Logistics distribution optimization method and device based on industrial Internet of Things, terminal and medium

Abstract

The invention discloses a logistics distribution optimization method and device based on an industrial internet of things, a terminal and a medium, which are applied to a distribution center system. The method comprises the following steps: obtaining production data corresponding to a product, inventory data corresponding to the product, historical order data corresponding to the product, current order data corresponding to the product, information data of a distribution center, traffic data of a distribution area corresponding to the distribution center and environment data of the distribution area corresponding to the distribution center; obtaining a first logistics distribution scheme through a preset first model; obtaining a second logistics distribution scheme through a preset second model; and updating a preset logistics distribution scheme based on the first logistics distribution scheme and the second logistics distribution scheme, and generating a target logistics distribution scheme. The invention aims to improve the logistics distribution effect of the product from the warehouse to each dealer through the Internet of Things technology and the neural network model.

Description

Technical Field

[0001] The present application relates to the field of logistics and distribution, and in particular to a logistics and distribution optimization method, device, terminal and medium based on industrial Internet of Things. Background Art

[0002] The logistics and distribution from warehouses to dealers is a complex process involving multiple links and steps. The entire logistics and distribution process requires efficient coordination and management to ensure that the goods can be delivered to each dealer accurately and in a timely manner.

[0003] However, the current management of logistics and distribution still relies on human management. Even if a systematic and automated management solution is adopted, there are still problems of low logistics and distribution efficiency caused by poor information flow and human decision-making errors in the process of products from warehouses to various distributors. Summary of the invention

[0004] The main purpose of this application is to provide a logistics distribution optimization method, device, terminal and medium based on industrial Internet of Things, aiming to improve the logistics distribution efficiency of products from warehouses to various dealers through Internet of Things technology and neural network models.

[0005] To achieve the above-mentioned purpose, the present application provides a logistics distribution optimization method based on industrial Internet of Things, which is applied to a distribution center system. The distribution center system includes a management platform, an object platform and a sensor network platform. The object platform includes a product sorting terminal and a product distribution terminal. The product sorting terminal is used to manage the process of sending products from various product warehouses in the factory to the distribution center. The product distribution terminal is used to manage the process of sending products from the distribution center to various dealers. The management platform and the object platform are connected in communication through the sensor network platform. The method includes:

[0006] Obtaining production data corresponding to the product, inventory data corresponding to the product, historical order data corresponding to the product, current order data corresponding to the product, distribution center information data, traffic data of the distribution area corresponding to the distribution center, and environmental data of the distribution area corresponding to the distribution center, wherein the production data is used to characterize the production status of the product, and the inventory data is used to characterize the inventory status of each product warehouse;

[0007] Obtaining a first logistics distribution plan based on the production data, the inventory data and the historical order data through a preset first model, wherein the first logistics distribution plan is used to guide the process of sending products from various product warehouses in the factory to the distribution center;

[0008] A second logistics distribution plan is obtained through a preset second model according to current order data corresponding to the product, information data of the distribution center, traffic data of the distribution area corresponding to the distribution center, and environmental data of the distribution area corresponding to the distribution center, wherein the second logistics distribution plan is used to guide the process of sending the product from the distribution center to each dealer;

[0009] Based on the first logistics distribution plan and the second logistics distribution plan, the preset logistics distribution plan is updated to generate a target logistics distribution plan.

[0010] Specifically, the preset first model includes a first input layer, a first hidden layer, and a first output layer;

[0011] The first logistics distribution plan is obtained according to the production data, the inventory data and the historical order data by using the preset first model, including:

[0012] Obtaining a first input vector through the first input layer according to the production data, the inventory data and the historical order data;

[0013] Obtaining a first output vector according to the first input vector through the first hidden layer;

[0014] Obtaining a production plan and an inventory plan for a product according to the first output vector through the first output layer;

[0015] The first logistics distribution plan is generated based on the production plan and the inventory plan.

[0016] Specifically, the first hidden layer includes a first sub-hidden layer and a second sub-hidden layer, the ratio of the number of nodes in the first sub-hidden layer to the number of nodes in the second sub-hidden layer is 2:1, and the activation function of the first sub-hidden layer and the activation function of the second sub-hidden layer are both ReLU functions;

[0017] The obtaining a first output vector according to the first input vector through the first hidden layer includes:

[0018] Obtaining a first intermediate vector according to the first input vector through the first sub-hidden layer;

[0019] The first output vector is obtained through the second sub-hidden layer according to the first intermediate vector.

[0020] Specifically, the first output layer includes a dense layer, a segmentation layer, a production plan generation layer, and an inventory plan generation layer;

[0021] The obtaining of a production plan and an inventory plan of a product according to the first output vector through the first output layer includes:

[0022] Obtaining a first information extraction vector according to the first output vector through the dense layer;

[0023] Extracting vectors according to the first information through the segmentation layer to obtain a production plan vector corresponding to the production plan and an inventory plan vector corresponding to the inventory plan;

[0024] Obtaining the production plan according to the production plan vector through the production plan generation layer;

[0025] The inventory plan generation layer is used to obtain the inventory plan according to the inventory plan vector.

[0026] Specifically, the preset second model includes a second input layer, a second hidden layer, and a second output layer;

[0027] The second logistics distribution plan is obtained by using the preset second model according to the current order data corresponding to the product, the distribution center information data, the traffic data of the distribution area corresponding to the distribution center, and the environmental data of the distribution area corresponding to the distribution center, including:

[0028] Obtaining a second input vector through the second input layer according to the current order data corresponding to the product, the distribution center information data, the traffic data of the distribution area corresponding to the distribution center, and the environmental data of the distribution area corresponding to the distribution center;

[0029] Obtaining a second output vector according to the second input vector through the second hidden layer;

[0030] Obtaining a delivery route plan, a delivery vehicle loading plan, and a delivery time plan according to the second output vector through the second output layer;

[0031] The second logistics distribution plan is generated based on the distribution route plan, the distribution vehicle loading plan and the distribution time plan.

[0032] Specifically, the second hidden layer includes a third sub-hidden layer and a fourth sub-hidden layer, the ratio of the number of nodes in the third sub-hidden layer to the number of nodes in the fourth sub-hidden layer is 2:1, and the activation function of the third sub-hidden layer and the activation function of the fourth sub-hidden layer are both ReLU functions;

[0033] The step of obtaining a second output vector according to the second input vector through the second hidden layer includes:

[0034] Obtaining a second intermediate vector according to the second input vector through the third sub-hidden layer;

[0035] The second output vector is obtained according to the second intermediate vector through the fourth sub-hidden layer.

[0036] Specifically, the second output layer includes a first fully connected layer, a second fully connected layer, and a third fully connected layer;

[0037] The method of obtaining a delivery route plan, a delivery vehicle loading plan, and a delivery time plan according to the second output vector through the second output layer includes:

[0038] Obtaining the delivery route plan according to the second output vector through the first fully connected layer;

[0039] Obtaining the delivery vehicle loading plan according to the second output vector through the second fully connected layer;

[0040] The delivery time plan is obtained according to the second output vector through the third fully connected layer.

[0041] To achieve the above-mentioned purpose, the present application further provides a logistics distribution optimization device based on industrial Internet of Things, which is applied to a distribution center system. The distribution center system includes a management platform, an object platform and a sensor network platform. The object platform includes a product sorting terminal and a product distribution terminal. The product sorting terminal is used to manage the process of sending products from various product warehouses in the factory to the distribution center. The product distribution terminal is used to manage the process of sending products from the distribution center to various dealers. The management platform and the object platform are connected in communication through the sensor network platform. The device includes:

[0042] The first unit is used to obtain production data corresponding to the product, inventory data corresponding to the product, historical order data corresponding to the product, current order data corresponding to the product, distribution center information data, traffic data of the distribution area corresponding to the distribution center, and environmental data of the distribution area corresponding to the distribution center, wherein the production data is used to characterize the production status of the product, and the inventory data is used to characterize the inventory status of each product warehouse;

[0043] A second unit is used to obtain a first logistics distribution plan according to the production data, the inventory data and the historical order data through a preset first model, wherein the first logistics distribution plan is used to guide the process of sending products from various product warehouses in the factory to the distribution center;

[0044] A third unit is used to obtain a second logistics distribution plan based on the current order data corresponding to the product, the distribution center information data, the traffic data of the distribution area corresponding to the distribution center, and the environmental data of the distribution area corresponding to the distribution center through a preset second model, wherein the second logistics distribution plan is used to guide the process of sending the product from the distribution center to each dealer;

[0045] The fourth unit is used to update the preset logistics distribution plan based on the first logistics distribution plan and the second logistics distribution plan to generate a target logistics distribution plan.

[0046] To achieve the above-mentioned purpose, the present application also provides a terminal, including a memory storing multiple instructions; the processor loads instructions from the memory to execute the steps in any one of the methods provided in the present application.

[0047] To achieve the above objectives, the present application also provides a medium, wherein the medium stores a plurality of instructions, wherein the instructions are suitable for a processor to load to execute the steps in any one of the methods provided in the present application.

[0048] The present application provides a method, device, terminal and medium for optimizing logistics distribution based on industrial Internet of Things. The method can first obtain production data corresponding to a product, inventory data corresponding to a product, historical order data corresponding to a product, current order data corresponding to a product, information data of a distribution center, traffic data of a distribution area corresponding to a distribution center and environmental data of a distribution area corresponding to a distribution center, wherein the production data is used to characterize the production status corresponding to the product, and the inventory data is used to characterize the inventory status of each product warehouse; then, through a pre-set first model, a first logistics distribution plan is obtained according to the production data, the inventory data and the historical order data, wherein the first logistics distribution plan is used to guide the process of sending products from each product warehouse of a factory to a distribution center;

[0049] Then, through a pre-set second model, a second logistics distribution plan is obtained according to the current order data corresponding to the product, the distribution center information data, the traffic data of the distribution area corresponding to the distribution center, and the environmental data of the distribution area corresponding to the distribution center, wherein the second logistics distribution plan is used to guide the process of sending the product from the distribution center to each dealer; based on the first logistics distribution plan and the second logistics distribution plan, the preset logistics distribution plan is updated, and finally the target logistics distribution plan is generated to improve the logistics distribution efficiency of the product.

[0050] This application can improve the logistics and distribution efficiency of products from warehouses to various dealers through Internet of Things technology and neural network models. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 A schematic diagram of a process flow provided for an embodiment of the present application;

[0052] Figure 2 A schematic diagram of a distribution center system provided in an embodiment of the present application;

[0053] Figure 3 A schematic diagram of the structure of the device provided in the embodiment of the present application;

[0054] Figure 4 A schematic diagram of the structure of a terminal provided in an embodiment of the present application. DETAILED DESCRIPTION

[0055] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of this application.

[0056] Since the current management of logistics and distribution still relies on human management, even if a systematic and automated management solution is adopted, there are still problems of low logistics and distribution efficiency caused by poor information flow and human decision-making errors in the process of products from warehouses to various distributors.

[0057] Therefore, the embodiments of the present application provide a logistics distribution optimization method, device, terminal and medium based on the industrial Internet of Things to solve practical technical problems.

[0058] In some embodiments, the device may be specifically integrated into an electronic device, which may be a terminal, a server or other device.

[0059] In some embodiments, the server may also be implemented in the form of a terminal.

[0060] Among them, the server can be an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN (Content Delivery Network), as well as big data and artificial intelligence platforms.

[0061] The terminal may be a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, a smart watch, etc., but is not limited thereto. The terminal and the server may be directly or indirectly connected via wired or wireless communication, and this application does not limit this.

[0062] It should be noted that the serial numbers of the following embodiments are not intended to limit the preferred order of the embodiments.

[0063] The embodiment of the present application provides a logistics distribution optimization method based on the industrial Internet of Things. The method can improve the logistics distribution effect of products from warehouses to various dealers through the Internet of Things technology and neural network models.

[0064] like Figure 1 and Figure 2 As shown, the method is applied to a distribution center system, which includes a management platform, an object platform and a sensor network platform. The object platform includes a product sorting terminal and a product distribution terminal. The product sorting terminal is used to manage the process of sending products from various product warehouses in the factory to the distribution center, and the product distribution terminal is used to manage the process of sending products from the distribution center to various dealers. The management platform and the object platform are communicatively connected via the sensor network platform.

[0065] In some embodiments, the management platform is responsible for the management and control of the entire system, including task scheduling, data processing, etc.

[0066] The object platform may refer to an object-oriented design framework for managing and processing various data objects in the system. The object platform provides a structured way to organize, store and operate data, making the system more flexible and scalable. Specifically, the object platform in the distribution center system includes a product sorting terminal and a product distribution terminal. The product sorting terminal is used to manage the process of sending products from various product warehouses in the factory to the distribution center, and the product distribution terminal is used to manage the process of sending products from the distribution center to various distributors.

[0067] The sensor network platform can be used as the middle layer of the production optimization system to collect data information from the object platform and is responsible for transmitting the input instructions of the operator from the management platform to the object platform.

[0068] The Internet of Things (IoT) refers to the technology that connects various physical devices, vehicles, terminals, sensors and other items through the Internet, enabling them to collect and exchange data and realize intelligent management and control. The core of the Internet of Things lies in the combination of "things" and "networks". Through network technology, sensor technology, embedded system technology, automatic identification technology, etc., the interconnection between objects is realized. The goal of the Internet of Things in the factory is to improve the efficiency of the industrial production process and the efficiency of product logistics and distribution through intelligence, automation and data analysis.

[0069] In some embodiments, based on the distribution center system, the product sorting terminal and the product distribution terminal are controlled by a management platform.

[0070] like Figure 1 , the specific process of the method can be as follows:

[0071] S110. Obtain production data corresponding to the product, inventory data corresponding to the product, historical order data corresponding to the product, current order data corresponding to the product, distribution center information data, traffic data of the distribution area corresponding to the distribution center, and environmental data of the distribution area corresponding to the distribution center, wherein the production data is used to characterize the production status of the product, and the inventory data is used to characterize the inventory status of each product warehouse.

[0072] In some embodiments, the production data may represent all information related to the production process of the product, including but not limited to production plan, production progress, raw material usage, production line status, quality inspection results, etc.; the inventory data may represent the quantity and status information of the products stored in each warehouse, including but not limited to inventory quantity, inventory location, storage time, storage time, etc.; the historical order data may represent all processed order information in the past period of time, including but not limited to order number, order time, order quantity, customer information, delivery time, etc.; the current order data may represent order information being processed, including but not limited to order number, order time, order quantity, expected delivery time, etc.; the distribution center information data may represent all information related to the distribution center, including but not limited to the geographical location, storage capacity, staffing, equipment status, etc. of the distribution center; the traffic data may represent traffic information related to the distribution area of ​​the distribution center, including but not limited to road conditions, traffic flow, traffic control, weather conditions, etc.; the environmental data may represent natural environment information related to the distribution area of ​​the distribution center, including but not limited to temperature, humidity, air quality, natural disaster warning, etc.

[0073] S120. Obtain a first logistics distribution plan through a pre-set first model according to the production data, the inventory data and the historical order data, wherein the first logistics distribution plan is used to guide the process of sending products from various product warehouses in the factory to the distribution center.

[0074] In some embodiments, the pre-set first model includes a first input layer, a first hidden layer, and a first output layer.

[0075] Specifically, the first logistics distribution plan is obtained according to the production data, the inventory data and the historical order data through the pre-set first model, including the steps S121 to S124 as shown below:

[0076] S121. Obtain a first input vector through the first input layer according to the production data, the inventory data and the historical order data.

[0077] In some embodiments, the production data, the inventory data, and the historical order data are converted into a format that can be processed by a neural network model, namely, the first input vector, through the first input layer.

[0078] S122. Obtain a first output vector through the first hidden layer according to the first input vector.

[0079] In some embodiments, the first hidden layer includes a first sub-hidden layer and a second sub-hidden layer, the ratio of the number of nodes in the first sub-hidden layer to the number of nodes in the second sub-hidden layer is 2:1, and the activation function of the first sub-hidden layer and the activation function of the second sub-hidden layer are both ReLU functions.

[0080] Specifically, obtaining the first output vector through the first hidden layer according to the first input vector includes the following steps S1221 to S1222:

[0081] S1221. Obtain a first intermediate vector according to the first input vector through the first sub-hidden layer.

[0082] S1222. Obtain the first output vector according to the first intermediate vector through the second sub-hidden layer.

[0083] Through the first sub-hidden layer and the second sub-hidden layer, the data of the pre-set first model is input with feature extraction and abstraction, and the patterns and rules in the data are gradually learned to provide support for the final decision. Common activation functions include ReLU (Rectified Linear Unit), Sigmoid, Tanh, etc. The ReLU function is used here because of its high computational efficiency and can effectively alleviate the gradient disappearance problem.

[0084] S123. Obtain a production plan and an inventory plan for a product through the first output layer and according to the first output vector.

[0085] In some embodiments, the first output layer includes a dense layer, a segmentation layer, a production plan generation layer, and an inventory plan generation layer.

[0086] Specifically, obtaining a production plan and an inventory plan of a product according to the first output vector through the first output layer includes the following steps S1231 to S1234:

[0087] S1231. Obtain a first information extraction vector through the dense layer according to the first output vector.

[0088] In some embodiments, the input of the dense layer is the first output vector from the first hidden layer. The output of the dense layer is a higher dimensional vector, which contains more information about the production plan and the inventory plan, that is, the first information extraction vector. The dense layer is used to increase the expressive power of the model so that the model can learn more complex patterns. It maps the input vector to a higher dimensional space through a weight matrix.

[0089] S1232. Extracting a vector according to the first information through the segmentation layer to obtain a production plan vector corresponding to the production plan and an inventory plan vector corresponding to the inventory plan.

[0090] Specifically, the first information extraction vector is split into two parts in a predetermined manner by the segmentation layer, and each part is responsible for generating a specific type of solution. For example, the vector of the first half may focus on generating a production plan, while the vector of the second half focuses on the inventory management strategy, that is, the production solution vector corresponding to the production solution and the inventory solution vector corresponding to the inventory solution.

[0091] S1233. Obtain the production plan through the production plan generation layer according to the production plan vector.

[0092] The input of the production plan generation layer is the vector related to the production plan generated by the segmentation layer, and the output of the production plan generation layer is a specific production plan, that is, the production plan, such as the quantity of each product to be produced, the production schedule, etc.

[0093] The production plan generation layer may also include additional neural network layers or use a specific algorithm to parse the input vector and generate a detailed production plan.

[0094] S1234. Obtain the inventory plan according to the inventory plan vector through the inventory plan generation layer.

[0095] The input of the inventory solution generation layer is the vector related to the inventory solution generated by the segmentation layer. The output of the inventory solution generation layer is a specific inventory management plan, that is, the inventory solution, such as the minimum inventory level that each warehouse needs to maintain, the timing of replenishment, etc. The inventory solution generation layer is similar to the production solution generation layer, that is, the inventory solution generation layer can also be composed of a series of neural network layers or algorithms to determine the best inventory management strategy.

[0096] S124. Generate the first logistics distribution plan based on the production plan and the inventory plan.

[0097] S130. Obtain a second logistics distribution plan through a pre-set second model according to the current order data corresponding to the product, the distribution center information data, the traffic data of the distribution area corresponding to the distribution center, and the environmental data of the distribution area corresponding to the distribution center, wherein the second logistics distribution plan is used to guide the process of sending the product from the distribution center to each dealer.

[0098] In some embodiments, the predetermined second model includes a second input layer, a second hidden layer, and a second output layer.

[0099] Specifically, the second logistics distribution plan is obtained by using the preset second model according to the current order data corresponding to the product, the distribution center information data, the traffic data of the distribution area corresponding to the distribution center, and the environmental data of the distribution area corresponding to the distribution center, including the steps S131 to S134 as shown below:

[0100] S131. Obtain a second input vector through the second input layer according to the current order data corresponding to the product, the distribution center information data, the traffic data of the distribution area corresponding to the distribution center, and the environmental data of the distribution area corresponding to the distribution center.

[0101] In some embodiments, the current order data corresponding to the product, the distribution center information data, the traffic data of the distribution area corresponding to the distribution center, and the environmental data of the distribution area corresponding to the distribution center are converted into a format that can be processed by the neural network model through the second input layer, that is, the second input vector.

[0102] S132. Obtain a second output vector through the second hidden layer according to the second input vector.

[0103] In some embodiments, the second hidden layer includes a third sub-hidden layer and a fourth sub-hidden layer, the ratio of the number of nodes in the third sub-hidden layer to the number of nodes in the fourth sub-hidden layer is 2:1, and the activation function of the third sub-hidden layer and the activation function of the fourth sub-hidden layer are both ReLU functions.

[0104] Specifically, the second hidden layer is used to obtain the second output vector according to the second input vector, including the steps S1321 to S1322 as shown below:

[0105] S1321. Obtain a second intermediate vector according to the second input vector through the third sub-hidden layer.

[0106] S1322. Obtain the second output vector according to the second intermediate vector through the fourth sub-hidden layer.

[0107] Through the third sub-hidden layer and the fourth sub-hidden layer, the data of the pre-set second model is input with feature extraction and abstraction, and the patterns and rules in the data are gradually learned to provide support for the final decision. Common activation functions include ReLU (Rectified Linear Unit), Sigmoid, Tanh, etc. The ReLU function is used here because of its high computational efficiency and can effectively alleviate the gradient disappearance problem.

[0108] S133. Obtain a delivery route plan, a delivery vehicle loading plan, and a delivery time plan through the second output layer according to the second output vector.

[0109] In some embodiments, the second output layer includes a first fully connected layer, a second fully connected layer, and a third fully connected layer.

[0110] Specifically, the second output layer obtains the delivery route plan, the delivery vehicle loading plan, and the delivery time plan according to the second output vector, including the steps S1331 to S1333 as shown below:

[0111] S1331. Obtain the delivery route plan according to the second output vector through the first fully connected layer.

[0112] S1332. Obtain the delivery vehicle loading plan according to the second output vector through the second fully connected layer.

[0113] S1333. Obtain the delivery time plan according to the second output vector through the third fully connected layer.

[0114] In some embodiments, the second output layer is a network layer based on multi-task learning.

[0115] Specifically, the first fully connected layer includes one or more fully connected layers, each neuron of which corresponds to a possible delivery route. The output can be converted into a probability distribution through a softmax function, and the route with the highest probability is selected as the recommended route, thereby generating the delivery route plan.

[0116] The second fully connected layer may include one or more fully connected layers, each neuron of which corresponds to a possible vehicle loading solution. The output may be a vector representing the loading amount or loading order of each vehicle, thereby generating the delivery vehicle loading solution.

[0117] The third fully connected layer may include one or more fully connected layers, and the output may be a numerical value representing the estimated delivery time, or a probability distribution of multiple time windows, thereby generating the delivery time plan.

[0118] S134: Generate the second logistics distribution plan based on the distribution route plan, the distribution vehicle loading plan, and the distribution time plan.

[0119] S140: Based on the first logistics distribution plan and the second logistics distribution plan, update the preset logistics distribution plan and generate a target logistics distribution plan.

[0120] In some embodiments, the first logistics distribution plan and the second logistics distribution plan can be directly integrated into an overall target logistics distribution plan. The feasibility and effectiveness of the target logistics distribution plan can be verified through simulation technology. When the effectiveness improvement is verified, the previously preset logistics distribution plan can be updated to determine and use the target logistics distribution plan.

[0121] In summary, this application provides a logistics distribution optimization method based on industrial Internet of Things, which improves the logistics distribution efficiency of products from warehouses to various dealers through Internet of Things technology and neural network models.

[0122] In order to better implement the above method, the embodiment of the present application also provides a logistics distribution optimization device based on the industrial Internet of Things, which can be integrated in an electronic device, and the electronic device can be a terminal, a server, etc. Among them, the terminal can be a mobile phone, a tablet computer, a smart Bluetooth device, a laptop, a personal computer, etc.; the server can be a single server or a server cluster composed of multiple servers.

[0123] For example, in this embodiment, the method of the embodiment of the present application will be described in detail by taking the specific integration of a logistics distribution optimization device based on the industrial Internet of Things in a terminal as an example.

[0124] For example, Figure 3 As shown, the logistics distribution optimization device 300 based on the industrial Internet of Things may include a first unit 301, a second unit 302, a third unit 303 and a fourth unit 304, which is applied to a distribution center system. The distribution center system includes a management platform, an object platform and a sensor network platform. The object platform includes a product sorting terminal and a product distribution terminal. The product sorting terminal is used to manage the process of sending products from various product warehouses in the factory to the distribution center. The product distribution terminal is used to manage the process of sending products from the distribution center to various dealers. The management platform and the object platform are connected in communication through the sensor network platform. The device includes:

[0125] The first unit 301 is used to obtain production data corresponding to the product, inventory data corresponding to the product, historical order data corresponding to the product, current order data corresponding to the product, distribution center information data, traffic data of the distribution area corresponding to the distribution center, and environmental data of the distribution area corresponding to the distribution center, wherein the production data is used to characterize the production status of the product, and the inventory data is used to characterize the inventory status of each product warehouse;

[0126] The second unit 302 is used to obtain a first logistics distribution plan according to the production data, the inventory data and the historical order data through a preset first model, wherein the first logistics distribution plan is used to guide the process of sending products from various product warehouses in the factory to the distribution center;

[0127] The third unit 303 is used to obtain a second logistics distribution plan based on the current order data corresponding to the product, the distribution center information data, the traffic data of the distribution area corresponding to the distribution center, and the environmental data of the distribution area corresponding to the distribution center through a preset second model, wherein the second logistics distribution plan is used to guide the process of sending the product from the distribution center to each dealer;

[0128] The fourth unit 304 is used to update the preset logistics distribution plan based on the first logistics distribution plan and the second logistics distribution plan to generate a target logistics distribution plan.

[0129] In specific implementation, the above units can be implemented as independent entities, or can be arbitrarily combined to be implemented as the same or several entities. The specific implementation of the above units can refer to the previous method embodiments, which will not be repeated here.

[0130] It can be seen from the above that the embodiments of the present application can improve the logistics and distribution efficiency of products from warehouses to various dealers through the Internet of Things technology and neural network models.

[0131] The embodiment of the present application also provides an electronic device, which can be a terminal, a server, etc. The terminal can be a mobile phone, a tablet computer, a smart Bluetooth device, a laptop, a personal computer, etc. The server can be a single server or a server cluster composed of multiple servers, etc.

[0132] In some embodiments, the product processing device can also be integrated into multiple electronic devices. For example, the product processing device can be integrated into multiple servers, and the multiple servers can implement the logistics distribution optimization method based on industrial Internet of Things of this application.

[0133] In this embodiment, the electronic device of this embodiment is a terminal as an example for detailed description, for example, Figure 4 As shown, it shows a schematic diagram of the structure of the terminal 400 involved in the embodiment of the present application, specifically:

[0134] The terminal 400 may include one or more processing core processors 401, one or more medium storages 402, a power supply 403, an input module 404, and a communication module 405. Those skilled in the art will appreciate that Figure 4 The structure of the terminal 400 shown in the figure does not constitute a limitation on the terminal 400, and may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0135] The processor 401 is the control center of the terminal 400. It uses various interfaces and lines to connect various parts of the entire terminal 400. It executes various functions of the terminal 400 and processes data by running or executing software programs and / or modules stored in the memory 402, and calling data stored in the memory 402, so as to monitor the terminal 400 as a whole. In some embodiments, the processor 401 may include one or more processing cores; in some embodiments, the processor 401 may integrate an application processor and a modem processor, wherein the application processor mainly processes the operating system, user interface, and application programs, and the modem processor mainly processes wireless communications. It is understandable that the above-mentioned modem processor may not be integrated into the processor 401.

[0136] The memory 402 can be used to store software programs and modules. The processor 401 executes various functional applications and data processing by running the software programs and modules stored in the memory 402. The memory 402 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area may store data created according to the use of the terminal 400, etc. In addition, the memory 402 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other volatile solid-state storage devices. Accordingly, the memory 402 may also include a memory controller to provide the processor 401 with access to the memory 402.

[0137] The terminal 400 also includes a power supply 403 for supplying power to various components. In some embodiments, the power supply 403 can be logically connected to the processor 401 through a power management system, so as to manage charging, discharging, and power consumption through the power management system. The power supply 403 can also include any components such as one or more DC or AC power supplies, recharging systems, power failure detection circuits, power converters or inverters, and power status indicators.

[0138] The terminal 400 may further include an input module 404, which may be used to receive input digital or character information and generate keyboard, mouse, joystick, optical or trackball signal input related to user settings and function control.

[0139] The terminal 400 may also include a communication module 405. In some embodiments, the communication module 405 may include a wireless module. The terminal 400 may perform short-range wireless transmission through the wireless module of the communication module 405, thereby providing the user with wireless broadband Internet access. For example, the communication module 405 may be used to help the user send and receive emails, browse web pages, and access streaming media.

[0140] Although not shown, the terminal 400 may also include a display unit, etc., which will not be described in detail herein. Specifically in this embodiment, the processor 401 in the terminal 400 will load the executable files corresponding to the processes of one or more applications into the memory 402 according to the following instructions, and the processor 401 will run the applications stored in the memory 402, thereby realizing various functions, as follows:

[0141] Obtaining production data corresponding to the product, inventory data corresponding to the product, historical order data corresponding to the product, current order data corresponding to the product, distribution center information data, traffic data of the distribution area corresponding to the distribution center, and environmental data of the distribution area corresponding to the distribution center, wherein the production data is used to characterize the production status of the product, and the inventory data is used to characterize the inventory status of each product warehouse;

[0142] Obtaining a first logistics distribution plan based on the production data, the inventory data and the historical order data through a preset first model, wherein the first logistics distribution plan is used to guide the process of sending products from various product warehouses in the factory to the distribution center;

[0143] A second logistics distribution plan is obtained through a preset second model according to current order data corresponding to the product, information data of the distribution center, traffic data of the distribution area corresponding to the distribution center, and environmental data of the distribution area corresponding to the distribution center, wherein the second logistics distribution plan is used to guide the process of sending the product from the distribution center to each dealer;

[0144] Based on the first logistics distribution plan and the second logistics distribution plan, the preset logistics distribution plan is updated to generate a target logistics distribution plan.

[0145] The specific implementation of the above operations can be found in the previous embodiments, which will not be described in detail here.

[0146] It can be seen from the above that the embodiments of the present application can improve the logistics and distribution efficiency of products from warehouses to various dealers through the Internet of Things technology and neural network models.

[0147] Those skilled in the art will appreciate that all or part of the steps in the various methods of the above embodiments may be completed by instructions, or by controlling related hardware through instructions. The instructions may be stored in a medium and loaded and executed by a processor.

[0148] To this end, an embodiment of the present application provides a medium in which a plurality of instructions are stored, and the instructions can be loaded by a processor to execute the steps in any one of the logistics distribution optimization methods based on the industrial Internet of Things provided in the embodiment of the present application. For example, the instructions can execute the following steps:

[0149] Obtaining production data corresponding to the product, inventory data corresponding to the product, historical order data corresponding to the product, current order data corresponding to the product, distribution center information data, traffic data of the distribution area corresponding to the distribution center, and environmental data of the distribution area corresponding to the distribution center, wherein the production data is used to characterize the production status of the product, and the inventory data is used to characterize the inventory status of each product warehouse;

[0150] Obtaining a first logistics distribution plan based on the production data, the inventory data and the historical order data through a preset first model, wherein the first logistics distribution plan is used to guide the process of sending products from various product warehouses in the factory to the distribution center;

[0151] A second logistics distribution plan is obtained through a preset second model according to current order data corresponding to the product, information data of the distribution center, traffic data of the distribution area corresponding to the distribution center, and environmental data of the distribution area corresponding to the distribution center, wherein the second logistics distribution plan is used to guide the process of sending the product from the distribution center to each dealer;

[0152] Based on the first logistics distribution plan and the second logistics distribution plan, the preset logistics distribution plan is updated to generate a target logistics distribution plan.

[0153] The medium may include: a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0154] According to one aspect of the present application, a computer program product or a computer program is provided, the computer program product or the computer program includes a computer instruction, the computer instruction is stored in a medium. A processor of a computer device reads the computer instruction from the medium, and the processor executes the computer instruction, so that the computer device executes the method provided in various optional implementations provided in the above embodiments.

[0155] Since the instructions stored in the medium can execute the steps in any one of the logistics and distribution optimization methods based on the industrial Internet of Things provided in the embodiments of the present application, the beneficial effects that can be achieved by any one of the logistics and distribution optimization methods based on the industrial Internet of Things provided in the embodiments of the present application can be achieved. For details, please refer to the previous embodiments and will not be repeated here.

[0156] The above is a detailed introduction to the logistics distribution optimization method, device, terminal and medium based on the industrial Internet of Things provided in the embodiments of the present application. Specific examples are used in this article to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method of the present application and its core idea; at the same time, for technical personnel in this field, according to the ideas of the present application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A logistics distribution optimization method based on industrial Internet of Things, characterized in that: Applied to a distribution center system, the distribution center system includes a management platform, an object platform and a sensor network platform, the object platform includes a product sorting terminal and a product distribution terminal, the product sorting terminal is used to manage the process of sending products from various product warehouses in the factory to the distribution center, the product distribution terminal is used to manage the process of sending products from the distribution center to various dealers, the management platform and the object platform are connected in communication through the sensor network platform, and the method includes: Obtaining production data corresponding to the product, inventory data corresponding to the product, historical order data corresponding to the product, current order data corresponding to the product, distribution center information data, traffic data of the distribution area corresponding to the distribution center, and environmental data of the distribution area corresponding to the distribution center, wherein the production data is used to characterize the production status of the product, and the inventory data is used to characterize the inventory status of each product warehouse; Obtaining a first logistics distribution plan based on the production data, the inventory data and the historical order data through a preset first model, wherein the first logistics distribution plan is used to guide the process of sending products from various product warehouses in the factory to the distribution center; A second logistics distribution plan is obtained through a preset second model according to current order data corresponding to the product, information data of the distribution center, traffic data of the distribution area corresponding to the distribution center, and environmental data of the distribution area corresponding to the distribution center, wherein the second logistics distribution plan is used to guide the process of sending the product from the distribution center to each dealer; Based on the first logistics distribution plan and the second logistics distribution plan, the preset logistics distribution plan is updated to generate a target logistics distribution plan.

2. The method according to claim 1, characterized in that The preset first model includes a first input layer, a first hidden layer, and a first output layer; The first logistics distribution plan is obtained according to the production data, the inventory data and the historical order data by using the preset first model, including: Obtaining a first input vector through the first input layer according to the production data, the inventory data and the historical order data; Obtaining a first output vector according to the first input vector through the first hidden layer; Obtaining a production plan and an inventory plan for a product according to the first output vector through the first output layer; The first logistics distribution plan is generated based on the production plan and the inventory plan.

3. The method according to claim 2, characterized in that The first hidden layer includes a first sub-hidden layer and a second sub-hidden layer, the ratio of the number of nodes in the first sub-hidden layer to the number of nodes in the second sub-hidden layer is 2:1, and the activation function of the first sub-hidden layer and the activation function of the second sub-hidden layer are both ReLU functions; The obtaining a first output vector according to the first input vector through the first hidden layer includes: Obtaining a first intermediate vector according to the first input vector through the first sub-hidden layer; The first output vector is obtained through the second sub-hidden layer according to the first intermediate vector.

4. The method according to claim 2, characterized in that The first output layer includes a dense layer, a segmentation layer, a production plan generation layer, and an inventory plan generation layer; The obtaining of a production plan and an inventory plan of a product according to the first output vector through the first output layer includes: Obtaining a first information extraction vector according to the first output vector through the dense layer; Extracting vectors according to the first information through the segmentation layer to obtain a production plan vector corresponding to the production plan and an inventory plan vector corresponding to the inventory plan; Obtaining the production plan according to the production plan vector through the production plan generation layer; The inventory plan generation layer is used to obtain the inventory plan according to the inventory plan vector.

5. The method according to claim 1, characterized in that The preset second model includes a second input layer, a second hidden layer, and a second output layer; The second logistics distribution plan is obtained by using the preset second model according to the current order data corresponding to the product, the distribution center information data, the traffic data of the distribution area corresponding to the distribution center, and the environmental data of the distribution area corresponding to the distribution center, including: Obtaining a second input vector through the second input layer according to the current order data corresponding to the product, the distribution center information data, the traffic data of the distribution area corresponding to the distribution center, and the environmental data of the distribution area corresponding to the distribution center; Obtaining a second output vector according to the second input vector through the second hidden layer; Obtaining a delivery route plan, a delivery vehicle loading plan, and a delivery time plan according to the second output vector through the second output layer; The second logistics distribution plan is generated based on the distribution route plan, the distribution vehicle loading plan and the distribution time plan.

6. The method according to claim 5, characterized in that The second hidden layer includes a third sub-hidden layer and a fourth sub-hidden layer, the ratio between the number of nodes in the third sub-hidden layer and the number of nodes in the fourth sub-hidden layer is 2:1, and the activation function of the third sub-hidden layer and the activation function of the fourth sub-hidden layer are both ReLU functions; The step of obtaining a second output vector according to the second input vector through the second hidden layer includes: Obtaining a second intermediate vector according to the second input vector through the third sub-hidden layer; The second output vector is obtained according to the second intermediate vector through the fourth sub-hidden layer.

7. The method according to claim 5, characterized in that The second output layer includes a first fully connected layer, a second fully connected layer, and a third fully connected layer; The method of obtaining a delivery route plan, a delivery vehicle loading plan, and a delivery time plan according to the second output vector through the second output layer includes: Obtaining the delivery route plan according to the second output vector through the first fully connected layer; Obtaining the delivery vehicle loading plan according to the second output vector through the second fully connected layer; The delivery time plan is obtained according to the second output vector through the third fully connected layer.

8. A logistics distribution optimization device based on industrial Internet of Things, characterized in that: Applied to a distribution center system, the distribution center system includes a management platform, an object platform and a sensor network platform, the object platform includes a product sorting terminal and a product distribution terminal, the product sorting terminal is used to manage the process of sending products from various product warehouses in the factory to the distribution center, the product distribution terminal is used to manage the process of sending products from the distribution center to various dealers, the management platform and the object platform are connected in communication through the sensor network platform, and the device includes: The first unit is used to obtain production data corresponding to the product, inventory data corresponding to the product, historical order data corresponding to the product, current order data corresponding to the product, distribution center information data, traffic data of the distribution area corresponding to the distribution center, and environmental data of the distribution area corresponding to the distribution center, wherein the production data is used to characterize the production status of the product, and the inventory data is used to characterize the inventory status of each product warehouse; A second unit is used to obtain a first logistics distribution plan according to the production data, the inventory data and the historical order data through a preset first model, wherein the first logistics distribution plan is used to guide the process of sending products from various product warehouses in the factory to the distribution center; A third unit is used to obtain a second logistics distribution plan based on the current order data corresponding to the product, the distribution center information data, the traffic data of the distribution area corresponding to the distribution center, and the environmental data of the distribution area corresponding to the distribution center through a preset second model, wherein the second logistics distribution plan is used to guide the process of sending the product from the distribution center to each dealer; The fourth unit is used to update the preset logistics distribution plan based on the first logistics distribution plan and the second logistics distribution plan to generate a target logistics distribution plan.

9. A terminal, characterized in that: The method comprises a processor and a memory, wherein the memory stores a plurality of instructions; the processor loads instructions from the memory to execute the steps in the method according to any one of claims 1 to 7.

10. A medium, characterized in that The medium stores a plurality of instructions, and the instructions are suitable for being loaded by a processor to execute the steps in the method according to any one of claims 1 to 7.

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