Intelligent distribution system for logistics order resources

Abstract

The invention relates to an intelligent distribution system for logistics order resources. The intelligent distribution system comprises an information acquisition part, a data capture part, a multi-layer building mechanism, a prediction processing mechanism, a store configuration mechanism, a prediction processing mechanism, a store configuration mechanism and the like. The logistics management level can be improved.

Description

Technical Field

[0001] The present invention relates to the field of logistics, and in particular to an intelligent distribution system for logistics order resources. Background Art

[0002] Internet retail refers to commodity trading activities conducted by both parties through the Internet, that is, the organization and transmission of information through the Internet, which realizes the transfer of ownership of tangible and intangible goods or the consumption of services. Buyers and sellers use e-commerce (online) applications to query transaction information (information flow), trade (capital flow) and deliver (logistics).

[0003] Internet retail has the following characteristics: First, it breaks the time and space limitations of traditional retail, and consumers can shop through the Internet anytime and anywhere. Second, Internet retail provides a rich selection of goods and price comparisons, so consumers can easily find the most suitable goods for themselves. Finally, Internet retail focuses on user experience and improves the user's shopping experience through personalized recommendations and high-quality services.

[0004] However, for the same retailer that has set up multiple unmanned retail stores in a set city block, the night time period is a key period for competition between Internet retail and other retail models. It is easy to get entangled in the dilemma between the number of unmanned retail stores opened at night and the energy to digest Internet retail orders. If the number of unmanned retail stores opened at night in a set city block is small relative to the number of Internet retail orders in the set city block, there is a risk of losing retail orders. On the contrary, if the number of unmanned retail stores opened at night in a set city block is too large relative to the number of Internet retail orders in the set city block, the risk of increased power resources and theft risks of unmanned retail stores is increased. Summary of the invention

[0005] In order to solve the technical problems in the prior art, the present invention provides a logistics order resource intelligent allocation system, which can use an artificial intelligence model to intelligently predict the number of retail store orders corresponding to the set city block in the early morning time period on the same day for the same retailer that has arranged multiple unmanned retail stores in a set city block, and then configure in advance the number of unmanned retail stores opened in the set city block in the early morning time period on the same day based on the intelligently predicted number of retail store orders corresponding to the set city block in the early morning time period on the same day, thereby achieving an effective dynamic balance between the number of unmanned retail stores opened at night and the energy for digesting Internet retail orders, taking into account multiple different needs of retail order maintenance, power resource saving and reduced theft risk.

[0006] According to the present invention, a logistics order resource intelligent allocation system is provided, the system comprising: An information collection component is used to obtain the number of orders from various retail stores corresponding to the early morning time periods of each previous day in a set city block, where there are multiple unmanned retail stores of the same retailer in the set city block; A data capture component, used to obtain block-related data of a set city block and each block-related data corresponding to each adjacent city block, wherein the block-related data of the set city block is the area, number of permanent residents and number of young people of the set city block, and the block-related data corresponding to each adjacent city block is the area, number of permanent residents and number of young people of the adjacent city block; A multi-layer building mechanism, used to perform multiple learning on the convolutional neural network to complete the multi-layer building of the convolutional neural network, and output the convolutional neural network after performing the multiple learning as an AI prediction body, wherein the number of times the convolutional neural network is learned is monotonically positively correlated with the number of blocks of each adjacent city block; The prediction processing mechanism is connected to the information collection component, the data capture component and the multi-layer assembly mechanism respectively, and is used to use the AI ​​prediction body to intelligently predict the number of retail store orders corresponding to the set city block in the early morning time period of the current day based on the duration of the early morning time period, the number of retail store orders corresponding to the set city block in the early morning time period of each previous day before the current day, the block association data of the set city block, and the block association data corresponding to each adjacent city block; A store configuration mechanism, connected to the prediction processing mechanism, configured in advance the number of unmanned retail stores opened in the set city block during the early morning time period of the day based on the number of retail store orders received corresponding to the set city block during the early morning time period of the day; Wherein, configuring in advance the number of unmanned retail stores opened in the early morning time period of the set city block based on the number of retail store orders corresponding to the set city block in the early morning time period of the set city block includes: configuring in advance the number of unmanned retail stores opened in the early morning time period of the set city block based on the maximum number of orders processed by a single unmanned retail store and the number of retail store orders corresponding to the set city block in the early morning time period of the set city block; Wherein, configuring in advance the number of unmanned retail stores opened in the early morning time period of the set city block based on the maximum number of orders processed by a single unmanned retail store and the number of retail store orders corresponding to the set city block received in the early morning time period of the set city block includes: dividing the number of retail store orders corresponding to the set city block received in the early morning time period of the set city block by the maximum number of orders processed by a single unmanned retail store to obtain a corresponding quotient value, and if the quotient value is not an integer, increasing the quotient value by one to serve as the number of unmanned retail stores opened in the early morning time period of the set city block, otherwise, directly using the quotient value as the number of unmanned retail stores opened in the early morning time period of the set city block; Among them, the convolutional neural network is subjected to multiple learning to complete the multi-layer construction of the convolutional neural network, and the convolutional neural network after the multiple learning is output as an AI prediction body, and the number of learning times of the convolutional neural network is monotonically positively correlated with the number of blocks of each adjacent city block, including: in each learning performed on the convolutional neural network, the known number of retail store orders corresponding to a certain city block in the early morning time period of a certain day is used as the output content of the convolutional neural network, the duration of the early morning time period, the number of each retail store order corresponding to the early morning time period of each day before the certain day, the block association data of the certain city block, and the block association data corresponding to each adjacent city block of the certain city block are used as the input content of the convolutional neural network to complete this learning operation.

[0007] The present invention has at least the following four key invention points: (1) For a set city block with multiple unmanned retail stores of the same retailer, an AI prediction body with a targeted structural design is used to predict the number of retail store orders corresponding to the set city block in the early morning time period of the current day based on the various territorial data of the set city block and the number of retail store orders corresponding to the set city block in the early morning time period of each day before the current day, thereby providing reliable reference information for the subsequent advance configuration of the number of unmanned retail stores opened in the set city block in the early morning time period of the current day, thereby realizing the intelligent allocation of retail store order resources; (2) The targeted design of the structure of the AI ​​prediction body that performs the prediction is reflected in that the AI ​​prediction body is a convolutional neural network that has performed multiple learnings, and the number of times the convolutional neural network has been learned is monotonically positively correlated with the number of blocks of each adjacent city block of the set city block; (3) Introducing a store configuration mechanism to configure in advance the number of unmanned retail stores opened in the early morning time period of the set city block based on the predicted number of retail store orders corresponding to the set city block in the early morning time period of the same day. Specifically, the number of retail store orders corresponding to the set city block in the early morning time period of the same day is divided by the maximum number of orders processed by a single unmanned retail store to obtain a corresponding quotient value. If the quotient value is not an integer, the quotient value is increased by one to serve as the number of unmanned retail stores opened in the early morning time period of the set city block. Otherwise, the quotient value is directly used as the number of unmanned retail stores opened in the early morning time period of the set city block; (4) In each learning operation performed on the convolutional neural network, the number of known retail store orders corresponding to a certain city block in the early morning time period of a certain day is used as the output content of the convolutional neural network, and the duration of the early morning time period, the number of retail store orders corresponding to the early morning time period of the certain city block in the past days before the certain day, the block-related data of the certain city block, and the block-related data corresponding to each adjacent city block of the certain city block are used as the input content of the convolutional neural network to complete this learning operation, thereby ensuring the learning effect of each learning operation of the convolutional neural network. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] 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 the logistics order resource intelligent allocation system according to implementation A of the present invention.

[0009] Figure 2 Schematic diagram of the internal structure of the logistics order resource intelligent allocation system according to implementation B of the present invention.

[0010] Figure 3 Schematic diagram of the internal structure of the logistics order resource intelligent allocation system according to Implementation Example C of the present invention. DETAILED DESCRIPTION

[0011] The implementation scheme of the logistics order resource intelligent allocation system of the present invention will be described in detail below with reference to the accompanying drawings.

[0012] Figure 1 This is a schematic diagram of the internal structure of a logistics order resource intelligent allocation system according to Embodiment A of the present invention, wherein the system comprises: An information collection component is used to obtain the number of orders from various retail stores corresponding to the early morning time periods of each previous day in a set city block, where there are multiple unmanned retail stores of the same retailer in the set city block; For example, the information collection component is used to obtain the number of retail store orders corresponding to the early morning time periods of each previous day in a set city block before the current day, wherein the multiple unmanned retail stores of the same retailer in the set city block include: the multiple unmanned retail stores of the same retailer have the same store size and the same type of goods sold; A data capture component, used to obtain block-related data of a set city block and each block-related data corresponding to each adjacent city block, wherein the block-related data of the set city block is the area, number of permanent residents and number of young people of the set city block, and the block-related data corresponding to each adjacent city block is the area, number of permanent residents and number of young people of the adjacent city block; A multi-layer building mechanism, used to perform multiple learning on the convolutional neural network to complete the multi-layer building of the convolutional neural network, and output the convolutional neural network after performing the multiple learning as an AI prediction body, wherein the number of times the convolutional neural network is learned is monotonically positively correlated with the number of blocks of each adjacent city block; The prediction processing mechanism is connected to the information collection component, the data capture component and the multi-layer assembly mechanism respectively, and is used to use the AI ​​prediction body to intelligently predict the number of retail store orders corresponding to the set city block in the early morning time period of the current day based on the duration of the early morning time period, the number of retail store orders corresponding to the set city block in the early morning time period of each previous day before the current day, the block association data of the set city block, and the block association data corresponding to each adjacent city block; A store configuration mechanism, connected to the prediction processing mechanism, configured in advance the number of unmanned retail stores opened in the set city block during the early morning time period of the day based on the number of retail store orders received corresponding to the set city block during the early morning time period of the day; Wherein, configuring in advance the number of unmanned retail stores opened in the early morning time period of the set city block based on the number of retail store orders corresponding to the set city block in the early morning time period of the set city block includes: configuring in advance the number of unmanned retail stores opened in the early morning time period of the set city block based on the maximum number of orders processed by a single unmanned retail store and the number of retail store orders corresponding to the set city block in the early morning time period of the set city block; Wherein, configuring in advance the number of unmanned retail stores opened in the early morning time period of the set city block based on the maximum number of orders processed by a single unmanned retail store and the number of retail store orders corresponding to the set city block received in the early morning time period of the set city block includes: dividing the number of retail store orders corresponding to the set city block received in the early morning time period of the set city block by the maximum number of orders processed by a single unmanned retail store to obtain a corresponding quotient value, and if the quotient value is not an integer, increasing the quotient value by one to serve as the number of unmanned retail stores opened in the early morning time period of the set city block, otherwise, directly using the quotient value as the number of unmanned retail stores opened in the early morning time period of the set city block; Among them, the convolutional neural network is subjected to multiple learning to complete the multi-layer formation of the convolutional neural network, and the convolutional neural network after the multiple learning is output as an AI prediction body, and the number of learning times of the convolutional neural network is monotonically positively correlated with the number of blocks of each adjacent city block, including: in each learning performed on the convolutional neural network, the number of known retail store orders corresponding to a certain city block in the early morning time period of a certain day is used as the output content of the convolutional neural network, the duration of the early morning time period, the number of retail store orders corresponding to the early morning time period of each day before the certain day, the block association data of the certain city block, and the block association data corresponding to each adjacent city block of the certain city block are used as the input content of the convolutional neural network to complete this learning operation; Among them, performing multiple learning on the convolutional neural network to complete the multi-layer formation of the convolutional neural network, and outputting the convolutional neural network after performing multiple learning as an AI prediction body, the number of times the convolutional neural network is learned is monotonically positively correlated with the number of blocks of each adjacent city block, including: using a content conversion formula to represent the content conversion relationship in which the number of times the convolutional neural network is learned is monotonically positively correlated with the number of blocks of each adjacent city block, the number of blocks of each adjacent city block is an input parameter of the content conversion formula, and the number of times the convolutional neural network is learned is an output parameter of the content conversion formula; Among them, the AI ​​prediction body is used to intelligently predict the number of retail store orders corresponding to the set city block in the early morning time period of the current day based on the duration of the early morning time period, the number of each retail store order corresponding to the early morning time period of each previous day of the set city block, the block association data of the set city block, and the block association data corresponding to each adjacent city block, including: synchronously inputting the duration of the early morning time period, the number of each retail store order corresponding to the early morning time period of each previous day of the set city block, the block association data of the set city block, and the block association data corresponding to each adjacent city block into the AI ​​prediction body and executing the AI ​​prediction body to obtain the number of retail store orders corresponding to the set city block in the early morning time period of the current day output by the AI ​​prediction body.

[0013] Figure 2 Schematic diagram of the internal structure of the logistics order resource intelligent allocation system according to implementation B of the present invention.

[0014] Compared with implementation scheme A, the logistics order resource intelligent allocation system shown in implementation scheme B may also include the following components: An ASIC processing chip is connected to the prediction processing mechanism, the information collection component, the data capture component and the multi-layer building mechanism respectively, and is used to provide configuration operations of working parameters for the prediction processing mechanism, the information collection component, the data capture component and the multi-layer building mechanism in a time-sharing manner; The ASIC processing chip is respectively connected to the prediction processing mechanism, the information collection component, the data capture component and the multi-layer assembly mechanism, and is used to provide the configuration operation of the working parameters for the prediction processing mechanism, the information collection component, the data capture component and the multi-layer assembly mechanism in a time-sharing manner, including: the prediction processing mechanism, the information collection component, the data capture component and the multi-layer assembly mechanism share the same working parameter configuration interface; Wherein, the prediction processing mechanism, the information collection component, the data capture component and the multi-layer building mechanism use different configuration address data; Wherein, a parallel connection of a communication data link is established between each of the prediction processing mechanism, the information collection component, the data capture component and the multi-layer assembly mechanism through a parallel data bus; Among them, the parallel connection of the communication data link established between the prediction processing mechanism, the information collection component, the data capture component and the multi-layer assembly mechanism through a parallel data bus includes: the parallel data bus is one of an 8-bit parallel data bus, a 16-bit parallel data bus and a 32-bit parallel data bus.

[0015] Figure 3 Schematic diagram of the internal structure of the logistics order resource intelligent allocation system according to Implementation Example C of the present invention.

[0016] Compared with implementation scheme A, the logistics order resource intelligent allocation system shown in implementation scheme C may also include the following components: The power support device is respectively connected to the prediction processing mechanism, the information collection component, the data capture component and the multi-layer assembly mechanism, and is used to provide power distribution support of different working voltages for the prediction processing mechanism, the information collection component, the data capture component and the multi-layer assembly mechanism in a time-sharing manner; The power support device is respectively connected to the prediction processing mechanism, the information collection component, the data capture component and the multi-layer assembly mechanism, and is used to provide power distribution support of different working voltages for the prediction processing mechanism, the information collection component, the data capture component and the multi-layer assembly mechanism in a time-sharing manner, including: two or more devices with the same working voltage requirements in the prediction processing mechanism, the information collection component, the data capture component and the multi-layer assembly mechanism use the same power supply line; Wherein, the power support device is respectively connected to the prediction processing mechanism, the information collection component, the data capture component and the multi-layer building mechanism, and is used to provide power distribution support of different working voltages for the prediction processing mechanism, the information collection component, the data capture component and the multi-layer building mechanism in a time-sharing manner, and further includes: the power support device is an uninterruptible power supply device; Wherein, the power support device is respectively connected to the prediction processing mechanism, the information collection component, the data capture component and the multi-layer assembly mechanism, and is used to provide power distribution support with different working voltages for the prediction processing mechanism, the information collection component, the data capture component and the multi-layer assembly mechanism in a time-sharing manner, and further includes: providing different working voltages including 3.3V working voltage for the prediction processing mechanism, the information collection component, the data capture component and the multi-layer assembly mechanism in a time-sharing manner; And wherein, the power support device is respectively connected to the prediction processing mechanism, the information collection component, the data capture component and the multi-layer assembly mechanism, and is used to provide power distribution support with different working voltages for the prediction processing mechanism, the information collection component, the data capture component and the multi-layer assembly mechanism in a time-sharing manner, and also includes: providing different working voltages including 5V working voltage for the prediction processing mechanism, the information collection component, the data capture component and the multi-layer assembly mechanism in a time-sharing manner.

[0017] In addition, in the logistics order resource intelligent allocation system, in each learning execution of the convolutional neural network, the known number of retail store orders corresponding to a certain city block in the early morning time period of a certain day is used as the output content of the convolutional neural network, and the duration of the early morning time period, the number of each retail store order corresponding to the early morning time period of each city block in the past days before the certain day, the block association data of the certain city block and the block association data corresponding to each adjacent city block of the certain city block are used as the input content of the convolutional neural network. Completing this learning operation includes: choosing to use the MATLAB toolbox to complete the test and simulation of each learning execution of the convolutional neural network.

[0018] The logistics order resource intelligent allocation system of the present invention solves the technical problem in the prior art that it is difficult for retail store management to balance retail order maintenance and retail store resource conservation. By adopting an artificial intelligence model with a customized structure, it intelligently predicts the retail order data of a set city block at night in the future based on a number of basic data screened in a targeted manner, thereby realizing the extraction and configuration of retail store resources in the set city block, thereby solving the above technical problem.

[0019] The foregoing description of the exemplary embodiments of the present invention is provided for the purpose of illustration and description. It is not intended to be exhaustive or to limit the present invention to the exact form disclosed. Obviously, many modifications and variations are obvious to those skilled in the art. The exemplary embodiments are selected and described in order to best illustrate the principles of the present invention and its practical application, so that other persons skilled in the art can understand the various embodiments and various variations of the present invention applicable to the specific purpose contemplated. It is intended that the scope of the present invention be limited by the appended claims and their equivalents.

Claims

1. A logistics order resource intelligent allocation system, characterized in that: The system comprises: An information collection component is used to obtain the number of orders from various retail stores corresponding to the early morning time periods of each previous day in a set city block, where there are multiple unmanned retail stores of the same retailer in the set city block; A data capture component, used to obtain block-related data of a set city block and each block-related data corresponding to each adjacent city block, wherein the block-related data of the set city block is the area, number of permanent residents and number of young people of the set city block, and the block-related data corresponding to each adjacent city block is the area, number of permanent residents and number of young people of the adjacent city block; A multi-layer building mechanism, used to perform multiple learning on the convolutional neural network to complete the multi-layer building of the convolutional neural network, and output the convolutional neural network after performing the multiple learning as an AI prediction body, wherein the number of times the convolutional neural network is learned is monotonically positively correlated with the number of blocks of each adjacent city block; The prediction processing mechanism is connected to the information collection component, the data capture component and the multi-layer assembly mechanism respectively, and is used to use the AI ​​prediction body to intelligently predict the number of retail store orders corresponding to the set city block in the early morning time period of the current day based on the duration of the early morning time period, the number of retail store orders corresponding to the set city block in the early morning time period of each previous day before the current day, the block association data of the set city block, and the block association data corresponding to each adjacent city block; A store configuration mechanism, connected to the prediction processing mechanism, configured in advance the number of unmanned retail stores opened in the set city block during the early morning time period of the day based on the number of retail store orders received corresponding to the set city block during the early morning time period of the day; Wherein, configuring in advance the number of unmanned retail stores opened in the early morning time period of the set city block based on the number of retail store orders corresponding to the set city block in the early morning time period of the set city block includes: configuring in advance the number of unmanned retail stores opened in the early morning time period of the set city block based on the maximum number of orders processed by a single unmanned retail store and the number of retail store orders corresponding to the set city block in the early morning time period of the set city block; Wherein, configuring in advance the number of unmanned retail stores opened in the early morning time period of the set city block based on the maximum number of orders processed by a single unmanned retail store and the number of retail store orders corresponding to the set city block received in the early morning time period of the set city block includes: dividing the number of retail store orders corresponding to the set city block received in the early morning time period of the set city block by the maximum number of orders processed by a single unmanned retail store to obtain a corresponding quotient value, and if the quotient value is not an integer, increasing the quotient value by one to serve as the number of unmanned retail stores opened in the early morning time period of the set city block, otherwise, directly using the quotient value as the number of unmanned retail stores opened in the early morning time period of the set city block; Among them, the convolutional neural network is subjected to multiple learning to complete the multi-layer construction of the convolutional neural network, and the convolutional neural network after the multiple learning is output as an AI prediction body, and the number of learning times of the convolutional neural network is monotonically positively correlated with the number of blocks of each adjacent city block, including: in each learning performed on the convolutional neural network, the known number of retail store orders corresponding to a certain city block in the early morning time period of a certain day is used as the output content of the convolutional neural network, the duration of the early morning time period, the number of each retail store order corresponding to the early morning time period of each day before the certain day, the block association data of the certain city block, and the block association data corresponding to each adjacent city block of the certain city block are used as the input content of the convolutional neural network to complete this learning operation.

2. The logistics order resource intelligent allocation system according to claim 1, characterized in that: Performing multiple learning on the convolutional neural network to complete the multi-layer formation of the convolutional neural network, and outputting the convolutional neural network after performing the multiple learning as an AI prediction body, wherein the number of times the convolutional neural network is learned is monotonically positively correlated with the number of blocks of each adjacent city block, including: using a content conversion formula to represent a content conversion relationship in which the number of times the convolutional neural network is learned is monotonically positively correlated with the number of blocks of each adjacent city block, the number of blocks of each adjacent city block is an input parameter of the content conversion formula, and the number of times the convolutional neural network is learned is an output parameter of the content conversion formula; Among them, the AI ​​prediction body is used to intelligently predict the number of retail store orders corresponding to the set city block in the early morning time period of the current day based on the duration of the early morning time period, the number of each retail store order corresponding to the early morning time period of each previous day of the set city block, the block association data of the set city block, and the block association data corresponding to each adjacent city block, including: synchronously inputting the duration of the early morning time period, the number of each retail store order corresponding to the early morning time period of each previous day of the set city block, the block association data of the set city block, and the block association data corresponding to each adjacent city block into the AI ​​prediction body and executing the AI ​​prediction body to obtain the number of retail store orders corresponding to the set city block in the early morning time period of the current day output by the AI ​​prediction body.

3. The logistics order resource intelligent allocation system according to claim 2, characterized in that: The system further comprises: An ASIC processing chip is connected to the prediction processing mechanism, the information collection component, the data capture component and the multi-layer building mechanism respectively, and is used to provide configuration operations of working parameters for the prediction processing mechanism, the information collection component, the data capture component and the multi-layer building mechanism in a time-sharing manner; Among them, the ASIC processing chip is respectively connected to the prediction processing mechanism, the information collection component, the data capture component and the multi-layer assembly mechanism, and is used to provide the configuration operation of working parameters for the prediction processing mechanism, the information collection component, the data capture component and the multi-layer assembly mechanism in a time-sharing manner, including: the prediction processing mechanism, the information collection component, the data capture component and the multi-layer assembly mechanism share the same working parameter configuration interface.

4. The logistics order resource intelligent allocation system as claimed in claim 3, characterized in that: The prediction processing mechanism, the information collection component, the data capture component and the multi-layer assembly mechanism use different configuration address data.

5. The logistics order resource intelligent allocation system as claimed in claim 3, characterized in that: Establishing parallel connections of communication data links between the prediction processing mechanism, the information collection component, the data capture component and the multi-layer assembly mechanism through parallel data buses; Among them, the parallel connection of the communication data link established between the prediction processing mechanism, the information collection component, the data capture component and the multi-layer assembly mechanism through a parallel data bus includes: the parallel data bus is one of an 8-bit parallel data bus, a 16-bit parallel data bus and a 32-bit parallel data bus.

6. The logistics order resource intelligent allocation system according to claim 2, characterized in that: The system further comprises: The power support device is respectively connected to the prediction processing mechanism, the information collection component, the data capture component and the multi-layer assembly mechanism, and is used to provide power distribution support of different working voltages for the prediction processing mechanism, the information collection component, the data capture component and the multi-layer assembly mechanism in a time-sharing manner; Among them, the power support device is respectively connected to the prediction processing mechanism, the information collection component, the data capture component and the multi-layer assembly mechanism, and is used to provide power distribution support with different working voltages for the prediction processing mechanism, the information collection component, the data capture component and the multi-layer assembly mechanism in a time-sharing manner, including: in the prediction processing mechanism, the information collection component, the data capture component and the multi-layer assembly mechanism, two or more devices with the same working voltage requirements use the same power supply line.

7. The logistics order resource intelligent allocation system according to claim 6, characterized in that: The power support device is respectively connected to the prediction processing mechanism, the information collection component, the data capture component and the multi-layer assembly mechanism, and is used to provide power distribution support with different working voltages for the prediction processing mechanism, the information collection component, the data capture component and the multi-layer assembly mechanism in a time-sharing manner, and also includes: the power support device is an uninterruptible power supply device.

8. The logistics order resource intelligent allocation system according to claim 7, characterized in that: The power support device is respectively connected to the prediction processing mechanism, the information collection component, the data capture component and the multi-layer assembly mechanism, and is used to provide power distribution support with different working voltages for the prediction processing mechanism, the information collection component, the data capture component and the multi-layer assembly mechanism in a time-sharing manner, and also includes: providing different working voltages including 3.3V working voltage for the prediction processing mechanism, the information collection component, the data capture component and the multi-layer assembly mechanism in a time-sharing manner.

9. The logistics order resource intelligent allocation system according to claim 8, characterized in that: The power support device is respectively connected to the prediction processing mechanism, the information collection component, the data capture component and the multi-layer assembly mechanism, and is used to provide power distribution support with different working voltages for the prediction processing mechanism, the information collection component, the data capture component and the multi-layer assembly mechanism in a time-sharing manner, and also includes: providing different working voltages including 5V working voltage for the prediction processing mechanism, the information collection component, the data capture component and the multi-layer assembly mechanism in a time-sharing manner.

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