Logistics distribution transport capacity regulation and control system

Abstract

The invention relates to a logistics distribution transport capacity regulation and control system which comprises a directional prediction device and a control device. The method is used for determining the number of residents in a current block, various configuration contents of the current block and a preset time length of a current time segment. The number of distribution orders of the express distribution company in previous time segments before the current time segment and the number of distribution orders corresponding to each day before the current day are set, and a predicted value of the number of distribution orders of the current block in the current time segment is intelligently analyzed through a feedforward neural network model; and the personnel allocation equipment is used for sending an outsourcing personnel allocation request when the predicted value exceeds the limit. Through the method and the device, the predicted value of the delivery order number of the express delivery company for the current block in the current time segment can be effectively obtained, and when it is determined that the delivery personnel in the current time segment are insufficient relative to the predicted value, the delivery personnel are requested to be allocated for the current time segment, so that the delivery efficiency and the operation cost are both considered.

Description

Technical Field

[0001] The present invention relates to the field of logistics management, and in particular to a logistics distribution capacity control system. Background Art

[0002] Logistics Management refers to the process of planning, organizing, directing, coordinating, controlling and supervising logistics activities in the process of social reproduction, based on the laws of the flow of material entities, and applying the basic principles and scientific methods of management, so as to achieve the best coordination and cooperation of various logistics activities, reduce logistics costs, and improve logistics efficiency and economic benefits. Part of the logistics supply chain activities is the process of planning, implementing and controlling the efficient and low-cost flow and storage of goods, services and related information from the place of production to the place of consumption in order to meet customer needs. Logistics refers to the use of modern information technology and equipment to achieve rationalized service models and advanced service processes.

[0003] There is still room for detailed management upgrades in current logistics management. For example, it is still impossible to effectively obtain the predicted value of the number of delivery orders for the current block in the current time segment of the express delivery company, which makes it impossible to determine whether there are insufficient delivery personnel in the current time segment and it is necessary to deploy outsourced personnel in the current time segment. As a result, the efficiency of delivery orders in each time segment cannot be guaranteed, or the unnecessary increase in personnel costs and economic costs is caused by the allocation of too many delivery personnel. Summary of the invention

[0004] In order to solve the above problems, the present invention provides a logistics distribution capacity control system, which can use an artificial intelligence model to intelligently predict the predicted value of the number of delivery orders of the express delivery company for the current block in the current time segment, and when the predicted value of the number of delivery orders of the express delivery company in the current time segment exceeds the threshold value of the number of delivery orders of the express delivery company in the preset time length, a request for outsourced personnel deployment for the current time segment is sent to a remote urban human resource management node, wherein the longer the preset time length, the larger the threshold value of the number of delivery orders for the preset time length, thereby ensuring the distribution efficiency of the delivery orders in each time segment, wherein the artificial intelligence model for executing the prediction of the number of delivery orders is a feedforward neural network model, and the feedforward neural network model is a model for completing multiple The feedforward neural network after the training operation, the number of feedforward neural network training operations is monotonically positively correlated with the number of active users of the set express delivery company in the current block, so that artificial intelligence models with different structures are designed for different express delivery companies, and multiple basic data are introduced to participate in the prediction of the number of delivery orders of the express delivery company for the current block in the current time segment. The multiple basic data involve the number of residents in the current block, the various configuration contents of the current block, the preset time length of the current time segment, the number of delivery orders of the set express delivery company in the past time segments before the current time segment, and the number of delivery orders corresponding to each day in the past of the set express delivery company before the current day, thereby ensuring the validity and stability of the prediction results.

[0005] According to the present invention, a logistics distribution capacity control system is provided, the system comprising: A content extraction mechanism, used to obtain various configuration contents of the set express delivery company for the current block, wherein the various configuration contents of the set express delivery company for the current block include the number of delivery personnel set by the set express delivery company in the current block, the number of active users in the current block, the geographical area of ​​the current block, and the total number of merchants providing delivery services for the current block; The data collection mechanism is used to obtain the number of delivery orders of the set express delivery company in the past time segments before the current time segment and to obtain the number of delivery orders of the set express delivery company corresponding to each of the past days before the current day; A sequential forming mechanism is used to sequentially perform multiple training operations on the feedforward neural network to obtain a feedforward neural network after completing the multiple training operations, and output the feedforward neural network after completing the multiple training operations as a feedforward neural network model, wherein the number of feedforward neural network training operations is monotonically positively correlated with the number of active users of the set express delivery company in the current block; A directional prediction device is connected to the content extraction mechanism, the data collection mechanism and the sequence formation mechanism respectively, and is used to use the number of residents in the current block, the configuration contents of the current block, the preset time length of the current time segment, the number of delivery orders of the set express delivery company in the past time segment before the current time segment, and the number of delivery orders of the set express delivery company in the past days before the current day as the input information of the feedforward neural network model item by item, so as to run the feedforward neural network model and obtain the predicted value of the number of delivery orders of the set express delivery company for the current block in the current time segment output by the feedforward neural network model; A personnel deployment device, connected to the directional prediction device, is used to send an outsourced personnel deployment request for the current time segment to a remote city human resource management node when the received predicted value of the number of delivery orders of the set express delivery company in the current time segment exceeds the threshold value of the number of delivery orders of the set express delivery company in the preset time length; The personnel deployment device is connected to the directional prediction device, and is used to send an outsourcing personnel deployment request for the current time segment to a remote city human resource management node when the received predicted value of the number of delivery orders of the set express delivery company in the current time segment exceeds the threshold value of the number of delivery orders of the set express delivery company in the preset time length, including: the longer the preset time length, the greater the threshold value of the number of delivery orders of the preset time length; Among them, the personnel deployment device is also used to temporarily suspend sending an outsourced personnel deployment request for the current time segment to the remote city human resource management node when the received predicted value of the number of delivery orders of the set express delivery company in the current time segment does not exceed the threshold value of the number of delivery orders of the set express delivery company in a preset time length.

[0006] It can be seen that the present invention has at least the following three important inventive features: Firstly: an artificial intelligence model is used to intelligently predict the predicted value of the number of delivery orders of the express delivery company for the current block in the current time segment, and when the predicted value of the number of delivery orders of the express delivery company in the current time segment exceeds the threshold of the number of delivery orders of the express delivery company in the preset time length, a request for outsourced personnel deployment for the current time segment is sent to the remote city human resource management node, wherein the longer the preset time length, the greater the threshold of the number of delivery orders for the preset time length, thereby ensuring the distribution efficiency of the delivery orders in each time segment; Secondly: the artificial intelligence model for performing delivery order prediction is a feedforward neural network model, which is a feedforward neural network that has completed multiple training operations. The number of feedforward neural network training operations is monotonically positively correlated with the number of active users of the set express delivery company in the current block, so that artificial intelligence models with different structures are designed for different express delivery companies; Again: multiple basic data are introduced to participate in setting the prediction of the number of delivery orders of the express delivery company for the current block in the current time segment. The multiple basic data involve the number of residents in the current block, the various configuration contents of the current block, the preset time length of the current time segment, the number of delivery orders of the express delivery company in the past time segments before the current time segment, and the number of delivery orders corresponding to each day before the current day by the express delivery company, thereby ensuring the validity and stability of the prediction results. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] The embodiments of the present invention will be described below with reference to the accompanying drawings, wherein: Figure 1 Schematic diagram of the internal structure of a logistics distribution capacity control system according to implementation A of the present invention.

[0008] Figure 2 Schematic diagram of the internal structure of a logistics distribution capacity control system according to implementation B of the present invention.

[0009] Figure 3 Schematic diagram of the internal structure of a logistics distribution capacity control system according to Implementation Example C of the present invention. DETAILED DESCRIPTION

[0010] The implementation scheme of the logistics distribution capacity control system of the present invention will be described in detail below with reference to the accompanying drawings.

[0011] Figure 1 This is a schematic diagram of the internal structure of a logistics distribution capacity control system according to Embodiment A of the present invention, wherein the system comprises: A content extraction mechanism, used to obtain various configuration contents of the set express delivery company for the current block, wherein the various configuration contents of the set express delivery company for the current block include the number of delivery personnel set by the set express delivery company in the current block, the number of active users in the current block, the geographical area of ​​the current block, and the total number of merchants providing delivery services for the current block; Specifically, the content extraction mechanism is used to obtain various configuration contents of the set express delivery company for the current block, and the various configuration contents of the set express delivery company for the current block are the number of delivery personnel set by the set express delivery company in the current block, the number of active users in the current block, the geographical area of ​​the current block, and the total number of businesses providing delivery services for the current block. In the content extraction mechanism, multiple information extraction components can be selected to respectively obtain the number of delivery personnel set by the set express delivery company in the current block, the number of active users in the current block, the geographical area of ​​the current block, and the total number of businesses providing delivery services for the current block; The data collection mechanism is used to obtain the number of delivery orders of the set express delivery company in the past time segments before the current time segment and to obtain the number of delivery orders of the set express delivery company corresponding to each of the past days before the current day; A sequential forming mechanism is used to sequentially perform multiple training operations on the feedforward neural network to obtain a feedforward neural network after completing the multiple training operations, and output the feedforward neural network after completing the multiple training operations as a feedforward neural network model, wherein the number of feedforward neural network training operations is monotonically positively correlated with the number of active users of the set express delivery company in the current block; A directional prediction device is connected to the content extraction mechanism, the data collection mechanism and the sequence formation mechanism respectively, and is used to use the number of residents in the current block, the configuration contents of the current block, the preset time length of the current time segment, the number of delivery orders of the set express delivery company in the past time segment before the current time segment, and the number of delivery orders of the set express delivery company in the past days before the current day as the input information of the feedforward neural network model item by item, so as to run the feedforward neural network model and obtain the predicted value of the number of delivery orders of the set express delivery company for the current block in the current time segment output by the feedforward neural network model; A personnel deployment device, connected to the directional prediction device, is used to send an outsourced personnel deployment request for the current time segment to a remote city human resource management node when the received predicted value of the number of delivery orders of the set express delivery company in the current time segment exceeds the threshold value of the number of delivery orders of the set express delivery company in the preset time length; The personnel deployment device is connected to the directional prediction device, and is used to send an outsourcing personnel deployment request for the current time segment to a remote city human resource management node when the received predicted value of the number of delivery orders of the set express delivery company in the current time segment exceeds the threshold value of the number of delivery orders of the set express delivery company in the preset time length, including: the longer the preset time length, the greater the threshold value of the number of delivery orders of the preset time length; The personnel deployment device is further used to temporarily suspend sending the outsourcing personnel deployment request for the current time segment to the remote city human resource management node when the received predicted value of the number of delivery orders of the set express delivery company in the current time segment does not exceed the threshold value of the number of delivery orders of the set express delivery company in the preset time length; Among them, the number of residents in the current block, the various configuration contents of the current block, the preset time length of the current time segment, the number of delivery orders set by the express delivery company in the past time segments before the current time segment, and the number of delivery orders set by the express delivery company in the past days before the current day are used as the input information of the feedforward neural network model item by item to run the feedforward neural network model, and the predicted value of the number of delivery orders set by the express delivery company for the current block in the current time segment output by the feedforward neural network model is obtained, including: the number of residents in the current block, the various configuration contents of the current block, the preset time length of the current time segment, the number of delivery orders set by the express delivery company in the past time segments before the current time segment, and the number of delivery orders set by the express delivery company in the past days before the current day are converted into binary values ​​and then synchronously input into the feedforward neural network model.

[0012] Figure 2 Schematic diagram of the internal structure of a logistics distribution capacity control system according to implementation B of the present invention.

[0013] Compared with implementation scheme A, the logistics distribution capacity control system shown in implementation scheme B may also include the following components: a vibration measuring mechanism, connected to the directional prediction device, the content extraction mechanism, the data collection mechanism and the order building mechanism, respectively, and used to measure the current vibration amplitude of each of the directional prediction device, the content extraction mechanism, the data collection mechanism and the order building mechanism; Wherein, the vibration measuring mechanism is respectively connected with the directional prediction device, the content extraction mechanism, the data collection mechanism and the order building mechanism, and is used to respectively measure the current vibration amplitude of each of the directional prediction device, the content extraction mechanism, the data collection mechanism and the order building mechanism, including: the vibration measuring mechanism includes a plurality of vibration measuring units, which are respectively connected with the directional prediction device, the content extraction mechanism, the data collection mechanism and the order building mechanism, so as to complete the respective measurement of the current vibration amplitude of each of the directional prediction device, the content extraction mechanism, the data collection mechanism and the order building mechanism; Wherein, the vibration measuring mechanism comprises a plurality of vibration measuring units, which are respectively connected to the directional prediction device, the content extraction mechanism, the data collection mechanism and the order building mechanism to complete the respective measurement of the current vibration amplitude of the directional prediction device, the content extraction mechanism, the data collection mechanism and the order building mechanism, including: the plurality of vibration measuring units are a plurality of vibration sensing circuits, which are respectively connected to the directional prediction device, the content extraction mechanism, the data collection mechanism and the order building mechanism to complete the respective measurement of the current vibration amplitude of the directional prediction device, the content extraction mechanism, the data collection mechanism and the order building mechanism; Wherein, the plurality of vibration measuring units are a plurality of vibration sensing circuits, which are used to be connected to the directional prediction device, the content extraction mechanism, the data collection mechanism and the order formation mechanism respectively, so as to complete the respective measurement of the current vibration amplitude of the directional prediction device, the content extraction mechanism, the data collection mechanism and the order formation mechanism respectively, including: the structures of the plurality of vibration sensing circuits are the same; Among them, the multiple vibration measurement units are multiple vibration sensing circuits, which are used to be connected to the directional prediction device, the content extraction mechanism, the data acquisition mechanism and the order assembly mechanism respectively, so as to complete the respective measurements of the current vibration amplitudes of the directional prediction device, the content extraction mechanism, the data acquisition mechanism and the order assembly mechanism, and also include: the multiple vibration sensing circuits have the same vibration measurement upper limit value and vibration measurement lower limit value.

[0014] Figure 3 Schematic diagram of the internal structure of a logistics distribution capacity control system according to Implementation Example C of the present invention.

[0015] Compared with implementation scheme A, the logistics distribution capacity control system shown in implementation scheme C may also include the following components: a data caching device, arranged near the directional prediction device, the content extraction mechanism, the data collection mechanism and the order building mechanism and connected to the directional prediction device, the content extraction mechanism, the data collection mechanism and the order building mechanism respectively, and used to provide data caching services for the directional prediction device, the content extraction mechanism, the data collection mechanism and the order building mechanism respectively; Wherein, the data cache device is arranged near the directional prediction device, the content extraction mechanism, the data collection mechanism and the order building mechanism and is respectively connected to the directional prediction device, the content extraction mechanism, the data collection mechanism and the order building mechanism, and is used to provide data cache services for the directional prediction device, the content extraction mechanism, the data collection mechanism and the order building mechanism respectively, including: the data cache device is connected to the directional prediction device, the content extraction mechanism, the data collection mechanism and the order building mechanism through different data cache interfaces; And wherein, the data caching device is arranged near the directional prediction device, the content extraction mechanism, the data collection mechanism and the order assembly mechanism and is respectively connected to the directional prediction device, the content extraction mechanism, the data collection mechanism and the order assembly mechanism, and is used to provide data caching services to the directional prediction device, the content extraction mechanism, the data collection mechanism and the order assembly mechanism respectively, and also includes: the data transmission bandwidth of different data caching interfaces adopted by the data caching device is the same.

[0016] In addition, in the logistics distribution capacity control system, the number of residents in the current block, the various configuration contents of the current block, the preset time length of the current time segment, the set number of delivery orders of the express delivery company in the past time segments before the current time segment, and the set number of delivery orders corresponding to the express delivery company in the past days before the current day are used as the input information of the feedforward neural network model item by item to run the feedforward neural network model, and obtain the predicted value of the number of delivery orders of the set express delivery company for the current block in the current time segment output by the feedforward neural network model, which also includes: running the feedforward neural network model to obtain the predicted value of the number of delivery orders of the set express delivery company in the current time segment represented by a binary value output by the feedforward neural network model.

[0017] The logistics distribution capacity control system of the present invention is used to address the technical problem in the prior art that express delivery companies are unable to accurately and effectively configure the number of delivery personnel that matches the number of delivery orders for future time segments. By adopting an artificial intelligence model, the predicted value of the number of delivery orders for the current block in the current time segment of the express delivery company is effectively obtained. When it is determined that the number of delivery personnel in the current time segment is insufficient compared to the predicted value, a request is made to allocate outsourced personnel for the current time segment, thereby taking into account both delivery efficiency and operating costs, and solving the above technical problems.

[0018] Those skilled in the art will readily appreciate the additional advantages and changes. Therefore, the present invention in its broader aspects is not limited to the specific details and representative embodiments shown and described herein. Therefore, various changes may be made without departing from the spirit or scope of the general inventive concept defined by the appended claims and their equivalents.

Claims

1. A logistics distribution capacity control system, characterized in that: The system comprises: A content extraction mechanism, used to obtain various configuration contents of the set express delivery company for the current block, wherein the various configuration contents of the set express delivery company for the current block include the number of delivery personnel set by the set express delivery company in the current block, the number of active users in the current block, the geographical area of ​​the current block, and the total number of merchants providing delivery services for the current block; The data collection mechanism is used to obtain the number of delivery orders of the set express delivery company in the past time segments before the current time segment and to obtain the number of delivery orders of the set express delivery company corresponding to each of the past days before the current day; A sequential forming mechanism is used to sequentially perform multiple training operations on the feedforward neural network to obtain a feedforward neural network after completing the multiple training operations, and output the feedforward neural network after completing the multiple training operations as a feedforward neural network model, wherein the number of feedforward neural network training operations is monotonically positively correlated with the number of active users of the set express delivery company in the current block; A directional prediction device is connected to the content extraction mechanism, the data collection mechanism and the sequence formation mechanism respectively, and is used to use the number of residents in the current block, the configuration contents of the current block, the preset time length of the current time segment, the number of delivery orders of the set express delivery company in the past time segment before the current time segment, and the number of delivery orders of the set express delivery company in the past days before the current day as the input information of the feedforward neural network model item by item, so as to run the feedforward neural network model and obtain the predicted value of the number of delivery orders of the set express delivery company for the current block in the current time segment output by the feedforward neural network model; A personnel deployment device, connected to the directional prediction device, is used to send an outsourced personnel deployment request for the current time segment to a remote city human resource management node when the received predicted value of the number of delivery orders of the set express delivery company in the current time segment exceeds the threshold value of the number of delivery orders of the set express delivery company in the preset time length; The personnel deployment device is connected to the directional prediction device, and is used to send an outsourcing personnel deployment request for the current time segment to a remote city human resource management node when the received predicted value of the number of delivery orders of the set express delivery company in the current time segment exceeds the threshold value of the number of delivery orders of the set express delivery company in the preset time length, including: the longer the preset time length, the greater the threshold value of the number of delivery orders of the preset time length; Among them, the personnel deployment device is also used to temporarily suspend sending an outsourced personnel deployment request for the current time segment to the remote city human resource management node when the received predicted value of the number of delivery orders of the set express delivery company in the current time segment does not exceed the threshold value of the number of delivery orders of the set express delivery company in a preset time length.

2. The logistics distribution capacity control system according to claim 1, characterized in that: The number of residents in the current block, the various configuration contents of the current block, the preset time length of the current time segment, the number of delivery orders set by the express delivery company in the past time segments before the current time segment, and the number of delivery orders set by the express delivery company in the past days before the current day are used as the input information of the feedforward neural network model item by item to run the feedforward neural network model, and the predicted value of the number of delivery orders set by the express delivery company for the current block in the current time segment output by the feedforward neural network model is obtained, including: the number of residents in the current block, the various configuration contents of the current block, the preset time length of the current time segment, the number of delivery orders set by the express delivery company in the past time segments before the current time segment, and the number of delivery orders set by the express delivery company in the past days before the current day are converted into binary values ​​and then synchronously input into the feedforward neural network model.

3. The logistics distribution capacity control system according to claim 2, characterized in that: The system further comprises: A vibration measuring mechanism, connected to the directional prediction device, the content extraction mechanism, the data collection mechanism and the order building mechanism, respectively, for measuring the current vibration amplitude of each of the directional prediction device, the content extraction mechanism, the data collection mechanism and the order building mechanism; Among them, the vibration measuring mechanism is respectively connected with the directional prediction device, the content extraction mechanism, the data collection mechanism and the order building mechanism, and is used to respectively measure the current vibration amplitudes of the directional prediction device, the content extraction mechanism, the data collection mechanism and the order building mechanism. The vibration measuring mechanism includes multiple vibration measuring units, which are respectively connected with the directional prediction device, the content extraction mechanism, the data collection mechanism and the order building mechanism to complete the respective measurements of the current vibration amplitudes of the directional prediction device, the content extraction mechanism, the data collection mechanism and the order building mechanism.

4. The logistics distribution capacity control system according to claim 3, characterized in that: The vibration measuring mechanism includes a plurality of vibration measuring units, which are used to be respectively connected to the directional prediction device, the content extraction mechanism, the data collection mechanism and the order building mechanism to complete the respective measurements of the current vibration amplitudes of the directional prediction device, the content extraction mechanism, the data collection mechanism and the order building mechanism. The plurality of vibration measuring units are a plurality of vibration sensing circuits, which are used to be respectively connected to the directional prediction device, the content extraction mechanism, the data collection mechanism and the order building mechanism to complete the respective measurements of the current vibration amplitudes of the directional prediction device, the content extraction mechanism, the data collection mechanism and the order building mechanism.

5. The logistics distribution capacity control system according to claim 4, characterized in that: The multiple vibration measurement units are multiple vibration sensing circuits, which are used to be connected to the directional prediction device, the content extraction mechanism, the data acquisition mechanism and the order building mechanism respectively, so as to complete the respective measurements of the current vibration amplitudes of the directional prediction device, the content extraction mechanism, the data acquisition mechanism and the order building mechanism, including: the structures of the multiple vibration sensing circuits are the same.

6. The logistics distribution capacity control system according to claim 5, characterized in that: The multiple vibration measurement units are multiple vibration sensing circuits, which are used to be connected to the directional prediction device, the content extraction mechanism, the data acquisition mechanism and the order assembly mechanism respectively, so as to complete the respective measurements of the current vibration amplitudes of the directional prediction device, the content extraction mechanism, the data acquisition mechanism and the order assembly mechanism. It also includes: the multiple vibration sensing circuits have the same vibration measurement upper limit value and vibration measurement lower limit value.

7. The logistics distribution capacity control system according to claim 2, characterized in that: The system further comprises: A data caching device is arranged near the directional prediction device, the content extraction mechanism, the data collection mechanism and the order assembly mechanism and is respectively connected to the directional prediction device, the content extraction mechanism, the data collection mechanism and the order assembly mechanism, and is used to provide data caching services for the directional prediction device, the content extraction mechanism, the data collection mechanism and the order assembly mechanism respectively.

8. The logistics distribution capacity control system according to claim 7, characterized in that: A data cache device is arranged near the directional prediction device, the content extraction mechanism, the data collection mechanism and the order assembly mechanism and is respectively connected to the directional prediction device, the content extraction mechanism, the data collection mechanism and the order assembly mechanism, and is used to provide data cache services for the directional prediction device, the content extraction mechanism, the data collection mechanism and the order assembly mechanism respectively, including: the data cache device is connected to the directional prediction device, the content extraction mechanism, the data collection mechanism and the order assembly mechanism through different data cache interfaces.

9. The logistics distribution capacity control system according to claim 8, characterized in that: A data cache device is arranged near the directional prediction device, the content extraction mechanism, the data collection mechanism and the order assembly mechanism and is respectively connected to the directional prediction device, the content extraction mechanism, the data collection mechanism and the order assembly mechanism, and is used to provide data cache services to the directional prediction device, the content extraction mechanism, the data collection mechanism and the order assembly mechanism respectively. It also includes: the data transmission bandwidth of different data cache interfaces used by the data cache device is the same.

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