An intelligent distribution dispatching system

Abstract

The present invention provides an intelligent distribution scheduling system, including: a carrier management module for managing carrier information; a warehouse management module for managing warehouse information; a strategy building module for establishing a distribution strategy from the first location of each warehouse to each outlet of each carrier and distribution parameter information corresponding to the distribution strategy; an order management module for acquiring and managing orders; and a distribution strategy adaptation module for adapting the distribution strategy to the order based on the order information of the order, the distribution parameter information of the distribution strategy and the preset adaptation principle. The intelligent distribution scheduling system of the present invention realizes the distribution scheduling of orders to meet the merchant's order distribution needs.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology, and in particular to an intelligent distribution scheduling system. Background Art

[0002] At present, with the rapid development of Internet technology, people's traditional shopping methods are gradually replaced by online shopping. Merchants send the purchased items to the buyers by express delivery; and in real life, people also have the need to send items in their daily lives; merchants often choose one company for delivery, and do not understand the difference between the express company and other companies in delivering the same items, so they cannot carry out better order delivery. Summary of the invention

[0003] One of the purposes of the present invention is to provide an intelligent delivery scheduling system to realize order delivery scheduling to meet the merchant's order delivery needs.

[0004] An intelligent distribution scheduling system provided by an embodiment of the present invention includes:

[0005] Carrier management module, used to manage carrier information;

[0006] Warehouse management module, used to manage warehouse information;

[0007] A strategy building module, used to establish a distribution strategy from the first location of each warehouse to each outlet of each carrier and distribution parameter information corresponding to the distribution strategy;

[0008] Order management module, used to obtain and manage orders;

[0009] The delivery strategy adaptation module is used to adapt the delivery strategy for the order based on the order information of the order, the delivery parameter information of the delivery strategy and the preset adaptation principle.

[0010] Preferably, the carrier management module includes:

[0011] The distribution area management unit is used to manage the reachable areas of each carrier;

[0012] The waybill management unit is used to manage the waybills of each carrier;

[0013] Logistics details information management unit, used to manage the logistics details information of each carrier;

[0014] The quotation management unit is used to manage the quotation information of each carrier.

[0015] Preferably, the adaptation principles include: timeliness first, lowest cost of optimal matching, and backup order.

[0016] Preferably, the intelligent distribution scheduling system further includes:

[0017] The personalized customization module for delivery labels is used to customize delivery labels according to customer needs.

[0018] Preferably, the intelligent delivery dispatching system further comprises: a delivery vehicle dispatching module for dispatching delivery vehicles;

[0019] The distribution vehicle scheduling module performs the following operations:

[0020] Treat the goods from the same warehouse and with the same distribution strategy as the same batch of goods for distribution;

[0021] Obtaining the first status information of each of the goods delivered in the same batch;

[0022] Determine the shipping location information of the same batch of delivered goods in the warehouse;

[0023] Acquire second status information of a plurality of vehicles to be dispatched;

[0024] Parse the shipping location information and determine the number of shipping points;

[0025] When the number of the shipping points is one, determining a delivery vehicle and determining a shipping order of the shipping points based on the first status information and the second status information;

[0026] When the number of shipping points is greater than one, determining a delivery vehicle and determining a loading route of the delivery vehicle and a shipping order of each shipping point based on the first status information, the shipping location information, and the second status information;

[0027] The shipping sequence is sent to the shipping equipment corresponding to the shipping point, and the shipping equipment ships according to the shipping sequence.

[0028] Preferably, when the number of the shipping points is one, the delivery vehicle scheduling module determines the delivery vehicle and determines the shipping order of the shipping points based on the first status information and the second status information, including:

[0029] Analyze the second state information to determine the three-dimensional loading space corresponding to the vehicle to be dispatched;

[0030] Using a preset first feature extraction rule to extract features from the first state information of all goods, and obtaining a plurality of first feature values;

[0031] Using a preset second feature extraction rule to extract features from the second state information to obtain a plurality of second feature values;

[0032] constructing a first extraction vector based on the plurality of first eigenvalues ​​and the plurality of second eigenvalues;

[0033] Obtaining a preset three-dimensional loading space segmentation rule library;

[0034] Determine a segmentation rule of the three-dimensional loading space based on the first extracted vector and the segmentation rule library;

[0035] Using segmentation rules to divide the three-dimensional loading space into multiple loading areas and determine the loading order of each loading area;

[0036] Determine the three-dimensional model of each of the goods in the same batch of delivered goods based on the first state information and a preset goods model library;

[0037] Based on multiple loading areas, loading order of loading areas, and 3D models, cargo is stacked in a 3D loading space;

[0038] Using a preset third feature extraction rule to extract features from the remaining space of the stacked three-dimensional loading space to obtain a plurality of third feature values;

[0039] constructing a first call vector based on the plurality of second eigenvalues ​​and the plurality of third eigenvalues;

[0040] Get the preset vehicle call library;

[0041] Determining the fitness of each vehicle to be dispatched based on the first call vector of each vehicle to be dispatched and the vehicle call library;

[0042] The vehicle with the highest suitability is called as the delivery vehicle;

[0043] Determine the delivery order of the delivery point by the stacking order of the goods in the three-dimensional loading space corresponding to the dispatched delivery vehicle;

[0044] Wherein, based on the first call vector of each vehicle to be dispatched and the vehicle call library, determining the adaptability of each vehicle to be dispatched includes:

[0045] The first call vector is matched with each adaptation vector in the vehicle call library one by one, and the fitness associated with the matching adaptation vector is obtained.

[0046] Preferably, stacking of goods in a three-dimensional loading space based on multiple loading areas, loading order of the loading areas, and a three-dimensional model includes:

[0047] Build a stacking drill model library based on the 3D models of each cargo;

[0048] Obtain each loading area in sequence according to the loading order, and use the stacking drill model library to perform cargo stacking drills;

[0049] The stacking walkthrough for the loading area is as follows:

[0050] Get the length and width of the bottom surface of the loading area;

[0051] The three-dimensional models of the same height in the stacking drill model library are plane-joined based on length and width to determine multiple joint groups;

[0052] Get the loading height of the loading area;

[0053] The loadable height and the height of each splicing group, select the splicing groups for stacking drills for the loading area, and determine multiple splicing groups for drills;

[0054] Based on multiple groups of splicing groups for drill, multiple stacking schemes to be screened are determined;

[0055] Get the first load-bearing value of the bottom of the loading area;

[0056] Obtaining the weight value and the second load-bearing value of the goods corresponding to each three-dimensional model in each stacking scheme;

[0057] Based on the relationship between the first load-bearing value and the sum of the weight values ​​of all goods in the stacking scheme to be screened, the relationship between the second load-bearing value of each goods and the sum of the weight values ​​of the goods thereon, the number of splicing groups and the position relationship of the splicing groups, the stacking scheme to be screened is evaluated to determine the score value of each stacking scheme to be screened;

[0058] The stacking scheme to be screened with the highest score is obtained as the stacking scheme of the loading area;

[0059] When the 3D model used in the stacking scenario of the loading area is deleted from the stacking drill model library.

[0060] Preferably, based on the relationship between the first load-bearing value and the sum of the weight values ​​of all goods in the stacking scheme to be screened, the relationship between the second load-bearing value of each goods and the sum of the weight values ​​of the goods thereon, the number of splicing groups and the position relationship of the splicing groups, the stacking scheme to be screened is evaluated to determine the score value of each stacking scheme to be screened, including:

[0061] Using a preset fourth feature extraction rule, feature extraction is performed on the relationship between the first load-bearing value and the sum of the weight values ​​of all goods in the stacking scheme to be screened, the relationship between the second load-bearing value of each goods and the sum of the weight values ​​of the goods thereon, the number of splicing groups, and the position relationship of the splicing groups to obtain multiple fourth feature values;

[0062] constructing a first scoring vector based on the plurality of fourth eigenvalues;

[0063] Obtain a preset stacking scheme evaluation library;

[0064] The first similarity between the first scoring vector and each first standard vector in the stacking scheme evaluation library is calculated. The first similarity calculation formula is as follows:

[0065]

[0066] Wherein, X1 represents the first similarity between the first scoring vector and the first standard vector; a i represents the i-th parameter value of the first scoring vector; b i represents the i-th parameter value of the first standard vector; n represents the total number of parameters of the first standard vector or the total number of parameters of the first scoring vector;

[0067] The scoring value corresponding to the first standard vector having the highest first similarity with the first scoring vector in the stacking solution evaluation library is obtained.

[0068] Preferably, when the number of shipping points is greater than one, determining a delivery vehicle and determining a loading route of the delivery vehicle and a shipping order of each shipping point based on the first status information, the shipping location information and the second status information includes:

[0069] Analyze the second state information to determine the three-dimensional loading space corresponding to the vehicle to be dispatched;

[0070] Based on the shipping location information, determine each shipping point on the road map corresponding to the preset warehouse;

[0071] Based on the principle of the shortest total route, each delivery point is connected in series to form at least one driving route;

[0072] Using a preset fifth feature extraction rule to extract features from the first state information of all goods at the first delivery point on each driving path, to obtain a plurality of fifth feature values;

[0073] Using a preset second feature extraction rule to extract features from the second state information to obtain a plurality of second feature values;

[0074] constructing a second scoring vector based on the plurality of fifth eigenvalues ​​and the plurality of second eigenvalues;

[0075] Obtain the preset path evaluation library;

[0076] Calculating a second similarity between the second scoring vector and each second standard vector in the path evaluation library;

[0077] Obtaining a score value associated with a second standard vector having the highest second similarity with the second score vector in the path evaluation library as a score value of the driving path corresponding to the second score vector;

[0078] The driving path corresponding to the highest score value is used as the loading path corresponding to the vehicle to be dispatched;

[0079] Using a preset first feature extraction rule to extract features from first state information of all goods at a first shipping point on the loading path, and obtaining a plurality of first feature values;

[0080] Using a preset second feature extraction rule to extract features from the second state information of the vehicle when entering the first delivery point, and obtaining a plurality of second feature values;

[0081] constructing a first extraction vector based on the plurality of first eigenvalues ​​and the plurality of second eigenvalues;

[0082] Obtaining a preset three-dimensional loading space segmentation rule library;

[0083] Determine a segmentation rule of the three-dimensional loading space based on the first extracted vector and the segmentation rule library;

[0084] Using segmentation rules to divide the three-dimensional loading space into multiple loading areas and determine the loading order of each loading area;

[0085] Determine a three-dimensional model of each of the goods at the first shipping point based on the first state information of the goods at the first shipping point and a preset goods model library;

[0086] Based on multiple loading areas, loading order of loading areas, and three-dimensional models of each cargo at the first shipping point, cargo is stacked in a three-dimensional loading space;

[0087] Acquire the three-dimensional loading space after stacking and update the second state information of the vehicle;

[0088] The cargo stacking of each shipping point on the driving path is processed in turn according to the cargo stacking processing method of the first shipping point;

[0089] When the goods at each delivery point on the driving path are stacked, a preset third feature extraction rule is used to extract features of the remaining space of the three-dimensional loading space after stacking to obtain multiple third feature values;

[0090] constructing a second call vector based on the plurality of second eigenvalues ​​and the plurality of third eigenvalues ​​when the vehicle enters the first delivery point;

[0091] Get the preset vehicle call library;

[0092] Determining the fitness of each vehicle to be dispatched based on the first call vector of each vehicle to be dispatched and the vehicle call library;

[0093] Call the vehicle with the highest suitability as the delivery vehicle;

[0094] The order of shipment of each shipment point is determined by the stacking order of the goods in the three-dimensional loading space corresponding to the called vehicle.

[0095] Preferably, the shipping equipment includes:

[0096] A conveying module, used for conveying goods;

[0097] An image acquisition module is used to obtain images of vehicles after they enter the delivery point;

[0098] An indication module, used for indicating the stacking position of the cargo in the vehicle;

[0099] A processor is electrically connected to the image acquisition module, the conveying module and the indicating module respectively;

[0100] The processor performs the following operations:

[0101] The image of the vehicle entering the delivery point is acquired through the image acquisition module;

[0102] Analyze the image and map the three-dimensional loading space to the vehicle's compartment;

[0103] Get the shipping order;

[0104] The control conveying module conveys the goods according to the shipping sequence and indicates the stacking position of the goods in the carriage through the indicating module.

[0105] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description, claims, and drawings.

[0106] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0107] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0108] Figure 1 A schematic diagram of an intelligent distribution scheduling system in an embodiment of the present invention;

[0109] Figure 2 The figure is an adaptation flow chart in an embodiment of the present invention. DETAILED DESCRIPTION

[0110] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0111] The embodiment of the present invention provides an intelligent distribution scheduling system. Figure 1 As shown, including:

[0112] Carrier management module 1 is used to manage carrier information; for example: create a new carrier, select the carrier logistics type, express or logistics, carrier name, whether to connect to the system, select whether the carrier provides parent-child waybill services, and fill in the special configuration required for carrier connection; in case of sudden or untimely updates of carriers, you can manually specify the suspension area. Determine the service scope of the carrier, such as national express, city distribution company, regional carrier; connect to each express company, and automatically update the suspension areas of each carrier;

[0113] Warehouse management module 2, used to manage warehouse information; warehouse information includes the location of the warehouse, etc.;

[0114] Strategy building module 3 is used to establish the distribution strategy from the first location of each warehouse to each outlet of each carrier and the distribution parameter information corresponding to the distribution strategy; for example: establish the available carriers corresponding to the warehouse; establish the default carrier sequence of the warehouse. When there is no distribution strategy or the designated decision courier is unreachable, the current warehouse traverses the backup carrier sequence, among which the backup carrier sequence is selected in order of priority;

[0115] Order management module 4, used to obtain and manage orders; orders sent by merchants through third-party platforms; third-party platforms include: Taobao, JD.com, etc.;

[0116] The distribution strategy adaptation module 5 is used to adapt the distribution strategy for the order based on the order information, the distribution parameter information of the distribution strategy and the preset adaptation principle. For example: according to the warehouse corresponding to the order, the delivery address, and whether the weight specifies the courier; matching the carrier;

[0117] The carrier management module 1 includes:

[0118] The delivery area management unit is used to manage the reachable areas of each carrier by setting delivery areas and stop delivery areas;

[0119] The waybill management unit is used to manage the waybills of each carrier; manage the merchant's balance information with each carrier, and issue a reminder when the balance is less than the minimum value set by the merchant.

[0120] The logistics details information management unit is used to manage the logistics details information of each carrier; obtain the location of the ordered goods in real time, etc.;

[0121] The quotation management unit is used to manage the quotation information of each carrier, that is, the quotation in the distribution strategy, specifically the transportation price from a warehouse to the outlet corresponding to the order address;

[0122] The adaptation principles include: timeliness first, optimal matching with the lowest cost, and bottom-line order. The system defaults to optimal matching with the lowest cost; of course, the adaptation principle can be changed according to the user's settings. Figure 2 To adapt the flow chart.

[0123] The system of the present invention has the following functions:

[0124] It supports docking with multiple express delivery platforms, obtaining express delivery service scope, express delivery bill number, and express delivery bill balance. It also warns of available bill balance and sends DingTalk messages to remind you to recharge the balance.

[0125] Supports distribution decisions for multiple business types: The DDS system can combine multiple distribution strategies based on optimal cost, timeliness priority, quality priority, and manual designation.

[0126] Distribution scheduling analysis: Based on the actual distribution situation, actual logistics cost analysis is conducted according to the transportation routes and service scope, and the order of optimal distribution costs is adjusted in reverse.

[0127] Distribution resource management: All downstream distribution carriers are managed uniformly through DDS, and a carrier evaluation system and unified quotation management are established. Distribution rule configuration is supported to achieve transparent management.

[0128] Supports re-scheduling of order carriers, and provides reachable couriers for re-scheduling for designated areas where couriers cannot reach.

[0129] In order to meet the needs of different customers, in one embodiment, the intelligent distribution scheduling system further includes:

[0130] The personalized delivery bill customization module is used to customize delivery bills according to customer needs. Customers include: warehouses, merchants, and carriers;

[0131] Diverse and flexible delivery labels: Provide different delivery label formats according to warehouse and merchant requirements and the requirements of live broadcast promotions at different stages. While meeting delivery requirements, increase customer service mentality, such as integrated delivery and installation services, door-to-door pickup and return, and door-to-door delivery.

[0132] In one embodiment, the intelligent delivery scheduling system further includes: a delivery vehicle scheduling module for scheduling delivery vehicles;

[0133] The distribution vehicle dispatch module performs the following operations:

[0134] The goods from the same warehouse and with the same distribution strategy are regarded as the same batch of goods for distribution; the same batch of goods for distribution refers to the goods shipped from the same warehouse, sent to the same location and transported by the same carrier;

[0135] Obtaining first status information of each of the goods delivered in the same batch; the first status information includes: goods size, goods weight and load-bearing capacity of the goods, etc.;

[0136] Determine the shipping location information of the same batch of delivered goods in the warehouse; because large warehouses often use multiple shipping points for shipment, the shipping point of each goods can be determined through the shipping location information;

[0137] Obtaining second status information of multiple vehicles to be dispatched; the second status information includes the internal dimensions of the compartment, whether the compartment is loaded with goods, the dimensions of the loaded goods, the types of goods, and the weight of the goods, etc. The vehicles to be dispatched include: empty vehicles or vehicles passing by a preset distance near the warehouse whose loadable volume is greater than the total volume of the batch of delivered goods;

[0138] Parse the shipping location information and determine the number of shipping points;

[0139] When the number of the shipping points is one, determining a delivery vehicle and determining a shipping order of the shipping points based on the first status information and the second status information;

[0140] When the number of shipping points is greater than one, determining a delivery vehicle and determining a loading route of the delivery vehicle and a shipping order of each shipping point based on the first status information, the shipping location information, and the second status information;

[0141] The shipping sequence is sent to the shipping equipment corresponding to the shipping point, and the shipping equipment ships according to the shipping sequence.

[0142] The working principle and beneficial effects of the above technical solution are:

[0143] By rationally selecting delivery vehicles based on the conditions of the dispatched vehicles, the shipment of goods, and the conditions of the goods themselves, rational use of vehicle resources can be achieved.

[0144] In one embodiment, when the number of the shipping points is one, the delivery vehicle scheduling module determines the delivery vehicle and determines the shipping order of the shipping points based on the first status information and the second status information, including:

[0145] Analyze the second state information to determine the three-dimensional loading space corresponding to the vehicle to be dispatched;

[0146] A preset first feature extraction rule is used to extract features from the first state information of all goods to obtain multiple first feature values; the first feature extraction rule is to determine the model number of the three-dimensional model corresponding to the first state information in the preset goods model library based on the first state information, use the model number as the first half of the first feature value, and extract the quantity of each model number as the second half of the first feature value; for example: the first feature value is 001080, the first three digits 001 represent the model number, and the last three digits 080 represent the quantity, indicating that the quantity of goods corresponding to the three-dimensional model numbered 001 in this batch of delivered goods is 80;

[0147] Using a preset second feature extraction rule to extract features from the second state information, a plurality of second feature values ​​are obtained; the second feature values ​​include: a feature value representing the length of the carriage, a feature value representing the width of the carriage, a feature value representing the height of the carriage, a feature value representing the height of the area where cargo is installed in the carriage, a feature value representing the width of the area where cargo is installed in the carriage, a feature value representing the length of the area where cargo is installed in the carriage, etc.;

[0148] Based on the plurality of first eigenvalues ​​and the plurality of second eigenvalues, a first extraction vector is constructed; the first eigenvalues ​​and the second eigenvalues ​​are arranged in a preset order to form parameters of the first extraction vector;

[0149] Obtaining a preset segmentation rule library of the three-dimensional loading space; the segmentation rule library is established in advance through a large amount of data analysis, and mainly stipulates the segmentation rules of the cargo-carrying area in the carriage, that is, the length of each device area; in the segmentation rule library, the segmentation rules are associated with the segmentation vectors in a one-to-one correspondence;

[0150] Based on the first extraction vector and the segmentation rule library, determine the segmentation rule of the three-dimensional loading space; match the first extraction vector with the segmentation vector one by one, and obtain the segmentation rule corresponding to the matching segmentation vector;

[0151] The segmentation rule is used to divide the three-dimensional loading space into multiple loading areas and determine the loading order of each loading area; taking a vehicle with a compartment door at the rear of the vehicle as an example, the segmentation rule stipulates the width of each loading area from the interior of the compartment to the compartment door, and the loading areas are sorted from large to small based on the distance from the loading area to the compartment door to obtain the installation order; when the three-dimensional loading space loaded with goods in the compartment is wind-cut according to the wind-cut rule, the first loading area corresponds to the space above the loaded goods;

[0152] Determine the three-dimensional model of each of the goods in the same batch of delivered goods based on the first state information and a preset goods model library;

[0153] Based on multiple loading areas, loading order of loading areas, and 3D models, cargo is stacked in a 3D loading space;

[0154] Using a preset third feature extraction rule to extract features of the remaining space of the stacked three-dimensional loading space, and obtaining a plurality of third feature values; the third feature values ​​include the volume of the remaining space, the length, width and height of the remaining space, etc.;

[0155] constructing a first call vector based on the plurality of second eigenvalues ​​and the plurality of third eigenvalues;

[0156] Obtain a preset vehicle call library; the vehicle call library is set based on the analysis of a large amount of empirical data; in the vehicle call library, the smaller the volume of the remaining space, the higher the adaptability; when the volume of the remaining space is the same, the adaptability of the vehicle loaded with goods is higher than that of the vehicle not loaded with goods;

[0157] Determining the fitness of each vehicle to be dispatched based on the first call vector of each vehicle to be dispatched and the vehicle call library;

[0158] The vehicle with the highest suitability is called as the delivery vehicle;

[0159] Determine the delivery order of the delivery point by the stacking order of the goods in the three-dimensional loading space corresponding to the dispatched delivery vehicle;

[0160] Wherein, based on the first call vector of each vehicle to be dispatched and the vehicle call library, determining the adaptability of each vehicle to be dispatched includes:

[0161] The first call vector is matched with each adaptation vector in the vehicle call library one by one to obtain the fitness degree associated with the matching adaptation vector; specifically, the similarity between the first call vector and the adaptation vector can be calculated, and when the similarity is greater than a preset threshold (for example, 0.95), it is determined that the first call vector matches the adaptation vector.

[0162] In one embodiment, stacking of goods in a three-dimensional loading space based on multiple loading areas, a loading order of the loading areas, and a three-dimensional model includes:

[0163] Build a stacking drill model library based on the 3D models of each cargo;

[0164] Obtain each loading area in sequence according to the loading order, and use the stacking drill model library to perform cargo stacking drills;

[0165] The stacking walkthrough for the loading area is as follows:

[0166] Get the length and width of the bottom surface of the loading area;

[0167] The three-dimensional models of the same height in the stacking drill model library are plane-stitched based on length and width to determine multiple splicing groups; for example: the length of the bottom of the loading area is 2 meters and the width is 1 meter; the heights of the three-dimensional models A, B, and C are all 20 cm, the length of the bottom of A is 2 meters and the width is 1 meter; the length of the bottom of B is 1.5 meters and the width is 1 meter; the length of the bottom of C is 1 meter and the width is 0.5 meters; splicing A, B, and C can form a splicing model with a length of 2 meters and a width of 1 meter, which is exactly the same as the bottom of the loading area.

[0168] Get the loading height of the loading area;

[0169] The loadable height and the height of each splicing group are used to select the splicing groups for stacking drills for the loading area, and multiple splicing groups for drills are determined; for example, the loadable height is 1.5 meters; there are eight splicing groups from A to H, and the heights are 0.3, 0.5, 0.7, 0.75, 0.75, 0.8, 1.0, 1.2, etc., so A and H are grouped together; B and G are grouped together; C and F are grouped together; D and E are grouped together; a total of 4 splicing groups for drills are determined;

[0170] Based on multiple groups of splicing groups for drill, multiple stacking schemes to be screened are determined; still eight groups of splicing groups from A to H are divided into four groups of splicing groups for drill; by exchanging the upper and lower order of the splicing groups, eight groups of stacking schemes to be screened can be obtained;

[0171] Obtain the first load-bearing value of the bottom of the loading area; if the bottom of the loading area is the floor of the carriage, the first load-bearing value is the load-bearing mass of the floor; if the bottom of the loading area is stacked goods, the first load-bearing value is the weight that can be placed on the upper end surface of the goods;

[0172] Obtain the weight value and second load-bearing value of the goods corresponding to each three-dimensional model in each stacking scheme; the second load-bearing value is the maximum weight value that the upper surface of the goods can bear;

[0173] Based on the relationship between the first load-bearing value and the sum of the weight values ​​of all the goods in the stacking scheme to be screened, the relationship between the second load-bearing value of each cargo and the sum of the weight values ​​of the goods above it, the number of splicing groups and the position relationship of the splicing groups, the stacking scheme to be screened is evaluated and the score value of each stacking scheme to be screened is determined; wherein, due to the bumps in the transportation process, the load-bearing value is required to be greater than about 10 times the mass value, but in order to ensure the safety of transportation, the weight of the transported goods must be guaranteed as much as possible, so when the first load-bearing value and the sum of the weight values ​​of all the goods in the stacking scheme to be screened When the sum is 10:1, the score is the highest; when it is higher than 10:1, the score gradually decreases, and when it is lower than 10:1, the score is directly set to zero; similarly, when the sum of the second load-bearing value of each cargo and the weight value of the cargo above it is 10:1, the score is the highest; when it is higher than 10:1, the score gradually decreases, and when it is lower than 10:1, the score is directly set to zero; the fewer the number of splicing groups, the higher the score; the score corresponding to the stacking scheme to be screened in which the height of the splicing group located below is greater than the height of the splicing group located above is greater than the stacking scheme to be screened in which the height of the splicing group located below is less than the height of the splicing group located above;

[0174] The stacking scheme to be screened with the highest score is the stacking scheme of the loading area; the stacking scheme to be screened with the highest score is the stacking scheme that best takes into account transportation safety and transportation efficiency among all the stacking schemes to be screened;

[0175] Delete the used 3D models from the stacking drill model library when the 3D models used in the stacking scheme of the loading area are deleted from the stacking drill model library to prevent the goods from being reused in the stacking drill.

[0176] In one embodiment, based on the relationship between the first load-bearing value and the sum of the weight values ​​of all goods in the stacking scheme to be screened, the relationship between the second load-bearing value of each goods and the sum of the weight values ​​of the goods thereon, the number of splicing groups, and the position relationship of the splicing groups, the stacking scheme to be screened is evaluated to determine the score value of each stacking scheme to be screened, including:

[0177] A preset fourth feature extraction rule is used to extract features of the relationship between the first load-bearing value and the sum of the weight values ​​of all goods in the stacking scheme to be screened, the relationship between the second load-bearing value of each goods and the sum of the weight values ​​of the goods above it, the number of splicing groups, and the position relationship of the splicing groups to obtain multiple fourth feature values; the fourth feature value includes: the first N smallest ratios of the first load-bearing value and the sum of the weight values ​​of all goods in the stacking scheme to be screened, the second load-bearing value of each goods and the sum of the weight values ​​of the goods above it, the number of splicing groups, the number of splicing groups whose height is greater than the height of the splicing group located above, the number of splicing groups whose height is less than the height of the splicing group located above, etc.;

[0178] Based on the multiple fourth eigenvalues, construct a first scoring vector; arrange the fourth eigenvalues ​​in a preset order to obtain the first scoring vector;

[0179] Obtaining a preset stacking scheme evaluation library; the stacking scheme evaluation library is manually set based on a large amount of data analysis in advance or is set by the system after a large amount of data analysis using a neural network model;

[0180] The first similarity between the first scoring vector and each first standard vector in the stacking scheme evaluation library is calculated. The first similarity calculation formula is as follows:

[0181]

[0182] Wherein, X1 represents the first similarity between the first scoring vector and the first standard vector; a i represents the i-th parameter value of the first scoring vector; b i represents the i-th parameter value of the first standard vector; n represents the total number of parameters of the first standard vector or the total number of parameters of the first scoring vector;

[0183] The scoring value corresponding to the first standard vector having the highest first similarity with the first scoring vector in the stacking solution evaluation library is obtained.

[0184] In one embodiment, when the number of shipping points is greater than one, determining a delivery vehicle and determining a loading route of the delivery vehicle and a shipping order of each shipping point based on the first status information, the shipping location information, and the second status information includes:

[0185] Analyze the second state information to determine the three-dimensional loading space corresponding to the vehicle to be dispatched;

[0186] Based on the shipping location information, determine each shipping point on the road map corresponding to the preset warehouse;

[0187] Based on the principle of the shortest total route, each delivery point is connected in series to form at least one driving route;

[0188] A preset fifth feature extraction rule is used to extract features from the first state information of all goods at the first delivery point on each driving path to obtain multiple fifth feature values; the fifth feature values ​​include: maximum goods mass, minimum goods mass, average value of goods mass, quantity between the maximum goods mass and the average value, quantity between the minimum goods mass and the average value, maximum goods height, minimum goods height, average value of goods height, quantity between the maximum goods height and the average value of goods height, quantity between the minimum goods height and the average value of goods height, etc.;

[0189] Using a preset second feature extraction rule to extract features from the second state information, a plurality of second feature values ​​are obtained; the second feature values ​​include: a feature value representing the length of the carriage, a feature value representing the width of the carriage, a feature value representing the height of the carriage, a feature value representing the height of the area where cargo is installed in the carriage, a feature value representing the width of the area where cargo is installed in the carriage, a feature value representing the length of the area where cargo is installed in the carriage, etc.;

[0190] constructing a second scoring vector based on the plurality of fifth eigenvalues ​​and the plurality of second eigenvalues;

[0191] Obtain a preset path evaluation library; the path evaluation library is established based on a large amount of data analysis;

[0192] Calculating a second similarity between the second scoring vector and each second standard vector in the path evaluation library;

[0193] Obtaining a score value associated with a second standard vector having the highest second similarity with the second score vector in the path evaluation library as a score value of the driving path corresponding to the second score vector;

[0194] The driving path corresponding to the highest score value is used as the loading path corresponding to the vehicle to be dispatched;

[0195] For example: when the rule is established that when it is found through analysis of the second eigenvalue that the three-dimensional loading space of the vehicle is a cuboid or a cube, a shipping point with more large and heavy goods is selected as the first shipping point of the loading path; when it is found through analysis of the second eigenvalue that the three-dimensional loading space of the vehicle is not a cuboid or a cube, a shipping point with more small and light goods is selected as the first shipping point of the loading path; by quantifying the above situation into a comprehensive analysis of the fifth eigenvalue and the second eigenvalue, the selection of the vehicle's cargo loading path is realized.

[0196] Using a preset first feature extraction rule to extract features from first state information of all goods at a first shipping point on the loading path, and obtaining a plurality of first feature values;

[0197] Using a preset second feature extraction rule to extract features from the second state information of the vehicle when entering the first delivery point, and obtaining a plurality of second feature values;

[0198] constructing a first extraction vector based on the plurality of first eigenvalues ​​and the plurality of second eigenvalues;

[0199] Obtaining a preset three-dimensional loading space segmentation rule library;

[0200] Determine a segmentation rule of the three-dimensional loading space based on the first extracted vector and the segmentation rule library;

[0201] Using segmentation rules to divide the three-dimensional loading space into multiple loading areas and determine the loading order of each loading area;

[0202] Determine a three-dimensional model of each of the goods at the first shipping point based on the first state information of the goods at the first shipping point and a preset goods model library;

[0203] Based on multiple loading areas, loading order of loading areas, and three-dimensional models of each cargo at the first shipping point, cargo is stacked in a three-dimensional loading space;

[0204] Acquire the three-dimensional loading space after stacking and update the second state information of the vehicle;

[0205] The cargo stacking of each shipping point on the driving path is processed in turn according to the cargo stacking processing method of the first shipping point;

[0206] When the goods at each delivery point on the driving path are stacked, a preset third feature extraction rule is used to extract features of the remaining space of the three-dimensional loading space after stacking to obtain multiple third feature values;

[0207] constructing a second call vector based on the plurality of second eigenvalues ​​and the plurality of third eigenvalues ​​when the vehicle enters the first delivery point;

[0208] Get the preset vehicle call library;

[0209] Determining the fitness of each vehicle to be dispatched based on the first call vector of each vehicle to be dispatched and the vehicle call library;

[0210] Call the vehicle with the highest suitability as the delivery vehicle;

[0211] The order of shipment of each shipment point is determined by the stacking order of the goods in the three-dimensional loading space corresponding to the called vehicle.

[0212] The working principle and beneficial effects of the above technical solution are:

[0213] The determination of the shipping order and the retrieval of delivery vehicles for multiple shipping points differ from that for a single shipping point in that each shipping point performs a stacking operation and selects a loading route. Through the stacking drill, the best shipping order is achieved, taking into account both transportation safety and cargo safety.

[0214] In one embodiment, the shipping device includes:

[0215] A conveying module, used for conveying goods;

[0216] An image acquisition module is used to obtain images of vehicles after they enter the delivery point;

[0217] An indication module, used for indicating the stacking position of the cargo in the vehicle;

[0218] A processor is electrically connected to the image acquisition module, the conveying module and the indicating module respectively;

[0219] The processor performs the following operations:

[0220] The image of the vehicle entering the delivery point is acquired through the image acquisition module;

[0221] Analyze the image and map the three-dimensional loading space to the vehicle's compartment;

[0222] Get the shipping order;

[0223] The control conveying module conveys the goods according to the shipping sequence and indicates the stacking position of the goods in the carriage through the indicating module.

[0224] The working principle and beneficial effects of the above technical solution are:

[0225] The image acquisition module can use multiple cameras, the conveying module includes a conveyor belt; the indication module includes: multiple holographic projection modules or infrared indication spotlights; when the vehicle is loading, the stacking position of the goods can be indicated in the vehicle box to assist in stacking, thereby ensuring that the stacking is carried out in a stacking order, thereby improving the efficiency of transportation and ensuring the safety of the goods during transportation by reasonably stacking the goods.

[0226] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. An intelligent distribution scheduling system, characterized in that: include: Carrier management module, used to manage carrier information; Warehouse management module, used to manage warehouse information; A strategy building module, used to establish a distribution strategy from the first location of each warehouse to each outlet of each carrier and distribution parameter information corresponding to the distribution strategy; Order management module, used to obtain and manage orders; A delivery strategy adaptation module, used for adapting the delivery strategy for the order based on the order information of the order, the delivery parameter information of the delivery strategy and a preset adaptation principle; The delivery vehicle scheduling module is used to schedule delivery vehicles; The delivery vehicle scheduling module performs the following operations: Treat the goods from the same warehouse and with the same distribution strategy as the same batch of goods for distribution; Obtaining the first status information of each of the goods delivered in the same batch; Determine the shipping location information of the same batch of delivered goods at the warehouse; Acquire second status information of a plurality of vehicles to be dispatched; Parsing the shipping location information to determine the number of shipping points; When the number of the shipping points is one, determining a delivery vehicle and determining a shipping order of the shipping points based on the first status information and the second status information includes: parsing the second state information to determine a three-dimensional loading space corresponding to the vehicle to be dispatched; Using a preset first feature extraction rule to extract features from the first state information of all goods, and obtaining a plurality of first feature values; Using a preset second feature extraction rule to perform feature extraction on the second state information to obtain a plurality of second feature values; constructing a first extraction vector based on a plurality of the first eigenvalues ​​and a plurality of the second eigenvalues; Obtaining a preset three-dimensional loading space segmentation rule library; Determining a segmentation rule for the three-dimensional loading space based on the first extraction vector and the segmentation rule library; Using the segmentation rule to divide the three-dimensional loading space into a plurality of loading areas and determine the loading order of each loading area; Determine the three-dimensional model of each of the goods in the same batch of delivered goods based on the first state information and a preset goods model library; Based on the plurality of loading areas, the loading order of the loading areas, and the three-dimensional model, stacking the goods in the three-dimensional loading space; Using a preset third feature extraction rule to extract features of the remaining space of the three-dimensional loading space after stacking, to obtain a plurality of third feature values; constructing a first call vector based on the plurality of the second eigenvalues ​​and the plurality of the third eigenvalues; Get the preset vehicle call library; Determining the fitness of each of the vehicles to be dispatched based on the first call vector of each of the vehicles to be dispatched and the vehicle call library; Calling the vehicle with the highest adaptability as the delivery vehicle; Determining the shipping order of the shipping point by the stacking order of the goods in the three-dimensional loading space corresponding to the called delivery vehicle; Wherein, determining the adaptability of each of the vehicles to be scheduled based on the first call vector of each of the vehicles to be scheduled and the vehicle call library includes: Matching the first call vector with each adaptation vector in the vehicle call library one by one, and obtaining the fitness degree associated with the matching adaptation vector; When the number of the shipping points is greater than one, determining a delivery vehicle and determining a loading route of the delivery vehicle and a shipping order of each of the shipping points based on the first status information, the shipping location information, and the second status information includes: parsing the second state information to determine a three-dimensional loading space corresponding to the vehicle to be dispatched; Based on the shipping location information, determining each shipping point on a preset road map corresponding to the warehouse; Connecting the shipping points in series based on the principle of the shortest total route to form at least one driving route; Using a preset fifth feature extraction rule to extract features from the first state information of all the goods at the first shipping point on each of the driving paths, to obtain a plurality of fifth feature values; Using a preset second feature extraction rule to perform feature extraction on the second state information to obtain a plurality of second feature values; constructing a second scoring vector based on the plurality of the fifth eigenvalues ​​and the plurality of the second eigenvalues; Obtain the preset path evaluation library; Calculating a second similarity between the second scoring vector and each second standard vector in the path evaluation library; Obtaining a score value associated with the second standard vector having the highest second similarity to the second score vector in the path evaluation library as a score value of the driving path corresponding to the second score vector; The driving path corresponding to the highest scoring value is used as the loading path corresponding to the vehicle to be dispatched; Using a preset first feature extraction rule to extract features from the first state information of all the goods at the first shipping point on the loading path, to obtain a plurality of first feature values; Using a preset second feature extraction rule to extract features from the second state information of the vehicle when entering the first shipping point, to obtain a plurality of second feature values; constructing a first extraction vector based on a plurality of the first eigenvalues ​​and a plurality of the second eigenvalues; Obtaining a preset three-dimensional loading space segmentation rule library; Determining a segmentation rule for the three-dimensional loading space based on the first extraction vector and the segmentation rule library; Using the segmentation rule to divide the three-dimensional loading space into a plurality of loading areas and determine the loading order of each loading area; Determine a three-dimensional model of each cargo at the first shipping point based on the first status information of the cargo at the first shipping point and a preset cargo model library; Based on the plurality of loading areas, the loading order of the loading areas, and the three-dimensional model of each cargo at the first shipping point, stacking the cargo in the three-dimensional loading space; Acquire the three-dimensional loading space after stacking and update the second state information of the vehicle; Processing the cargo stacking of each of the shipping points on the driving path in turn according to the cargo stacking processing method of the first shipping point; When the goods at each of the delivery points on the driving path are stacked, a preset third feature extraction rule is used to extract features of the remaining space of the three-dimensional loading space after stacking to obtain a plurality of third feature values; constructing a second call vector based on the plurality of second feature values ​​and the plurality of third feature values ​​when the vehicle enters the first delivery point; Get the preset vehicle call library; Determining the fitness of each of the vehicles to be dispatched based on the first call vector of each of the vehicles to be dispatched and the vehicle call library; Calling the vehicle with the highest adaptability as the delivery vehicle; Determining the shipping order of each shipping point according to the stacking order of the goods in the three-dimensional loading space corresponding to the called vehicle; The shipping sequence is sent to the shipping equipment at the corresponding shipping point, and the shipping equipment ships according to the shipping sequence.

2. The intelligent distribution scheduling system according to claim 1, characterized in that: The carrier management module includes: A distribution area management unit, used to manage the reachable areas of each of the carriers; A waybill management unit, used to manage the waybills of each of the carriers; A logistics details information management unit, used to manage the logistics details information of each of the carriers; The quotation management unit is used to manage the quotation information of each of the carriers.

3. The intelligent distribution scheduling system according to claim 1, characterized in that: The adaptation principles include: timeliness first, lowest cost, and backup order.

4. The intelligent distribution scheduling system according to claim 1, characterized in that: Also includes: The personalized customization module for delivery labels is used to customize delivery labels according to customer needs.

5. The intelligent distribution scheduling system according to claim 1, characterized in that: The stacking of goods in the three-dimensional loading space based on the plurality of loading areas, the loading order of the loading areas, and the three-dimensional model includes: Building a stacking drill model library based on the three-dimensional models of each cargo; Acquire each of the loading areas in sequence according to the loading order, and perform cargo stacking drills using a stacking drill model library; The stacking walkthrough for the loading area is as follows: Obtaining the length and width of the bottom surface of the loading area; Performing plane splicing on the three-dimensional models of the same height in the stacked drill model library based on the length and width to determine a plurality of splicing groups; Obtaining a loadable height of the loading area; The loadable height and the height of each splicing group are used to select the splicing groups for stacking drills in the loading area, and to determine multiple groups of the splicing groups for the drills; Based on the plurality of splicing groups for the drill, determining a plurality of stacking schemes to be screened; Obtaining a first load-bearing value of the bottom of the loading area; Obtaining the weight value and the second load-bearing value of the goods corresponding to each three-dimensional model in each of the stacking schemes; Based on the relationship between the first load-bearing value and the sum of the weight values ​​of all goods in the stacking scheme to be screened, the relationship between the second load-bearing value of each of the goods and the sum of the weight values ​​of the goods thereon, the number of splicing groups and the positional relationship of the splicing groups, the stacking scheme to be screened is evaluated to determine the score value of each of the stacking schemes to be screened; Acquire the stacking scheme to be screened with the highest score as the stacking scheme for the loading area; The three-dimensional model used in the stacking scheme of the loading area is deleted from the stacking drill model library.

6. The intelligent distribution scheduling system according to claim 5, characterized in that: The stacking scheme to be screened is evaluated based on the relationship between the first load-bearing value and the sum of the weight values ​​of all goods in the stacking scheme to be screened, the relationship between the second load-bearing value of each of the goods and the sum of the weight values ​​of the goods thereon, the number of splicing groups and the position relationship of the splicing groups, and the score value of each of the stacking schemes to be screened is determined, including: Using a preset fourth feature extraction rule, feature extraction is performed on the relationship between the first load-bearing value and the sum of the weight values ​​of all goods in the to-be-screened stacking scheme, the relationship between the second load-bearing value of each of the goods and the sum of the weight values ​​of the goods thereon, the number of splicing groups, and the positional relationship of the splicing groups to obtain a plurality of fourth feature values; Based on the plurality of fourth eigenvalues, construct a first scoring vector; Obtain a preset stacking scheme evaluation library; The first similarity between the first scoring vector and each first standard vector in the stacking scheme evaluation library is calculated, and the first similarity calculation formula is as follows: Wherein, X1 represents the first similarity between the first scoring vector and the first standard vector; a i represents the i-th parameter value of the first scoring vector; b i represents the i-th parameter value of the first standard vector; n represents the total number of parameters of the first standard vector or the total number of parameters of the first scoring vector; Obtain a score value in the stacking solution evaluation library that is associated with the first standard vector having the highest first similarity to the first score vector.

7. The intelligent distribution scheduling system according to claim 1, characterized in that: The shipping equipment includes: A conveying module, used for conveying goods; An image acquisition module, used to acquire images of the vehicle after it enters the shipping point; an indication module, used for indicating the stacking position of the cargo in the vehicle; A processor, electrically connected to the image acquisition module, the conveying module and the indication module respectively; The processor performs the following operations: Acquire an image of the vehicle after entering the delivery point through the image acquisition module; parsing the image and mapping and associating the three-dimensional loading space with a compartment of the vehicle; Obtaining the shipping sequence; The conveying module is controlled to convey the goods according to the shipping sequence and the stacking position of the goods is indicated in the carriage through the indicating module.

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