A smart logistics supply chain management method and system

Abstract

The present invention relates to the field of logistics supply chain technology, and discloses an intelligent logistics supply chain management method and system, the method comprising: obtaining order data of the supply chain in real time; performing order analysis according to the receipt information, generating a delivery task based on the order analysis result, and determining the first delivery path of the current delivery task; planning the optimal path for logistics delivery of supply chain orders; monitoring and updating the inventory information of the supply chain in real time, and dynamically adjusting the inventory capacity. The present invention can realize the division of orders, realize refined order management, determine multiple order sets that can be delivered one way at a time, determine the delivery tasks of the delivery orders that meet the conditions through the delivery target model, dynamically adjust the optimal path, and can maximize the delivery efficiency, shorten the delivery time and reduce the logistics cost. Real-time monitoring of inventory information can improve the visibility of inventory, and can manage inventory more scientifically.

Description

Technical Field

[0001] The present invention relates to the field of logistics supply chain technology, and in particular to an intelligent logistics supply chain management method and system. Background Art

[0002] With the development of Internet technology and the popularization of Internet application users, the types and areas of logistics have also expanded. At present, with the rapid development of e-commerce, online transactions have become a more frequently used commodity trading method in people's daily lives. The e-commerce logistics supply chain has developed rapidly, and the role of logistics supply chain transportation in various industries has become more and more important. The logistics transportation and distribution tasks have become more arduous, and the efficiency requirements for transportation and distribution are also higher. Distribution route planning is an important part of logistics supply chain management, which mainly completes the part of cargo transportation.

[0003] The existing traditional logistics supply chain path planning method is usually that the transportation personnel directly search on the navigation software, but this navigation path cannot be efficiently and accurately planned based on the delivery time and transportation cost of multiple orders. It is inefficient, has high operating costs, and low user satisfaction. Relying solely on the previous manual determination of delivery routes is no longer suitable for the current logistics supply chain management situation. Summary of the invention

[0004] The purpose of this section is to summarize some aspects of embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0005] In view of the above existing problems, the present invention is proposed. Therefore, the present invention provides an intelligent logistics supply chain management method to solve the above problems.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, the present invention provides an intelligent logistics supply chain management method, comprising:

[0008] Obtain supply chain order data in real time;

[0009] Based on the order data, obtaining the delivery information in the order, performing order analysis according to the delivery information, generating a delivery task based on the order analysis result, and determining a first delivery path for the current delivery task;

[0010] Based on the delivery tasks, plan the optimal path for supply chain order logistics delivery and update the order status in real time;

[0011] Based on the order data, the inventory information of the supply chain is monitored and updated in real time, inventory change events are recorded, and inventory changes are predicted to dynamically adjust the inventory capacity of goods.

[0012] As a preferred solution of the intelligent logistics supply chain management method of the present invention, the real-time acquisition of supply chain order data includes:

[0013] Acquire the order data to be delivered received by the management terminal in real time, and extract the delivery address in the order according to the order data;

[0014] Based on the area where the delivery address is located, according to the preset area range, determine the area range to which the area where the delivery address of the current order is located belongs, and determine the delivery site of the order;

[0015] According to the to-be-delivered order data, key order features are extracted, where the key order features include: order time, product name, product quantity, delivery address, and estimated delivery time.

[0016] As a preferred solution of the intelligent logistics supply chain management method of the present invention, the order analysis according to the receipt information includes:

[0017] Generate a first delivery order set for each delivery site according to the order delivery sites to which the orders to be delivered belong;

[0018] According to the estimated delivery time of the order, the delivery order set is sorted in descending order to generate a second delivery order set of the current delivery site, and if there is a newly generated order to be delivered, the second delivery order set is updated in real time;

[0019] Define the time window length and the distance between orders, and each order meets the single delivery constraint. Construct the delivery target model and constraints, which can be expressed as:

[0020] ;

[0021] ;

[0022] ;

[0023] ;

[0024] ;

[0025] ;

[0026] in, Indicates the total number of orders, , Indicates the order index number. Indicates order Estimated delivery time window, Indicates order To order Straight-line distance from address, Indicates the maximum time window length allowed between orders, Indicates the distance threshold between orders, , , represents a binary variable.

[0027] As a preferred solution of the intelligent logistics supply chain management method of the present invention, determining the first delivery path of the current delivery task includes:

[0028] According to the orders in the second delivery order set, the order with the largest ranking is taken as the target order in turn, and the delivery target model is used to solve the order, and the delivery task is determined according to the solution result;

[0029] The delivery task includes extracting a target order from the second delivery order set and generating a one-way task set centered on the target order;

[0030] According to the delivery addresses and delivery site addresses of all orders in the one-way task set, the map coordinate information and site coordinate information of all orders are obtained, the site coordinates are used as the starting point of the path, the coordinates of the target order are used as the first delivery node, and the starting point and the first delivery node are connected with the shortest distance as the path target to generate the first delivery path of the one-way task set.

[0031] As a preferred solution of the intelligent logistics supply chain management method of the present invention, the optimal path for planning supply chain order logistics distribution includes:

[0032] Step 1: Determine other order nodes in the current path by combining the coordinate information of orders other than the target order in the one-way task set;

[0033] Step 2: Based on the first delivery path, select the order node closest to the latest delivery node in the one-way task set, and if the current order node meets the estimated delivery time window of the order, then determine the current order node as the next delivery node of the first delivery path;

[0034] Step 3, repeating step 2 until all orders in the one-way task set have completed the judgment of the delivery nodes, added to the delivery nodes in the delivery path, and connecting all the delivery nodes in sequence with the shortest distance as the path target to generate a second delivery path;

[0035] Step 4: Select two delivery nodes in the second delivery path, exchange two pairs of edges of each pair of edges of the two delivery nodes, determine whether each order node meets the estimated delivery time window of the order, and if so, generate at least one third delivery path;

[0036] Step 5: Compare the third delivery path with the second delivery path one by one, calculate the change in path length between the two paths, and determine the path with the shortest distance between the third delivery path and the second delivery path as the optimal path for the current one-way task set.

[0037] As a preferred solution of the intelligent logistics supply chain management method of the present invention, the real-time monitoring and updating of supply chain inventory information and recording of inventory change events include:

[0038] Based on the data of the orders to be delivered, the order status, logistics status, and inventory data are monitored in real time, and the inventory information of the ordered goods is updated in real time, and an inventory warning threshold is set. If the real-time inventory of the goods is less than the inventory warning threshold, an inventory warning is issued;

[0039] Record inventory change events, including inventory change category, time, quantity, and inventory level, and associate change events with business.

[0040] As a preferred solution of the intelligent logistics supply chain management method of the present invention, the dynamic adjustment of commodity inventory capacity includes:

[0041] Based on historical order data and inventory consumption event records, machine learning algorithms are used to predict inventory changes in the future.

[0042] Generate a product inventory replenishment list based on the forecast results and the product inventory safety threshold;

[0043] Dynamically adjust product pricing strategies and formulate product marketing activities based on product attribute values.

[0044] In a second aspect, the present invention provides an intelligent logistics supply chain management system, comprising:

[0045] Acquisition module, used to obtain supply chain order data in real time;

[0046] An analysis module, configured to obtain delivery information in the order based on the order data, perform order analysis based on the delivery information, generate a delivery task based on the order analysis result, and determine a first delivery path for the current delivery task;

[0047] A planning module is used to plan the optimal path for supply chain order logistics distribution based on the distribution task and update the order status in real time;

[0048] The inventory management module is used to monitor and update the inventory information of the supply chain in real time based on the order data, record inventory change events, predict inventory changes, and dynamically adjust the inventory capacity of goods.

[0049] In a third aspect, the present invention provides an electronic device, comprising:

[0050] Memory and processor;

[0051] The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the intelligent logistics supply chain management method are implemented.

[0052] In a fourth aspect, the present invention provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the intelligent logistics supply chain management method.

[0053] Compared with the prior art, the present invention has the following beneficial effects: the present invention realizes the division of orders by acquiring the order information that needs to be delivered and determining the delivery site to which the order belongs; realizes refined order management by analyzing the receipt information in the order, determines a plurality of order sets that can be delivered one way at a time, determines the delivery tasks of the delivery orders that meet the conditions through the delivery target model, can dynamically adjust the optimal path according to the current delivery task, can maximize the delivery efficiency, shorten the delivery time and reduce the logistics cost; real-time monitoring of inventory information can improve the visibility of inventory, and by recording inventory change events and making predictions and adjustments, can manage inventory more scientifically and reduce the risk of inventory backlog and out-of-stock goods. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative labor.

[0055] Figure 1 The figure is a schematic diagram of the overall process of an intelligent logistics supply chain management method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0056] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0057] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0058] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0059] The present invention is described in detail with reference to schematic diagrams. When describing the embodiments of the present invention, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0060] At the same time, in the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "upper, lower, inner and outer" are based on the directions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as limiting the present invention. In addition, the terms "first, second or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0061] In the present invention, unless otherwise clearly specified and limited, the terms "install, connect, connect" should be understood in a broad sense, for example: it can be a fixed connection, a detachable connection or an integral connection; it can also be a mechanical connection, an electrical connection or a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0062] Embodiment 1:

[0063] Reference Figure 1, is an embodiment of the present invention, and provides an intelligent logistics supply chain management method, comprising:

[0064] S100, real-time access to supply chain order data;

[0065] Preferably, real-time acquisition of supply chain order data includes:

[0066] Obtain the order data to be delivered received by the management terminal in real time, and extract the delivery address in the order based on the order data;

[0067] Based on the area where the delivery address is located, according to the preset area range, determine the address area range to which the delivery address of the current order belongs, and determine the delivery site of the order;

[0068] Based on the data of orders to be delivered, the key features of the orders are extracted. The key features of the orders include: order time, product name, product quantity, delivery address and estimated delivery time.

[0069] Specifically, the management terminal will receive new order information generated in real time on the user side. The initial state of the new order is defined as an order to be delivered. The order data to be delivered includes delivery information, product information, and basic order information. The delivery information includes the name, telephone number, and delivery address of the consignee, etc. The product information includes the product name, product data, product price, etc. The basic order information includes the order time, order amount, and expected delivery time range. In this embodiment, multiple delivery sites will be defined in each large delivery area, and the delivery area range will be divided for each delivery site. The area range can be set according to the actual local environment, and there is no overlapping area in each area range. According to the area range to which the delivery address of each order belongs, it is determined which delivery site will deliver the order. Further, key features related to delivery in the order data to be delivered are extracted, including but not limited to the order time, product name, product quantity, delivery address, and expected delivery time.

[0070] It should be noted that this step can obtain the order information that needs to be delivered, quickly respond to changes in customer demand, and determine the delivery site to which the order belongs, thereby realizing the division of orders, and the delivery sites in the area can efficiently complete the order delivery, optimize the allocation of delivery resources, and improve resource utilization.

[0071] S200, based on the order data, obtaining the delivery information in the order, and performing order analysis according to the delivery information, generating a delivery task based on the order analysis result, and determining a first delivery path of the current delivery task;

[0072] Preferably, order analysis based on delivery information includes:

[0073] Step 1: Generate a first delivery order set for each delivery site according to the order delivery sites to which the orders to be delivered belong;

[0074] Specifically, all orders to be delivered at the delivery sites are collected, and these orders are integrated to generate the first delivery order set at each delivery site. If there is a newly generated order to be delivered, the order is added and the first delivery order set is updated to ensure that no order to be delivered is missed.

[0075] Step 2: sort the delivery order set in descending order according to the estimated delivery time of the order, generate a second delivery order set for the current delivery site, and update the second delivery order set in real time if there is a newly generated order to be delivered;

[0076] Specifically, the estimated delivery time of the order is automatically generated by the system. The generation rules can take into account the order placement time and the delivery address of the order, and estimate the estimated delivery time period of the order, which is composed of the time interval of the earliest estimated delivery time and the latest estimated delivery time. In this embodiment, the time interval of the estimated delivery time period can be defined as 0.5h, and the orders are arranged in descending order according to the estimated delivery time, which fully considers the time characteristics of the orders, gives priority to the delivery of orders with the shortest current delivery time, improves user satisfaction, and enhances user stickiness.

[0077] Step 3: Define the time window length and the distance between orders, and each order meets the single delivery constraint, which is achieved by minimizing the total delivery path length. Construct the delivery target model and constraints, which are expressed as:

[0078] ;

[0079] The constraints are expressed as:

[0080] ;

[0081] ;

[0082] The above two constraints can ensure that the actual delivery time of two orders is the same when they are combined for delivery, which means that if the order and Orders are arranged on the same delivery route (i.e. ), their time windows have sufficient overlap.

[0083] ;

[0084] ;

[0085] The above constraints mean that for any order , it can only belong to one delivery;

[0086] ;

[0087] The above constraint means that if the straight-line distance between two order addresses exceeds , then these two orders will not be considered for combined delivery;

[0088] ;

[0089] in, Indicates the total number of orders, , Indicates the order index number. Indicates order Estimated delivery time window, Indicates order To order Straight-line distance from address, Indicates the maximum time window length allowed between orders, which means that in a delivery journey, the overlap of the estimated delivery time windows between orders must have at least a certain amount of overlap time. Indicates the distance threshold between orders, , , represents a binary variable, Indicates order and Orders On the same one-way delivery route, otherwise 0, Indicates order Selected into the current delivery batch.

[0090] Preferably, determining the first delivery path of the current delivery task includes:

[0091] Step 1: according to the orders in the second delivery order set, the order with the largest ranking is taken as the target order in turn, and the delivery target model is used to solve the problem, and the delivery task is determined according to the solution result;

[0092] The delivery task includes extracting a target order from the second delivery order set and generating a one-way task set centered on the target order;

[0093] Specifically, the orders in the second delivery order set are sorted according to the estimated delivery time of the orders, and the order with the largest sorting is taken as the target order, that is, the order ranked first in the current second delivery order set is taken as the target order, and solved by the delivery target model, wherein commercial optimization software (such as CPLEX, Gurobi) or open source solvers (such as SCIP, CBC) can be used to solve, so as to obtain other orders in the second delivery set that meet the one-way delivery requirements of the target order, and generate a one-way task set, which represents multiple order sets that can be delivered in the same batch, which can save costs and improve delivery efficiency.

[0094] Step 2: According to the delivery addresses and delivery site addresses of all orders in the one-way task set, the map coordinate information and site coordinate information of all orders are obtained. The site coordinates are used as the starting point of the path, the coordinates of the target order are used as the first delivery node, and the shortest distance is used as the path target to connect the starting point and the first delivery node to generate the first delivery path of the one-way task set.

[0095] It should be noted that in this embodiment, considering that in each delivery task, the estimated delivery time of the target order arrives first, and the estimated delivery times of other orders in the one-way task set are all after the target order, the coordinates of the target order are used as the first delivery node, and the first delivery path is preliminarily planned to ensure the priority of the target order delivery.

[0096] By analyzing the delivery information in the order, we can better understand the actual needs of customers and achieve refined order management; by arranging them in descending order by the estimated delivery time and determining multiple order sets that can be delivered in one go, we can maximize delivery efficiency and reduce the cost caused by multiple round trips; by determining the delivery order tasks that meet the conditions through the delivery target model, we can reduce unnecessary distances, shorten delivery time, and reduce logistics costs; at the same time, ensuring that orders are delivered on time can improve customer satisfaction and enhance brand image.

[0097] S300, based on delivery tasks, plans the optimal path for supply chain order logistics delivery and updates the order status in real time.

[0098] Preferably, planning the optimal path for supply chain order logistics distribution includes:

[0099] Step 1: Determine other order nodes in the current path by combining the coordinate information of orders other than the target order in the one-way task set;

[0100] Step 2: Based on the first delivery path, select the order node closest to the latest delivery node in the one-way task set, and if the current order node meets the estimated delivery time window of the order, then determine the current order node as the next delivery node of the first delivery path;

[0101] Specifically, according to the first delivery path, before this step is implemented, initially, there are only the site starting point and the first delivery node of the target order in the path. When adding the second delivery node, select the order node closest to the first delivery node among the nodes of other orders in the one-way task set except the target order, and at the same time meet the estimated delivery time window of the order, then the order node is used as the second delivery node. If the estimated delivery time window of the order is not met, the order node is skipped; when adding the third delivery node, select the order node closest to the first delivery node, and so on;

[0102] Step 3: Repeat step 2 until all orders in the one-way task set have completed the judgment of the delivery nodes, added to the delivery nodes in the delivery path, and connected all the delivery nodes in sequence with the shortest distance as the path target to generate a second delivery path;

[0103] Specifically, if, in the process of determining the order delivery node, until the node judgment of all orders is completed, there is still a situation that the estimated delivery time window is not met, the order is removed from the one-way task set and updated and arranged in the second delivery order set;

[0104] Step 4: Select two delivery nodes in the second delivery path, exchange two pairs of edges of each pair of edges of the two delivery nodes, and determine whether each order node meets the estimated delivery time window of the order. If so, generate at least one third delivery path;

[0105] Step 5: Compare the third delivery path with the second delivery path one by one, calculate the change in path length between the two paths, and determine the path with the shortest distance between the third delivery path and the second delivery path as the optimal path for the current one-way task set.

[0106] Exemplarily, the order node closest to the latest delivery node in the one-way task set is selected, where the distance calculation formula is expressed as:

[0107] ;

[0108] in, , Respectively indicate orders , The address coordinates of

[0109] Further, select the next order It is expressed as:

[0110] ;

[0111] in, represents the set of delivery nodes determined in the first delivery path;

[0112] Further, two delivery nodes of the order in the second delivery path are selected and , so that , these two points can be any two points, usually choose points that are farther apart to produce a larger change, and then swap the path between the two points. Specifically, swap the edges and , and create new edges and , calculate the change in path length between two paths, expressed as:

[0113] ;

[0114] in, Indicates the change in path length after exchanging two pairs of edges in the path. This change is used to evaluate whether the path will become shorter or longer after the exchange process. If it is a negative value, it means that the path becomes shorter after the exchange; if it is a positive value, it means that the path becomes longer after the exchange.

[0115] For example, it is determined that the current order node meets the estimated delivery time window of the order. , and its estimated delivery time window is ,in Indicates the earliest delivery time. Indicates the latest delivery time, obtained from the current location To order Destination Driving time , and in Service hours , currently need to ensure:

[0116] ;

[0117] in, Represents the current time, which is the cumulative service time of completed orders plus the travel time from the delivery station to the current order location;

[0118] Initialization time, the time when starting from the distribution station It can be set to the time when delivery starts. Every time we complete an order, we need to update the current time : If at any time , the current path is not feasible.

[0119] It should be noted that this process ensures that the time window constraints of each order are taken into account when planning the route, thereby ensuring that the actual delivery time of all orders is within the estimated delivery time range.

[0120] Finally, the order status is updated in real time, including the delivery personnel information, order process status, delivery location, and other abnormal situations (returns / out of stock / complaints) and other data.

[0121] Dynamically adjust the optimal route based on the current delivery task to ensure smooth delivery; real-time update of order status allows customers to understand the progress of their orders at any time, increases transparency, responds to abnormal situations more quickly, and reduces delays and customer complaints; optimized route planning can reduce invalid mileage and reduce transportation costs.

[0122] S400, based on order data, monitors and updates the inventory information of the supply chain in real time, records inventory change events, predicts inventory changes, and dynamically adjusts the inventory capacity of goods.

[0123] Preferably, real-time monitoring and updating of supply chain inventory information and recording of inventory change events include:

[0124] Based on the data of orders to be delivered, the order status, logistics status, and inventory data are monitored in real time, and the inventory information of the ordered goods is updated in real time. The inventory warning threshold is set. If the real-time inventory of the goods is less than the inventory warning threshold, an inventory warning is issued;

[0125] Record inventory change events, including inventory change category, time, quantity, and inventory level, and associate change events with business.

[0126] Specifically, real-time monitoring data, when an order is confirmed, automatically update inventory information to reflect inventory changes, set thresholds and alarm mechanisms, detect abnormal situations (such as insufficient inventory, order delays, etc.), and notify in time. The inventory warning threshold can be determined based on actual operating conditions; record inventory change events, including inventory consumption and inventory increase events. Inventory consumption events may include each shipment, internal use or expired product cleanup, and inventory increase events may include inventory replenishment or return events. Consumption events are associated with business activities such as orders, production activities, and returns to facilitate subsequent tracking and analysis.

[0127] Preferably, dynamically adjusting the commodity inventory capacity includes:

[0128] Based on historical order data and inventory consumption event records, machine learning algorithms are used to predict inventory changes in the future.

[0129] Generate a product inventory replenishment list based on the forecast results and the product inventory safety threshold;

[0130] Dynamically adjust product pricing strategies and formulate product marketing activities based on product attribute values.

[0131] Specifically, historical order data, inventory change events and other data are used to predict inventory changes in a certain period of time in the future through statistical analysis or machine learning models. At the same time, factors such as seasonal fluctuations and promotional activities are taken into consideration, and the prediction model parameters are adjusted. Based on the prediction results, the replenishment process is automatically triggered to ensure that the inventory level is maintained within a reasonable range. Among them, a reasonable safety stock level, namely the inventory safety threshold, is set according to historical fluctuations and prediction errors to cope with uncertainties, regularly optimize the inventory structure, reduce expired or unsalable inventory, and improve turnover.

[0132] It should be noted that real-time monitoring of inventory information can improve inventory visibility, help managers understand inventory status in a timely manner, and avoid out-of-stock or overstock situations. By recording inventory change events and making predictions, inventory can be managed more scientifically, reducing inventory backlogs and out-of-stock risks. Dynamic adjustment of inventory capacity can adjust inventory levels according to actual demand and avoid storage costs and capital occupation costs caused by excessive inventory. Predicting inventory changes can help companies prepare in advance and improve their ability to respond to changes in market demand.

[0133] In summary, the present invention can realize the optimization of the comprehensive logistics supply chain from order reception, analysis, distribution route planning to inventory management. This method not only improves logistics efficiency and customer satisfaction, but also reduces operating costs and enhances the competitiveness of enterprises.

[0134] The above is a schematic scheme of an intelligent logistics supply chain management method of this embodiment. It should be noted that the technical scheme of the intelligent logistics supply chain management system and the technical scheme of the above-mentioned intelligent logistics supply chain management method belong to the same concept. For the details not described in detail in the technical scheme of the intelligent logistics supply chain management system in this embodiment, please refer to the description of the technical scheme of the above-mentioned intelligent logistics supply chain management method.

[0135] The intelligent logistics supply chain management system in this embodiment includes:

[0136] Acquisition module, used to obtain supply chain order data in real time;

[0137] The analysis module is used to obtain the delivery information in the order based on the order data, and perform order analysis based on the delivery information, generate a delivery task based on the order analysis result, and determine the first delivery path of the current delivery task;

[0138] The planning module is used to plan the optimal path for supply chain order logistics distribution based on the distribution tasks, and update the order status in real time;

[0139] The inventory management module is used to monitor and update the inventory information of the supply chain in real time based on order data, record inventory change events, predict inventory changes, and dynamically adjust the inventory capacity of goods.

[0140] This embodiment also provides an electronic device suitable for intelligent logistics supply chain management, including:

[0141] Memory and processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the intelligent logistics supply chain management method proposed in the above embodiment.

[0142] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the method for realizing intelligent logistics supply chain management as proposed in the above embodiment is implemented.

[0143] The storage medium proposed in this embodiment and the method for realizing intelligent logistics supply chain management proposed in the above embodiment belong to the same inventive concept. The technical details not fully described in this embodiment can be referred to the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0144] Through the above description of the implementation methods, the technicians in the relevant field can clearly understand that the present invention can be implemented by means of software and necessary general hardware, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory (FLASH), hard disk or optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, server, or network device, etc.) to execute the methods of various embodiments of the present invention.

[0145] Embodiment 2:

[0146] Referring to Table 1, an embodiment of the present invention provides an intelligent logistics supply chain management method. In order to verify its beneficial effects, comparison results of two solutions are provided.

[0147] In order to evaluate the effect of the method of the present invention, a simulation experiment was conducted and compared with the traditional logistics management method. Table 1 lists the performance indicators and their example numerical comparisons.

[0148] Table 1: Comparison of indicators

[0149] Method of the present invention Traditional methods Average order processing and delivery time / min 45 80 Order path optimization rate / % 36 14 One-way delivery improvement rate / % 88 62 Delivery delay rate / % 2 10 Transport (vehicle) utilization rate / % 60 85 Labor cost reduction rate / % 26 8 User satisfaction (1-10) 9 6

[0150] As can be seen from Table 1, the present invention can quickly respond to order demands and shorten the overall time for order processing and delivery through automated processes and planning algorithms. In terms of order path optimization, that is, considering the length and time of the order delivery path, the present invention is 12% higher than the traditional method. In the case of many orders, the present invention maximizes the planning of one-way multiple orders on the basis of ensuring the delivery time limit, and also ensures the distance in one-way multiple orders, and the efficiency is improved by 26%. The overall delivery delay rate is only 2%, and the transportation (vehicle) utilization rate, that is, the full load rate of the delivery goods of the one-way transportation tool, is also better than the traditional method. At the same time, it reduces labor costs, does not require many delivery personnel to complete the delivery task of the same number of orders, and user satisfaction is also high. Therefore, the application effect of the present invention is fully verified.

[0151] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. An intelligent logistics supply chain management method, characterized in that: include: Obtain supply chain order data in real time; Based on the order data, obtaining the delivery information in the order, performing order analysis according to the delivery information, generating a delivery task based on the order analysis result, and determining a first delivery path for the current delivery task; Based on the delivery tasks, plan the optimal path for supply chain order logistics delivery and update the order status in real time; Based on the order data, the inventory information of the supply chain is monitored and updated in real time, inventory change events are recorded, and inventory changes are predicted to dynamically adjust the inventory capacity of goods; The order analysis according to the delivery information includes: Generate a first delivery order set for each delivery site according to the order delivery sites to which the orders to be delivered belong; According to the estimated delivery time of the order, the first delivery order set is sorted in descending order to generate a second delivery order set of the current delivery site, and if there is a newly generated order to be delivered, the second delivery order set is updated in real time; Define the time window length and the distance between orders, and each order meets the single delivery constraint. Construct the delivery target model and delivery constraints, which are expressed as: ； ； ； ； ； ； in, Indicates the total number of orders, , Indicates the order index number. Indicates order Estimated delivery time window, Indicates order To order The straight-line distance to the delivery address, Indicates the maximum time window length allowed between orders, Indicates the distance threshold between orders, , , represents a binary variable; According to the orders in the second delivery order set, the order with the largest ranking is taken as the target order in turn, and the delivery target model is used to solve the order, and the delivery task is determined according to the solution result; The delivery task includes extracting a target order from the second delivery order set and generating a one-way task set centered on the target order; The optimal path for planning supply chain order logistics distribution includes: Step 1: Determine other order nodes in the current path by combining the coordinate information of orders other than the target order in the one-way task set; Step 2: Based on the first delivery path, select the order node closest to the latest delivery node in the one-way task set, and if the current order node meets the estimated delivery time window of the order, then determine the current order node as the next delivery node of the first delivery path; Step 3, repeating step 2 until all orders in the one-way task set have completed the judgment of the delivery nodes, adding them to the delivery nodes in the delivery path, and connecting all the delivery nodes in sequence with the shortest distance as the path target to generate a second delivery path; Step 4: Select two delivery nodes in the second delivery path, exchange two pairs of edges of each pair of edges of the two delivery nodes, determine whether each order node meets the estimated delivery time window of the order, and if so, generate at least one third delivery path; Step 5: Compare the third delivery path with the second delivery path one by one, calculate the change in path length between the two paths, and determine the shortest path between the third delivery path and the second delivery path as the optimal path for the current one-way task set.

2. The intelligent logistics supply chain management method according to claim 1, characterized in that: The real-time acquisition of supply chain order data includes: Acquire the to-be-delivered orders received by the management terminal in real time, and extract the delivery address in the orders according to the to-be-delivered orders; Based on the area where the delivery address is located, according to the preset area range, determine the area range to which the delivery address of the order to be delivered belongs, and determine the delivery site of the order to be delivered; Key order features are extracted based on the order to be delivered, where the key order features include: order time, product name, product quantity, delivery address, and estimated delivery time.

3. The intelligent logistics supply chain management method according to claim 2, characterized in that: Determining the first delivery path of the current delivery task includes: According to the delivery addresses and delivery site addresses of all orders in the one-way task set, the map coordinate information and site coordinate information of all orders are obtained, the site coordinates are used as the starting point of the path, the coordinates of the target order are used as the first delivery node, and the starting point and the first delivery node are connected with the shortest distance as the path target to generate the first delivery path of the one-way task set.

4. The intelligent logistics supply chain management method according to claim 1, characterized in that: The real-time monitoring and updating of supply chain inventory information and recording of inventory change events include: Based on the data of the orders to be delivered, the order status, logistics status, and inventory data are monitored in real time, and the inventory information of the ordered goods is updated in real time, and an inventory warning threshold is set. If the real-time inventory of the goods is less than the inventory warning threshold, an inventory warning is issued; Record inventory change events, including inventory change category, time, quantity, and inventory level, and associate inventory change events with business.

5. The intelligent logistics supply chain management method according to claim 4, characterized in that: The dynamically adjusting commodity inventory capacity includes: Based on historical order data and inventory consumption event records, machine learning algorithms are used to predict inventory changes in the future. Generate a product inventory replenishment list based on the forecast results and the product inventory safety threshold; Dynamically adjust product pricing strategies and formulate product marketing activities based on product attribute values.

6. An intelligent logistics supply chain management system, used in the intelligent logistics supply chain management method according to any one of claims 1 to 5, characterized in that: include, Acquisition module, used to obtain supply chain order data in real time; An analysis module, configured to obtain delivery information in the order based on the order data, perform order analysis based on the delivery information, generate a delivery task based on the order analysis result, and determine a first delivery path for the current delivery task; A planning module is used to plan the optimal path for supply chain order logistics distribution based on the distribution task and update the order status in real time; The inventory management module is used to monitor and update the inventory information of the supply chain in real time based on the order data, record inventory change events, predict inventory changes, and dynamically adjust the inventory capacity of goods.

7. An electronic device, characterized in that: include: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the intelligent logistics supply chain management method described in any one of claims 1 to 5 are implemented.

8. A computer-readable storage medium, characterized in that: It stores computer executable instructions, which, when executed by a processor, can implement the steps of the intelligent logistics supply chain management method described in any one of claims 1 to 5.

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