Logistics management method and system based on Internet of Things

Abstract

The invention discloses a logistics management method and system based on the Internet of Things, and the method comprises the steps: obtaining a to-be-delivered commodity picture through a camera device when a to-be-delivered commodity is located on a first conveying belt, receiving an order data flow, a to-be-uploaded commodity library, and a package management library, dividing a to-be-delivered order set based on different commodity specifications in the order data stream, establishing a three-dimensional commodity uploading sequence model in combination with the to-be-delivered order set and the to-be-uploaded commodity library, and controlling the uploading sequence of the to-be-uploaded commodities according to the three-dimensional commodity uploading sequence model. And according to the to-be-delivered commodity picture, identifying the specification of the to-be-delivered commodity, matching a to-be-delivered order set which is being executed in the three-dimensional commodity uploading sequence model, and when it is identified that the to-be-delivered order set contains a combined commodity order, transmitting the combined commodity order to a second transmission belt. The method has the characteristics of improving order processing efficiency and reducing operation cost.

Description

Technical Field

[0001] The present invention relates to the technical field of logistics management, and in particular to a logistics management method and system based on the Internet of Things. Background Art

[0002] With the rapid development of Internet technology and the profound changes in people's lifestyles, online shopping has become an extremely convenient and popular consumption model. In order to stand out in the fierce market competition and attract more customers, merchants sell a wide range of products with different styles and types, and also conduct marketing activities in a variety of combinations to stimulate consumers' desire to buy. However, the existing systems mostly rely on manual sorting or simple priority rules based on two-dimensional data, which makes it difficult to efficiently process massive orders. Especially in the scenario of combined orders (such as promotional bundled products), there is a lack of intelligent classification and scheduling strategies, which leads to increased sorting error rate and insufficient resource utilization. Traditional methods cannot respond to changes in order status in real time (such as urgent order insertion, inventory fluctuations or customer returns), and the rigid processing flow of the system often causes problems such as conveyor belt congestion and order delays. Therefore, it is necessary to design a logistics management method and system based on the Internet of Things to improve order processing efficiency and reduce operating costs. Summary of the invention

[0003] The purpose of the present invention is to provide a logistics management method and system based on the Internet of Things to solve the problems raised in the above background technology.

[0004] In order to solve the above technical problems, the present invention provides the following technical solutions: a logistics management method based on the Internet of Things, the operation steps of the method include:

[0005] Step S1: obtaining a picture of the goods to be shipped when the goods to be shipped are on the first conveyor belt through a camera device, receiving an order data stream, a goods library to be uploaded, and a packaging management library, wherein the goods library to be uploaded refers to the inventory data of the goods to be shipped, and the packaging management library stores the packaging requirements of each product;

[0006] Step S2: dividing the to-be-shipped order set based on the different commodity specifications in the order data stream, establishing a three-dimensional commodity upload sequence model in combination with the to-be-shipped order set and the to-be-uploaded commodity library, and controlling the upload sequence of the to-be-uploaded commodities according to the three-dimensional commodity upload sequence model;

[0007] Step S3: Identify the specifications of the goods to be shipped based on the pictures of the goods to be shipped, match the set of orders to be shipped that are 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 combination product order, transmit the combination product order to the second conveyor belt, and update the order data stream in real time to enable an abnormal modification mechanism for abnormal order data.

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

[0009] Step S21: Divide the received order data stream into the to-be-shipped order sets according to the specifications of the to-be-shipped goods, allocate each order data to the corresponding to-be-shipped order set, and perform time priority sorting on each order data in the to-be-shipped order set according to the order placing time and the corresponding delivery time requirement of each order data;

[0010] Step S22: Establish the three-dimensional commodity upload sequence model with the parametric encoding of the to-be-shipped order set as the discrete value on the X-axis, the demand quantity of the corresponding commodity 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 commodity upload sequence model with the minimum upload quantity N of the to-be-uploaded commodities corresponding in the to-be-uploaded commodity library min , and perform priority division on each to-be-shipped order set, and control the sequence and quantity of the to-be-uploaded commodities, where the minimum upload quantity refers to the minimum unit quantity of the to-be-uploaded commodities to be processed, allocated, or uploaded to the delivery system separately.

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

[0013] Step S211: Extract the order data with buyer remarks on the logistics delivery time requirement in the order data stream, and perform standardization processing on the time requirement. Assign different weight values ω to the order placing time of the corresponding order data according to the urgency of the logistics delivery time requirement, where the weight value of the order data without buyer remarks on the logistics delivery time requirement is 1;

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

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

[0016] Step S231: Establish a three-dimensional curve truncation model, and perform dynamic segmentation on the three-dimensional curves of each arbitrary to-be-shipped order set in the three-dimensional commodity upload sequence model:

[0017]

[0018] Wherein, It represents the dynamic segment length of the three-dimensional curve of the i-th order in the a-th order set to be shipped in the Z-axis direction. represents the demand for the i-th order in the a-th set of pending orders, Q s represents the inventory gradient factor, represents the time priority function value of the i-th order in the a-th set of pending orders, Γ a represents the priority index of the i-th order in the a-th set of pending orders, α represents the time decay compensation coefficient, β represents the transportation capacity adjustment coefficient, and m represents the multiple of the minimum upload quantity;

[0019] Step S232: In the three-dimensional product upload sequence model, the discrete point where the priority index is greater than the threshold is taken as the starting point, and the discrete point where the priority index is equal to the threshold is taken as the cutoff point. The three-dimensional curves corresponding to the starting point and the cutoff point are intercepted, and the order data therein is extracted as the first processing data. The three-dimensional curves that meet the requirements are intercepted in turn according to the priority index, and a subset of orders to be shipped are obtained and sorted in turn, and they are numbered in turn according to the time order of interception.

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

[0021] Step S31: according to the subset of to-be-shipped orders being executed in the three-dimensional goods loading sequence model, identifying whether the to-be-shipped goods image is consistent with the corresponding to-be-shipped order set data, and triggering an exception handling mechanism when they are inconsistent, and retrieving the to-be-shipped order subset that is consistent with the to-be-shipped goods image and has the previous number;

[0022] Step S32: When identifying whether the subset of orders to be shipped contains the combination commodity order, the second conveyor belt is opened when the combination commodity order is contained, and when the conveyor belt transports the commodities to be shipped, when the commodities to be shipped pass through the set area, the commodities to be shipped are transported to the vacant waiting combination area of ​​the second conveyor belt and the waiting combination area of ​​commodities to be shipped that are waiting for the first conveyor belt, and when the commodities to be shipped are transported to the corresponding position coordinates, the commodities to be shipped are transported to the corresponding waiting combination area.

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

[0024] Furthermore, the system includes a data collection module and a three-dimensional product upload sequence model building module:

[0025] The data acquisition module is used to obtain pictures of the goods to be shipped when the goods to be shipped are located on the first conveyor belt through a camera device, receive the order data stream, the goods library to be uploaded, and the packaging management library. The goods library to be uploaded refers to the inventory data of the goods to be shipped, and the packaging requirements of each commodity are stored in the packaging management library;

[0026] The three-dimensional commodity upload sequence model establishment module is used to divide the order set to be shipped based on the different specifications of the commodities in the order data stream, establish a three-dimensional commodity upload sequence model in combination with the order set to be shipped and the goods library to be uploaded, and control the upload sequence of the goods to be uploaded according to the three-dimensional commodity upload sequence model.

[0027] Further, 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 according to the pictures of the goods to be shipped, match the order set to be shipped that is being executed in the three-dimensional commodity upload sequence model, when it is recognized that the order set to be shipped contains combined commodity orders, transmit the combined commodity orders to the second conveyor belt, and update the order data stream in real time to enable an exception modification mechanism for order exception data.

[0029] In a third aspect of the present application, there is provided 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, the electronic device realizes the method described in the first aspect of the present application.

[0030] In a fourth aspect of the present application, there is provided a computer-readable storage medium for storing a computer program. When the computer program runs on a computer, the computer executes the method described in the first aspect of the present application.

[0031] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: By constructing a three-dimensional commodity upload sequence model, the present invention can intelligently prioritize and control the order of commodities based on the information of the order data stream and the goods library to be uploaded. This method not only improves the efficiency of order processing but also reduces the sorting error rate. Especially when dealing with combined orders (such as promotional bundled commodities), it shows a higher level of intelligence. Existing systems often suffer from problems such as conveyor belt congestion and order delays due to their inability to respond in real time to order status changes (such as the insertion of emergency orders, inventory fluctuations, or customer order cancellations). However, the present invention can flexibly respond to various order status changes by updating the order data stream in real time and dynamically adjusting the three-dimensional commodity upload sequence model, ensuring the smooth progress of the logistics process, thereby improving the order processing efficiency and reducing the operating cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. They are used together with the embodiments of the present invention to explain the present invention and do not constitute a limitation to the present invention. In the accompanying drawings:

[0033] Figure 1 It is a schematic flowchart diagram of a logistics management method based on the Internet of Things provided in the first embodiment of the present invention.

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

[0035] Figure 3 It is a schematic diagram of an electronic device according to an embodiment of the present application. Detailed implementation manners

[0036] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0037] This embodiment can be applied to the scenario of commodity order packing and processing. This method can be executed by a logistics management system based on the Internet of Things provided in this embodiment. Figure 1 It is a schematic flowchart diagram of a logistics management method based on the Internet of Things provided in the first embodiment of the present invention. The method specifically includes the following steps:

[0038] Step S1: Obtain a picture of the goods to be shipped when the goods to be shipped are located on the first conveyor belt through a camera device, and receive the order data stream, the goods library to be uploaded, and the packaging management library. The goods library to be uploaded refers to the inventory data of the goods to be shipped, and the packaging requirements of each commodity are stored in the packaging management library;

[0039] Step S2: Divide the order set to be shipped based on the different specifications of the commodities in the order data stream, establish a three-dimensional commodity upload sequence model in combination with the order set to be shipped and the goods library to be uploaded, and control the upload sequence of the goods to be uploaded according to the three-dimensional commodity upload sequence model;

[0040] Step S3: Identify the specifications of the goods to be shipped according to the picture of the goods to be shipped, match the order set to be shipped that is being executed in the three-dimensional commodity upload sequence model. When it is identified that the order set to be shipped contains a combined commodity order, transmit the combined commodity order to the second conveyor belt, and update the order data stream in real time to enable an exception modification mechanism for order exception data.

[0041] Specifically, by using a camera device to obtain real-time pictures of the goods to be shipped, and combining the order data stream, the goods library to be uploaded, and the packaging management library, an automated and accurate goods management and upload process is achieved. Specifically, this process divides the order sets to be shipped based on different product specifications, and establishes a three-dimensional goods upload sequence model, thereby effectively controlling the sequence of goods to be uploaded and improving the warehouse operation efficiency. At the same time, by identifying the product specifications in the pictures of the goods to be shipped, the corresponding order sets are timely matched and executed. Especially for combined goods orders, they can be automatically transferred to the second conveyor belt for special processing, and the order data stream is updated in real time, and an exception modification mechanism is enabled for abnormal order data. This series of operations not only improves the accuracy and timeliness of order processing, but also effectively reduces human errors and enhances the overall intelligence level of logistics management.

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

[0043] Step S21: Divide the received order data stream into the order sets to be shipped according to the product specifications of the goods to be shipped, allocate each order data to the corresponding order set to be shipped, and perform time priority sorting on each order data in the order sets to be shipped according to the order placing time and the corresponding delivery time requirements of each order data;

[0044] Step S22: Establish the three-dimensional goods upload sequence model with the parameterized encoding of the order set to be shipped as the X-axis discrete value, the demand quantity of the corresponding product specification 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 goods upload sequence model with the minimum upload quantity N of the goods to be uploaded in the goods library to be uploaded min , and perform priority division on each order set to be shipped, control the sequence and quantity of the goods to be uploaded. Among them, the minimum upload quantity refers to the minimum unit quantity of the goods to be uploaded that is separately processed, allocated, or uploaded to the shipping system. Specifically, sellers usually put goods of the same specification together and store them in boxes or other containers. When processing the shipment of goods, the goods are extracted in whole boxes according to the order data, and the goods in the whole box are successively inverted onto the first conveyor belt, and order allocation is performed according to the order data requirements. If the goods to be uploaded are stored in boxes as a unit, the minimum upload quantity is the quantity of the goods to be uploaded in 1 box.

[0046] Specifically, the received order data stream is carefully divided and sorted according to time priority, realizing the effective management and optimized sorting of the order set to be shipped, providing an accurate data basis for the establishment of the subsequent three-dimensional commodity upload sequence model. A three-dimensional commodity upload sequence model is established with the parameterized coding of the order set to be shipped, the demand quantity of each order data for the commodity specifications, and the time priority function value as the axes, and the three-dimensional curves of the combined commodity orders are thickly marked, realizing the three-dimensional and visual display of the order data, facilitating the intuitive grasp of the order processing situation. Combining the three-dimensional commodity upload sequence model with the minimum upload quantity in the commodity library to be uploaded, the priority of each order set to be shipped is divided, and the order and quantity of the commodities to be uploaded are controlled, realizing the reasonable allocation and optimized utilization of logistics resources, improving the efficiency and accuracy of logistics management, and reducing the operating costs.

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

[0048] Step S211: Extract the order data with buyer remarks on the logistics delivery time requirement in the order data stream, and perform standardization processing on the time requirement. Different weight values ω are assigned 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 the order data without buyer remarks on the logistics delivery time requirement is 1;

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

[0050] Specifically, by extracting the order data with buyer remarks on the logistics delivery time requirement in the order data stream, performing standardization processing on the time requirement, and assigning different weight values to the order time of the corresponding order data according to the urgency of the logistics delivery time requirement, the rapid response and processing of urgent orders are realized.

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

[0052] Step S231: Establish a three-dimensional curve truncation model to dynamically segment the three-dimensional curves of each arbitrary order set to be shipped in the three-dimensional commodity upload sequence model:

[0053]

[0054] In the formula, Denote the dynamic segmentation length of the three-dimensional curve of the \(i\)-th order in the \(a\)-th order set to be shipped in the Z-axis direction. Denote the demand quantity of the \(i\)-th order in the \(a\)-th order set to be shipped, \(Q\). s Denote the inventory gradient factor. Denote the time priority function value of the \(i\)-th order in the \(a\)-th order set to be shipped, \(\Gamma\). a Denote the priority index of the \(i\)-th order in the \(a\)-th order set to be shipped. \(\alpha\) denotes the time decay compensation coefficient, \(\beta\) denotes the transportation capacity adjustment coefficient, and \(m\) denotes the multiple of the minimum upload quantity for uploading.

[0055] Step S232: In the three-dimensional commodity upload sequence model, take the discrete points where the priority index is greater than the threshold as the starting points, and the discrete points where the priority index is equal to the threshold as the truncation points. Intercept the corresponding three-dimensional curves according to the starting points and the truncation points, extract the order data therein as the first processed data, intercept the conforming three-dimensional curves in sequence according to the priority index, obtain the subsets of orders to be shipped and sort them in sequence for waiting, and number them in sequence according to the intercepted time order.

[0056] Specifically, during a certain logistics peak period, the system receives a large number of orders, including multiple urgent orders and ordinary orders. According to the traditional processing method, these orders may be processed in sequence according to the received order, but this often leads to the situation that urgent orders cannot be shipped in time, affecting the buyer experience. However, in the logistics management system of the present invention, the system first establishes a three-dimensional commodity upload sequence model according to the order data stream. Dynamically segment the three-dimensional curves of each order set to be shipped through the three-dimensional curve truncation model. For example, for a certain urgent order, due to its high time priority function value (indicating that the buyer has a more urgent expectation for the shipping time), the system will intelligently divide its priority in the three-dimensional curve truncation model according to factors such as the demand quantity of the order, the inventory gradient factor, and the time priority function value.

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

[0058] Step S2321: Identify the order data with the same customer ID and shipping address in the order data stream, obtain the commodity characteristics of the corresponding order data, and integrate the commodity characteristics and the conflict coefficient of the packaging requirements quality inspection in the packaging management library, where the commodity characteristics include: commodity size, commodity weight, commodity category.

[0059] Step S2322: When the conflict coefficient is greater than the threshold, no combination processing is performed on the corresponding order to be shipped. When the conflict coefficient is less than the threshold, combination processing is performed on the corresponding order data, and the time priority function value of the combined order is uniformly adjusted to the minimum value within the group. Among them, for the goods sent by the same customer to the same address, based on their commodity characteristics, it is intelligently judged which goods are suitable for combined packaging, aiming to reduce the use of packaging materials, thereby reducing logistics costs, and at the same time reducing the customer's pick-up times and the waiting time for the goods to arrive, improving the overall service efficiency and customer satisfaction.

[0060] Specifically, in the actual scenario of logistics management, buyers often have different requirements for the logistics delivery time. Some buyers may hope that the goods can be delivered as soon as possible, so they have strict requirements for the delivery time; while some buyers may be relatively tolerant of the delivery time. Traditional logistics management methods often ignore this difference, resulting in the failure to process urgent orders in a timely manner, which in turn leads to problems such as buyer dissatisfaction and order delays. By extracting the buyer's remarks information in the order data stream, especially the requirements for the logistics delivery time, and standardizing these requirements, the system can accurately understand the buyer's needs. At the same time, according to the urgency of the logistics delivery time requirements, different weight values are assigned to the order placement times of the corresponding order data, which further ensures that the system can give priority to processing urgent orders. When the system processes orders according to the priority indicators, urgent orders can be processed first, thus ensuring the timeliness and accuracy of the orders.

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

[0062] Step S31: According to the subset of orders to be shipped being executed in the three-dimensional goods loading order model, identify whether the pictures of the goods to be shipped are consistent with the data in the corresponding set of orders to be shipped. When they are inconsistent, trigger the exception handling mechanism and retrieve the subset of orders to be shipped that is consistent with the pictures of the goods to be shipped and has a higher number.

[0063] Step S32: When identifying whether the subset of orders to be shipped contains the combined commodity orders, when the combined commodity orders are contained, activate the second conveyor belt. When the conveyor belt is transporting the goods to be shipped, when the goods to be shipped pass through the set area, transfer the goods to be shipped to the empty waiting combination area of the second conveyor belt and the waiting combination area of the goods to be shipped waiting for the first conveyor belt. When the goods to be shipped are transported to the corresponding position coordinates, transfer the goods to be shipped to the corresponding waiting combination area.

[0064] Specifically, through the matching verification and exception handling mechanism of the pictures of goods to be shipped and order data, the accurate identification and error correction of the shipped goods are realized, ensuring the accuracy of shipping; at the same time, by intelligently identifying combined orders and opening a second conveyor belt for waiting combined processing, the automated and efficient scheduling of combined orders is realized, reducing manual intervention and improving the efficiency and flexibility of order processing.

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

[0066] Specifically, by real-time processing of the customer return information received in the subset of orders to be shipped, timely deleting the corresponding order data, and intelligently retrieving the order data that is consistent with the deleted order data and has the smallest time priority function value for replacement, the dynamic optimization adjustment of order data is realized, avoiding the impact of return orders on the logistics process, reducing order delays and resource waste caused by returns, further improving the accuracy and efficiency of order processing, and enhancing the response speed of logistics management and customer service quality.

[0067] Based on the same inventive concept as the above method embodiment, the embodiment of the present invention further provides an Internet of Things-based logistics management system. Figure 2 It is a schematic diagram of the module composition of an Internet of Things-based logistics management system provided by the embodiment of the present invention. As Figure 2 shown, the system includes a data acquisition module, a three-dimensional commodity upload sequence model establishment module, an order upload channel processing module, and an order exception processing module:

[0068] The data acquisition module is used to obtain pictures of goods to be shipped when the goods to be shipped are located on the first conveyor belt through a camera device, receive order data streams, a goods library to be uploaded, and a packaging management library. The goods library to be uploaded refers to the inventory data of the goods to be shipped, and the packaging requirements of each commodity are stored in the packaging management library;

[0069] The three-dimensional commodity upload sequence model establishment module is used to divide the set of orders to be shipped based on the different specifications of commodities in the order data stream, establish a three-dimensional commodity upload sequence model in combination with the set of orders to be shipped and the goods library to be uploaded, and control the upload sequence of the goods to be uploaded according to the three-dimensional commodity upload sequence model;

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

[0071] The order exception processing module is used to update the order data flow in real time, dynamically update the three-dimensional goods upload sequence model, and enable an exception modification mechanism for order exception data.

[0072] It should be noted that although several units or subunits of the device are mentioned in the above detailed description, this division is merely exemplary and not mandatory. In fact, according to the embodiments of the present application, the features and functions of the 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 embodiment, an electronic device is also provided in an embodiment of the present application, 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, the electronic device implements the control method in the above embodiment.

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

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

[0076] The memory 2001 can be a volatile memory, such as a random-access memory (RAM); the memory 2001 can also be a non-volatile memory, such as a read-only memory, a flash memory, a hard disk drive (HDD), or a solid-state drive (SSD); or the memory 2001 is any other medium that can be used to carry or store a desired computer program in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto. The memory 2001 can be a combination of the above memories.

[0077] The processor 2002 can include one or more central processing units (CPUs) or be a digital processing unit, etc. The processor 2002 is used to implement the above audio data processing method when calling the computer program stored in the memory 2001.

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

[0079] In the embodiments of the present application, the specific connection medium between the memory 2001, the communication module 2003, and the processor 2002 is not limited. In the embodiments of the present application Figure 3 it is described that the memory 2001 and the processor 2002 are connected through a bus 2004, and the bus 2004 is described by an arrow in Figure 3 The connection manners between other components are only for illustrative purposes and are not to be construed as limiting. The bus 2004 can be divided into an address bus, a data bus, a control bus, etc. For the sake of convenience of description, Figure 3 only one arrow is used to describe it in

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

[0081] Based on the same inventive concept as the above method embodiments, an embodiment of the present invention further provides a computer program product. The computer program product includes a computer program. When the program product runs on an electronic device, the computer program is used to enable the electronic device to execute the steps in the control method according to various exemplary embodiments described above in this specification. The program product may adopt any combination of one or more readable media. These computer program instructions may be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing device generate means for implementing the functions specified in Figure 1 one process or multiple processes and / or Figure 1 one block or multiple blocks.

[0082] Although the preferred embodiments of the present application have been described, those skilled in the art can make additional changes and modifications once they learn the basic creative concept. Therefore, the appended claims are intended to be construed to include the preferred embodiments and all changes and modifications falling within the scope of the present application.

Claims

1. A logistics management method based on the Internet of Things, characterized in that: Obtain pictures of the goods to be shipped when the goods to be shipped are located on the first conveyor belt through a camera device; Receive order data streams, goods libraries to be uploaded, and packaging management libraries. The goods libraries to be uploaded refer to the inventory data of the goods to be shipped, and the packaging requirements of each commodity are stored in the packaging management library; Divide the order sets to be shipped based on the different specifications of the goods in the order data stream, establish a three-dimensional goods upload sequence model by combining the order sets to be shipped and the goods libraries to be uploaded, and control the upload sequence of the goods to be uploaded according to the three-dimensional goods upload sequence model; Identify the specifications of the goods to be shipped according to the pictures of the goods to be shipped, match the order sets to be shipped that are being executed in the three-dimensional goods upload sequence model. When it is identified that the order sets to be shipped contain combined commodity orders, transmit the combined commodity orders to the second conveyor belt, and update the order data stream in real time to enable an exception modification mechanism for order exception data.

2. The logistics management method based on the Internet of Things according to claim 1, wherein: The step of dividing the order sets to be shipped based on the different specifications of the goods in the order data stream, establishing a three-dimensional goods upload sequence model by combining the order sets to be shipped and the goods libraries to be uploaded, and controlling the upload sequence of the goods to be uploaded according to the three-dimensional goods upload sequence model includes: Divide the order sets to be shipped according to the specifications of the goods to be shipped for the received order data stream, allocate each order data to the corresponding order sets to be shipped, and perform time-priority sorting on each order data in the order sets to be shipped according to the order time and the corresponding delivery time requirements of each order data; Establish the three-dimensional goods upload sequence model with the parametric coding of the order sets to be shipped as the discrete value on the X-axis, the demand quantity of the corresponding commodity specifications in each order data as the Y-axis, and the time-priority function value as the Z-axis; Combine the three-dimensional commodity upload sequence model with the minimum upload quantity N corresponding to the commodity to be uploaded in the commodity library to be uploaded, min , perform priority division on each set of orders to be shipped, and control the sequence and quantity of the commodities to be uploaded. Among them, the minimum upload quantity refers to the minimum unit quantity of the commodities to be uploaded that are processed, allocated, or uploaded to the shipping system separately.

3. The logistics management method based on the Internet of Things according to claim 2, characterized in that: The step of dividing the order sets to be shipped according to the specifications of the goods to be shipped for the received order data stream, allocating each order data to the corresponding order sets to be shipped, and performing time-priority sorting on each order data in the order sets to be shipped according to the order time and the corresponding delivery time requirements of each order data includes: Extract the order data in the order data stream with buyer remarks on the logistics delivery time requirements, and perform standardization processing on the time requirements. Assign different weight values ω to the order time of the corresponding order data according to the urgency of the logistics delivery time requirements. Among them, the weight value of the order data without buyer remarks on the logistics delivery time requirements is 1; Sort each order data in the order sets to be shipped in descending order according to the time-priority function ft = A - bt - ω·t0), where A represents the outbound preparation time after the order data is promised to be placed in the platform, b represents the attenuation rate, t represents the current time, and t0 represents the order time of the order data.

4. A logistics management method based on the Internet of Things according to claim 3, characterized in that: Combining the three-dimensional commodity upload sequence model with the minimum upload quantity N corresponding to the commodity to be uploaded in the commodity library to be uploaded, min prioritize each set of orders to be shipped, and control the sequence and quantity of the commodities to be uploaded, where the minimum upload quantity refers to the minimum unit quantity of the commodities to be uploaded that are processed, allocated, or uploaded to the shipping system individually, including: Establish a three-dimensional curve truncation model to dynamically segment the three-dimensional curves of each arbitrary order set to be shipped in the three-dimensional goods upload sequence model; In the formula, represents the dynamic segmented length in the Z-axis direction of the 3D curve of the i-th order in the a-th order set to be shipped, represents the demand of the i-th order in the a-th order set to be shipped, Q s represents the inventory gradient factor, represents the time priority function value of the i-th order in the a-th order set to be shipped, Γ a represents the priority index of the i-th order in the a-th order set to be shipped, α represents the time decay compensation coefficient, β represents the transportation capacity adjustment coefficient, and m represents the multiple of the minimum upload quantity for uploading; In the three-dimensional commodity upload sequence model, the discrete points where the priority index is greater than the threshold are used as the starting points, and the discrete points where the priority index is equal to the threshold are used as the truncation points. According to the three-dimensional curves corresponding to the starting points and the truncation points, the curves are intercepted, and the order data therein is extracted as the first processed data. According to the priority index, the corresponding three-dimensional curves are intercepted in sequence, a subset of the orders to be shipped is obtained and sorted in sequence for waiting, and numbered in the order of the interception time.

5. The logistics management method based on the Internet of Things according to claim 4, characterized in that: The step of identifying the specifications of the goods to be shipped according to the pictures of the goods to be shipped and matching the subset of the orders to be shipped being executed in the three-dimensional commodity upload sequence model. When it is identified that the subset of the orders to be shipped contains combined commodity orders, the combined commodity orders are transmitted to the second conveyor belt, including: According to the subset of the orders to be shipped being executed in the three-dimensional commodity loading sequence model, it is identified whether the pictures of the goods to be shipped are consistent with the data in the corresponding subset of the orders to be shipped. When they are inconsistent, an exception handling mechanism is triggered, and the subset of the orders to be shipped that is consistent with the pictures of the goods to be shipped and has a smaller serial number is retrieved. When it is identified whether the subset of the orders to be shipped contains the combined commodity orders, when the combined commodity orders are contained, the second conveyor belt is activated. When the goods to be shipped are being transported on the conveyor belt, when the goods to be shipped pass through a set area, the goods to be shipped are transported to the empty waiting combination area of the second conveyor belt, as well as the waiting combination area of the goods to be shipped waiting for the first conveyor belt. When the goods to be shipped are transported to the corresponding position coordinates, the goods to be shipped are transported to the corresponding waiting combination area.

6. The logistics management method based on the Internet of Things according to claim 5, characterized in that: The step of updating the order data stream in real time and activating an exception modification mechanism for order exception data, including: when the order data corresponding to the subset of the orders to be shipped and the customer return information is captured, the corresponding order data in the subset of the orders to be shipped is excluded, and the order data that is consistent with the excluded order data and has the smallest time priority function value is retrieved and replaces the corresponding order data.

7. An Internet of Things-based logistics management system, characterized in that: The system includes a data acquisition module and a three-dimensional commodity upload sequence model establishment module: The data acquisition module is used to obtain pictures of the goods to be shipped when the goods to be shipped are on the first conveyor belt through a camera device, and receive the order data stream, the goods to be uploaded library, and the packaging management library. The goods to be uploaded library refers to the inventory data of the goods to be shipped, and the packaging requirements of each commodity are stored in the packaging management library. The three-dimensional commodity upload sequence model establishment module is used to divide the subset of the orders to be shipped based on the different specifications of the commodities in the order data stream, establish a three-dimensional commodity upload sequence model in combination with the subset of the orders to be shipped and the goods to be uploaded library, and control the upload sequence of the goods to be uploaded according to the three-dimensional commodity upload sequence model.

8. The logistics management system based on the Internet of Things according to claim 7, characterized in that: The system includes an order upload channel processing module: The order upload channel processing module is used to identify the specifications of the goods to be shipped based on the pictures of the goods to be shipped, match the set of orders to be shipped that are being executed in the three-dimensional goods upload sequence model. When it is identified that the set of orders to be shipped contains combined goods orders, the combined goods orders are transmitted to the second conveyor belt, and the order data stream is updated in real time, and an exception modification mechanism is enabled for order exception data.

9. An electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, the electronic device realizes the method according to any one of claims 1 to 6.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium is used to store a computer program. When the computer program runs on a computer, the computer executes the method according to any one of claims 1 to 6.

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