Intelligent logistics management method and system based on big data, and storage medium

Abstract

The invention provides an intelligent logistics management method and system based on big data and a storage medium, and relates to the technical field of logistics management, and the method comprises the steps: obtaining order information and transport vehicle information; determining the number of transport vehicles according to the order information and the transport vehicle information; obtaining the number of delivery transport vehicles and historical transport time of similar orders; determining a priority delivery task according to the order information, the transportation time of the similar order and the number of delivery transportation vehicles; acquiring historical order information; obtaining a goods price and a goods storage quantity; determining a first relation function; and according to the first relation function, the order quantity, the cargo storage quantity, the cargo price and the transport vehicle quantity, managing the cargo inventory. According to the invention, the number of transport vehicles required by the order and the priority delivery task can be determined, and the cargo inventory is managed according to the cargo inventory, so that the accuracy and comprehensiveness of logistics management are improved.

Description

Technical Field

[0001] The present invention relates to the field of logistics management technology, and in particular to a smart logistics management method, system and storage medium based on big data. Background Art

[0002] In the related art, CN115115151A discloses a logistics management method and logistics system, which belongs to the field of logistics technology. The scheme includes: the management end enters product order information; the management end determines the logistics code and anti-counterfeiting code for each product according to the product order information; the printing end prints the corresponding logistics code and anti-counterfeiting code on each product; the warehouse end packages all products into at least one product group; the warehouse end marks each product and each product group according to the logistics code and anti-counterfeiting code; the management end receives a scan request for the logistics code and / or anti-counterfeiting code; the management end responds to the scan request and outputs the corresponding information. In the embodiment of this scheme, based on the logistics code and anti-counterfeiting code of each product, there is a unique correspondence between the logistics code and the anti-counterfeiting code, and each product is bound by the double code. In this way, when it is necessary to obtain the logistics-related information of a product, it is only necessary to scan the logistics code and / or anti-counterfeiting code on the product to query the corresponding information, so as to realize flexible management of logistics.

[0003] CN118446615A discloses a smart logistics management system, which belongs to the field of logistics management technology. The smart logistics management system includes: an order management module and a route planning module; the order management module is used to parse multiple logistics orders of a target vehicle to obtain multiple order information; determine multiple feature vectors corresponding to each order information according to the information category contained in each order information; classify and store the multiple logistics orders according to the multiple feature vectors; the route planning module is used to plan the optimal path for the target vehicle according to the multiple order information. This solution can improve the efficiency of logistics order classification, ensure that the target vehicle completes the logistics order in the shortest time, reduce logistics distribution time, and improve logistics distribution efficiency.

[0004] Based on the above related technologies, the efficiency of logistics distribution can be improved. However, the related technologies do not take into account the impact of order delivery priority and cargo inventory on logistics management, that is, it is impossible to manage logistics according to the order of order delivery and cargo inventory status.

[0005] The information disclosed in the background technology section of this application is only intended to deepen the understanding of the general background technology of this application, and should not be regarded as an admission or any form of suggestion that the information constitutes the prior art already known to those skilled in the art. Summary of the invention

[0006] The present invention provides a smart logistics management method, system and storage medium based on big data, which can solve the technical problem that related technologies cannot manage logistics according to the delivery sequence of orders and the inventory status of goods.

[0007] According to a first aspect of the present invention, there is provided a smart logistics management method based on big data, comprising:

[0008] Obtaining order information and transport vehicle information of multiple orders in the current time period, wherein the order information includes: order destination, order required delivery time, order release time, order transportation distance, cargo quantity, cargo weight and cargo volume; the transport vehicle information includes: maximum safe carrying weight and maximum safe carrying volume;

[0009] Determine the number of transport vehicles according to the order information and the transport vehicle information;

[0010] Obtaining the number of transport vehicles available for delivery at multiple times in the current time period and the historical transport time of similar orders in multiple historical time periods, wherein the destination of the similar orders is the same as the destination of the order in the current time period;

[0011] Determine the priority delivery task according to the order information, the delivery time of similar orders and the number of available delivery vehicles;

[0012] Acquire historical order information of multiple historical time periods, wherein the historical order information includes: historical cargo storage quantity, historical cargo inventory, historical cargo price, historical order quantity and historical transportation vehicle quantity;

[0013] Get the price and storage quantity of goods in the current time period;

[0014] Determine a first relationship function of a change in the inventory quantity of goods according to the historical order information;

[0015] The cargo inventory is managed according to the first relationship function, the order quantity, the cargo storage quantity, the cargo price and the number of transport vehicles.

[0016] According to a second aspect of the present invention, there is provided a smart logistics management system based on big data, comprising:

[0017] An information acquisition module, used to acquire order information and transport vehicle information of multiple orders in the current time period, wherein the order information includes: order destination, order required delivery time, order release time, order transportation distance, cargo quantity, cargo weight and cargo volume; the transport vehicle information includes: maximum safe carrying weight and maximum safe carrying volume;

[0018] A transport vehicle quantity module, used to determine the number of transport vehicles according to the order information and the transport vehicle information;

[0019] An order logistics analysis module, used to obtain the number of transport vehicles that can be delivered at multiple times in the current time period and the historical transportation time of similar orders in multiple historical time periods, wherein the destination of the similar orders is the same as the destination of the order in the current time period;

[0020] A delivery task determination module, used to determine a priority delivery task according to the order information, the transportation time of similar orders and the number of transport vehicles available for delivery;

[0021] A historical information acquisition module is used to acquire historical order information of multiple historical time periods, wherein the historical order information includes: historical cargo storage quantity, historical cargo inventory, historical cargo price, historical order quantity and historical transportation vehicle quantity;

[0022] The cargo information module is used to obtain the cargo price and cargo storage quantity in the current time period;

[0023] A first relationship function module, used to determine a first relationship function of the change in the inventory quantity of goods according to the historical order information;

[0024] The inventory management module is used to manage the inventory of goods according to the first relationship function, the order quantity, the storage quantity of the goods, the price of the goods and the number of transport vehicles.

[0025] Technical effect: According to the present invention, the number of transport vehicles required for an order can be determined based on the order information and the transport vehicle information. Furthermore, the delivery priority of the order can be evaluated based on the order information, the number of transport vehicles required for the order, the number of transport vehicles that can be dispatched, and the transportation time of similar orders, and the priority delivery task can be determined. The cargo inventory can be managed based on the historical order information, the order quantity, the number of transport vehicles, the cargo storage quantity, and the cargo price, thereby improving the accuracy and comprehensiveness of logistics management. When determining the number of transport vehicles, the number of transport vehicles can be determined based on the order information and the transport vehicle information. During the calculation process, the number of transport vehicles required for the order can be determined based on the total weight of the cargo and the total volume of the cargo, thereby improving the accuracy of the number of transport vehicles. When determining the dispatch priority coefficient, the dispatch priority coefficient can be determined based on order information, the transportation time of similar orders, and the number of transport vehicles that can be dispatched. During the calculation process, the shortage of transport vehicle resources can be determined based on the number of transport vehicles that can be dispatched. According to the shortage of transport vehicle resources, the method for determining the dispatch priority coefficient is judged. When transport vehicle resources are relatively scarce, the dispatch priority of the order is evaluated based on the number of transport vehicles, the order transportation distance, and the urgency of the order. When transport vehicles are relatively sufficient, the dispatch priority of the order is evaluated based on the execution priority and urgency of the order, thereby improving the accuracy and comprehensiveness of the dispatch priority coefficient. When determining the first relationship function, the first relationship function of the change in the inventory of goods can be determined based on historical order information, accurately describing the relationship between the storage quantity of goods, the inventory of goods, the number of orders, the number of transport vehicles, and the price of goods, thereby improving the accuracy and comprehensiveness of the first relationship function.

[0026] It should be understood that the above general description and the following detailed description are exemplary and explanatory only and do not limit the present invention. Other features and aspects of the present invention will become more apparent from the following detailed description of exemplary embodiments with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other embodiments can be obtained based on these drawings without creative work.

[0028] Figure 1 A schematic diagram of a process flow of a smart logistics management method based on big data according to an embodiment of the present invention is exemplarily shown;

[0029] Figure 2 A block diagram of a smart logistics management system based on big data according to an embodiment of the present invention is exemplarily shown. DETAILED DESCRIPTION

[0030] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0031] The technical solution of the present invention is described in detail with specific embodiments below. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments.

[0032] Figure 1 A schematic flow chart of a digital twin monitoring method for a fertilizer production process according to an embodiment of the present invention is exemplarily shown, and the method includes:

[0033] Step S101, obtaining order information and transport vehicle information of multiple orders in the current time period, wherein the order information includes: order destination, order required delivery time, order release time, order transportation distance, cargo quantity, cargo weight and cargo volume, and the transport vehicle information includes: maximum safe carrying weight and maximum safe carrying volume;

[0034] Step S102, determining the number of transport vehicles according to the order information and the transport vehicle information;

[0035] Step S103, obtaining the number of transport vehicles that can be delivered at multiple times in the current time period and the historical transport time of similar orders in multiple historical time periods, wherein the destination of the similar orders is the same as the destination of the order in the current time period;

[0036] Step S104, determining a priority delivery task according to the order information, the delivery time of similar orders and the number of available delivery vehicles;

[0037] Step S105, obtaining historical order information of multiple historical time periods, wherein the historical order information includes: historical cargo storage quantity, historical cargo inventory, historical cargo price, historical order quantity and historical transportation vehicle quantity;

[0038] Step S106, obtaining the price and storage quantity of goods in the current time period;

[0039] Step S107, determining a first relationship function of a change in the inventory quantity of goods according to the historical order information;

[0040] Step S108, managing the cargo inventory according to the first relationship function, the order quantity, the cargo storage quantity, the cargo price and the number of transport vehicles.

[0041] According to the big data-based smart logistics management method of an embodiment of the present invention, the number of transport vehicles required for an order can be determined based on the order information and transport vehicle information. Furthermore, the delivery priority of the order can be evaluated based on the order information, the number of transport vehicles required for the order, the number of transport vehicles that can be delivered and the transportation time of similar orders, and the priority delivery task can be determined. The cargo inventory can be managed based on historical order information, order quantity, number of transport vehicles, cargo storage quantity and cargo price, thereby improving the accuracy and comprehensiveness of logistics management.

[0042] According to one embodiment of the present invention, in step S101, order information and transport vehicle information of multiple orders in the current time period are obtained, wherein the order information includes: order destination, order required delivery time, order release time, order transportation distance, cargo quantity, cargo weight and cargo volume, and the transport vehicle information includes: maximum safe carrying weight and maximum safe carrying volume.

[0043] For example, through an order management system (such as an OMS system), the order destination, required delivery time, order release time, order transportation distance and quantity of goods for multiple orders are determined; the goods are weighed by weighing equipment (such as a platform scale, electronic scale, etc.) to determine the weight of the goods; the volume of the goods is measured by professional volume measurement tools (such as a 3D scanner, laser rangefinder, etc.) to determine the volume of each cargo; the maximum safe load-bearing weight and maximum safe load-bearing volume of the transport vehicle are determined through the manufacturer's label and cargo box size of the transport vehicle.

[0044] According to an embodiment of the present invention, in step S102, the number of transport vehicles is determined based on the order information and the transport vehicle information.

[0045] According to an embodiment of the present invention, determining the number of transport vehicles according to the order information and the transport vehicle information includes: determining the number of transport vehicles required for the i-th order according to formula (1): i ,

[0046]

[0047] Among them, max is the maximum value function, is the ceiling rounding function, Hw i，e is the weight of the e-th item of the i-th order in the current time period, Cl z Ht is the maximum safe load of the transport vehicle.i，e is the volume of the e-th item of the i-th order in the current time period, Cl t is the maximum safe carrying capacity of the transport vehicle, e≤E, E is the quantity of goods, and both e and E are positive integers.

[0048] According to one embodiment of the present invention, The rounded-up value of the ratio of the total weight of the goods of the i-th order in the current time period to the maximum safe load weight of the transport vehicle, indicating the number of transport vehicles determined according to the total weight of the goods of the i-th order. It is the rounded-up value of the ratio of the total volume of the cargo of the i-th order in the current time period to the maximum safe carrying volume of the transport vehicle. It indicates the number of transport vehicles determined according to the total volume of the cargo of the i-th order. Since the cargo cannot be divided into multiple transport vehicles, the above two ratios need to be rounded up to ensure that all the cargo is transported. The maximum value of the rounded-up value of the ratio of the total weight of the cargo to the maximum safe carrying weight of the transport vehicle and the rounded-up value of the ratio of the total volume of the cargo to the maximum safe carrying volume of the transport vehicle can be used to determine the situation where the maximum number of transport vehicles is required, thereby ensuring that all cargo can be safely transported to the destination in terms of both the total weight and the total volume of the cargo.

[0049] In this way, the number of transport vehicles can be determined based on the order information and transport vehicle information. During the calculation process, the number of transport vehicles required for the order can be determined based on the total weight of the goods and the total volume of the goods, thereby improving the accuracy of the number of transport vehicles.

[0050] According to one embodiment of the present invention, in step S103, the number of deliverable transport vehicles at multiple moments in the current time period and the historical transport time of similar orders in multiple historical time periods are obtained, wherein the destination of the similar orders is the same as the destination of the order in the current time period.

[0051] For example, through the management system, the operating status of the transport vehicle (such as idle, being dispatched, etc.) is obtained, the number of idle transport vehicles is used as the number of transport vehicles that can be dispatched, the historical start time and historical delivery time of multiple similar orders are obtained, and the difference between the historical delivery time and the historical start time is used as the historical transportation time.

[0052] According to an embodiment of the present invention, in step S104, a priority delivery task is determined based on the order information, the transportation time of similar orders and the number of available delivery vehicles.

[0053] According to one embodiment of the present invention, step S104 includes:

[0054] Determine a delivery priority coefficient according to the order information, the delivery time of similar orders and the number of available delivery vehicles;

[0055] Sort multiple orders in descending order according to their delivery priority coefficients to determine the order delivery priority sequence;

[0056] According to the order delivery priority sequence, a priority delivery task is determined.

[0057] For example, the delivery priority of the order is evaluated based on the order information, the transportation time of similar orders and the number of available transport vehicles, and the delivery priority coefficient is determined; the delivery priority coefficients of multiple orders are sorted in descending order from large to small. The larger the delivery priority coefficient of an order, the smaller the sequence number of the corresponding order in the order delivery priority sequence; the smaller the sequence number of an order in the order delivery priority sequence, the earlier the delivery task is arranged.

[0058] According to one embodiment of the present invention, the delivery priority coefficient is determined according to the order information, the transportation time of the similar orders and the number of the transport vehicles that can be delivered, including: determining the delivery priority coefficient Zxs of the i-th order at the k-th moment in the current time period according to formula (2): i，k ,

[0059]

[0060] Among them, if is the conditional function, α1, α2, α3, α4 and α5 are preset weights, CAR k is the number of transport vehicles that can be dispatched at the kth moment in the current time period, CAR T To preset the number of transport vehicles, Casl i is the number of transport vehicles required for the i-th order, Lu i is the order transportation distance of the i-th order, T i，r is the shipping time of similar orders of the ith order in the rth historical time period, t k is the kth moment of the current time period, T sd，i is the required delivery time of the i-th order in the current time period, T fb，i is the order release time of the i-th order in the current time period, R is the number of historical time periods, n is the number of orders, i≤n, r≤R, i, n, r and R are all positive integers.

[0061] According to one embodiment of the present invention, is the relative difference between the number of transport vehicles required for the i-th order and the average number of transport vehicles required for multiple orders. The larger the ratio, the smaller the number of transport vehicles required for the i-th order relative to the average number of transport vehicles required for multiple orders. is the relative difference between the order transportation distance of the ith order and the average order transportation distance of multiple orders. The larger the ratio is, the shorter the order transportation distance of the ith order is relative to the average order transportation distance of multiple orders. k -Tf b，i is the difference between the kth moment of the current time period and the order release time of the ith order of the current time period, indicating the waiting time of the ith order. is the average shipping time of similar orders to the i-th order in the historical time period, and represents the service time required for the i-th order. is the ratio of the sum of the waiting time and the required service time of the ith order to the required service time, indicating the response ratio of the ith order. The larger the ratio, the longer the waiting time of the order, or the shorter the required service time, indicating that the execution priority of the order is higher. sd，i -t k is the difference between the required delivery time of the i-th order in the current time period and the k-th moment in the current time period, indicating the remaining completion time of the i-th order. It is the relative difference between the average remaining completion time of multiple orders and the remaining completion time of the i-th order. The larger the ratio is, the shorter the remaining completion time of the i-th order is relative to the average remaining completion time of multiple orders, indicating that the order is more urgent.

[0062] According to one embodiment of the present invention, in formula (2), the conditional function The value of includes the following two cases: k ≤CAR T When the condition is met, the number of transport vehicles that can be dispatched at the kth moment in the current time period is less than or equal to the preset transport vehicle number threshold, indicating that the transport vehicle resources that can be dispatched in the current time period are in short supply, and the value of the condition function is It indicates that the delivery priority coefficient of the i-th order is determined according to the number of transport vehicles required for the order, the order transportation distance and the urgency of the order. The larger the coefficient is, the higher the delivery priority of the order is. When the resources of available transport vehicles are scarce, the orders with fewer transport vehicles, shorter order transportation distance and higher urgency are given priority. This can ensure that the transport vehicles can complete more delivery tasks in a short time and ensure that urgent orders can be delivered on time, thereby improving the overall distribution efficiency, reducing the waiting time of the transport vehicles, and improving the utilization rate of the transport vehicles. k ≤CAR T When the condition is met, it means that the resources of the transport vehicles available for delivery in the current time period are sufficient, and the value of the condition function is It means that the delivery priority coefficient of the i-th order is determined according to the execution priority and urgency of the order. The larger the coefficient is, the higher the delivery priority of the order is. When the resources of available transport vehicles are sufficient, orders with high execution priority and high urgency are given priority, so that transport vehicles are given priority to more important and urgent delivery tasks, thereby ensuring that key tasks are completed in a timely manner and avoiding waste of resources.

[0063] In this way, the delivery priority coefficient can be determined based on order information, transportation time of similar orders and the number of dispatchable transport vehicles. During the calculation process, the shortage of transport vehicle resources can be determined based on the number of dispatchable transport vehicles. According to the shortage of transport vehicle resources, the method of determining the delivery priority coefficient can be judged. When transport vehicle resources are relatively scarce, the delivery priority of the order is evaluated based on the number of transport vehicles, order transportation distance and the urgency of the order. When transport vehicles are relatively sufficient, the delivery priority of the order is evaluated based on the execution priority and urgency of the order, thereby improving the accuracy and comprehensiveness of the delivery priority coefficient.

[0064] According to one embodiment of the present invention, in step S105, historical order information of multiple historical time periods is obtained, wherein the historical order information includes: historical cargo storage quantity, historical cargo inventory, historical cargo price, historical order quantity and historical transport vehicle quantity.

[0065] For example, the date of the current time period is November 10th, and the management system obtains the cargo storage quantity (the cargo quantity at the beginning of the time period), cargo inventory (the cargo quantity at the end of the time period), cargo price, order quantity and transport vehicle number (the number of vehicles required for transport orders) on the 10th of each month in the previous six months as the historical cargo storage quantity, historical cargo inventory, historical cargo price, historical order quantity and historical transport vehicle number.

[0066] According to an embodiment of the present invention, in step S106, the price of goods and the storage quantity of goods in the current time period are obtained.

[0067] For example, the price of goods can be obtained through e-commerce platform inquiries and supplier consultations, and the storage quantity of goods can be obtained through the warehouse management system.

[0068] According to an embodiment of the present invention, in step S107, a first relationship function of the change in the inventory quantity of goods is determined based on the historical order information.

[0069] According to an embodiment of the present invention, determining a first relationship function of the change in the inventory of goods based on the historical order information includes: obtaining a first undetermined coefficient equation of the first relationship function based on formula (3),

[0070]

[0071] Among them, Kcsl r is the historical inventory of goods in the rth historical time period, Hw r is the historical cargo storage quantity in the rth historical time period, Δt is the duration of the time period, SQ r is the number of historical orders in the rth historical time period, XS r is the number of historical transport vehicles in the rth historical time period, HG r is the historical commodity price of the rth historical time period, γ1, γ2, γ3, γ4, γ5, γ6 and γ7 are the first undetermined coefficients of the first undetermined coefficient equation;

[0072] Solving the first undetermined coefficient according to the historical order information to obtain a solution value of the first undetermined coefficient;

[0073] The first relationship function is obtained according to the solved value of the first undetermined coefficient and the first undetermined coefficient equation.

[0074] According to one embodiment of the present invention, is the ratio of the difference between the historical cargo storage quantity in the rth historical time period and the historical cargo inventory in the rth historical time period to the length of the time period, indicating the cargo inventory decline rate in the rth historical time period, (γ1SQ r +γ2) indicates that the order quantity and the inventory decline rate are positively correlated. When the order quantity is larger, the quantity of goods required is larger, and the inventory decline rate in the warehouse is larger. (γ3XS r +γ4) indicates that the number of transport vehicles and the rate of decline in cargo inventory are positively correlated. When the number of transport vehicles is greater, the amount of cargo transported is greater, the cargo inventory is consumed faster, and the rate of decline in cargo inventory is greater. It means that there is a negative correlation between the price of goods and the rate of decline in goods inventory. When the price of goods is higher, the demand for goods will decrease or the sales volume will decrease. The inventory of goods may slow down the consumption rate due to the decrease in demand and sales volume, and the rate of decline in goods inventory will be smaller.

[0075] According to one embodiment of the present invention, fitting can be performed based on multiple parameters involved in the above-mentioned undetermined coefficient equation, that is, fitting can be performed based on historical cargo inventory, historical cargo storage quantity, historical order quantity, historical transport vehicle quantity and historical cargo price to solve the above-mentioned multiple first undetermined coefficients. There are 7 first undetermined coefficients, namely, γ1, γ2, γ3, γ4, γ5, γ6 and γ7. According to historical cargo inventory, historical cargo storage quantity, historical order quantity, historical transport vehicle quantity and historical cargo price, the above-mentioned 7 first undetermined coefficients are solved to obtain the solution values ​​of the above-mentioned 7 first undetermined coefficients, and the solution values ​​of the above-mentioned 7 first undetermined coefficients are substituted into the first relationship function to determine the first undetermined function.

[0076] In this way, the first relationship function of the change in cargo inventory can be determined based on historical order information, accurately describing the relationship between cargo storage quantity, cargo inventory, order quantity, number of transport vehicles and cargo price, thereby improving the accuracy and comprehensiveness of the first relationship function.

[0077] According to an embodiment of the present invention, in step S108, the cargo inventory is managed according to the first relationship function, the order quantity, the cargo storage quantity, the cargo price and the transport vehicle quantity.

[0078] According to one embodiment of the present invention, step S108 includes:

[0079] Determine the cargo inventory according to the first relationship function, the cargo price, the order quantity, the number of transport vehicles and the cargo storage quantity;

[0080] Determine the goods inventory result according to the goods inventory, the preset minimum inventory threshold and the preset maximum inventory threshold;

[0081] A method for managing the goods inventory is determined based on the goods inventory result.

[0082] For example, the price of goods, the number of orders, the number of transport vehicles and the number of goods stored are substituted into the first relationship function to determine the inventory of goods; if the inventory of goods is less than the preset minimum inventory threshold, it means that there is a shortage of goods, and the result of the goods inventory is -1; if the inventory of goods is greater than or equal to the preset minimum inventory threshold and less than or equal to the preset maximum inventory threshold, it means that the inventory of goods is in good condition, and the result of the goods inventory is 0; if the inventory of goods is greater than the preset maximum inventory threshold, it means that there is an excess of goods, and the result of the goods inventory is 1; according to the result of the goods inventory, the inventory status is evaluated to determine the management method of the goods inventory.

[0083] According to an embodiment of the present invention, determining a management method for the goods inventory according to the goods inventory result includes:

[0084] If the goods inventory result is greater than the preset inventory status threshold, an overstock warning is issued;

[0085] If the goods inventory result is equal to the preset inventory status threshold, no inventory warning will be issued;

[0086] If the goods inventory result is less than the preset inventory status threshold, an inventory shortage warning is issued.

[0087] For example, if the goods inventory result is greater than the preset inventory status threshold, it means that the inventory is in excess, and an inventory excess warning is issued. At the same time, the goods price, product promotion, etc. are adjusted to prevent losses caused by inventory backlogs; if the goods inventory result is equal to the preset inventory status threshold, it means that the inventory is in normal status. At this time, no inventory warning is issued, and the goods inventory is continuously monitored; if the goods inventory result is less than the preset inventory status threshold, it means that the inventory is in shortage, and an inventory shortage warning is issued. At the same time, the efficiency of goods procurement is improved, the quantity of goods procurement is increased, and it is ensured that there is enough goods to meet order requirements.

[0088] According to the smart logistics management method based on big data of the embodiment of the present invention, the number of transport vehicles required for an order can be determined based on the order information and the transport vehicle information. Furthermore, the delivery priority of the order can be evaluated based on the order information, the number of transport vehicles required for the order, the number of transport vehicles that can be dispatched, and the transportation time of similar orders, and the priority delivery task can be determined. The cargo inventory can be managed based on the historical order information, the order quantity, the number of transport vehicles, the cargo storage quantity, and the cargo price, thereby improving the accuracy and comprehensiveness of logistics management. When determining the number of transport vehicles, the number of transport vehicles can be determined based on the order information and the transport vehicle information. During the calculation process, the number of transport vehicles required for the order can be determined based on the total weight of the cargo and the total volume of the cargo, thereby improving the accuracy of the number of transport vehicles. When determining the dispatch priority coefficient, the dispatch priority coefficient can be determined based on order information, the transportation time of similar orders, and the number of transport vehicles that can be dispatched. During the calculation process, the shortage of transport vehicle resources can be determined based on the number of transport vehicles that can be dispatched. According to the shortage of transport vehicle resources, the method for determining the dispatch priority coefficient is judged. When transport vehicle resources are relatively scarce, the dispatch priority of the order is evaluated based on the number of transport vehicles, the order transportation distance, and the urgency of the order. When transport vehicles are relatively sufficient, the dispatch priority of the order is evaluated based on the execution priority and urgency of the order, thereby improving the accuracy and comprehensiveness of the dispatch priority coefficient. When determining the first relationship function, the first relationship function of the change in the inventory of goods can be determined based on historical order information, accurately describing the relationship between the storage quantity of goods, the inventory of goods, the number of orders, the number of transport vehicles, and the price of goods, thereby improving the accuracy and comprehensiveness of the first relationship function.

[0089] Figure 2 A block diagram of a smart logistics management system based on big data according to an embodiment of the present invention is exemplarily shown, and the system includes:

[0090] An information acquisition module, used to acquire order information and transport vehicle information of multiple orders in the current time period, wherein the order information includes: order destination, order required delivery time, order release time, order transportation distance, cargo quantity, cargo weight and cargo volume; the transport vehicle information includes: maximum safe carrying weight and maximum safe carrying volume;

[0091] A transport vehicle quantity module, used to determine the number of transport vehicles according to the order information and the transport vehicle information;

[0092] An order logistics analysis module, used to obtain the number of transport vehicles that can be delivered at multiple times in the current time period and the historical transportation time of similar orders in multiple historical time periods, wherein the destination of the similar orders is the same as the destination of the order in the current time period;

[0093] A delivery task determination module, which determines a priority delivery task according to the order information, the transportation time of similar orders and the number of available delivery vehicles;

[0094] A historical information acquisition module is used to acquire historical order information of multiple historical time periods, wherein the historical order information includes: historical cargo storage quantity, historical cargo inventory, historical cargo price, historical order quantity and historical transportation vehicle quantity;

[0095] The cargo information module is used to obtain the cargo price and cargo storage quantity in the current time period;

[0096] A first relationship function module, used to determine a first relationship function of the change in the inventory quantity of goods according to the historical order information;

[0097] The inventory management module is used to manage the inventory of goods according to the first relationship function, the order quantity, the storage quantity of the goods, the price of the goods and the number of transport vehicles.

[0098] The present invention may be a method, an apparatus, a system and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for executing various aspects of the present invention.

[0099] It should be understood by those skilled in the art that the embodiments of the present invention described above and shown in the accompanying drawings are only examples and do not limit the present invention. The purpose of the present invention has been fully and effectively achieved. The functional and structural principles of the present invention have been demonstrated and explained in the embodiments, and the embodiments of the present invention may be deformed or modified in any way without departing from the principles.

Claims

1. A smart logistics management method based on big data, characterized in that: include: Obtaining order information and transport vehicle information of multiple orders in the current time period, wherein the order information includes: order destination, order required delivery time, order release time, order transportation distance, cargo quantity, cargo weight and cargo volume; the transport vehicle information includes: maximum safe carrying weight and maximum safe carrying volume; Determine the number of transport vehicles according to the order information and the transport vehicle information; Obtaining the number of transport vehicles available for delivery at multiple times in the current time period and the historical transport time of similar orders in multiple historical time periods, wherein the destination of the similar orders is the same as the destination of the order in the current time period; Determine the priority delivery task according to the order information, the delivery time of similar orders and the number of available delivery vehicles; Acquire historical order information of multiple historical time periods, wherein the historical order information includes: historical cargo storage quantity, historical cargo inventory, historical cargo price, historical order quantity and historical transportation vehicle quantity; Get the price and storage quantity of goods in the current time period; Determine a first relationship function of a change in the inventory quantity of goods according to the historical order information; The cargo inventory is managed according to the first relationship function, the order quantity, the cargo storage quantity, the cargo price and the number of transport vehicles.

2. The intelligent logistics management method based on big data according to claim 1 is characterized in that: Determining the number of transport vehicles according to the order information and the transport vehicle information includes: According to the formula Determine the number of transport vehicles required for the i-th order Casl i , where max is the maximum value function, is the ceiling rounding function, Hw i,e is the weight of the e-th item of the i-th order in the current time period, Cl z Ht is the maximum safe load of the transport vehicle. i，e is the volume of the e-th item of the i-th order in the current time period, Cl t is the maximum safe carrying capacity of the transport vehicle, e≤E, E is the quantity of goods, and both e and E are positive integers.

3. The intelligent logistics management method based on big data according to claim 1 is characterized in that: Determine the priority delivery task according to the order information, the transportation time of similar orders and the number of available delivery vehicles, including: Determine a delivery priority coefficient according to the order information, the delivery time of similar orders and the number of available delivery vehicles; Sort multiple orders in descending order according to their delivery priority coefficients to determine the order delivery priority sequence; According to the order delivery priority sequence, a priority delivery task is determined.

4. The intelligent logistics management method based on big data according to claim 3 is characterized in that: Determine the delivery priority coefficient according to the order information, the transportation time of similar orders and the number of transport vehicles available for delivery, including: According to the formula Determine the delivery priority coefficient Zxs of the i-th order at the k-th moment in the current time period i，k , where if is the conditional function, α1, α2, α3, α4 and α5 are preset weights, and CAR k is the number of transport vehicles that can be dispatched at the kth moment in the current time period, CAR T To preset the number of transport vehicles, Casl i is the number of transport vehicles required for the i-th order, Lu i is the order transportation distance of the i-th order, T i，r is the shipping time of similar orders of the ith order in the rth historical time period, t k is the kth moment of the current time period, T sd，i is the required delivery time of the i-th order in the current time period, T fb,i is the order release time of the i-th order in the current time period, R is the number of historical time periods, n is the number of orders, i≤n, r≤R, i, n, r and R are all positive integers.

5. The intelligent logistics management method based on big data according to claim 1 is characterized in that: Determining a first relationship function of a change in the inventory quantity of goods according to the historical order information includes: According to the formula Obtain the first undetermined coefficient equation of the first relationship function, where Kcsl r is the historical inventory of goods in the rth historical time period, Hw r is the historical cargo storage quantity in the rth historical time period, Δt is the duration of the time period, SQ r is the number of historical orders in the rth historical time period, XS r is the number of historical transport vehicles in the rth historical time period, HG r is the historical commodity price of the rth historical time period, γ1, γ2, γ3, γ4, γ5, γ6 and γ7 are the first undetermined coefficients of the first undetermined coefficient equation; Solving the first undetermined coefficient according to the historical order information to obtain a solution value of the first undetermined coefficient; The first relationship function is obtained according to the solved value of the first undetermined coefficient and the first undetermined coefficient equation.

6. The intelligent logistics management method based on big data according to claim 1 is characterized in that: Managing the cargo inventory according to the first relationship function, the order quantity, the cargo storage quantity, the cargo price and the number of transport vehicles includes: Determine the cargo inventory according to the first relationship function, the cargo price, the order quantity, the number of transport vehicles and the cargo storage quantity; Determine the goods inventory result according to the goods inventory, the preset minimum inventory threshold and the preset maximum inventory threshold; A method for managing the goods inventory is determined based on the goods inventory result.

7. The intelligent logistics management method based on big data according to claim 6 is characterized in that: Determine the management method of the goods inventory according to the goods inventory results, including: If the goods inventory result is greater than the preset inventory status threshold, an overstock warning is issued; If the goods inventory result is equal to the preset inventory status threshold, no inventory warning will be issued; If the goods inventory result is less than the preset inventory status threshold, an inventory shortage warning is issued.

8. A smart logistics management system based on big data, characterized in that: include: An information acquisition module, used to acquire order information and transport vehicle information of multiple orders in the current time period, wherein the order information includes: order destination, order required delivery time, order release time, order transportation distance, cargo quantity, cargo weight and cargo volume; the transport vehicle information includes: maximum safe carrying weight and maximum safe carrying volume; A transport vehicle quantity module, used to determine the number of transport vehicles according to the order information and the transport vehicle information; An order logistics analysis module, used to obtain the number of transport vehicles that can be delivered at multiple times in the current time period and the historical transportation time of similar orders in multiple historical time periods, wherein the destination of the similar orders is the same as the destination of the order in the current time period; A delivery task determination module, used to determine a priority delivery task according to the order information, the transportation time of similar orders and the number of transport vehicles available for delivery; A historical information acquisition module is used to acquire historical order information of multiple historical time periods, wherein the historical order information includes: historical cargo storage quantity, historical cargo inventory, historical cargo price, historical order quantity and historical transportation vehicle quantity; The cargo information module is used to obtain the cargo price and cargo storage quantity in the current time period; A first relationship function module, used to determine a first relationship function of the change in the inventory quantity of goods according to the historical order information; The inventory management module is used to manage the inventory of goods according to the first relationship function, the order quantity, the storage quantity of the goods, the price of the goods and the number of transport vehicles.

9. A computer-readable storage medium, characterized in that: Computer program instructions are stored thereon, and when the computer program instructions are executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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