A logistics management method and system based on the Internet of Things

Abstract

The application discloses a logistics management method and system based on an Internet of Things, and the method comprises the following steps: acquiring a picture of a to-be-shipped commodity when the to-be-shipped commodity is located on a first conveying belt through a camera, receiving an order data stream, a to-be-uploaded commodity database and a packaging management database, dividing a to-be-shipped order set based on different commodity specifications in the order data stream, combining the to-be-shipped order set and the to-be-uploaded commodity database to establish a three-dimensional commodity uploading sequence model, controlling the to-be-uploaded commodity uploading sequence according to the three-dimensional commodity uploading sequence model, identifying the to-be-shipped commodity specification according to the picture of the to-be-shipped commodity, matching the to-be-shipped order set being executed in the three-dimensional commodity uploading sequence model, when it is identified that the to-be-shipped order set contains a combined commodity order, transmitting the combined commodity order to a second conveying belt, and the application has the characteristics of improving order processing efficiency and reducing operation cost.

Description

Technical Field

[0001] This invention relates to the field of logistics management technology, specifically to a logistics management method and system based on the Internet of Things. Background Technology

[0002] With the rapid development of internet technology and profound changes in people's lifestyles, online shopping has become an extremely convenient and popular consumption model. To stand out in the fierce market competition and attract more customers, merchants sell a dazzling array of diverse products and engage in various marketing campaigns to stimulate consumer purchasing desires. However, existing systems largely rely on manual sorting or simple priority rules based on two-dimensional data, making it difficult to efficiently handle massive orders. Especially in scenarios involving bundled orders (such as promotional bundled products), the lack of intelligent classification and scheduling strategies leads to increased sorting error rates, insufficient resource utilization, and the inability of traditional methods to respond in real-time to changes in order status (such as urgent order insertions, inventory fluctuations, or customer order cancellations). The rigid processing flow of these systems often causes problems such as conveyor belt congestion and order delays. Therefore, it is essential to design an IoT-based logistics management method and system to improve order processing efficiency and reduce operating costs. Summary of the Invention

[0003] The purpose of this invention is to provide a logistics management method and system based on the Internet of Things (IoT) to solve the problems mentioned in the background art.

[0004] To address the aforementioned technical problems, this invention provides the following technical solution: a logistics management method based on the Internet of Things, the method comprising the following operational steps:

[0005] Step S1: Obtain an image of the goods to be shipped when they are on the first conveyor belt using a camera device; receive order data stream, goods to be uploaded database, and packaging management database. The goods to be uploaded database refers to the inventory data of the goods to be shipped, and the packaging management database stores the packaging requirements of each product.

[0006] Step S2: Divide the order sets to be shipped into different categories based on the different product specifications in the order data stream, establish a three-dimensional product upload order model by combining the order sets to be shipped and the product library to be uploaded, and control the upload order of the products to be uploaded according to the three-dimensional product upload order model;

[0007] Step S3: Identify the specifications of the products to be shipped based on the images of the products to be shipped, match the set of orders to be shipped currently being executed in the three-dimensional product upload sequence model, and when it is identified that the set of orders to be shipped contains a combined product order, transmit the combined product order to the second conveyor belt, and update the order data stream in real time to enable the abnormal modification mechanism for abnormal order data.

[0008] Furthermore, step S2 further includes the following steps:

[0009] Step S21: Divide the received order data stream into the order set to be shipped according to the specifications of the goods to be shipped, allocate each order data to the corresponding order set to be shipped, and sort the order data in the order set to be shipped by time priority according to the order placement time and corresponding shipping time requirements.

[0010] Step S22: Establish the three-dimensional product upload order model with the parameterized encoding of the order set to be shipped as the discrete value of the X-axis, the demand for the corresponding product specifications in each order data as the Y-axis, and the time priority function value as the Z-axis;

[0011] Step S23: Combine the three-dimensional product upload order model with the minimum upload quantity N of the products to be uploaded in the product library. min The system prioritizes each set of orders to be shipped, controlling the order and quantity of the products to be uploaded. The minimum upload quantity refers to the minimum number of products to be processed, allocated, or uploaded to the shipping system individually.

[0012] Furthermore, step S21 further includes the following steps:

[0013] Step S211: Extract order data from the order data stream that contains buyer remarks regarding logistics delivery time requirements, and perform time requirement standardization processing. Assign different weight values ​​ω to the order time of the corresponding order data according to the urgency of the logistics delivery time requirement. Among them, the weight value of order data where the buyer does not make any remarks regarding logistics delivery time requirements is 1.

[0014] Step S212: Sort each order data in the order set to be shipped in descending order according to the time priority function f(t)=Ab(t-ω·t0), where A represents the outbound preparation time of the order data after the order is promised to be placed in the platform, b represents the decay rate, t represents the current time, and t0 represents the order placement time of the order data.

[0015] Furthermore, step S23 further includes the following steps:

[0016] Step S231: Establish a three-dimensional curve truncation model, and dynamically segment the three-dimensional curve of any set of orders to be shipped in the three-dimensional product upload sequence model:

[0017]

[0018] In the formula, Let represent the dynamic segment length of the 3D curve of the i-th order in the a-th order set to be shipped, along the Z-axis. Let Q represent the demand quantity of the i-th order in the a-th order set to be shipped. s Indicates the inventory gradient factor. Γ represents the time priority function value of the i-th order in the a-th order set to be shipped. a Let α represent the priority index of the i-th order in the a-th order set to be shipped, α represent the time decay compensation coefficient, β represent the transportation capacity adjustment coefficient, and m represent the multiple of the minimum upload quantity.

[0019] Step S232: In the three-dimensional product upload order model, the discrete points where the priority index is greater than the threshold are taken as the starting points, and the discrete points where the priority index is equal to the threshold are taken as the cutoff points. The three-dimensional curves corresponding to the starting points and the cutoff points are cut off, and the order data is extracted as the first processing data. The three-dimensional curves that meet the priority index are cut off in sequence to obtain the subset of orders to be shipped and sort them in sequence. The orders are numbered in sequence according to the time order of the cutoff.

[0020] Furthermore, step S3 further includes the following steps:

[0021] Step S31: Based on the subset of pending orders being executed in the three-dimensional goods loading sequence model, identify whether the pending goods image is consistent with the corresponding pending order set data. If they are inconsistent, trigger the exception handling mechanism and retrieve the pending order subset that is consistent with the pending goods image and has the earlier number.

[0022] Step S32: When identifying whether the subset of orders to be shipped contains the combined product order, if the combined product order is present, the second conveyor belt is activated. When the conveyor belt transports the products to be shipped, the products to be shipped are transported to the empty waiting combination area of ​​the second conveyor belt and the waiting combination area of ​​the products to be shipped that are waiting for the first conveyor belt when the products to be shipped pass through the set area. When the products to be shipped are transported to the corresponding position coordinates, the products to be shipped are transported to the corresponding waiting combination area.

[0023] Furthermore, step S3 further includes: when receiving customer return information in the currently executing subset of orders to be shipped, removing the corresponding order data in the subset of orders to be shipped, retrieving the order data that is consistent with the removed order data and has the smallest time priority function value, and replacing the corresponding order data.

[0024] Furthermore, the system includes a data acquisition module and a 3D product upload sequence model building module:

[0025] The data acquisition module is used to acquire images of the goods to be shipped when they are on the first conveyor belt using a camera device, and to receive order data streams, a goods database to be uploaded, and a packaging management database. The goods database to be uploaded refers to the inventory data of the goods to be shipped, and the packaging management database stores the packaging requirements of each product.

[0026] The three-dimensional product upload order model establishment module is used to divide the order set to be shipped into different categories based on the different product specifications in the order data stream, establish a three-dimensional product upload order model by combining the order set to be shipped and the product library to be uploaded, and control the upload order of the products to be uploaded according to the three-dimensional product upload order model.

[0027] Furthermore, the system includes an order upload channel processing module:

[0028] The order upload channel processing module is used to identify the specifications of the goods to be shipped based on the images of the goods to be shipped, match the set of orders to be shipped currently being executed in the three-dimensional goods upload sequence model, and when it is identified that the set of orders to be shipped contains a combined goods order, transmit the combined goods order to the second conveyor belt, and update the order data stream in real time to enable the abnormal modification mechanism for abnormal order data.

[0029] A third aspect of this application provides an electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, it causes the electronic device to perform the method described in the first aspect of this application.

[0030] A fourth aspect of this application provides a computer-readable storage medium for storing a computer program that, when run on a computer, causes the computer to perform the method described in the first aspect of this application.

[0031] Compared with existing technologies, the beneficial effects achieved by this invention are as follows: By constructing a three-dimensional product upload sequence model, this invention can intelligently prioritize and control the order of products based on the order data stream and the information in the product database to be uploaded. This method not only improves the efficiency of order processing but also reduces the sorting error rate. It demonstrates a higher level of intelligence, especially when handling combined orders (such as promotional bundled products). Existing systems often suffer from problems such as conveyor belt congestion and order delays due to their inability to respond in real time to changes in order status (such as urgent order insertions, inventory fluctuations, or customer order cancellations). This invention, by updating the order data stream in real time and dynamically adjusting the three-dimensional product upload sequence model, can flexibly respond to various order status changes, ensuring smooth logistics processes, thereby improving order processing efficiency and reducing operating costs. Attached Figure Description

[0032] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0033] Figure 1 This is a flowchart illustrating a logistics management method based on the Internet of Things (IoT) provided in Embodiment 1 of the present invention.

[0034] Figure 2 This is a schematic diagram of the module composition of a logistics management system based on the Internet of Things provided in Embodiment 2 of the present invention.

[0035] Figure 3 This is a schematic diagram of an electronic device according to an embodiment of this application. Detailed Implementation

[0036] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0037] This embodiment can be applied to the scenario of product order packaging and processing. This method can be executed by an IoT-based logistics management system provided in this embodiment. Figure 1 This is a flowchart illustrating an IoT-based logistics management method according to Embodiment 1 of the present invention. The method specifically includes the following steps:

[0038] Step S1: Obtain an image of the goods to be shipped when they are on the first conveyor belt using a camera device; receive order data stream, goods to be uploaded database, and packaging management database. The goods to be uploaded database refers to the inventory data of the goods to be shipped, and the packaging management database stores the packaging requirements of each product.

[0039] Step S2: Divide the order sets to be shipped into different categories based on the different product specifications in the order data stream, establish a three-dimensional product upload order model by combining the order sets to be shipped and the product library to be uploaded, and control the upload order of the products to be uploaded according to the three-dimensional product upload order model;

[0040] Step S3: Identify the specifications of the products to be shipped based on the images of the products to be shipped, match the set of orders to be shipped currently being executed in the three-dimensional product upload sequence model, and when it is identified that the set of orders to be shipped contains a combined product order, transmit the combined product order to the second conveyor belt, and update the order data stream in real time to enable the abnormal modification mechanism for abnormal order data.

[0041] Specifically, by acquiring real-time images of goods awaiting shipment using camera devices and combining this with order data streams, a goods-to-be-uploaded database, and a packaging management database, an automated and precise goods management and uploading process is achieved. Specifically, this process categorizes orders for shipment based on different product specifications and establishes a three-dimensional goods upload sequence model, effectively controlling the order of goods to be uploaded and improving warehouse operational efficiency. Simultaneously, by identifying product specifications in the images of goods awaiting shipment, the system promptly matches and executes corresponding order sets. Particularly for combined goods orders, it automatically transmits them to a second conveyor belt for special processing and updates the order data stream in real time, initiating an error correction mechanism for abnormal order data. This series of operations not only improves the accuracy and timeliness of order processing but also effectively reduces human error and enhances the overall intelligence level of logistics management.

[0042] In some preferred embodiments, step S2 further includes the following steps:

[0043] Step S21: Divide the received order data stream into the order set to be shipped according to the specifications of the goods to be shipped, allocate each order data to the corresponding order set to be shipped, and sort the order data in the order set to be shipped by time priority according to the order placement time and corresponding shipping time requirements.

[0044] Step S22: Establish the three-dimensional product upload order model with the parameterized encoding of the order set to be shipped as the discrete value of the X-axis, the demand for the corresponding product specifications in each order data as the Y-axis, and the time priority function value as the Z-axis;

[0045] Step S23: Combine the three-dimensional product upload order model with the minimum upload quantity N of the products to be uploaded in the product library. min The system prioritizes each set of orders awaiting shipment, controlling the order and quantity of goods to be uploaded. The minimum upload quantity refers to the minimum unit quantity of goods to be processed, allocated, or uploaded to the shipping system. Specifically, sellers typically group goods of the same specifications together and store them in boxes or other containers. When processing the shipment, the system extracts goods from the boxes based on the order data and sequentially inverts the goods within the boxes onto the first conveyor belt. Orders are then allocated according to the order data requirements. If the goods to be uploaded are stored in boxes, the minimum upload quantity is the number of goods to be uploaded in one box.

[0046] Specifically, the received order data stream is meticulously segmented and prioritized by time, enabling effective management and optimized sorting of the order sets to be shipped. This provides a precise data foundation for the subsequent establishment of a 3D product upload sequence model. A 3D product upload sequence model is built using the parametric encoding of the order sets to be shipped, the demand for product specifications in each order, and the time priority function value as axes. The 3D curves of combined product orders are bolded, achieving a three-dimensional and visual display of order data, facilitating intuitive understanding of order processing. By combining the 3D product upload sequence model with the minimum upload quantity in the product library to be uploaded, priority is assigned to each order set to be shipped, and the order and quantity of products to be uploaded are controlled. This achieves reasonable allocation and optimized utilization of logistics resources, improving the efficiency and accuracy of logistics management and reducing operating costs.

[0047] In some preferred embodiments, step S21 further includes the following steps:

[0048] Step S211: Extract order data from the order data stream that contains buyer remarks regarding logistics delivery time requirements, and perform time requirement standardization processing. Assign different weight values ​​ω to the order time of the corresponding order data according to the urgency of the logistics delivery time requirement. Among them, the weight value of order data where the buyer does not make any remarks regarding logistics delivery time requirements is 1.

[0049] Step S212: Sort each order data in the order set to be shipped in descending order according to the time priority function f(t)=Ab(t-ω·t0), where A represents the outbound preparation time of the order data after the order is promised to be placed in the platform, b represents the decay rate, t represents the current time, and t0 represents the order placement time of the order data.

[0050] Specifically, by extracting order data from the order data stream containing buyer notes regarding logistics delivery time requirements, and standardizing these time requirements, different weight values ​​are assigned to the order placement time of the corresponding order data based on the urgency of the logistics delivery time requirement, enabling rapid response and processing of urgent orders.

[0051] In some preferred embodiments, step S23 further includes the following steps:

[0052] Step S231: Establish a three-dimensional curve truncation model, and dynamically segment the three-dimensional curve of any set of orders to be shipped in the three-dimensional product upload sequence model:

[0053]

[0054] In the formula, Let represent the dynamic segment length of the 3D curve of the i-th order in the a-th order set to be shipped, along the Z-axis. Let Q represent the demand quantity of the i-th order in the a-th order set to be shipped. s Indicates the inventory gradient factor. Γ represents the time priority function value of the i-th order in the a-th order set to be shipped. a Let α represent the priority index of the i-th order in the a-th order set to be shipped, α represent the time decay compensation coefficient, β represent the transportation capacity adjustment coefficient, and m represent the multiple of the minimum upload quantity.

[0055] Step S232: In the three-dimensional product upload order model, the discrete points where the priority index is greater than the threshold are taken as the starting points, and the discrete points where the priority index is equal to the threshold are taken as the cutoff points. The three-dimensional curves corresponding to the starting points and the cutoff points are cut off, and the order data is extracted as the first processing data. The three-dimensional curves that meet the priority index are cut off in sequence to obtain the subset of orders to be shipped and sort them in sequence. The orders are numbered in sequence according to the time order of the cutoff.

[0056] Specifically, during a peak logistics period, the system receives a large number of orders, including multiple urgent and regular orders. Traditionally, these orders might be processed sequentially based on the order they were received, but this often leads to delayed shipments of urgent orders, impacting the buyer experience. However, in the logistics management system of this invention, the system first establishes a three-dimensional product upload order model based on the order data flow. A three-dimensional curve truncation model is then used to dynamically segment the three-dimensional curves of each set of orders awaiting shipment. For example, for an urgent order, due to its high time priority function value (indicating a more urgent expectation from the buyer regarding delivery time), the system intelligently prioritizes it in the three-dimensional curve truncation model based on factors such as the order's demand, inventory gradient factor, and time priority function value.

[0057] In some preferred embodiments, step S232 further includes the following steps:

[0058] Step S2321: Identify order data in the order data stream where the customer ID and delivery address are consistent, obtain the product characteristics of the corresponding order data, integrate the product characteristics and compare them with the conflict coefficient of the packaging requirements quality inspection in the packaging management library, wherein the product characteristics include: product size, product weight, and product category;

[0059] Step S2322: When the conflict coefficient is greater than the threshold, the corresponding orders to be shipped are not combined. When the conflict coefficient is less than the threshold, the corresponding order data is combined, and the time priority function value of the combined orders is uniformly adjusted to the minimum value within the group. In this process, attention is paid to goods shipped by the same customer to the same address. Based on the characteristics of the goods, it is intelligently determined which goods are suitable for combination packaging, which aims to reduce the use of packaging materials, thereby reducing logistics costs. At the same time, it reduces the number of times customers pick up their packages and the waiting time for the goods to arrive, thereby improving overall service efficiency and customer satisfaction.

[0060] Specifically, in real-world logistics management scenarios, buyers' requirements for delivery time often vary. Some buyers may want goods delivered as quickly as possible and therefore have strict requirements for delivery time; while others may be more lenient. Traditional logistics management methods often overlook this difference, leading to urgent orders not being processed in a timely manner, resulting in buyer dissatisfaction and order delays. By extracting buyer remarks from the order data stream, especially regarding delivery time requirements, and standardizing these requirements, the system can accurately understand buyer needs. Furthermore, assigning different weights to the order placement time based on the urgency of the delivery time requirement further ensures that the system prioritizes urgent orders. When the system processes orders according to priority indicators, urgent orders are given priority, thus ensuring the timeliness and accuracy of orders.

[0061] In some preferred embodiments, step S3 further includes the following steps:

[0062] Step S31: Based on the subset of pending orders being executed in the three-dimensional goods loading sequence model, identify whether the pending goods image is consistent with the corresponding pending order set data. If they are inconsistent, trigger the exception handling mechanism and retrieve the pending order subset that is consistent with the pending goods image and has the earlier number.

[0063] Step S32: When identifying whether the subset of orders to be shipped contains the combined product order, if the combined product order is present, the second conveyor belt is activated. When the conveyor belt transports the products to be shipped, the products to be shipped are transported to the empty waiting combination area of ​​the second conveyor belt and the waiting combination area of ​​the products to be shipped that are waiting for the first conveyor belt when the products to be shipped pass through the set area. When the products to be shipped are transported to the corresponding position coordinates, the products to be shipped are transported to the corresponding waiting combination area.

[0064] Specifically, by matching and verifying images of goods to be shipped with order data and by implementing an anomaly handling mechanism, accurate identification and error correction of goods to be shipped are achieved, ensuring the accuracy of shipments. At the same time, by intelligently identifying combined orders and opening a second conveyor belt for waiting and combining processing, automated and efficient scheduling of combined orders is achieved, reducing manual intervention and improving the efficiency and flexibility of order processing.

[0065] In some preferred embodiments, step S3 further includes: when a customer return information is received in the subset of orders to be shipped that is being executed, removing the corresponding order data in the subset of orders to be shipped, retrieving the order data that is consistent with the removed order data and has the smallest time priority function value, and replacing the corresponding order data.

[0066] Specifically, by processing customer return information received in the pending order subset in real time, promptly removing corresponding order data, and intelligently retrieving and replacing the order data with the order data that is consistent with the removed order data and has the smallest time priority function value, dynamic optimization and adjustment of order data is achieved. This avoids the impact of return orders on the logistics process, reduces order delays and resource waste caused by returns, further improves the accuracy and efficiency of order processing, and enhances the responsiveness of logistics management and the quality of customer service.

[0067] Based on the same inventive concept as the above-described method embodiments, this invention also provides an Internet of Things-based logistics management system. Figure 2 A schematic diagram of the module composition of an Internet of Things-based logistics management system provided in an embodiment of the present invention is shown below. Figure 2 As shown, the system includes a data acquisition module, a 3D product upload sequence model building module, an order upload channel processing module, and an order exception handling module.

[0068] The data acquisition module is used to acquire images of the goods to be shipped when they are on the first conveyor belt using a camera device, and to receive order data streams, a goods database to be uploaded, and a packaging management database. The goods database to be uploaded refers to the inventory data of the goods to be shipped, and the packaging management database stores the packaging requirements of each product.

[0069] The three-dimensional product upload order model building module is used to divide the order set to be shipped into different categories based on the different product specifications in the order data stream, combine the order set to be shipped and the product library to be uploaded to build a three-dimensional product upload order model, and control the upload order of the products to be uploaded according to the three-dimensional product upload order model.

[0070] The order upload channel processing module is used to identify the specifications of the goods to be shipped based on the image of the goods to be shipped, match the set of orders to be shipped being executed in the three-dimensional goods upload sequence model, and when it is identified that the set of orders to be shipped contains a combined goods order, transmit the combined goods order to the second conveyor belt, and update the order data stream in real time to enable the abnormal modification mechanism for abnormal order data.

[0071] The order anomaly handling module is used to update the order data stream in real time, dynamically update the three-dimensional product upload order model, and enable an anomaly modification mechanism for abnormal order data.

[0072] It should be noted that although several units or sub-units of the device have been mentioned in the detailed description above, this division is merely exemplary and not mandatory. In fact, according to embodiments of this application, the features and functions of two or more units described above can be embodied in one unit. Conversely, the features and functions of one unit described above can be further divided and embodied by multiple units.

[0073] Based on the same inventive concept as the above method embodiments, this application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it enables the electronic device to implement the control method described in the above embodiments.

[0074] In one embodiment, the electronic device may be a server, and in this embodiment, the structure of the electronic device may be as follows: Figure 3 As shown, it includes a memory 2001, a communication module 2003, and one or more processors 2002.

[0075] The memory 2001 is used to store computer programs executed by the processor 2002. The memory 2001 may mainly include a program storage area and a data storage area. The program storage area may store the operating system and programs required to run instant messaging functions, etc.; the data storage area may store various instant messaging information and operation instruction sets, etc.

[0076] Memory 2001 may be volatile memory, such as random-access memory (RAM); memory 2001 may also be non-volatile memory, such as read-only memory, flash memory, hard disk drive (HDD), or solid-state drive (SSD); or memory 2001 may be any other medium capable of carrying or storing a desired computer program having the form of instructions or data structures and accessible by a computer, but is not limited thereto. Memory 2001 may be a combination of the above-mentioned memories.

[0077] Processor 2002 may include one or more central processing units (CPUs) or digital processing units, etc. Processor 2002 is used to implement the above-mentioned audio data processing method when calling computer programs stored in memory 2001.

[0078] The communication module 2003 is used to communicate with terminal devices and other servers.

[0079] This application embodiment does not limit the specific connection medium between the memory 2001, communication module 2003, and processor 2002. This application embodiment... Figure 3 The memory 2001 and the processor 2002 are connected via a bus 2004, which is in... Figure 3 The connections between other components are illustrated with arrows and are for illustrative purposes only, not as limiting information. The Bus 2004 can be divided into address bus, data bus, control bus, etc. For ease of description, Figure 3 The text uses only one arrow to describe it, but does not indicate that there is only one bus or a bus of a certain type.

[0080] Based on the same inventive concept as the above-described method embodiments, embodiments of the present invention also provide a computer-readable storage medium for storing a computer program. When the computer program is run on a computer, it enables the electronic device to implement the control method described in the above embodiments. The computer-readable storage medium can be a readable signal medium or a readable storage medium. A readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof.

[0081] Based on the same inventive concept as the above-described method embodiments, embodiments of the present invention also provide a computer program product, which includes a computer program that, when run on an electronic device, causes the electronic device to perform the steps of the control methods described above according to various exemplary embodiments of this application. The program product may take the form of any combination of one or more readable media. These computer program commands can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing device to produce a machine, such that the commands executed by the processor of the computer or other programmable data processing device generate a process for implementing... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0082] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

Claims

1. A logistics management method based on the Internet of Things, characterized in that: S1. Obtain an image of the goods to be shipped when they are on the first conveyor belt using a camera device; S2. Receive order data stream, product database to be uploaded, and packaging management database. The product database to be uploaded refers to the inventory data of the products to be shipped, and the packaging management database stores the packaging requirements of each product. S3. Based on the different product specifications in the order data stream, divide the pending order sets into sets of orders to be shipped, and establish a three-dimensional product upload order model by combining the pending order sets and the product upload library, and control the upload order of the products to be uploaded according to the three-dimensional product upload order model; including: The received order data stream is divided into the order set to be shipped according to the specifications of the goods to be shipped. Each order data is assigned to the corresponding order set to be shipped. The order data in the order set to be shipped is sorted by time priority according to the order placement time and the corresponding shipping time requirements. The three-dimensional product upload order model is established using the parameterized encoding of the order set to be shipped as the discrete value of the X-axis, the demand for the corresponding product specifications in each order data as the Y-axis, and the time priority function value as the Z-axis. The three-dimensional product upload order model is combined with the minimum upload quantity of the products to be uploaded in the product library. The system prioritizes each set of orders awaiting shipment, controlling the upload order and quantity of the products to be uploaded. The minimum upload quantity refers to the minimum number of products to be processed, allocated, or uploaded to the shipping system individually. This includes: establishing a three-dimensional curve truncation model; dynamically segmenting the three-dimensional curves of any set of orders awaiting shipment in the three-dimensional product upload order model; using discrete points where the priority index is greater than a threshold as starting points and discrete points where the priority index is equal to the threshold as truncation points; extracting order data from the corresponding three-dimensional curves based on the starting points and truncation points as first processing data; sequentially trunculating the matching three-dimensional curves according to the priority index to obtain subsets of orders awaiting shipment, sorting them sequentially, and numbering them according to the truncation time order. S4. Identify the specifications of the goods to be shipped based on the images of the goods to be shipped, match the set of orders to be shipped currently being executed in the three-dimensional product upload order model, and when it is identified that the set of orders to be shipped contains a combined product order, transmit the combined product order to the second conveyor belt, and update the order data stream in real time to enable the abnormal modification mechanism for abnormal order data.

2. The logistics management method based on the Internet of Things according to claim 1, characterized in that: The process of dividing the received order data stream into the pending order set according to the specifications of the goods to be shipped, allocating each order data to the corresponding pending order set, and sorting the order data in the pending order set according to the order placement time and corresponding shipping time requirements includes: Extract order data from the order data stream containing buyer remarks regarding logistics delivery time requirements, and standardize these time requirements. Assign different weight values ​​to the order placement time of the corresponding order data based on the urgency of the logistics delivery time requirement. Among them, the weight of order data where the buyer did not specify the logistics delivery time requirement is 1; In the set of orders awaiting shipment, each order data is sorted according to a time priority function. Sort in descending order, where, This indicates the order preparation time after the order data is placed on the platform, where b represents the decay rate. Indicates the current time. This indicates the order placement time of the order data.

3. The logistics management method based on the Internet of Things according to claim 2, characterized in that: The three-dimensional product upload order model is combined with the minimum upload quantity of the products to be uploaded in the product library. The system prioritizes each set of orders awaiting shipment, controlling the upload order and quantity of the products to be uploaded. The minimum upload quantity refers to the minimum unit quantity of products to be processed, allocated, or uploaded to the shipping system individually, including: A three-dimensional curve truncation model is established, and the three-dimensional curve of any set of orders to be shipped in the three-dimensional product upload sequence model is dynamically segmented: In the formula, Indicates the first The dynamic segment length of the 3D curve of the i-th order in a set of orders awaiting shipment along the Z-axis. Indicates the first The demand quantity of the i-th order in a set of orders awaiting shipment. Indicates the inventory gradient factor. Indicates the first The time priority function value of the i-th order in a set of orders awaiting shipment. Indicates the first The priority indicator of the i-th order in a set of pending orders. This represents the time decay compensation coefficient. This represents the transport capacity adjustment factor. This indicates a multiple of the minimum upload limit.

4. The logistics management method based on the Internet of Things according to claim 3, characterized in that: The step of identifying the specifications of the goods to be shipped based on the image of the goods to be shipped, matching them with the set of orders to be shipped currently being executed in the 3D product upload sequence model, and when it is identified that the set of orders to be shipped contains a combined product order, transmitting the combined product order to the second conveyor belt includes: Based on the subset of pending orders being executed in the three-dimensional product loading sequence model, identify whether the product image to be shipped is consistent with the corresponding data in the pending order set. If they are inconsistent, trigger the exception handling mechanism and retrieve the subset of pending orders that is consistent with the product image to be shipped and has the earlier number. When identifying whether the subset of orders to be shipped contains the combined product order, if the combined product order is present, the second conveyor belt is activated. When the product to be shipped is being transported on the second conveyor belt, it is transferred to the empty waiting combination area of ​​the second conveyor belt and the waiting combination area of ​​the product to be shipped that is waiting for the first conveyor belt when the product to be shipped passes through the set area. When the product to be shipped reaches the corresponding position coordinates, it is transferred to the corresponding waiting combination area.

5. The logistics management method based on the Internet of Things according to claim 4, characterized in that: The real-time update of the order data stream and the activation of an abnormal modification mechanism for abnormal order data include: when order data corresponding to customer return information is captured in the order set to be shipped, the corresponding order data in the order set to be shipped is removed, the order data that is consistent with the removed order data and has the smallest time priority function value is retrieved, and the corresponding order data is replaced.

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