Management strategy mapping system for intelligent logistics
Through the BP neural network model, intelligent prediction of the logistics data of express companies has been solved, which is the problem of inaccurate prediction of logistics data in the existing technology, and has achieved the improvement of transmission and reception efficiency and resource utilization.
Patent Information
- Application Number
- CN202411496643.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-10-25
- Publication Date
- 2025-02-11
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
The prior art cannot accurately predict the number of receipts and sendings of courier companies in the set operation area and time segments, resulting in the inability to effectively allocate recipients and sending personnel, affecting efficiency and resource utilization.
The BP neural network model is adopted to conduct intelligent predictions by inputting historical logistics data and logistics association information, determine the number of received and sent items on the day, and dynamically allocate personnel based on these quantities.
It realizes intelligent prediction of logistics data in the operating area and time segment of express delivery companies, ensuring balance of transmission and reception efficiency and efficient utilization of logistics resources.
Smart Images
Figure CN119417334A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of smart logistics, and more specifically, to a management strategy mapping system for smart logistics. Background Art
[0002] Logistics is the dynamic state of material materials such as goods in space and time. Smart logistics refers to a modern logistics model that uses intelligent technologies such as intelligent software and hardware, the Internet of Things, and big data to achieve refined, dynamic, and visual management of all aspects of logistics, improve the intelligent analysis and decision-making and automated operation execution capabilities of the logistics system, and enhance the efficiency of logistics operations.
[0003] Smart logistics requires the establishment of a future-oriented supply chain with three major characteristics: advanced, interconnected and intelligent. The concept of "smart supply chain" generates real-time information through sensors, RFID tags, brakes, GPS and other equipment and systems, and pays more attention to integrating the Internet of Things, sensor networks and the existing Internet. Through sophisticated, dynamic and scientific management, logistics can be automated, visualized, controllable, intelligent and networked, thereby improving resource utilization and productivity.
[0004] However, the prior art is unable to accurately predict the number of express deliveries and the number of express pick-ups for each express company in a set operating area at a set time segment on the day; it is also unable to determine the number of recipients assigned to the express company for the set operating area at the set time segment on the day based on the number of express deliveries for each express company in the set operating area at the set time segment on the day; and it is also unable to determine the number of senders assigned to the express company for the set operating area at the set time segment on the day based on the number of express deliveries for the set operating area at the set time segment on the day, resulting in the inability to take into account both the receiving and sending efficiency and the full utilization of limited logistics resources. Summary of the invention
[0005] In order to solve the technical problems in the prior art, the present invention provides a management strategy mapping system for smart logistics, which inputs the number of express receipts and the number of express delivery of each express delivery of the target express company for the set operating area in the same set time segment in the past multiple days, multiple express-related information and the time length of the set time segment into a BP neural network model in parallel, and executes the BP neural network model to obtain the number of express receipts and the number of express delivery of each express delivery of the target express company for the set operating area in the set time segment of the day output by the BP neural network model, thereby completing the intelligent prediction of future time logistics data based on historical logistics data and logistics-related data, and determining the number of recipients assigned by the target express company for the set operating area in the set time segment of the day based on the number of express receipts by the target express company for the set operating area in the set time segment of the day, and determining the number of delivery personnel assigned by the target express company for the set operating area in the set time segment of the day based on the number of express delivery of each express delivery by the target express company for the set operating area in the set time segment of the day, thereby improving the utilization rate of limited logistics resources while ensuring the efficiency of receiving and sending.
[0006] According to the present invention, a management strategy mapping system for smart logistics is provided, the system comprising: A multiple remodeling device is used to analyze a BP neural network model, in which the BP neural network is trained for each time to obtain a BP neural network after each training and used as the BP neural network model; The receiving and sending collection device is used to obtain the number of each express delivery received and the number of each express delivery sent by the target express company for the set operating area in the same set time segment over the past multiple days; An information analysis device, used to obtain the number of operating areas managed by the target express company, the number of express delivery personnel employed by the target express company, and the area and number of residents in the set operating area, so as to output multiple express-related information; a parameter mapping mechanism, connected to the multiple remodeling device, the receiving and sending collection device and the information analysis device respectively, for inputting the number of each express delivery received and the number of each express delivery sent by the target express company for the set operating area in the same set time segment in the past multiple days, multiple express related information and the time length of the set time segment in parallel into the BP neural network model, and executing the BP neural network model to obtain the number of each express delivery received and the number of each express delivery sent by the target express company for the set operating area in the set time segment on the current day output by the BP neural network model; a dynamic mapping mechanism, connected to the parameter mapping mechanism, for determining the number of recipients assigned by the target express company for the set operating area at the set time segment on the day based on the number of each express delivery received by the target express company for the set operating area at the set time segment on the day, and also determining the number of senders assigned by the target express company for the set operating area at the set time segment on the day based on the number of each express delivery sent by the target express company for the set operating area at the set time segment on the day; Among them, the multiple remodeling device is used to analyze the BP neural network model, in which the BP neural network is trained each time to obtain the BP neural network after each training and as the BP neural network model, including: the number of times the BP neural network is trained is positively correlated with the number of operating areas managed by the target express company and is also positively correlated with the area occupied by the set operating area; Among them, the number of recipients assigned by the target express company for the set operating area at the set time segment on the day is determined based on the number of express deliveries received by the target express company for the set operating area at the set time segment on the day, and the number of senders assigned by the target express company for the set operating area at the set time segment on the day is also determined based on the number of express deliveries sent by the target express company for the set operating area at the set time segment on the day, including: the more the number of express deliveries received by the target express company for the set operating area at the set time segment on the day, the more recipients the target express company will allocate for the set operating area at the set time segment on the day.
[0007] Therefore, the present invention has at least the following beneficial technical effects: Technical effect A: multiple remodeling devices are used to analyze the BP neural network model. In the BP neural network model, the BP neural network is trained for each time to obtain the BP neural network after each training and used as the BP neural network model. The number of times the BP neural network is trained is positively correlated with the number of operating areas managed by the target express company and is also positively correlated with the area occupied by the set operating area, thereby completing the structural customization of the BP neural network model; Technical effect B: The number of express parcels received and the number of express parcels sent by the target express company for the set operating area in the same set time segment in the past multiple days, multiple express-related information and the time length of the set time segment are input into the BP neural network model in parallel, and the BP neural network model is executed to obtain the number of express parcels received and the number of express parcels sent by the target express company for the set operating area in the set time segment on the current day output by the BP neural network model, thereby completing the intelligent prediction of future time logistics data based on historical logistics data and logistics-related data, and providing key information for the subsequent dynamic management of personnel resources; Technical effect C: The number of recipients assigned to the target express company for the set operating area at the set time segment on the day is determined based on the number of each express delivery received by the target express company for the set operating area at the set time segment on the day; the number of senders assigned to the target express company for the set operating area at the set time segment on the day is also determined based on the number of each express delivery sent by the target express company for the set operating area at the set time segment on the day, thereby improving the utilization rate of limited logistics resources while ensuring the efficiency of receiving and sending.
[0008] The management strategy mapping system for smart logistics of the present invention operates intelligently and has certain pertinence. Since the BP neural network model can be used to intelligently predict the number of express receipts and the number of express delivery of each express delivery company for the set operation area at the set time segment of the day, reliable information is provided for the dynamic deployment of express delivery personnel and recipients at the set time segment of the day. BRIEF DESCRIPTION OF THE DRAWINGS
[0009] Those skilled in the art may better understand the numerous advantages of the present invention by referring to the accompanying drawings, in which: Figure 1 It is a schematic diagram of the internal structure of a management strategy mapping system for smart logistics according to the first embodiment of the present invention.
[0010] Figure 2 It is a schematic diagram of the internal structure of a management strategy mapping system for smart logistics according to the second embodiment of the present invention.
[0011] Figure 3 It is a schematic diagram of the internal structure of a management strategy mapping system for smart logistics according to the third embodiment of the present invention. DETAILED DESCRIPTION
[0012] The implementation scheme of the management strategy mapping system for smart logistics of the present invention will be described in detail below with reference to the accompanying drawings.
[0013] Figure 1 1 is a schematic diagram of the internal structure of a management strategy mapping system for smart logistics according to a first embodiment of the present invention, the system comprising: A multiple remodeling device is used to analyze a BP neural network model, in which the BP neural network is trained for each time to obtain a BP neural network after each training and used as the BP neural network model; For example, the device is reshaped multiple times to analyze a BP neural network model, in which the BP neural network is trained each time to obtain the BP neural network after each training and use it as the BP neural network model, including: selecting and using a MATLAB toolbox to implement the simulation and testing of the data processing process of training the BP neural network each time to obtain the BP neural network after each training and use it as the BP neural network model; The receiving and sending collection device is used to obtain the number of each express delivery received and the number of each express delivery sent by the target express company for the set operating area in the same set time segment over the past multiple days; An information analysis device, used to obtain the number of operating areas managed by the target express company, the number of express delivery personnel employed by the target express company, and the area and number of residents in the set operating area, so as to output multiple express-related information; a parameter mapping mechanism, connected to the multiple remodeling device, the receiving and sending collection device and the information analysis device respectively, for inputting the number of each express delivery received and the number of each express delivery sent by the target express company for the set operating area in the same set time segment in the past multiple days, multiple express related information and the time length of the set time segment in parallel into the BP neural network model, and executing the BP neural network model to obtain the number of each express delivery received and the number of each express delivery sent by the target express company for the set operating area in the set time segment on the current day output by the BP neural network model; a dynamic mapping mechanism, connected to the parameter mapping mechanism, for determining the number of recipients assigned by the target express company for the set operating area at the set time segment on the day based on the number of each express delivery received by the target express company for the set operating area at the set time segment on the day, and also determining the number of senders assigned by the target express company for the set operating area at the set time segment on the day based on the number of each express delivery sent by the target express company for the set operating area at the set time segment on the day; Among them, the multiple remodeling device is used to analyze the BP neural network model, in which the BP neural network is trained each time to obtain the BP neural network after each training and as the BP neural network model, including: the number of times the BP neural network is trained is positively correlated with the number of operating areas managed by the target express company and is also positively correlated with the area occupied by the set operating area; Wherein, determining the number of recipients assigned by the target express company to the set operating area at the set time segment on the day based on the number of each express delivery received by the target express company for the set operating area at the set time segment on the day, and determining the number of senders assigned by the target express company to the set operating area at the set time segment on the day based on the number of each express delivery sent by the target express company for the set operating area at the set time segment on the day includes: the more the number of each express delivery received by the target express company for the set operating area at the set time segment on the day, the more recipients the determined target express company will assign to the set operating area at the set time segment on the day; Among them, the number of receiving personnel assigned to the target express company for the set operating area at the set time segment on the day is determined based on the number of express deliveries of each express delivery by the target express company for the set operating area at the set time segment on the day, and the number of sending personnel assigned to the target express company for the set operating area at the set time segment on the day is determined based on the number of express deliveries of each express delivery by the target express company for the set operating area at the set time segment on the day, and it also includes: the more the number of express deliveries of each express delivery by the target express company for the set operating area at the set time segment on the day, the more sending personnel the determined target express company will allocate for the set operating area at the set time segment on the day.
[0014] Figure 2 It is a schematic diagram of the internal structure of a management strategy mapping system for smart logistics according to the second embodiment of the present invention.
[0015] and Figure 1 different, Figure 2 The management strategy mapping system for smart logistics in the system may also include the following components: A decibel measuring mechanism, connected to the parameter mapping mechanism, the multiple reshaping device, the transceiver collection device and the information analysis device, respectively, for measuring the decibel of nearby noise of the parameter mapping mechanism, the multiple reshaping device, the transceiver collection device and the information analysis device respectively; Wherein, the decibel measurement mechanism is respectively connected with the parameter mapping mechanism, the multiple reshaping device, the transceiver collection device and the information analysis device, and is used to respectively measure the decibels of the noise near the parameter mapping mechanism, the multiple reshaping device, the transceiver collection device and the information analysis device, including: the decibel measurement mechanism includes a plurality of decibel measurement units, which are respectively connected with the parameter mapping mechanism, the multiple reshaping device, the transceiver collection device and the information analysis device, so as to complete the measurement of the decibels of the noise near the parameter mapping mechanism, the multiple reshaping device, the transceiver collection device and the information analysis device; Wherein, the decibel measurement mechanism includes a plurality of decibel measurement units, which are respectively connected to the parameter mapping mechanism, the multiple reshaping device, the transceiver collection device and the information analysis device to complete the respective measurement of the decibel of the nearby noise of the parameter mapping mechanism, the multiple reshaping device, the transceiver collection device and the information analysis device, including: the plurality of decibel measurement units are a plurality of decibel sensor circuits, which are respectively connected to the parameter mapping mechanism, the multiple reshaping device, the transceiver collection device and the information analysis device to complete the respective measurement of the decibel of the nearby noise of the parameter mapping mechanism, the multiple reshaping device, the transceiver collection device and the information analysis device; Wherein, the multiple decibel measurement units are multiple decibel sensing circuits, which are used to be connected to the parameter mapping mechanism, the multiple reshaping device, the transceiver collection device and the information analysis device respectively, so as to complete the measurement of the decibel of the noise near each of the parameter mapping mechanism, the multiple reshaping device, the transceiver collection device and the information analysis device respectively, including: the structures of the multiple decibel sensing circuits are the same; Among them, the multiple decibel measurement units are multiple decibel sensing circuits, which are used to be connected to the parameter mapping mechanism, the multiple reshaping device, the transceiver acquisition device and the information analysis device respectively, so as to complete the separate measurement of the decibels of the nearby noise of the parameter mapping mechanism, the multiple reshaping device, the transceiver acquisition device and the information analysis device respectively, and also include: the multiple decibel sensing circuits have the same decibel measurement upper limit value and decibel measurement lower limit value.
[0016] Figure 3 It is a schematic diagram of the internal structure of a management strategy mapping system for smart logistics according to the third embodiment of the present invention.
[0017] and Figure 1 different, Figure 3 The management strategy mapping system for smart logistics in the system may also include the following components: A voice control processing device is arranged near the parameter mapping mechanism, the multiple reshaping device, the transceiver collection device and the information analysis device, and is used to provide the parameter mapping mechanism, the multiple reshaping device, the transceiver collection device and the information analysis device with the voice control services they need respectively; The voice control processing device is arranged near the parameter mapping mechanism, the multiple reshaping device, the transceiver collection device and the information analysis device, and is used to provide the parameter mapping mechanism, the multiple reshaping device, the transceiver collection device and the information analysis device with the voice control services they need respectively, including: the voice control processing device includes a sound collection unit, a numerical conversion unit and a signal sending unit; And wherein, the voice control processing device is arranged near the parameter mapping mechanism, the multiple reshaping device, the transceiver collection device and the information analysis device, and is used to provide the parameter mapping mechanism, the multiple reshaping device, the transceiver collection device and the information analysis device with the voice control services they need respectively, and further comprises: the numerical conversion unit is connected to the sound collection unit and the signal sending unit respectively; And wherein, the voice control processing device is arranged near the parameter mapping mechanism, the multiple reshaping device, the transceiver acquisition device and the information analysis device, and is used to provide the parameter mapping mechanism, the multiple reshaping device, the transceiver acquisition device and the information analysis device with the voice control services they need respectively. It also includes: the sound acquisition unit is used to collect sound signals near the parameter mapping mechanism, the multiple reshaping device, the transceiver acquisition device and the information analysis device, and whether the sound signals collected respectively belong to sound control signals, so as to only send the sound control signals to the numerical conversion unit for analysis of the control signals.
[0018] In addition, in the management strategy mapping system for smart logistics, the number of express parcels received and the number of express parcels sent by the target express company for the set operating area in the same set time segment in the past multiple days, multiple express-related information and the time length of the set time segment are input into the BP neural network model in parallel, and the BP neural network model is executed to obtain the number of express parcels received and the number of express parcels sent by the target express company for the set operating area in the set time segment on the current day output by the BP neural network model, including: using a numerical simulation mode to implement the test and simulation of the data processing process of inputting the number of express parcels received and the number of express parcels sent by the target express company for the set operating area in the same set time segment in the past multiple days, multiple express-related information and the time length of the set time segment in parallel into the BP neural network model, and executing the BP neural network model to obtain the number of express parcels received and the number of express parcels sent by the target express company for the set operating area in the set time segment on the current day output by the BP neural network model.
[0019] While there has been described what are considered to be preferred embodiments of the present invention, those skilled in the art will recognize that other and further modifications may be made thereto without departing from the spirit of the invention, and it is intended to claim all such embodiments that fall within the true scope of the invention.
Claims
1. A management strategy mapping system for smart logistics, characterized in that: The system comprises: A multiple remodeling device is used to analyze a BP neural network model, in which the BP neural network is trained for each time to obtain a BP neural network after each training and used as the BP neural network model; The receiving and sending collection device is used to obtain the number of each express delivery received and the number of each express delivery sent by the target express company for the set operating area in the same set time segment over the past multiple days; An information analysis device, used to obtain the number of operating areas managed by the target express company, the number of express delivery personnel employed by the target express company, and the area and number of residents in the set operating area, so as to output multiple express-related information; a parameter mapping mechanism, connected to the multiple remodeling device, the receiving and sending collection device and the information analysis device respectively, for inputting the number of each express delivery received and the number of each express delivery sent by the target express company for the set operating area in the same set time segment in the past multiple days, multiple express related information and the time length of the set time segment in parallel into the BP neural network model, and executing the BP neural network model to obtain the number of each express delivery received and the number of each express delivery sent by the target express company for the set operating area in the set time segment on the current day output by the BP neural network model; a dynamic mapping mechanism, connected to the parameter mapping mechanism, for determining the number of recipients assigned by the target express company for the set operating area at the set time segment on the day based on the number of each express delivery received by the target express company for the set operating area at the set time segment on the day, and also determining the number of senders assigned by the target express company for the set operating area at the set time segment on the day based on the number of each express delivery sent by the target express company for the set operating area at the set time segment on the day; Among them, the multiple remodeling device is used to analyze the BP neural network model, in which the BP neural network is trained each time to obtain the BP neural network after each training and as the BP neural network model, including: the number of times the BP neural network is trained is positively correlated with the number of operating areas managed by the target express company and is also positively correlated with the area occupied by the set operating area; Among them, the number of recipients assigned by the target express company for the set operating area at the set time segment on the day is determined based on the number of express deliveries received by the target express company for the set operating area at the set time segment on the day, and the number of senders assigned by the target express company for the set operating area at the set time segment on the day is also determined based on the number of express deliveries sent by the target express company for the set operating area at the set time segment on the day, including: the more the number of express deliveries received by the target express company for the set operating area at the set time segment on the day, the more recipients the target express company will allocate for the set operating area at the set time segment on the day.
2. The management strategy mapping system for smart logistics according to claim 1, characterized in that: The number of receiving personnel assigned to the target express company for the set operating area at the set time segment on the day is determined based on the number of express deliveries of each express delivery by the target express company for the set operating area at the set time segment on the day, and the number of sending personnel assigned to the target express company for the set operating area at the set time segment on the day is determined based on the number of express deliveries of each express delivery by the target express company for the set operating area at the set time segment on the day, and the number also includes: the more the number of express deliveries of each express delivery by the target express company for the set operating area at the set time segment on the day, the more sending personnel the determined target express company will assign to the set operating area at the set time segment on the day.
3. The management strategy mapping system for smart logistics according to claim 2, characterized in that: The system further comprises: A decibel measuring mechanism, connected to the parameter mapping mechanism, the multiple reshaping device, the transceiver collection device and the information analysis device, respectively, for measuring the decibel of nearby noise of the parameter mapping mechanism, the multiple reshaping device, the transceiver collection device and the information analysis device respectively; Among them, the decibel measuring mechanism is respectively connected to the parameter mapping mechanism, the multiple reshaping device, the transceiver acquisition device and the information analysis device, and is used to respectively measure the decibels of the nearby noise of the parameter mapping mechanism, the multiple reshaping device, the transceiver acquisition device and the information analysis device. The decibel measuring mechanism includes multiple decibel measuring units, which are respectively connected to the parameter mapping mechanism, the multiple reshaping device, the transceiver acquisition device and the information analysis device to complete the separate measurement of the nearby noise decibels of the parameter mapping mechanism, the multiple reshaping device, the transceiver acquisition device and the information analysis device.
4. The management strategy mapping system for smart logistics according to claim 3, characterized in that: The decibel measurement mechanism includes a plurality of decibel measurement units, which are used to be respectively connected to the parameter mapping mechanism, the multiple reshaping device, the transceiver acquisition device and the information analysis device to complete the respective measurements of the decibels of noise near the parameter mapping mechanism, the multiple reshaping device, the transceiver acquisition device and the information analysis device. The plurality of decibel measurement units are a plurality of decibel sensing circuits, which are used to be respectively connected to the parameter mapping mechanism, the multiple reshaping device, the transceiver acquisition device and the information analysis device to complete the respective measurements of the decibels of noise near the parameter mapping mechanism, the multiple reshaping device, the transceiver acquisition device and the information analysis device.
5. The management strategy mapping system for smart logistics according to claim 4, characterized in that: The multiple decibel measurement units are multiple decibel sensing circuits, which are used to be connected to the parameter mapping mechanism, the multiple reshaping device, the transceiver acquisition device and the information analysis device respectively, so as to complete the separate measurement of the nearby noise decibels of the parameter mapping mechanism, the multiple reshaping device, the transceiver acquisition device and the information analysis device respectively, including: the structures of the multiple decibel sensing circuits are the same.
6. The management strategy mapping system for smart logistics according to claim 5, characterized in that: The multiple decibel measurement units are multiple decibel sensing circuits, which are used to be connected to the parameter mapping mechanism, the multiple reshaping device, the transceiver acquisition device and the information analysis device respectively, so as to complete the separate measurement of the decibels of the nearby noise of the parameter mapping mechanism, the multiple reshaping device, the transceiver acquisition device and the information analysis device respectively, and also include: the multiple decibel sensing circuits have the same decibel measurement upper limit value and decibel measurement lower limit value.
7. The management strategy mapping system for smart logistics according to claim 2, characterized in that: The system further comprises: The voice control processing device is arranged near the parameter mapping mechanism, the multiple reshaping device, the transceiver collection device and the information analysis device, and is used to provide the parameter mapping mechanism, the multiple reshaping device, the transceiver collection device and the information analysis device with the voice control services they need respectively.
8. The management strategy mapping system for smart logistics according to claim 7, characterized in that: The voice control processing device is arranged near the parameter mapping mechanism, the multiple reshaping device, the transceiver collection device and the information analysis device, and is used to provide the parameter mapping mechanism, the multiple reshaping device, the transceiver collection device and the information analysis device with the required voice control services respectively, including: the voice control processing device includes a sound collection unit, a numerical conversion unit and a signal sending unit.
9. The management strategy mapping system for smart logistics according to claim 8, characterized in that: The voice control processing device is arranged near the parameter mapping mechanism, the multiple reshaping device, the transceiver collection device and the information analysis device, and is used to provide the parameter mapping mechanism, the multiple reshaping device, the transceiver collection device and the information analysis device with the voice control services they need, and further includes: the numerical conversion unit is connected to the sound collection unit and the signal sending unit respectively; Among them, the voice control processing device is arranged near the parameter mapping mechanism, the multiple reshaping device, the transceiver acquisition device and the information analysis device, and is used to provide the parameter mapping mechanism, the multiple reshaping device, the transceiver acquisition device and the information analysis device with their respective required voice control services. It also includes: the sound acquisition unit is used to collect sound signals near the parameter mapping mechanism, the multiple reshaping device, the transceiver acquisition device and the information analysis device, and whether the respectively collected sound signals belong to sound control signals, so as to only send the sound control signals to the numerical conversion unit for analysis of the control signals.