. 4, when the "mountain" is formed, the warehouse cone and the store cone are solid in the three-dimensional simulation model, and when the "basin" is formed, the warehouse cone and the store cone are concave in the three-dimensional simulation model.
The construction of a three-dimensional simulation model reflecting a warehouse, a store and a road based on two-dimensional coordinate data and altitude data includes the steps of: and generating diversion trenches representing the roads in the three-dimensional simulation model according to the trend of the roads in the two-dimensional map data, wherein the cross sections of the diversion trenches are rectangular and have the same depth and width. The trend of the diversion trench is formed according to the original trend of the road, so that the diversion trench is connected with the warehouse cone frustum and the store cone frustum. The depth and width of the unified diversion trench and the fact that the diversion trench has no difference in the height direction are specified to highlight the route of the diversion trench reaction road, so that other influencing conditions are homogenized.
As a preferable scheme, in the three-dimensional simulation model, the length of the diversion trench adopts the length of an original road, and the width and the depth of the diversion trench are 1m.
As a further preferable scheme, the diversion trench extends to the edge cut-off of the warehouse cone table and the shop cone table, and is different from the common design concept, the volume characteristics of the nodes are not reflected in general in order to realize route planning, and the route planning is realized on the basis of the storage and the morning attribute of the nodes by the simulation of fluid mechanics, so that the roads are omitted when extending to the warehouse cone table and the shop cone table, the business attribute of the logistics nodes is reflected by the seemingly inaccurate method, particularly the logistics transportation and storage cost is considered, and the route planning is more in accordance with the purpose of the logistics refinement operation of the whole system.
The logistics is planned and calculated according to the normal logistics direction, namely a warehouse-to-store mode in the prior technical scheme. In the present application, a store is used as a starting side of the logistics simulation to perform simulation and planning as a preferable scheme.
It should be noted that, although the three-dimensional simulation model is constructed by taking meter as a unit, the three-dimensional simulation model can be scaled in the same proportion during actual calculation and display.
Specifically, the method for planning a logistics path for a commodity order according to a path formed by a three-dimensional position of a warehouse and a road between the warehouse and a store in a three-dimensional simulation model comprises the following steps: generating transportation volume data according to the data of the commodity of the type in the purchase order; generating a simulated fluid of a corresponding volume in the three-dimensional simulation model according to the transport volume data; and setting a gravity parameter in the three-dimensional simulation model, and planning a logistics line according to the time and the path of the simulated fluid flowing from the store cone to the storage cone.
For example, a certain beverage 3 boxes are included in the purchase order, the volume value of the beverage 3 boxes is 3L through collecting and calculating data, so that 3L of fluid is generated according to the volume value, free fluid movement is performed on the shop cone of the shop, friction force is not assumed to exist on the diversion grooves, the time from the shop cone to the final flow into the storage cone is used as a route selection standard, and when a plurality of lines exist from the shop cone to the storage cone, the lines can be respectively simulated.
The gravity parameter in the three-dimensional simulation model is set to accelerate the operation time.
By the simulation method, simulation of the flowing process and acquisition of the goods taking addresses are carried out on all the goods SKUs in the purchase order of one store, so that a goods taking address list containing the goods taking addresses of all the goods SKUs in the purchase order of the store is generated. This list is shown in fig. 5 (in the figure, for convenience of understanding, the pick-up address, that is, the warehouse address is replaced by the supplier name, and the delivery address is replaced by the store name, the following description). I.e., by simulation, a warehouse for each commodity SKU of store a is selected.
And then, summarizing the goods taking address list of all shops into a purchasing relation list, wherein the purchasing relation list comprises the delivery addresses, the goods taking addresses, the purchasing order numbers and the commodity SKUs with corresponding relations. The purchase relationship table is shown in FIG. 6, which aggregates the sources of supplies for the commodity SKUs in the store purchase orders in all systems.
As shown in fig. 6, the pick addresses are then combined according to the same pick address in the purchase relationship table to generate a supply relationship table, where the supply relationship table includes a pick address, a set of delivery addresses, a set of pick SKUs, a set of purchase order numbers, and a total shipping volume.
The shipping volume of each column is analyzed for the ability to generate a logistic order according to the supply relationship table. Specifically, the method comprises the following steps:
judging whether the transport volume corresponding to one pick-up address in the supply relation table is larger than a preset maximum transport capacity volume threshold value or not; if the maximum capacity volume threshold is greater, splitting the total transport volume.
Specifically, the maximum capacity volume threshold is the maximum volume of the vehicle that the system can dispense, and exceeding the maximum capacity volume threshold indicates that the system can dispense vehicles is exceeded, thus requiring the total amount of transport volume to be split. The principle of splitting is that sorting of the transport volumes of each store is carried out according to the delivery address, then the transport volumes of smaller stores are split preferentially to form a single column in a supply relation table, then whether the residual transport volumes left after splitting are larger than the maximum volume of the vehicle is judged, and if so, the split is carried out.
Alternatively, orders for larger stores may be split preferentially according to the maximum capacity volume threshold.
Then, in the following judging step, if the transport volume is smaller than the maximum transport volume threshold, judging whether the transport volume corresponding to one pick-up address in the supply relation table is larger than the minimum transport volume threshold.
If greater than the minimum capacity volume threshold, a carrier vehicle will be matched for a pick-up address in the supply relationship table.
If the total transport volume is less than the minimum capacity volume threshold, the total transport volume is combined with the transport volumes corresponding to the other pick-up addresses.
Where the minimum capacity volume threshold corresponds to the minimum volume of the vehicle that the system is capable of dispensing, being less than the maximum capacity volume threshold and greater than the minimum capacity volume threshold, it is stated that the shipping volume of the row of the supply relationship table may be directly dispensed to the appropriate type of carrier vehicle, such as a minibus, an ivek, a minivan, a large van, etc.
When the minimum capacity volume threshold is less than, the shipping volume that specifies the row of the supply relationship table cannot fill the smallest capacity carrier vehicle, and a waste of capacity occurs. At this point, such transport volumes need to be combined.
Specifically, the merging of transport volumes may employ a scheme that merges transport volumes smaller than a minimum capacity volume threshold (also including new transport volumes of new rows of the split generated new supply relationship table) in the same manner as the shipping address in the supply relationship table, and allocates a vehicle if the merged transport volumes satisfy less than the maximum capacity volume threshold and greater than the minimum capacity volume threshold.
If the maximum capacity volume threshold is still not met but is greater than the minimum capacity volume threshold, typically still less than the minimum capacity volume threshold, the process proceeds as follows.
The first scheme is to generate order orders for logistics tasks corresponding to the capacity volumes, send the orders to clients of a certain range of carrier vehicles and distribute the orders in a carrier vehicle order mode.
The second approach is to match the unassigned shipping volume to the carrier vehicle for the generated logistics order based on the included similar (90% to 110% float range) sub-shipping volumes (shipping volumes for one shipping address) of the generated logistics order, generating a mid-stream logistics order, and after the vehicle completes the assigned sub-shipping volumes, going to the mid-stream logistics order for pick-up and shipping.
Preferably, after matching the unassigned shipping volume to the carrier vehicle for the logistics order, the mid-stream logistics order is not generated immediately, but rather all the logistics orders comprising similar sub-shipping volumes are completed, all the navigation lines are generated, and the combination of the logistics order and the shortest distance of the mid-stream logistics order is completed as the final matching result.
By the scheme, the logistics orders can be generated according to the original transportation volume, the split transportation volume and the combined transportation volume; matching carrier vehicles for each of the logistics orders.
In some embodiments, a dynamic simulation-based logistics planning apparatus includes:
the model construction module is used for constructing a three-dimensional simulation model corresponding to a commodity SKU, wherein a shop cone table, a warehouse cone table and a diversion trench are respectively constructed in the three-dimensional model to represent shops, warehouses and roads, and the shop cone table and the warehouse cone table have opposite generation directions.
And the fluid generation module is used for generating a simulation fluid corresponding to one commodity SKU in the purchase order according to the purchase order of the store.
And the fluid simulation module is used for simulating the flowing process of the simulated fluid from the store cone frustum corresponding to the store which initiates the purchase order to all the store cone frustum corresponding to the store in a preset range in the three-dimensional simulation model corresponding to the commodity SKU.
And the time recording module is used for recording all the flowing processes corresponding to one commodity SKU in one purchase order and extracting the completion time of the flowing processes.
And the address inquiry module is used for inquiring the warehouse cone frustum corresponding to the flowing process with the shortest finishing time and taking the warehouse cone frustum corresponding to the warehouse position as the goods taking address of the commodity SKU in the purchase order.
As shown in fig. 8, the electronic device 800 may include a processing means (e.g., a central processor, a graphics processor, etc.) 801, which may perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 802 or a program loaded from a storage means 808 into a Random Access Memory (RAM) 803. In the RAM803, various programs and data required for the operation of the electronic device 800 are also stored. The processing device 801, the ROM802, and the RAM803 are connected to each other by a bus 804. An input/output (I/O) interface 805 is also connected to the bus 804.
In general, the following devices may be connected to the I/O interface 805: input devices 806 including, for example, a touch screen, touchpad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, and the like: an output device 807 including, for example, a Liquid Crystal Display (LCD), speakers, vibrators, etc.; including storage 808, such as magnetic tape, hard disk, etc.: communication means 809. The communication means 809 may allow the electronic device 800 to communicate wirelessly or by wire with other devices to exchange data. While fig. 8 shows an electronic device 800 having various means, it is to be understood that not all of the illustrated means are required to be implemented or provided. More or fewer devices may be implemented or provided instead. Each block shown in fig. 8 may represent one device or a plurality of devices as needed.
In particular, according to some embodiments of the present disclosure, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, some embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method shown in the flow chart. In such embodiments, the computer program may be downloaded and installed from a network via communication device 809, or from storage device 808, or from ROM 802. The above-described functions defined in the methods of some embodiments of the present disclosure are performed when the computer program is executed by the processing device 801.
It should be noted that, in some embodiments of the present disclosure, the computer readable medium may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
In some embodiments of the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In some embodiments of the present disclosure, however, the computer-readable signal medium may comprise a data signal propagated in baseband or as part of a carrier wave, with the computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, fiber optic cables, RF (radio frequency), and the like, or any suitable combination of the foregoing.
In some implementations, the clients, servers may communicate using any currently known or future developed network protocol, such as HTTP (Hyper Text Transfer Protocol ), and may be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), the internet (e.g., the internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future developed networks.
The computer readable medium may be contained in the electronic device described above: or may exist alone without being incorporated into the electronic device. The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: constructing a three-dimensional simulation model corresponding to a commodity SKU, wherein a shop cone table, a warehouse cone table and a diversion trench are respectively constructed in the three-dimensional model to represent a shop, a warehouse and a road, and the shop cone table and the warehouse cone table have opposite generation directions; generating a simulated fluid corresponding to a commodity SKU in the purchase order according to the purchase order of the store; simulating a flowing process of the simulated fluid from the store cone frustum corresponding to the store which initiates the purchase order to the store cone frustum corresponding to all stores in a preset range in the three-dimensional simulation model corresponding to the commodity SKU; recording all the flowing processes corresponding to one commodity SKU in one purchase order and extracting the completion time of the flowing processes; inquiring a warehouse cone frustum corresponding to the flowing process with the shortest finishing time, and taking the warehouse cone frustum corresponding to the warehouse position as a goods taking address of the commodity SKU in the purchase order.
Computer program code for carrying out operations for some embodiments of the present disclosure may be written in one or more programming languages, including an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, or combinations thereof: such as the "C" language or similar programming language. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider).
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s).
It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures.
For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The functions described above herein may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: a Field Programmable Gate Array (FPGA), an Application Specific Integrated Circuit (ASIC), an Application Specific Standard Product (ASSP), a system on a chip (SOC), a Complex Programmable Logic Device (CPLD), and the like.
The foregoing description is only of the preferred embodiments of the present disclosure and description of the principles of the technology being employed. It will be appreciated by those skilled in the art that the scope of the invention in the embodiments of the present disclosure is not limited to the specific combination of the above technical features, but encompasses other technical features formed by any combination of the above technical features or their equivalents without departing from the spirit of the invention. Such as the above-described features, are mutually substituted with (but not limited to) the features having similar functions disclosed in the embodiments of the present disclosure.

Claims (10)

1. A logistics planning method based on dynamic data updating is characterized in that:
the logistics planning method based on dynamic data updating comprises the following steps:
constructing a three-dimensional simulation model corresponding to a commodity SKU, wherein a shop cone table, a warehouse cone table and a diversion trench are respectively constructed in the three-dimensional model to represent a shop, a warehouse and a road, and the shop cone table and the warehouse cone table have opposite generation directions;
generating a simulated fluid corresponding to a commodity SKU in the purchase order according to the purchase order of the store;
simulating a flowing process of the simulated fluid from the store cone frustum corresponding to the store which initiates the purchase order to the store cone frustum corresponding to all stores in a preset range in the three-dimensional simulation model corresponding to the commodity SKU;
recording all the flowing processes corresponding to one commodity SKU in one purchase order and extracting the completion time of the flowing processes;
inquiring a warehouse cone frustum corresponding to the flowing process with the shortest finishing time, and taking the position of the warehouse cone frustum corresponding to the warehouse as a goods taking address of the commodity SKU in the purchase order;
The construction of the three-dimensional simulation model corresponding to the commodity SKU comprises the following steps: acquiring two-dimensional coordinate data of a warehouse and a store in two-dimensional map data, acquiring two-dimensional coordinate data of a road in the two-dimensional map data, generating height data of the warehouse according to commodity inventory data of the warehouse, generating height data of the store according to commodity order data of the store, and constructing a three-dimensional simulation model reflecting the warehouse, the store and the road based on the two-dimensional coordinate data and the height data;
the construction of the three-dimensional simulation model reflecting the warehouse, the store and the road based on the two-dimensional coordinate data and the height data comprises the following steps: generating a two-dimensional coordinate center point of the warehouse according to the two-dimensional coordinate data of the warehouse; constructing a warehouse cone table to represent a warehouse, wherein the central axis of the warehouse cone table is vertical to the horizontal plane of the three-dimensional simulation model and passes through the two-dimensional coordinate center point of the warehouse, and the height of the warehouse cone table is in direct proportion to the height data of the warehouse; collecting maximum inventory data of commodities in a warehouse; generating the bottom radius of the warehouse cone frustum according to the maximum inventory capacity data; collecting the last ex-warehouse data of commodities in a warehouse; generating the radius of the upper bottom of the warehouse cone frustum according to the last ex-warehouse data;
The construction of a three-dimensional simulation model reflecting a warehouse, a store and a road based on two-dimensional coordinate data and altitude data includes the steps of: generating a two-dimensional coordinate center point of the store according to the two-dimensional coordinate data of the store; constructing a shop cone table representing a shop, wherein the central axis of the shop cone table is vertical to the horizontal plane of the three-dimensional simulation model and passes through the two-dimensional coordinate center point of the shop, and the height of the shop cone table is in direct proportion to the height data of the shop; collecting historical maximum purchase quantity data of commodities of a store; generating the bottom radius of the cone frustum of the store according to the historical maximum purchase quantity data; collecting historical minimum purchase quantity data of commodities of a store; and generating the radius of the upper bottom of the cone frustum of the store according to the historical minimum purchase quantity data.
2. The dynamic data update-based logistics planning method of claim 1, wherein:
the logistics planning method based on dynamic data updating further comprises the following steps:
and simulating the flowing process and acquiring the goods taking addresses for all the goods SKUs in the purchase order of one store, so as to generate a goods taking address list containing the goods taking addresses of all the goods SKUs in the purchase order of the store.
3. The dynamic data update-based logistics planning method of claim 2, wherein:
the logistics planning method based on dynamic data updating further comprises the following steps:
and summarizing the goods taking address lists of all stores into a purchasing relation list, wherein the purchasing relation list comprises a delivery address, a goods taking address, a purchasing order number and a commodity SKU with corresponding relations.
4. A method of logistics planning based on dynamic data update of claim 3, wherein:
the logistics planning method based on dynamic data updating further comprises the following steps:
and combining the goods taking addresses according to the same goods taking addresses in the purchase relation table to generate a supply relation table, wherein the supply relation table comprises the goods taking addresses, the goods delivering address sets, the goods taking SKU sets, the purchase order number sets and the total transport volume amount which have corresponding relations.
5. The dynamic data update-based logistics planning method of claim 4, wherein:
the logistics planning method based on dynamic data updating further comprises the following steps:
judging whether the transport volume corresponding to one pick-up address in the supply relation table is larger than a preset maximum transport capacity volume threshold value or not;
If the maximum capacity volume threshold is greater, splitting the total transport volume.
6. The dynamic data update-based logistics planning method of claim 5, wherein:
the logistics planning method based on dynamic data updating further comprises the following steps:
if the transport volume is smaller than the maximum transport volume threshold, judging whether the transport volume corresponding to one pick-up address in the supply relation table is larger than the minimum transport volume threshold;
if the minimum capacity volume threshold is greater than the minimum capacity volume threshold, matching a pickup address in the supply relationship table with a carrier vehicle;
and if the total transport volume is smaller than the minimum transport capacity volume threshold, merging the total transport volume with the transport volumes corresponding to other pick addresses.
7. The dynamic data update-based logistics planning method of claim 6, wherein:
the logistics planning method based on dynamic data updating further comprises the following steps:
generating a logistics order according to the original transportation volume, the split transportation volume and the combined transportation volume;
matching carrier vehicles for each of the logistics orders.
8. A dynamic simulation-based logistics planning apparatus, comprising:
The model construction module is used for constructing a three-dimensional simulation model corresponding to a commodity SKU, wherein a shop cone table, a warehouse cone table and a diversion trench are respectively constructed in the three-dimensional model to represent shops, warehouses and roads, and the shop cone table and the warehouse cone table have opposite generation directions;
the fluid generation module is used for generating a simulation fluid corresponding to one commodity SKU in the purchase order according to the purchase order of the store;
the fluid simulation module is used for simulating the flowing process of the simulated fluid from the store cone station corresponding to the store which initiates the purchase order to all the store cone stations corresponding to the stores in a preset range in the three-dimensional simulation model corresponding to the commodity SKU;
the time recording module is used for recording all the flowing processes corresponding to one commodity SKU in one purchase order and extracting the completion time of the flowing processes;
the address inquiry module is used for inquiring a warehouse cone frustum corresponding to the flowing process with the shortest finishing time and taking the warehouse cone frustum corresponding to the warehouse position as a goods taking address of the commodity SKU in the purchase order;
Wherein, the constructing a three-dimensional simulation model corresponding to a commodity SKU comprises: acquiring two-dimensional coordinate data of a warehouse and a store in two-dimensional map data, acquiring two-dimensional coordinate data of a road in the two-dimensional map data, generating height data of the warehouse according to commodity inventory data of the warehouse, generating height data of the store according to commodity order data of the store, and constructing a three-dimensional simulation model reflecting the warehouse, the store and the road based on the two-dimensional coordinate data and the height data;
the construction of the three-dimensional simulation model reflecting the warehouse, the store and the road based on the two-dimensional coordinate data and the height data comprises the following steps: generating a two-dimensional coordinate center point of the warehouse according to the two-dimensional coordinate data of the warehouse; constructing a warehouse cone table to represent a warehouse, wherein the central axis of the warehouse cone table is vertical to the horizontal plane of the three-dimensional simulation model and passes through the two-dimensional coordinate center point of the warehouse, and the height of the warehouse cone table is in direct proportion to the height data of the warehouse; collecting maximum inventory data of commodities in a warehouse; generating the bottom radius of the warehouse cone frustum according to the maximum inventory capacity data; collecting the last ex-warehouse data of commodities in a warehouse; generating the radius of the upper bottom of the warehouse cone frustum according to the last ex-warehouse data;
The construction of a three-dimensional simulation model reflecting a warehouse, a store and a road based on two-dimensional coordinate data and altitude data includes the steps of: generating a two-dimensional coordinate center point of the store according to the two-dimensional coordinate data of the store; constructing a shop cone table representing a shop, wherein the central axis of the shop cone table is vertical to the horizontal plane of the three-dimensional simulation model and passes through the two-dimensional coordinate center point of the shop, and the height of the shop cone table is in direct proportion to the height data of the shop; collecting historical maximum purchase quantity data of commodities of a store; generating the bottom radius of the cone frustum of the store according to the historical maximum purchase quantity data; collecting historical minimum purchase quantity data of commodities of a store; and generating the radius of the upper bottom of the cone frustum of the store according to the historical minimum purchase quantity data.
9. An electronic device, comprising:
one or more processors;
a storage device having one or more programs stored thereon;
the one or more programs, when executed by the one or more processors, cause the processor to implement the method of any of claims 1-7.
10. A computer readable medium having stored thereon a computer program, wherein the computer program when executed by a processor implements the method of any of claims 1 to 7.
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Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPH11250128A (en) * 1997-12-29 1999-09-17 Kazuhiko Kurematsu Device and method for controlling shipment of commodity and recording medium recorded with commodity shipment control program
JP2004196553A (en) * 2004-04-09 2004-07-15 Penta Ocean Constr Co Ltd Object movement management method, in-warehouse physical distribution management system, and program for them
CN106557906A (en) * 2016-11-30 2017-04-05 长威信息科技发展股份有限公司 A kind of warehouse management method and system based on three-dimensional modeling
CN107274013A (en) * 2017-06-08 2017-10-20 北京惠赢天下网络技术有限公司 Direct kinds of goods allocator, system and server based on automatic control logistics
CN110136265A (en) * 2018-02-02 2019-08-16 北京京东尚科信息技术有限公司 Merchandise display method, apparatus, terminal device and retail trade system
CN110288167A (en) * 2019-07-02 2019-09-27 湖南艾瓦特网络科技有限公司 Kinds of goods Allocation plan method, apparatus, electronic equipment and readable storage medium storing program for executing
CN111724225A (en) * 2019-03-21 2020-09-29 北京京东尚科信息技术有限公司 Liquid commodity display method, terminal device, storage medium and electronic device
CN113112208A (en) * 2021-04-29 2021-07-13 国网山西省电力公司运城供电公司 Electric power material storage management method based on three-dimensional visualization model

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105096015B (en) * 2014-04-23 2021-01-08 菜鸟智能物流控股有限公司 Commodity object information processing method and system

Patent Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPH11250128A (en) * 1997-12-29 1999-09-17 Kazuhiko Kurematsu Device and method for controlling shipment of commodity and recording medium recorded with commodity shipment control program
JP2004196553A (en) * 2004-04-09 2004-07-15 Penta Ocean Constr Co Ltd Object movement management method, in-warehouse physical distribution management system, and program for them
CN106557906A (en) * 2016-11-30 2017-04-05 长威信息科技发展股份有限公司 A kind of warehouse management method and system based on three-dimensional modeling
CN107274013A (en) * 2017-06-08 2017-10-20 北京惠赢天下网络技术有限公司 Direct kinds of goods allocator, system and server based on automatic control logistics
CN110136265A (en) * 2018-02-02 2019-08-16 北京京东尚科信息技术有限公司 Merchandise display method, apparatus, terminal device and retail trade system
CN111724225A (en) * 2019-03-21 2020-09-29 北京京东尚科信息技术有限公司 Liquid commodity display method, terminal device, storage medium and electronic device
CN110288167A (en) * 2019-07-02 2019-09-27 湖南艾瓦特网络科技有限公司 Kinds of goods Allocation plan method, apparatus, electronic equipment and readable storage medium storing program for executing
CN113112208A (en) * 2021-04-29 2021-07-13 国网山西省电力公司运城供电公司 Electric power material storage management method based on three-dimensional visualization model

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
A-Star算法在三维物流模拟中的应用研究;冯晓川等;《林产工业》;第45卷(第9期);第52页左栏第1段-第53页左栏第2段 *
Real-time path planning of unmanned aerial vehicle for target tracking and obstacle avoidance in complex dynamic environment;Yao Peng et al.;《Aerospace Science and Technology》;第47卷;摘要 *

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