A smart management platform and management method for logistics park

Abstract

The present invention relates to the technical field related to logistics management, and specifically includes a smart management platform and management method for a logistics park, including: based on the smart management platform, connecting M receiving ends and N supply ends, collecting a site feature set and adding a time limit factor, receiving a logistics request, matching an initial path according to site features and time factors, and determining a logistics transit node; optimizing resources to obtain an available resource scheduling sequence, setting a logistics path, and managing a logistics park, thereby solving the technical problems of fixed logistics path planning, low resource scheduling efficiency of a logistics park, and insufficient coordination and balancing capabilities of logistics transit nodes, and realizing real-time optimization scheduling according to site features and time limit factors, improving the resource scheduling efficiency of the logistics park, and improving the coordination efficiency between logistics transit nodes through a multi-node interdependent balancing mechanism, and achieving flexible logistics scheduling planning, and adapting to the resource allocation and path optimization needs during logistics peak periods.

Description

Technical Field

[0001] The present invention relates to the technical field related to logistics management, and in particular to an intelligent management platform and management method for a logistics park. Background Art

[0002] With the rapid development of the logistics industry, logistics parks, as important logistics distribution centers, have a management efficiency that directly affects the operation of the overall logistics system. At present, the supply and receiving ends in logistics parks present different characteristics such as dispersion, concentration and variability. Most of them rely on fixed route planning, lack flexibility, and cannot cope with the complex demands between different logistics nodes. At the same time, the time requirements in the logistics process are becoming more and more stringent. The existing management system has limited ability to optimize the time of logistics routes, making it difficult to achieve efficient matching and optimized management under multiple scenario requirements.

[0003] In summary, the existing technology has technical problems such as fixed logistics path planning, low resource scheduling efficiency of logistics parks, and insufficient coordination and balancing capabilities of logistics transfer nodes. Summary of the invention

[0004] This application provides an intelligent management method for a logistics park, aiming to solve the technical problems in the prior art of fixed logistics route planning, low resource scheduling efficiency of the logistics park, and insufficient coordination and balancing capabilities of logistics transit nodes.

[0005] In view of the above problems, the technical solution to implement this application is:

[0006] On the one hand, the present application provides a smart management platform for a logistics park, which includes: a first feature collection module, which is used to connect M receiving terminals based on the smart management platform to collect a basic feature set of a type of place, wherein the basic feature of a type of place corresponds to a decentralized demand node, a centralized demand node, and a variable demand node;

[0007] The second feature collection module is used to connect N supply terminals based on the smart management platform to collect the basic feature set of the second type of places, where the basic features of the second type of places include decentralized supply nodes, centralized supply nodes, and variable supply nodes;

[0008] A time limit factor adding module, used to add a time limit factor, wherein the time limit factor is any one of a shortening time limit factor, an extending time limit factor, and a supplementing time limit factor;

[0009] A path matching module, configured to match the basic feature set of the first type of place and the basic feature set of the second type of place according to the time limit factor and set an initial logistics path after any one or more of the M receiving ends send a logistics request to the smart management platform;

[0010] A primary available resource optimization module is used to fix the path starting point and path end point of the initial logistics path, determine the logistics transfer nodes under different route constraints, and introduce a multi-node competition mechanism to perform a primary available resource optimization to obtain a first available resource scheduling sequence;

[0011] A secondary available resource optimization module is used to optimize the secondary available resources by balancing the interdependence between logistics transfer nodes under different route constraints to obtain a second available resource scheduling sequence;

[0012] The logistics management module is used to set the logistics path based on the initial logistics path through the first available resource scheduling sequence and the second available resource scheduling sequence, and respond to the logistics request to establish a short-term communication connection between any one or more of the M receiving ends and any one or more of the N supply ends, and send it to the associated logistics park to manage the physical flow process from the supply end to the receiving end.

[0013] On the other hand, the present application provides a smart management method for a logistics park, which includes: based on a smart management platform, connecting M receiving terminals, collecting a basic feature set of a type of place, wherein the basic feature of a type of place corresponds to a decentralized demand node, a centralized demand node, and a variable demand node;

[0014] Based on the smart management platform, N supply terminals are connected to collect the basic feature set of the second type of places, where the basic features of the second type of places include decentralized supply nodes, centralized supply nodes, and variable supply nodes;

[0015] Adding a time limit factor, wherein the time limit factor is any one of a shortening time limit factor, an extending time limit factor, and a supplementing time limit factor;

[0016] After any one or more of the M receiving ends sends a logistics request to the smart management platform, matching is performed according to the basic feature set of the first type of place and the basic feature set of the second type of place and the time limit factor to set an initial logistics path;

[0017] The starting point and the end point of the initial logistics path are fixed, the logistics transfer nodes under different route constraints are determined, and a multi-node competition mechanism is introduced to perform an available resource optimization to obtain a first available resource scheduling sequence;

[0018] Through the interdependence balance between logistics transfer nodes under different route constraints, secondary available resource optimization is performed to obtain the second available resource scheduling sequence;

[0019] On the basis of the initial logistics path, the logistics path is set through the first available resource scheduling sequence and the second available resource scheduling sequence, and in response to the logistics request, a short-term communication connection is established between any one or more of the M receiving ends and any one or more of the N supply ends, and sent to the associated logistics park to manage the physical flow process from the supply end to the receiving end.

[0020] In summary, one or more technical solutions provided in this application solve the technical problems of fixed logistics route planning, low resource scheduling efficiency of logistics parks, and insufficient coordination and balancing capabilities of logistics transit nodes. It realizes real-time optimization scheduling based on site characteristics and time constraints, improves the resource scheduling efficiency of logistics parks, and improves the coordination efficiency between logistics transit nodes through the interdependent balancing mechanism of multiple nodes. It also achieves flexible logistics scheduling planning and adapts to the resource allocation and route optimization needs during logistics peak periods. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 A structural diagram of a smart management platform for a logistics park is provided for this application;

[0022] Figure 2 A flow chart of a smart management method for a logistics park is provided for this application.

[0023] Explanation of the accompanying drawings: first feature collection module M100, second feature collection module M200, time limit factor adding module M300, path matching module M400, primary available resource optimization module M500, secondary available resource optimization module M600, logistics management module M700. DETAILED DESCRIPTION

[0024] Embodiment 1

[0025] The present application is described in detail below with reference to the accompanying drawings. Figure 1 As shown, the present application provides a smart management platform for a logistics park, which includes:

[0026] The first feature collection module M100 is used to connect M receiving ends based on the smart management platform to collect the basic feature set of a type of place, wherein the basic features of the type of place correspond to decentralized demand nodes, centralized demand nodes, and variable demand nodes; the second feature collection module M200 is used to connect N supply ends based on the smart management platform to collect the basic feature set of the type of place, wherein the basic features of the type of place include decentralized supply nodes, centralized supply nodes, and variable supply nodes.

[0027] Specifically, the smart management platform is an intelligent platform for managing logistics parks through network technology, which can integrate multiple management functions, such as logistics route planning, resource scheduling, time management, etc. The receiving end (M) is the node that receives logistics from the supply end, usually a storage facility. Correspondingly, the basic feature set of the first type of place refers to a data set that describes the characteristics of the receiving end, which is classified according to the demand type, including decentralized demand nodes (multiple receiving points with widely distributed demand), centralized demand nodes (a few centralized receiving points) and variable demand nodes (receiving points with changing demand at any time). The supply end (N) is the supply source node, usually a supplier or distribution center. Correspondingly, the basic feature set of the second type of place refers to a data set that describes the characteristics of the supply end, which is classified according to the supply type, including decentralized supply nodes (supply resources are widely distributed), centralized supply nodes (resources are concentrated in specific locations) and variable supply nodes (supply conditions fluctuate greatly).

[0028] Execution steps: The smart management platform is connected to M receiving ends through IoT devices and networks. Each receiving end has different site characteristics, such as decentralized demand nodes (such as the delivery address of a single consumer), centralized demand nodes (such as the distribution center of a large retailer) or variable demand nodes (such as temporary demand points during seasonality or promotions). The characteristics of these nodes mainly reflect the distribution and dynamics of demand. In order to collect these characteristics, data collection tools, such as GPS positioning, are used to determine the demand characteristics of each node.

[0029] Connect N supply ends, and also collect the basic feature sets of the second category of places through IoT devices and data collection tools. Each supply end has different site characteristics, including decentralized supply nodes (such as multiple small warehouses, which serve as regional distribution nodes and quickly respond to logistics needs in surrounding areas), centralized supply nodes (such as large distribution centers), and variable supply nodes (such as supply points for fruit and vegetable products, whose supply volume fluctuates with seasonal changes). The characteristics of these nodes mainly reflect the distribution of logistics resources, storage capacity, and supply flexibility. The above steps provide data support for subsequent analysis by collecting site feature data from the receiving end and the supply end.

[0030] The time limit factor adding module M300 is used to add a time limit factor, where the time limit factor is any one of a shortening time limit factor, an extending time limit factor, and a completing time limit factor.

[0031] Specifically, time constraints refer to constraints or adjustments made according to time requirements during logistics route planning and resource scheduling. The time constraints are any one of shortening time constraints, extending time constraints, and making up time constraints. Shortening time constraints are used when time is tight and logistics activities need to be completed in a shorter time than normal time, such as emergency delivery, expedited service, etc. Extending time constraints are used when there is no rush for delivery and special needs require logistics activities to take longer than normal time, such as the logistics peak during holidays, when the storage time of the warehouse needs to be extended to meet the demand. Making up time constraints are used to fill in possible time differences to ensure that logistics activities can still be completed within the time expected by the customer and avoid arriving too early or too late. For example, if the transportation is delayed by one day due to unforeseen factors, the delivery can be completed within the time expected by the customer by shipping one day in advance or adjusting the transportation plan.

[0032] Execution steps: The time limit factor is any one of the shortening time limit factor, the extending time limit factor, and the completing time limit factor. Furthermore, in the first case (shortening time limit factor), when the logistics request needs to be processed urgently, the platform will add a shortening time limit factor, and optimize the transportation route by using real-time traffic data and logistics scheduling data, select the shortest route, and minimize the number of transfers and stay time on the way. Tools such as the API of the urban logistics platform can be used to obtain real-time traffic information. For example, a delivery task needs to be delivered from A to B within 24 hours. By shortening the time limit, direct routes are given priority, and the path is automatically adjusted according to real-time traffic information to avoid congestion.

[0033] In the second case (extended time limit factor), when there is a delay note for a logistics task, an extended time limit factor is added to make better use of resources. For example, for logistics peaks during holidays, an extended time limit factor can be set to adapt to the increase in demand. At the same time, through resource scheduling algorithms (such as linear programming algorithms), the task is combined with other logistics tasks, and the most suitable time period is determined to arrange transportation by sharing transportation resources.

[0034] In the third case (filling time limit factor), in logistics route planning, if it is found that the transportation time is uneven (for example, the delivery time is too early or too late), the filling time limit factor is used to balance the time arrangement. Use data analysis tools (such as Python's pandas or R language) to analyze the time distribution of historical delivery data, identify potential time differences, and fill in the time. For example, when delivering to multiple locations, if it is found that the delivery time of a certain location is earlier than that of other locations, by increasing the waiting time and selecting a more distant transfer point, ensure that the goods at all locations can arrive within a close time.

[0035] In summary, by adding time limitation factors, the smart management platform can flexibly adjust logistics routes and scheduling resources according to specific needs, thereby achieving efficient logistics management.

[0036] The path matching module M400 is used to match the basic feature set of the first type of places and the basic feature set of the second type of places according to the time limit factors after any one or more of the M receiving ends sends a logistics request to the smart management platform, and set the initial logistics path.

[0037] Specifically, the M receiving ends refer to multiple locations or entities in the logistics network that need to receive goods or services; the basic feature set of the first type of locations represents the distribution characteristics of the demand locations; and the basic feature set of the second type of locations represents the distribution characteristics of the supply locations.

[0038] Execution steps: When any one or more of the M receiving ends sends a logistics request to the smart management platform, the logistics path planning process is started according to the request. First, the matching relationship between the receiving end and the supply end is analyzed according to the basic feature set of the first type of place (the characteristics of the receiving end) and the basic feature set of the second type of place (the characteristics of the supply end). In the matching process, the time limit factor must also be considered to ensure that the logistics path not only meets the spatial matching but also meets the timeliness requirements. Specifically,

[0039] After receiving the logistics request, the platform will call the basic feature sets of the first-class places and the second-class places related to the task from the database. For example, if a company needs to receive goods from multiple scattered locations, the company's receiving end belongs to the scattered demand node, and if the supplier warehouses are concentrated in a certain area, they belong to the concentrated supply node. Then, a matching algorithm (such as the Dijkstra algorithm based on the shortest path) is used to perform a preliminary match between the receiving end and the supply end. Furthermore, if the receiving end is scattered and the supply end is concentrated, the distribution plan from the supply end to multiple receiving ends is given priority, usually with the help of logistics optimization tools such as Or-Tools or similar logistics planning engines.

[0040] Next, the matching results are adjusted according to the time constraints specified in the logistics request. Assuming that the logistics request needs to be delivered within 12 hours, and the receiving end is scattered, the path is optimized by shortening the time constraint, reducing the transfer nodes, and giving priority to faster transportation methods, such as air transportation or highway transportation. After combining the characteristics of the receiving end and the supply end and the time constraints, one or more initial logistics paths are generated. The platform will give priority to the route with the shortest path and the best time in the matching results as the initial path. For example, when the receiving end is a plurality of scattered locations, the platform chooses to start from the centralized supply node, pass through the main transportation hub, and distribute to multiple receiving ends.

[0041] In summary, after receiving a logistics request, the smart management platform generates an initial logistics path by matching the characteristics of the receiving and supply ends and combining time constraints, and provides a basis for subsequent resource scheduling and optimization. This process makes full use of the synergy of real-time data tools and the supply chain to ensure the efficient execution of the logistics process.

[0042] The primary available resource optimization module M500 is used to fix the starting point and the end point of the initial logistics path, determine the logistics transfer nodes under different route constraints, and introduce a multi-node competition mechanism to perform a primary available resource optimization to obtain a first available resource scheduling sequence; the secondary available resource optimization module M600 is used to perform a secondary available resource optimization through the mutual dependence balance between the logistics transfer nodes under different route constraints to obtain a second available resource scheduling sequence.

[0043] Specifically, the initial logistics path refers to the first logistics path generated by the logistics request, including the path starting point (the location where the goods depart from the supply end) and the path end point (the location where the goods arrive at the receiving end); the logistics transit node is the intermediate point where the goods are transshipped and distributed during the logistics process, which can be a warehouse, distribution center, etc. The transit node has different route options, which will affect the overall distribution efficiency; the multi-node competition mechanism refers to the selection of the optimal transit node through competition among multiple transit nodes (such as based on factors such as cost and timeliness); the primary available resource optimization is to optimize the resources for the transit nodes on the initial path to ensure that the best resources are used to complete the distribution task under the given constraints; the first available resource scheduling sequence is used to characterize the optimal resource allocation plan obtained after the primary resource optimization, including the selected transit nodes, transportation tools, time scheduling, etc.; the secondary available resource optimization is to balance resource utilization by further optimizing the interdependence between transit nodes to obtain a more refined optimization scheduling plan; the second available resource scheduling sequence is used to characterize the better resource allocation plan obtained after the secondary optimization to ensure the optimal utilization of resources under complex conditions.

[0044] Execution steps: In the logistics distribution process, an initial logistics path is first generated. The initial logistics path connects the path starting point (usually the supply end) and the path end point (usually the receiving end). In order to optimize logistics efficiency, it is necessary to set up several logistics transfer nodes in the middle of the path, and allocate resources through a multi-node competition mechanism under different route constraints. Specifically, the starting point and end point of the initial logistics path are fixed, and the positions of the supply end and the receiving end are clarified; several possible transfer nodes are selected in the path according to the route constraints. The constraints can be time constraints (such as the traffic hours on the highway), resource constraints (such as some nodes may not have sufficient storage capacity), or transportation mode restrictions (such as some nodes can only be transported by rail).

[0045] Multi-node competition is carried out according to the resource availability of transit nodes (such as storage capacity, transportation tools, geographical location, etc.). The multi-node competition mechanism allows multiple nodes to compete to become transit nodes at the same time. They are sorted by factors such as transportation time (commonly used optimization algorithms include Dijkstra shortest path algorithm, dynamic programming algorithm, linear programming, etc., which are used for path planning and resource scheduling) to select the optimal node; an available resource optimization is carried out based on the logistics transit nodes, and the first available resource scheduling sequence is generated by analyzing the existing transportation tools (such as trucks, airplanes, trains, etc.) and available time windows.

[0046] After determining the logistics transfer nodes, the dependencies between different routes will be considered. For example, transportation from A to B may affect the transportation time and resource arrangements from B to C (A to C will pass through B). In order to further improve efficiency, the interdependence between each node is analyzed and these relationships are balanced through secondary available resource optimization. At this time, more detailed adjustments are made based on factors such as the distance and timeliness between transfer nodes to ensure that resources are optimally utilized in the overall distribution chain and generate a second available resource scheduling sequence.

[0047] In summary, after fixing the initial logistics path, the first available resource scheduling sequence and the second available resource scheduling sequence are obtained through a multi-node competition mechanism and primary and secondary resource optimization, respectively, to ensure the efficiency and timeliness of the logistics process and improve the overall operational efficiency of logistics.

[0048] The logistics management module M700 is used to set the logistics path based on the initial logistics path through the first available resource scheduling sequence and the second available resource scheduling sequence, and respond to the logistics request to establish a short-term communication connection between any one or more of the M receiving ends and any one or more of the N supply ends, and send it to the associated logistics park to manage the physical flow process from the supply end to the receiving end.

[0049] Specifically, the first available resource scheduling sequence refers to the logistics resource scheduling order obtained through the first resource optimization under the initial logistics path, which involves the resource usage time, allocation and transit node selection; the second available resource scheduling sequence refers to the second resource optimization based on the first optimization, further considering the dependency relationship and resource coordination between transit nodes, and generating a more accurate scheduling plan; the short-term communication connection is a temporary data transmission connection established between the logistics supply end and the receiving end, which is used for the rapid transmission and confirmation of logistics requests and resource scheduling information.

[0050] Execution steps: Generate an initial logistics path based on the logistics request, and on this basis, combine the first available resource scheduling sequence and the second available resource scheduling sequence to optimize the use of resources at different nodes to ensure that materials can be delivered efficiently. The optimization of the scheduling sequence takes into account the choice of transit nodes and transportation tools, as well as the dependency of resources under different paths. After two optimizations, the optimal logistics path is generated; when the logistics path and resource scheduling are determined, it is necessary to establish a short-term communication connection between the supply end and the receiving end. This is a temporary connection for data exchange, mainly used to transmit the confirmation of logistics requests and the information of distribution paths. Through this connection, the status information of the receiving end and the supply end can be updated in real time to ensure the smooth progress of the logistics process.

[0051] The logistics path and scheduling information are sent to the relevant logistics park. The logistics park is responsible for the actual flow management of goods from the supply end to the receiving end. In the logistics park, the specific material flow is arranged according to the scheduling plan, such as the distribution and loading of goods at the transfer node, and the scheduling of transportation tools. The supply end loads and transports the goods through the logistics park, and the receiving end receives and distributes the goods through the logistics park. According to the logistics path and scheduling sequence, the status of the goods is monitored in real time to ensure that the logistics process proceeds as planned.

[0052] Through the above steps, the precise setting of logistics routes and the effective scheduling of resources can be achieved, while ensuring the real-time transmission of information and the safe delivery of goods during the logistics process.

[0053] Furthermore, the path matching module M400 is used to execute the following method:

[0054] The item type information is uploaded to the smart management platform through the N supply terminals; after receiving the item type information, a logistics request is issued to the smart management platform through any one or more of the M receiving terminals to match the service type and obtain a logistics service instruction; according to the basic feature set of the first type of places and the basic feature set of the second type of places, and in accordance with the time limitation factors, the logistics distribution time zone is matched, wherein the logistics distribution time zone is used to activate the logistics service instruction.

[0055] Specifically, the item type information includes material type, substance type, cargo type, commodity type, and item type; the logistics distribution time zone refers to the logistics distribution operations performed within a specific time period, and the logistics services must be activated and executed within these time zones; the logistics service instructions are specific logistics operation instructions used to guide the execution of the distribution service based on the request of the receiving end and the item information of the supply end.

[0056] Execution steps: matching is performed according to the basic feature sets of the first category of places and the basic feature sets of the second category of places according to the time limitation factors, including: the supply end uploads the information of the types of items that can be currently supplied through the smart management platform, records the inventory information of the supply end and transmits it to the smart management platform; the receiving end sends a logistics request to the smart management platform, and the logistics request can be initiated by different receiving ends, and the appropriate service type is matched according to the needs of the receiving end. In this process, the best logistics service type is matched according to the basic feature sets of the first category of places and the basic feature sets of the second category of places; after receiving the logistics request, the smart management platform refers to the basic feature sets of the first category of places and the basic feature sets of the second category of places, and takes into account the time limitation factors. The time limitation factors also include the geographical locations of the receiving end and the supply end, demand characteristics, supply characteristics, and time requirements for logistics operations.

[0057] After the logistics service type is matched, the logistics distribution time zone is matched. The distribution time zone is set to ensure that the logistics service instructions can be executed in the appropriate time period, taking into account the characteristics of the site (such as busy hours in commercial areas, quiet hours in residential areas) and the actual operability of logistics (such as avoiding traffic peaks) to avoid time conflicts in logistics transportation; after matching the distribution time zone, the logistics service instructions are activated. The logistics service instructions instruct the supply side to start preparing the goods, arrange transportation tools, and monitor the distribution process in real time. Through the feature matching of the first and second category sites, combined with the time limit factor, the service type and distribution time zone are matched, and the logistics service instructions are activated to ensure that the goods flow from the supply side to the receiving side on time, safely and efficiently.

[0058] Furthermore, the path matching module is also used to execute the following method:

[0059] Connect the decentralized demand nodes in the basic characteristics of the first type of place with the centralized supply nodes in the basic characteristics of the second type of place, and match them with the first path combination; connect the centralized demand nodes in the basic characteristics of the first type of place with the decentralized supply nodes in the basic characteristics of the second type of place, and match them with the second path combination; based on the first path combination and the second path combination, extend the variable demand nodes in the basic characteristics of the first type of place backward to obtain the first distribution path combination; based on the first path combination and the second path combination, extend the variable supply nodes in the basic characteristics of the second type of place forward to obtain the second distribution path combination; associate the first distribution path combination and the second distribution path combination with the time limitation factors to match the logistics distribution time zone.

[0060] Specifically, path combination refers to matching the logistics paths of the supply end and the receiving end, and forming a logistics route combination after considering the node characteristics of supply and demand; backward extension refers to adding more demand nodes from the original path toward the receiving end; forward extension refers to adding more supply nodes from the original path toward the supply end; the logistics distribution time zone is used to represent the time window defined in the logistics distribution process, and logistics distribution operations are performed within this time to ensure timeliness.

[0061] Execution steps: According to the basic feature set of the first category of places and the basic feature set of the second category of places, and in accordance with the time limitation factors, matching the logistics distribution time zone, including, in the logistics distribution process, first connecting the decentralized demand nodes in the basic features of the first category of places with the centralized supply nodes in the basic features of the second category of places to form a first path combination, the purpose is to use the powerful supply capacity of the centralized supply nodes to cover multiple decentralized demand nodes and optimize the supply chain efficiency; connecting the centralized demand nodes with the decentralized supply nodes to form a second path combination, the second path combination can meet the large demand of the centralized demand locations, the decentralized supply nodes provide multiple small batches of items, and the supply of items at large demand points is met through multiple supply points.

[0062] Based on the first path combination that has been generated, backward extension can be performed, that is, more variable demand nodes are added from the current logistics path to expand the logistics service scope. This operation is suitable for situations where demand fluctuates over time. Through backward extension, it is possible to flexibly respond to changes in these demands. For example, if the demand for several small retail stores in the original plan is uncertain, but as the actual situation changes, they generate additional order demand, the original first path is extended to add new demand points to the distribution plan to form the first distribution path combination.

[0063] Similar to backward extension, forward extension is performed based on the second path combination, that is, expanding the logistics path and adding more variable supply nodes. This is suitable for situations where the supply side is uncertain. By adding more supply points, the stability of logistics supply can be guaranteed. For example, if the supply of some small supply points is insufficient, and other areas have additional variable supply points, these new supply points can be included in the logistics path through forward extension to form a second distribution path combination to ensure that the supermarket has sufficient supply.

[0064] After generating the first distribution path combination and the second distribution path combination, the optimal logistics distribution time zone is determined based on the actual demand and supply nodes and time constraints. The distribution time zone can ensure that logistics is carried out within the appropriate time window to avoid delivery delays due to time conflicts between supply and demand. Through the above steps, it is ensured that the logistics chain can efficiently complete the distribution task under the influence of different node characteristics and time factors, and can flexibly respond to changes in demand or supply.

[0065] Furthermore, the path matching module is also used to execute the following method:

[0066] Based on the first path combination and the second path combination, the variable demand nodes in the basic characteristics of the first type of place are used as alternative transit points for a first matching, and P extended delivery paths are obtained; based on the first path combination, the second path combination, and the P extended delivery paths, the variable demand nodes in the basic characteristics of the first type of place are used as alternative transit points for a second matching, and P′ extended delivery paths are obtained; based on the P extended delivery paths and the P′ extended delivery paths, backward extension optimization of the delivery paths is performed to obtain a first delivery path combination, and the first delivery path combination associates and maps the P extended delivery paths corresponding to the first-level redundancy and the P′ extended delivery paths corresponding to the second-level redundancy.

[0067] Specifically, the first path combination and the second path combination refer to the distribution paths formed by connecting different types of nodes. The first path combination connects the dispersed demand nodes of the first type of places with the concentrated supply nodes of the second type of places; the second path combination connects the concentrated demand nodes of the first type of places with the dispersed supply nodes of the second type of places; variable demand nodes refer to demand nodes with fluctuating demand; alternative transit points refer to nodes that are used as alternative options in path planning. Logistics can pass through or rely on these nodes to achieve flexible scheduling. In the logistics distribution network, alternative transit points can be used as places for cargo distribution, sorting or transshipment when necessary to optimize distribution paths and improve distribution efficiency; extending distribution paths The path is a new logistics distribution path generated by expanding the original path combination by adding variable demand nodes; the first matching and second matching refer to the matching process of alternative transit points at different levels. The first matching is the process of preliminary screening and evaluation of alternative transit points, while the second matching is to further optimize the path planning on the basis of the first matching to generate a more optimized distribution route; the first-level redundancy and second-level redundancy refer to the additional distribution paths set up in the distribution path combination to cope with possible emergencies or changes in demand. The first-level redundancy corresponds to the extended distribution path obtained after the first matching, and the second-level redundancy corresponds to the extended distribution path obtained after the second matching.

[0068] Execution steps: Based on the first path combination and the second path combination, the variable demand nodes in the basic characteristics of the first type of place are extended backward to obtain a first distribution path combination, including, on the basis of the existing first path combination and the second path combination, the variable demand nodes in the basic characteristics of the first type of place are used as alternative transfer points, and a matching is performed to generate P extended distribution paths. The purpose of the first matching is to increase the flexibility of the distribution route and adapt to the changes of the demand nodes without changing the initial path; after the first matching, a second matching is performed based on the generated P extended distribution paths and the original path combination, and the variable demand nodes are used as transfer points again to re-optimize the path planning and generate P′ extended distribution paths. The purpose of the second matching is to further enhance the redundancy of the path to ensure that efficient operation can be maintained even when logistics conditions change.

[0069] The backward extension optimization based on P extended distribution paths and P' extended distribution paths is to extend the distribution path to as many areas as possible by adding more variable demand nodes in the matched path, thereby improving the coverage and path redundancy. Through this optimization process, the first distribution path combination finally generated can more flexibly cope with uncertainty and changes; the first distribution path combination is mapped with the first-level redundant and second-level redundant paths to ensure that the distribution path has good fault tolerance. The first-level redundant path corresponds to P extended paths, and the second-level redundant path corresponds to P' extended distribution paths. This means that when a problem occurs at any node, the redundant path can be automatically selected to ensure that logistics is not affected. Through the above steps, a flexible and efficient distribution network is established to cope with possible demand changes in the future.

[0070] Furthermore, the path matching module is also used to execute the following method:

[0071] Through the first distribution path combination, the first segmentation limiting factor is set with the P1 extended distribution paths corresponding to the first-level redundancy and the P1′ extended distribution paths corresponding to the second-level redundancy; through the second distribution path combination, the second segmentation limiting factor is set with the P2 extended distribution paths corresponding to the first-level redundancy and the P2′ extended distribution paths corresponding to the second-level redundancy, wherein P1+P2=P, and the second distribution path combination associates and maps the P2 extended distribution paths corresponding to the first-level redundancy and the P2′ extended distribution paths corresponding to the second-level redundancy; the first segmentation limiting factor and the second segmentation limiting factor are combined to obtain the time limiting factor.

[0072] Specifically, P1 extended distribution path refers to the P1 backup path obtained by backward extension of the variable demand nodes in the basic characteristics of the first type of place through the first path combination, which is used to extend and cover more demand nodes in the logistics distribution process; P2 extended distribution path refers to the P2 backup path obtained by backward extension of the variable demand nodes in the basic characteristics of the first type of place through the second path combination; the segmentation limitation factor refers to the time restriction conditions set for a certain section of the path, including the earliest and latest delivery times allowed in this section of the path after the first-level redundancy, and the earliest and latest delivery times allowed in this section of the path after the second-level redundancy.

[0073] Execution steps: by associating the first distribution path combination and the second distribution path combination with the time limitation factor, matching the logistics distribution time zone, including, in the first distribution path combination, introducing P1 extended distribution paths corresponding to the first-level redundancy and P1′ extended distribution paths corresponding to the second-level redundancy, setting the first segmentation limitation factor, the segmentation limitation factor refers to the time constraint conditions set in different distribution path segments, such as requiring that the delivery must be completed within a specified time range within a certain path segment, the purpose is to ensure that the time arrangement of logistics distribution is in line with the actual distribution capacity and demand fluctuations, and to ensure the smooth progress of logistics.

[0074] For the second distribution path combination, P2 extended distribution paths corresponding to the first-level redundancy and P2′ extended distribution paths corresponding to the second-level redundancy are also introduced, and the second segmentation limiting factors are set. By setting these limiting factors, the time requirements between the nodes of the second path combination can be flexibly adjusted to ensure that in emergency situations, redundant paths can be quickly enabled for adjustment. For example, a logistics path may be delayed due to weather reasons, and the segmentation time factors will be set in advance for the second distribution path combination. For example, the goods must arrive at the designated node before a certain time point, otherwise the second-level redundant path will be enabled to ensure that logistics continues to proceed smoothly.

[0075] Combining the first segment limiting factor and the second segment limiting factor to form an overall time limiting factor means that, on the entire logistics distribution path, not only are specific time limits set for each segment of the path, but the distribution time zones are also optimized and matched as a whole to ensure that the distribution of each path node can be within the appropriate time range, so that all distribution activities can be completed within the set time frame.

[0076] Furthermore, the path matching module is also used to execute the following method:

[0077] Connect the data storage unit, use the associated external conditions as screening conditions, export historical delivery instances, and fit the delivery time distribution density map; based on the delivery time distribution density map, compare the total number of deliveries corresponding to the historical delivery instances, add a mode limiting factor; based on the delivery time distribution density map, compare the maximum delivery time corresponding to the historical delivery instances, add a median limiting factor; based on the delivery time distribution density map, compare the average delivery time corresponding to the historical delivery instances, add an average limiting factor; use the mode limiting factor, median limiting factor, and average limiting factor to initialize the configuration of the first segment limiting factor.

[0078] Specifically, the delivery time distribution density map is a statistical chart based on historical delivery data that shows the frequency of occurrence of different delivery times, which helps to understand the distribution pattern of each time length; the mode limiter refers to the delivery time limit set by the mode (the value with the highest frequency) in the historical data to ensure that most common situations can meet the requirements; the median limiter is a time limit set based on the median (the middle value of all sorted data) in the historical delivery data to balance extremely long or short delivery times; the average limiter is a delivery time factor set by the average value in the historical data to ensure the overall efficiency of the delivery process; the first segment limiter refers to the time limit set for a certain section of the delivery route.

[0079] The execution step sets the first segmentation limiting factor through the first delivery path combination, the P1 extended delivery paths corresponding to the first-level redundancy, and the P1′ extended delivery paths corresponding to the second-level redundancy, including: firstly, by connecting the data storage unit, extracting the historical delivery instances matching the current delivery conditions, the historical delivery instances including the starting point, end point, logistics route, delivery time and other important information of the delivery, and filtering according to the associated external conditions (such as weather, traffic, holidays, etc.) to ensure that the extracted data is targeted. For example, if bad weather is encountered during delivery, the delivery instances with similar conditions will be exported for reference.

[0080] A delivery time distribution density map is fitted from the exported historical delivery data. The delivery time distribution density map is used to show the frequency distribution of different delivery times. Through analysis, we can intuitively see within what time range delivery is most frequent. For example, if it is found that most delivery times are concentrated in 2 to 3 hours, this interval will be displayed as a peak area on the map. By analyzing the delivery time distribution density map, we can find the mode (the value with the highest frequency) in the delivery time data and set it as the mode limiting factor.

[0081] In the delivery time distribution density map, find the median of the data to help determine the midpoint of the delivery time. For example, if most of the delivery times for a certain route are between 1.5 hours and 3 hours, select the middle value as the median limit. For example, assuming the median is 2.5 hours, set the median limit factor to ensure that most delivery times are not too long. Analyze the data in historical delivery instances, find the average delivery time, and use it as another time limit factor. For example, if the average delivery time of historical data is 2.2 hours, set the average limit factor to ensure that the actual delivery time is as close to this time as possible to avoid being too long or too short.

[0082] The first segment limiting factor will be initialized and configured comprehensively through the majority limiting factor, median limiting factor and average limiting factor. The first segment limiting factor will be used to set the delivery time of a certain path (if it is the first level redundancy, it corresponds to P1 extended delivery paths and P2 extended delivery paths) to ensure that it is within the reasonable range of historical delivery, thereby optimizing the entire logistics process. For example, the first segment limiting factor of a certain path can be set to 2 hours. Preferably, the majority limiting factor, median limiting factor and average limiting factor are referred to to ensure the smooth completion of the delivery task.

[0083] Furthermore, the path matching module is also used to execute the following method:

[0084] If the time limitation factor is a time shortening limitation factor, based on the delivery time distribution density map, extract the time distribution kurtosis and time distribution skewness; take the time distribution kurtosis as the central symmetry point to determine whether the time distribution skewness is negative; if the time distribution skewness is negative, according to the median limitation factor, merge the mode limitation factor and the mean limitation factor to update the first segment limitation factor in the time limitation factor.

[0085] Specifically, the kurtosis of the delivery time distribution reflects the concentration of the delivery time distribution. A higher kurtosis means that most delivery times are concentrated in a smaller range, and a lower kurtosis means that the delivery time distribution is more dispersed. The skewness of the delivery time distribution indicates the symmetry of the delivery time distribution. If the skewness is negative, it means a shorter time, and a positive skewness means a longer time.

[0086] The execution step uses the mode limiting factor, the median limiting factor, and the mean limiting factor to initialize the configuration of the first segment limiting factor, including: the time limiting factor is any one of the shortening time limiting factor, the extending time limiting factor, and the completing time limiting factor. Specifically, if the time limiting factor is the shortening time limiting factor, in short, when facing a scenario that needs to be optimized or shortened (such as express delivery), the delivery time distribution density map shows the time distribution of different delivery tasks, and the kurtosis (data concentration) and skewness (data symmetry) of the time distribution are extracted from the delivery time distribution density map; determine whether the delivery time distribution is negatively skewed, which means that shorter delivery times occur more frequently, while longer delivery times occur less frequently; in the case of negative skewness, taking the time distribution kurtosis as the central symmetry point means that the duration of most delivery tasks will be around This peak value, the center point is used to adjust the subsequent time limiting factors; in the case of negative skew, based on the median limiting factor, the mode limiting factor and the average limiting factor are integrated (for example, assuming that the median of the delivery time is 2 hours, the mode is 1.5 hours, and the average is 2.5 hours, |1.5-2|=0.5, |2.5-2|=0.5, the weight corresponding to the mode is 0.5, and the weight corresponding to the average is 0.5, (0.5×2+0.5×1.5)=1.75, (0.5×2+0.5×2.5)=2.25, therefore, the first segment is limited to 1.75-2.25 hours). Through this fusion, the planning of delivery time is ensured to be more accurate and efficient. According to the above analysis results, the first segment limiting factor is updated to ensure that the delivery time is reasonably shortened during the path planning and resource optimization process, thereby improving logistics efficiency.

[0087] Through the above steps, the mode, median and mean of the delivery time are used to optimize the time-limiting factors to ensure that the delivery time is shortened during logistics route planning without affecting the overall efficiency. By analyzing historical data, the peak and distribution characteristics of the logistics task duration are determined. Combined with actual logistics needs, the limiting factors are reasonably configured to improve the overall efficiency of the logistics park.

[0088] Furthermore, the path matching module is also used to execute the following method:

[0089] The kurtosis of the duration distribution is taken as the central symmetry point to determine whether the skewness of the duration distribution is positively skewed; if the skewness of the duration distribution is positively skewed, the first segment limitation factor in the time limitation factor is updated according to the mean limitation factor, the median limitation factor and the mode limitation factor.

[0090] The execution steps, if the time limiting factor is a time extension limiting factor, include: first, it is necessary to extract the kurtosis of the time distribution from the historical delivery data. The kurtosis reflects the concentration of the data and is used to determine the most concentrated interval of the time. By taking the kurtosis as the center point, the subsequent time adjustment will be symmetrical around this center; then, the skewness of the delivery time will be analyzed, that is, whether the delivery task duration tends to be shorter (positive skewness) or longer (negative skewness). Positive skewness means that most tasks take a shorter time, while fewer tasks take a longer time. In this case, it is suitable to extend the time; if the skewness is determined to be positive, the time needs to be adjusted according to the average limiting factor. First, according to the average limiting factor, the median limiting factor and the mode limiting factor are integrated to fine-tune the duration (for example, assuming that the average delivery duration is 2.5 hours, the median is 2 hours, and the mode is 1.5 hours, |2-2.5|=0.5, |1.5-2.5|=1, the weight corresponding to the median is 0.33, and the weight corresponding to the mode is 0.67, so the first segment limiting time is optimized to (0.67×2.5+0.33×2)=2.335, (0.33×2.5+0.67×1.5)

[0091] =1.83, the first segment time is limited to 1.83-2.335 hours), after comprehensive analysis of the mean, median and mode, these limiting factors of the extended time are integrated to generate the limiting factors of the first segment to ensure that in the process of extending the time, the delivery time is reasonably extended and the task time is not too long or the efficiency is too low.

[0092] When dealing with extended time constraints, we first analyze the kurtosis and skewness of the time distribution, and then calculate the extended time based on the positive skewness. By integrating the mode, median and mean, we can reasonably extend the delivery time and ensure that the time allocation for most tasks is reasonable.

[0093] In summary, the beneficial effects of the embodiments of the present application are:

[0094] 1. By collecting the location characteristics of the receiving end and the supply end and introducing time constraints, dynamic planning of the initial logistics path is realized, so that the path can be flexibly adjusted to adapt to real-time logistics needs, thereby improving the flexibility and accuracy of the logistics path.

[0095] 2. Introducing a multi-node competition mechanism, through the optimization of primary and secondary available resources, rationally allocating resources between logistics transit nodes, avoiding resource waste and scheduling bottlenecks, and significantly improving resource utilization and logistics response speed.

[0096] 3. According to the different time requirements of logistics tasks, the appropriate time limit factors are automatically matched, such as shortening, extending or making up the duration, so that logistics tasks can be managed more effectively and the overall logistics operation efficiency is improved.

[0097] 4. Due to the adoption of the first distribution path combination, the P1 extended distribution paths corresponding to the primary redundancy and the P1′ extended distribution paths corresponding to the secondary redundancy are used to set the first segmentation limiting factor; the second distribution path combination is used to set the P2 extended distribution paths corresponding to the primary redundancy and the P2′ extended distribution paths corresponding to the secondary redundancy, where P1+P2=P, and the second distribution path combination is associated with the mapping of the P2 extended distribution paths corresponding to the primary redundancy and the P2′ extended distribution paths corresponding to the secondary redundancy; the first segmentation limiting factor and the second segmentation limiting factor are combined to obtain the time limiting factor. Overall, the distribution time zone is optimized and matched to ensure that the distribution of each path node can be within the appropriate time range, so that all distribution activities can be completed within the set time frame.

[0098] Embodiment 2

[0099] Based on the same inventive concept as the smart management platform of a logistics park in the aforementioned embodiment, Figure 2 As shown, the embodiment of the present application provides a smart management method for a logistics park, which includes:

[0100] Based on the smart management platform, M receiving terminals are connected to collect a set of basic characteristics of a type of place, where the basic characteristics of a type of place correspond to decentralized demand nodes, centralized demand nodes, and variable demand nodes;

[0101] Based on the smart management platform, N supply terminals are connected to collect the basic feature set of the second type of places, where the basic features of the second type of places include decentralized supply nodes, centralized supply nodes, and variable supply nodes;

[0102] Adding a time limit factor, wherein the time limit factor is any one of a shortening time limit factor, an extending time limit factor, and a supplementing time limit factor;

[0103] After any one or more of the M receiving ends sends a logistics request to the smart management platform, matching is performed according to the basic feature set of the first type of place and the basic feature set of the second type of place and the time limit factor to set an initial logistics path;

[0104] The starting point and the end point of the initial logistics path are fixed, the logistics transfer nodes under different route constraints are determined, and a multi-node competition mechanism is introduced to perform an available resource optimization to obtain a first available resource scheduling sequence;

[0105] Through the interdependence balance between logistics transfer nodes under different route constraints, secondary available resource optimization is performed to obtain the second available resource scheduling sequence;

[0106] On the basis of the initial logistics path, the logistics path is set through the first available resource scheduling sequence and the second available resource scheduling sequence, and in response to the logistics request, a short-term communication connection is established between any one or more of the M receiving ends and any one or more of the N supply ends, and sent to the associated logistics park to manage the physical flow process from the supply end to the receiving end.

[0107] Further, according to the basic feature set of the first type of places and the basic feature set of the second type of places, matching is performed according to the time limit factor, and the method of the present application includes:

[0108] Uploading item type information to the smart management platform through the N supply terminals;

[0109] After receiving the item type information, a logistics request is sent to the smart management platform through any one or more of the M receiving terminals to match the service type and obtain a logistics service instruction;

[0110] According to the basic feature set of the first type of places and the basic feature set of the second type of places, the logistics delivery time zone is matched according to the time limitation factor, wherein the logistics delivery time zone is used to activate the logistics service instruction.

[0111] Further, according to the basic feature set of the first type of place and the basic feature set of the second type of place, and according to the time limiting factors, the logistics distribution time zone is matched, and the application method includes:

[0112] Connecting the decentralized demand nodes in the basic characteristics of the first type of places with the centralized supply nodes in the basic characteristics of the second type of places, and matching the first path combination;

[0113] Connecting the centralized demand node in the basic characteristics of the first type of place with the decentralized supply node in the basic characteristics of the second type of place, and matching a second path combination;

[0114] Based on the first path combination and the second path combination, backward extension is performed with the variable demand nodes in the basic characteristics of the first type of place to obtain a first delivery path combination;

[0115] Based on the first path combination and the second path combination, forward extension is performed with the variable supply nodes in the basic characteristics of the two types of places to obtain a second delivery path combination;

[0116] The first delivery path combination and the second delivery path combination are associated with the time limit factor to match the logistics delivery time zone.

[0117] Further, based on the first path combination and the second path combination, the variable demand node in the basic characteristics of the first type of place is extended backward to obtain a first delivery path combination. The method of the present application includes:

[0118] Based on the first path combination and the second path combination, the variable demand nodes in the basic characteristics of the first type of place are matched as alternative transfer points to obtain P extended delivery paths;

[0119] Based on the first path combination, the second path combination, and the P extended delivery paths, a variable demand node in the basic characteristics of the first type of place is used as an alternative transfer point for secondary matching to obtain P′ extended delivery paths;

[0120] Based on the P extended delivery paths and the P′ extended delivery paths, a backward extension optimization of the delivery paths is performed to obtain a first delivery path combination, wherein the first delivery path combination associates and maps the P extended delivery paths corresponding to the primary redundancy and the P′ extended delivery paths corresponding to the secondary redundancy.

[0121] Furthermore, by associating the first delivery path combination and the second delivery path combination with the time limiting factor to match the logistics delivery time zone, the present application method includes:

[0122] By combining the first delivery paths, P1 extended delivery paths corresponding to the primary redundancy and P1′ extended delivery paths corresponding to the secondary redundancy, a first segmentation limiting factor is set;

[0123] Through the second delivery path combination, the second segmentation limiting factor is set with P2 extended delivery paths corresponding to the primary redundancy and P2′ extended delivery paths corresponding to the secondary redundancy, wherein P1+P2=P, and the second delivery path combination is associated with and maps P2 extended delivery paths corresponding to the primary redundancy and P2′ extended delivery paths corresponding to the secondary redundancy;

[0124] The first segment limiting factor and the second segment limiting factor are combined to obtain the time limiting factor.

[0125] Further, by combining the first delivery path, P1 extended delivery paths corresponding to the primary redundancy and P1′ extended delivery paths corresponding to the secondary redundancy, a first segmentation limiting factor is set, and the method of the present application includes:

[0126] Connect the data storage unit, use the associated external conditions as the screening conditions, export the historical delivery instances, and fit the delivery time distribution density map;

[0127] Based on the delivery time distribution density map, and comparing the total number of deliveries corresponding to the historical delivery instances, adding a mode limiting factor;

[0128] Based on the delivery time distribution density map, and comparing with the maximum delivery time corresponding to the historical delivery instance, a median limiting factor is added;

[0129] Based on the delivery time distribution density map, comparing the average delivery time corresponding to the historical delivery instance, adding an average limiting factor;

[0130] The first segmentation limiting factor is initialized and configured using the mode limiting factor, the median limiting factor, and the mean limiting factor.

[0131] Furthermore, the mode limiting factor, the median limiting factor, and the average limiting factor are used to initialize and configure the first segment limiting factor. The method of the present application includes:

[0132] If the time limiting factor is a shortening time limiting factor, based on the delivery time distribution density map, extract the time distribution kurtosis and time distribution skewness;

[0133] Taking the duration distribution kurtosis as the central symmetry point, determining whether the duration distribution skewness is negative;

[0134] If the duration distribution skewness is negative, the first segmentation limiting factor in the time limiting factor is updated according to the median limiting factor, the mode limiting factor and the mean limiting factor.

[0135] Furthermore, if the time limiting factor is an extended time limiting factor, the application method includes:

[0136] Taking the duration distribution kurtosis as the central symmetry point, determining whether the duration distribution skewness is positive skewness;

[0137] If the duration distribution skewness is positive, the median limiting factor and the mode limiting factor are integrated according to the mean limiting factor to update the first segment limiting factor in the time limiting factor.

[0138] In summary, any step can be stored as a computer instruction or program in an unlimited computer memory, and can be called and recognized by an unlimited computer processor, and no unnecessary restrictions are made here.

[0139] Furthermore, the above-mentioned technical scheme only reflects the preferred technical scheme of the technical scheme of the embodiment of the present application. Some changes that may be made to some parts thereof by technicians in this technical field all reflect the novel principles of the embodiment of the present application. Obviously, technicians in this field can make various changes and modifications to the present application without departing from the scope of the present application.

Claims

1. A smart management platform for a logistics park, characterized in that: include: The first feature collection module is used to connect M receiving terminals based on the smart management platform to collect a basic feature set of a type of place, wherein the basic features of a type of place correspond to a decentralized demand node, a centralized demand node, and a variable demand node; The second feature collection module is used to connect N supply terminals based on the smart management platform to collect the basic feature set of the second type of places, where the basic features of the second type of places include decentralized supply nodes, centralized supply nodes, and variable supply nodes; A time limit factor adding module, used to add a time limit factor, wherein the time limit factor is any one of a shortening time limit factor, an extending time limit factor, and a supplementing time limit factor; A path matching module, configured to match the basic feature set of the first type of place and the basic feature set of the second type of place according to the time limit factor and set an initial logistics path after any one or more of the M receiving ends send a logistics request to the smart management platform; A primary available resource optimization module is used to fix the path starting point and path end point of the initial logistics path, determine the logistics transfer nodes under different route constraints, and introduce a multi-node competition mechanism to perform a primary available resource optimization to obtain a first available resource scheduling sequence; A secondary available resource optimization module is used to optimize the secondary available resources by balancing the interdependence between logistics transfer nodes under different route constraints to obtain a second available resource scheduling sequence; A logistics management module, configured to set a logistics path based on the initial logistics path through the first available resource scheduling sequence and the second available resource scheduling sequence, and to respond to the logistics request, establish a short-term communication connection between any one or more of the M receiving ends and any one or more of the N supply ends, and send the short-term communication connection to the associated logistics park to manage the physical flow process from the supply end to the receiving end; According to the basic feature set of the first type of places and the basic feature set of the second type of places, matching is performed according to the time limit factor, including: Uploading item type information to the smart management platform through the N supply terminals; After receiving the item type information, a logistics request is sent to the smart management platform through any one or more of the M receiving terminals to match the service type and obtain a logistics service instruction; According to the basic feature set of the first type of place and the basic feature set of the second type of place, and according to the time limiting factor, matching the logistics distribution time zone, wherein the logistics distribution time zone is used to activate the logistics service instruction; According to the basic feature set of the first type of place and the basic feature set of the second type of place, and according to the time limiting factors, matching the logistics distribution time zone includes: Connecting the decentralized demand nodes in the basic characteristics of the first type of places with the centralized supply nodes in the basic characteristics of the second type of places, and matching the first path combination; Connecting the centralized demand node in the basic characteristics of the first type of place with the decentralized supply node in the basic characteristics of the second type of place, and matching a second path combination; Based on the first path combination and the second path combination, backward extension is performed with the variable demand nodes in the basic characteristics of the first type of place to obtain a first delivery path combination; Based on the first path combination and the second path combination, forward extension is performed with the variable supply nodes in the basic characteristics of the two types of places to obtain a second delivery path combination; The first delivery path combination and the second delivery path combination are associated with the time limit factor to match the logistics delivery time zone.

2. The intelligent management platform for a logistics park as claimed in claim 1, characterized in that: Based on the first path combination and the second path combination, a first delivery path combination is obtained by performing a backward extension based on the variable demand nodes in the basic characteristics of the first type of place, including: Based on the first path combination and the second path combination, the variable demand nodes in the basic characteristics of the first type of place are matched as alternative transfer points to obtain P extended delivery paths; Based on the first path combination, the second path combination, and the P extended delivery paths, a variable demand node in the basic characteristics of the first type of place is used as an alternative transfer point for secondary matching to obtain P′ extended delivery paths; Based on the P extended delivery paths and the P′ extended delivery paths, a backward extension optimization of the delivery paths is performed to obtain a first delivery path combination, wherein the first delivery path combination associates and maps the P extended delivery paths corresponding to the primary redundancy and the P′ extended delivery paths corresponding to the secondary redundancy.

3. A smart management platform for a logistics park as claimed in claim 2, characterized in that: By associating the first delivery path combination and the second delivery path combination with the time limiting factor, matching the logistics delivery time zone includes: By combining the first delivery paths, P1 extended delivery paths corresponding to the primary redundancy and P1′ extended delivery paths corresponding to the secondary redundancy, a first segmentation limiting factor is set; Through the second delivery path combination, the second segmentation limiting factor is set with P2 extended delivery paths corresponding to the primary redundancy and P2′ extended delivery paths corresponding to the secondary redundancy, wherein P1+P2=P, and the second delivery path combination is associated with and maps P2 extended delivery paths corresponding to the primary redundancy and P2′ extended delivery paths corresponding to the secondary redundancy; The first segment limiting factor and the second segment limiting factor are combined to obtain the time limiting factor.

4. A smart management platform for a logistics park as claimed in claim 3, characterized in that: The first segmentation limiting factor is set by combining the first delivery path, the P1 extended delivery paths corresponding to the primary redundancy, and the P1′ extended delivery paths corresponding to the secondary redundancy, including: Connect the data storage unit, use the associated external conditions as the screening conditions, export the historical delivery instances, and fit the delivery time distribution density map; Based on the delivery time distribution density map, and comparing the total number of deliveries corresponding to the historical delivery instances, adding a mode limiting factor; Based on the delivery time distribution density map, and comparing with the maximum delivery time corresponding to the historical delivery instance, a median limiting factor is added; Based on the delivery time distribution density map, comparing the average delivery time corresponding to the historical delivery instance, adding an average limiting factor; The first segmentation limiting factor is initialized and configured using the mode limiting factor, the median limiting factor, and the mean limiting factor.

5. The intelligent management platform for a logistics park as claimed in claim 4, characterized in that: Using the mode limiting factor, the median limiting factor, and the average limiting factor, initializing and configuring the first segment limiting factor includes: If the time limiting factor is a shortening time limiting factor, based on the delivery time distribution density map, extract the time distribution kurtosis and time distribution skewness; Taking the duration distribution kurtosis as the central symmetry point, determining whether the duration distribution skewness is negative; If the duration distribution skewness is negative, the first segmentation limiting factor in the time limiting factor is updated according to the median limiting factor, the mode limiting factor and the mean limiting factor.

6. A smart management platform for a logistics park as claimed in claim 5, characterized in that: If the time limit factor is a time limit factor that extends the duration, it includes: Taking the duration distribution kurtosis as the central symmetry point, determining whether the duration distribution skewness is positive; If the duration distribution skewness is positive, the median limiting factor and the mode limiting factor are integrated according to the mean limiting factor to update the first segment limiting factor in the time limiting factor.

7. A smart management method for a logistics park, characterized in that: A smart management platform for a logistics park for implementing any one of claims 1 to 6, comprising: Based on the smart management platform, M receiving terminals are connected to collect a set of basic characteristics of a type of place, where the basic characteristics of a type of place correspond to decentralized demand nodes, centralized demand nodes, and variable demand nodes; Based on the smart management platform, N supply terminals are connected to collect the basic feature set of the second type of places, where the basic features of the second type of places include decentralized supply nodes, centralized supply nodes, and variable supply nodes; Adding a time limit factor, wherein the time limit factor is any one of a shortening time limit factor, an extending time limit factor, and a completing time limit factor; After any one or more of the M receiving ends sends a logistics request to the smart management platform, the initial logistics route is set according to the basic feature set of the first type of place and the basic feature set of the second type of place and the time limit factor; The starting point and the end point of the initial logistics path are fixed, the logistics transfer nodes under different route constraints are determined, and a multi-node competition mechanism is introduced to perform an available resource optimization to obtain a first available resource scheduling sequence; Through the interdependence balance between logistics transfer nodes under different route constraints, secondary available resource optimization is performed to obtain the second available resource scheduling sequence; On the basis of the initial logistics path, the logistics path is set through the first available resource scheduling sequence and the second available resource scheduling sequence, and in response to the logistics request, a short-term communication connection is established between any one or more of the M receiving ends and any one or more of the N supply ends, and sent to the associated logistics park to manage the physical flow process from the supply end to the receiving end.

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