An order attribution system and method based on multi-level net point cooperation

Abstract

The application is suitable for the technical field of order attribution, and provides an order attribution system and method based on multi-level network point cooperation. The application constructs network point virtual fences of multiple business network points, plans order attribution data of the multiple business network points, calculates multiple comprehensive balance indexes, judges whether there is local adjacent imbalance, cooperatively reconstructs the fences of adjacent imbalanced network points and reattributes the orders, constructs multiple optimized virtual fences, and updates and generates optimized attribution data, and plans order distribution routes in the multiple optimized virtual fences. According to the network point virtual fences and the order attribution data of multiple business network points in the same level, the comprehensive balance indexes are calculated and compared with the adjacent ones, the adjacent imbalanced network points are selected, the fences are cooperatively reconstructed and the orders are reattributed, the corresponding order attribution dynamic optimization is carried out according to the dynamic fluctuation of the orders, the imbalance problem of different network points is avoided, and the overall service quality and distribution efficiency are improved.

Description

Technical Field

[0001] This invention belongs to the field of order attribution technology, and particularly relates to an order attribution system and method based on multi-level network collaboration. Background Technology

[0002] Order attribution is the process of allocating orders to specific execution outlets or service units in a business system with multiple outlets or service entities operating collaboratively, based on factors such as the spatial location and service scope of the orders.

[0003] In existing technologies, order attribution to different outlets is usually done by matching orders to pre-set, fixed service areas. However, order demand has significant dynamic fluctuations. This static order attribution method can easily cause imbalances between different outlets, thereby affecting overall service quality and delivery efficiency. Summary of the Invention

[0004] The purpose of this invention is to provide an order attribution system and method based on multi-level network collaboration, aiming to solve the technical problems existing in the prior art mentioned in the background.

[0005] The embodiments of the present invention are implemented as follows: A method for order attribution based on multi-level branch network collaboration, the method specifically includes the following steps: Acquire business structure data and business order data, divide business outlets into multiple levels, construct virtual fences for multiple business outlets, and plan the order attribution data for multiple business outlets; Analyze the virtual fences and order attribution data of multiple business outlets at the same level, and calculate multiple comprehensive equilibrium indices; Compare the overall balance index of adjacent business outlets to determine whether there is a local imbalance, and select the adjacent unbalanced outlets if there is a local imbalance. Collaboratively reconstruct fences and reassign orders to adjacent unbalanced network points, construct multiple optimized virtual fences, and update and generate optimized assignment data; Based on the optimized attribution data, plan order delivery routes within multiple optimized virtual fences.

[0006] As a further limitation of the technical solution of this embodiment of the invention, the steps of obtaining business structure data and business order data, performing multi-level division of business outlets, constructing virtual fences for multiple business outlets, and planning the order attribution data for multiple business outlets specifically include the following steps: Obtain business structure data; Based on the aforementioned business structure data, business outlets are divided into multiple levels. Obtain the initial area of ​​multiple business outlets; Based on the multiple initial areas, construct virtual fences for multiple business outlets; Obtain business order data; Based on multiple virtual fences of the business outlets, the business order data is matched for attribution, and the order attribution data of multiple business outlets is planned.

[0007] As a further limitation of the technical solution of this invention, the analysis of virtual fences and order attribution data of multiple business outlets at the same level to calculate multiple comprehensive equilibrium indices specifically includes the following steps: Analyze the virtual fences and order attribution data of multiple business outlets at the same level to determine the number of orders and the area of ​​fences at the same level. The quantities of multiple orders at the same level and the areas of multiple fences at the same level are normalized to calculate multiple normalized quantity values ​​and multiple normalized area values; Based on multiple normalized quantity values ​​and multiple normalized area values, a comprehensive equilibrium index is calculated for multiple business outlets at the same level.

[0008] As a further limitation of the technical solution of this embodiment of the invention, the calculation formula for the plurality of normalized quantity values ​​is as follows: ; in, For business outlets The normalized quantity value, For business outlets The number of orders at the same level, For business outlets The number of orders at the same level, For the first A collection of business outlets at different levels; The formulas for calculating the normalized area values ​​are as follows: ; in, For business outlets The normalized area value, For business outlets The area of ​​the same level of fence, For business outlets The area of ​​the same level of fence; The formulas for calculating the various comprehensive equilibrium indices are as follows: ; ; in, For business outlets The comprehensive equilibrium index, and These are the preset weighting coefficients.

[0009] As a further limitation of the technical solution of this embodiment of the invention, the step of comparing the comprehensive balance index of adjacent business outlets, determining whether there is a local imbalance, and selecting adjacent unbalanced outlets when a local imbalance exists specifically includes the following steps: Compare the overall equilibrium index of adjacent business outlets and calculate multiple equilibrium difference values; The multiple equilibrium difference values ​​are compared with the preset standard difference values ​​to determine whether there is a local adjacent imbalance. When there is a local imbalance between adjacent values, select the abnormal difference value from the multiple balance difference values; Based on the abnormal difference values, adjacent unbalanced points are identified.

[0010] As a further limitation of the technical solution of this embodiment of the invention, the calculation formula for the plurality of equilibrium difference values ​​is as follows: ; in, For adjacent business outlets With business outlets The equilibrium difference value, For business outlets The comprehensive equilibrium index.

[0011] As a further limitation of the technical solution of this invention, the step of collaboratively reconstructing fences and reassigning orders to adjacent unbalanced network points, constructing multiple optimized virtual fences, and updating the generated optimized assignment data specifically includes the following steps: Calculate the boundary adjustment amount based on the abnormal difference value; According to the boundary adjustment amount, the adjacent unbalanced points are reconstructed collaboratively to obtain multiple optimized virtual fences. Based on the multiple optimized virtual fences, corresponding orders are reassigned, and optimized assignment data is updated and generated.

[0012] As a further limitation of the technical solution of this embodiment of the invention, the calculation formula for the boundary adjustment amount is: ; in, Representing adjacent unbalanced points and uneven network points , For boundary adjustment amount, These are abnormal difference values. This is the preset adjustment coefficient.

[0013] As a further limitation of the technical solution of this invention embodiment, the step of planning order delivery routes in multiple optimized virtual fences according to the optimized attribution data specifically includes the following steps: From the optimized attribution data, extract the branch order data of multiple business outlets at the lowest level; Identify multiple order data from various outlets to determine multiple order addresses; Obtain the optimized virtual fence map of multiple business outlets at the lowest level; In multiple virtual fence maps, delivery routes are planned according to multiple order addresses to generate delivery routes for multiple business outlets at the lowest level.

[0014] A multi-level network collaboration-based order attribution system for executing any of the above-described multi-level network collaboration-based order attribution methods, the system comprising a data initialization processing module, a balance index calculation module, an adjacent imbalance comparison module, an optimization, reconstruction, and update module, and a delivery route planning module, wherein: The data initialization processing module is used to acquire business structure data and business order data, perform multi-level division of business outlets, construct virtual fences for multiple business outlets, and plan the order attribution data for multiple business outlets. The equilibrium index calculation module is used to analyze the virtual fences and order attribution data of multiple business outlets at the same level and calculate multiple comprehensive equilibrium indices. The adjacent imbalance comparison module is used to compare the comprehensive balance index of adjacent business outlets, determine whether there is a local adjacent imbalance, and select the adjacent unbalanced outlet when a local adjacent imbalance exists. The optimization and reconstruction update module is used to collaboratively reconstruct fences and reassign orders to adjacent unbalanced network points, build multiple optimized virtual fences, and update and generate optimized assignment data. The delivery route planning module is used to plan order delivery routes in multiple optimized virtual fences based on the optimized attribution data.

[0015] Compared with the prior art, the beneficial effects of the present invention are: This invention can perform comprehensive balance index calculation and adjacent comparison based on the virtual fences and order attribution data of multiple business outlets at the same level, select adjacent unbalanced outlets, and perform collaborative fence reconstruction and order reassignment. It can also dynamically optimize order attribution according to the dynamic fluctuations of orders, avoid the imbalance problem between different outlets, and improve the overall service quality and delivery efficiency. Attached Figure Description

[0016] Figure 1A flowchart of an order attribution method based on multi-level branch network collaboration provided in an embodiment of the present invention is shown; Figure 2 The following is an application architecture diagram of the order attribution system based on multi-level network collaboration provided by an embodiment of the present invention. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0018] Understandably, in existing technologies, order attribution to different outlets is usually done by matching orders to pre-set, fixed service areas. However, order demand has significant dynamic fluctuations, and this static order attribution method can easily cause imbalances between different outlets, thereby affecting overall service quality and delivery efficiency.

[0019] To address the aforementioned issues, this invention discloses an order attribution system and method based on multi-level network collaboration. This system acquires business structure data and business order data, performs multi-level division of business outlets, constructs virtual fences for multiple business outlets, and plans order attribution data for multiple business outlets. It analyzes the virtual fences and order attribution data of multiple business outlets at the same level, calculating multiple comprehensive equilibrium indices. It compares the comprehensive equilibrium indices of adjacent business outlets to determine if there are local imbalances, and selects adjacent imbalanced outlets if such imbalances exist. It then reconstructs collaborative fences and reassigns orders to adjacent imbalanced outlets, constructing multiple optimized virtual fences and updating optimized attribution data. Finally, it plans order delivery routes within the multiple optimized virtual fences according to the optimized attribution data. This system can calculate comprehensive equilibrium indices and compare adjacent outlets based on the virtual fences and order attribution data of multiple business outlets at the same level, selecting adjacent imbalanced outlets for collaborative fence reconstruction and order reassignment. It can dynamically optimize order attribution based on dynamic order fluctuations, avoiding imbalances between different outlets and improving overall service quality and delivery efficiency.

[0020] Specifically, Figure 1 A flowchart of an order attribution method based on multi-level network collaboration provided by an embodiment of the present invention is shown.

[0021] In a preferred embodiment of the present invention, an order attribution method based on multi-level network collaboration specifically includes the following steps: Step S101: Obtain business structure data and business order data, perform multi-level division of business outlets, construct virtual fences for multiple business outlets, and plan the order attribution data for multiple business outlets.

[0022] In this embodiment of the invention, business structure data is acquired, and business outlets are divided into multiple levels according to the business structure data. This allows for the identification of multiple business outlets at the same level. By acquiring the initial area of ​​multiple business outlets, and using these multiple business outlets as centers, service boundaries for different business outlets are determined according to their respective initial areas. This constructs virtual fences for multiple business outlets. Simultaneously, business order data for the current stage is acquired, and based on the virtual fences for multiple outlets, the business order data is matched to determine the location of orders, thus planning the order attribution data for multiple business outlets.

[0023] It is understandable that the initial area of ​​different business outlets is preset, and even different business outlets belonging to the same level may have different initial areas.

[0024] Specifically, in another preferred embodiment provided by the present invention, the steps of acquiring business structure data and business order data, performing multi-level division of business outlets, constructing virtual fences for multiple business outlets, and planning order attribution data for multiple business outlets specifically include the following steps: Obtain business structure data; Based on the aforementioned business structure data, business outlets are divided into multiple levels. Obtain the initial area of ​​multiple business outlets; Based on the multiple initial areas, construct virtual fences for multiple business outlets; Obtain business order data; Based on multiple virtual fences of the business outlets, the business order data is matched for attribution, and the order attribution data of multiple business outlets is planned.

[0025] Furthermore, the order attribution method based on multi-level branch network collaboration also includes the following steps: Step S102: Analyze the virtual fences and order attribution data of multiple business outlets at the same level, and calculate multiple comprehensive equilibrium indices.

[0026] In this embodiment of the invention, the virtual fences and order attribution data of multiple business outlets at the same level are analyzed to determine the number of orders and fence areas corresponding to the multiple business outlets at the same level. Then, the number of orders and fence areas are normalized to calculate multiple normalized quantity values ​​and multiple normalized area values. Finally, based on these normalized quantity and area values, a comprehensive equilibrium index for the multiple business outlets at the same level is calculated. Specifically, the formula for calculating the multiple normalized quantity values ​​is as follows: ; in, For business outlets The normalized quantity value, For business outlets The number of orders at the same level, For business outlets The number of orders at the same level, For the first A collection of business outlets at different levels; The formula for calculating multiple normalized area values ​​is as follows: ; in, For business outlets The normalized area value, For business outlets The area of ​​the same level of fence, For business outlets The area of ​​the same level of fence; The formulas for calculating multiple comprehensive equilibrium indices are as follows: ; ; in, For business outlets The comprehensive equilibrium index, and These are the preset weighting coefficients.

[0027] Specifically, in another preferred embodiment provided by the present invention, the analysis of virtual fences and order attribution data of multiple business outlets at the same level, and the calculation of multiple comprehensive equilibrium indices, specifically includes the following steps: Analyze the virtual fences and order attribution data of multiple business outlets at the same level to determine the number of orders and the area of ​​fences at the same level. The quantities of multiple orders at the same level and the areas of multiple fences at the same level are normalized to calculate multiple normalized quantity values ​​and multiple normalized area values; Based on multiple normalized quantity values ​​and multiple normalized area values, a comprehensive equilibrium index is calculated for multiple business outlets at the same level.

[0028] Furthermore, the order attribution method based on multi-level branch network collaboration also includes the following steps: Step S103: Compare the comprehensive balance index of adjacent business outlets to determine whether there is a local imbalance between adjacent outlets, and select the adjacent unbalanced outlets when there is a local imbalance between adjacent outlets.

[0029] In this embodiment of the invention, the comprehensive balance index of adjacent business outlets is compared to calculate the balance difference value between adjacent business outlets. Then, multiple balance difference values ​​are compared with a preset standard difference value to determine if there is any local adjacent imbalance. Specifically, if all balance difference values ​​are less than the standard difference value, it is determined that there is no local adjacent imbalance; if one or more balance difference values ​​are not less than the standard difference value, it is determined that there is a local adjacent imbalance. In this case, an abnormal difference value not less than the standard difference value is selected from the multiple balance difference values. Then, based on the abnormal difference value, adjacent unbalanced outlets are selected from the multiple adjacent business outlets. Specifically, the calculation formula for the multiple balance difference values ​​is as follows: ; in, For adjacent business outlets With business outlets The equilibrium difference value, For business outlets The comprehensive equilibrium index.

[0030] Specifically, in another preferred embodiment provided by the present invention, the step of comparing the comprehensive balance index of adjacent business outlets to determine whether there is a local imbalance, and selecting adjacent imbalanced outlets when a local imbalance exists, specifically includes the following steps: Compare the overall equilibrium index of adjacent business outlets and calculate multiple equilibrium difference values; The multiple equilibrium difference values ​​are compared with the preset standard difference values ​​to determine whether there is a local adjacent imbalance. When there is a local imbalance between adjacent values, select the abnormal difference value from the multiple balance difference values; Based on the abnormal difference values, adjacent unbalanced points are identified.

[0031] Furthermore, the order attribution method based on multi-level branch network collaboration also includes the following steps: Step S104: Collaboratively reconstruct fences and reassign orders to adjacent unbalanced network points, construct multiple optimized virtual fences, and update and generate optimized assignment data.

[0032] In this embodiment of the invention, based on the abnormal difference value, the boundary adjustment amount is calculated, and the adjustment increase points and adjustment decrease points in adjacent unbalanced network points are determined. Then, according to the boundary adjustment amount, the boundary of the adjustment increase points in adjacent unbalanced network points is expanded, and the boundary of the adjustment decrease points in adjacent unbalanced network points is reduced, realizing the collaborative fence reconstruction of adjacent unbalanced network points, obtaining multiple optimized virtual fences. Based on the multiple optimized virtual fences, the corresponding orders are reassigned, and the optimized assignment data is updated and generated. Specifically, the formula for calculating the boundary adjustment amount is: ; in, Representing adjacent unbalanced points and uneven network points , For boundary adjustment amount, These are abnormal difference values. This is the preset adjustment coefficient.

[0033] Specifically, in another preferred embodiment provided by the present invention, the step of collaboratively reconstructing fences and reassigning orders to adjacent unbalanced network points, constructing multiple optimized virtual fences, and updating the generated optimized assignment data specifically includes the following steps: Calculate the boundary adjustment amount based on the abnormal difference value; According to the boundary adjustment amount, the adjacent unbalanced points are reconstructed collaboratively to obtain multiple optimized virtual fences. Based on the multiple optimized virtual fences, corresponding orders are reassigned, and optimized assignment data is updated and generated.

[0034] Furthermore, the order attribution method based on multi-level branch network collaboration also includes the following steps: Step S105: Based on the optimized attribution data, plan order delivery routes in multiple optimized virtual fences.

[0035] In this embodiment of the invention, order data of multiple business outlets at the lowest level are extracted from the optimized attribution data. By performing address identification on the order data of multiple business outlets at the lowest level, multiple order addresses are determined. At the same time, a virtual fence map of optimized virtual fences for multiple business outlets at the lowest level is obtained. Then, in the multiple virtual fence maps, delivery routes are planned according to the multiple order addresses to generate delivery routes for orders of multiple business outlets at the lowest level. After that, automatic control of unmanned delivery or route navigation for manual delivery can be performed according to the multiple order delivery routes.

[0036] Understandably, the lowest-level business outlets are those that need to deliver orders to the order addresses, and can carry out unmanned or manual delivery.

[0037] Specifically, in another preferred embodiment provided by the present invention, the step of planning order delivery routes in multiple optimized virtual fences according to the optimized attribution data specifically includes the following steps: From the optimized attribution data, extract the branch order data of multiple business outlets at the lowest level; Identify multiple order data from various outlets to determine multiple order addresses; Obtain the optimized virtual fence map of multiple business outlets at the lowest level; In multiple virtual fence maps, delivery routes are planned according to multiple order addresses to generate delivery routes for multiple business outlets at the lowest level.

[0038] Furthermore, Figure 2 The following is an application architecture diagram of the order attribution system based on multi-level network collaboration provided by an embodiment of the present invention.

[0039] Specifically, in another preferred embodiment provided by the present invention, an order attribution system based on multi-level network collaboration includes: The data initialization processing module 101 is used to acquire business structure data and business order data, perform multi-level division of business outlets, construct virtual fences for multiple business outlets, and plan the order attribution data for multiple business outlets.

[0040] In this embodiment of the invention, the data initialization processing module 101 acquires business structure data, performs multi-level division of business outlets according to the business structure data, and can identify multiple business outlets at the same level. By acquiring the initial area of ​​multiple business outlets, and taking multiple business outlets as the center, the service boundaries of different business outlets are determined according to the corresponding initial area, and a virtual fence for multiple business outlets is constructed. At the same time, the business order data of the current stage is acquired, and based on the virtual fence of multiple outlets, the business order data is matched for order location attribution, and the order attribution data of multiple business outlets is planned.

[0041] The equilibrium index calculation module 102 is used to analyze the virtual fences and order attribution data of multiple business outlets at the same level and calculate multiple comprehensive equilibrium indices.

[0042] In this embodiment of the invention, the equilibrium index calculation module 102 analyzes the virtual fences and order attribution data of multiple business outlets at the same level to determine the number of orders and fence areas corresponding to the multiple business outlets at the same level. Then, it normalizes the number of orders and fence areas to calculate multiple normalized quantity values ​​and multiple normalized area values. Finally, based on these normalized quantity and area values, it calculates the comprehensive equilibrium index of the multiple business outlets at the same level. Specifically, the formula for calculating the multiple normalized quantity values ​​is as follows: ; in, For business outlets The normalized quantity value, For business outlets The number of orders at the same level, For business outlets The number of orders at the same level, For the first A collection of business outlets at different levels; The formula for calculating multiple normalized area values ​​is as follows: ; in, For business outlets The normalized area value, For business outlets The area of ​​the same level of fence, For business outlets The area of ​​the same level of fence; The formulas for calculating multiple comprehensive equilibrium indices are as follows: ; ; in, For business outlets The comprehensive equilibrium index, and These are the preset weighting coefficients.

[0043] The adjacent imbalance comparison module 103 is used to compare the comprehensive balance index of adjacent business outlets, determine whether there is a local adjacent imbalance, and select the adjacent unbalanced outlets when there is a local adjacent imbalance.

[0044] In this embodiment of the invention, the adjacent imbalance comparison module 103 compares the comprehensive balance index of adjacent business outlets, calculates the balance difference value between adjacent business outlets, and then compares multiple balance difference values ​​with a preset standard difference value to determine whether there is a local adjacent imbalance. Specifically, if all balance difference values ​​are less than the standard difference value, it is determined that there is no local adjacent imbalance; if one or more balance difference values ​​are not less than the standard difference value, it is determined that there is a local adjacent imbalance. In this case, an abnormal difference value not less than the standard difference value is selected from the multiple balance difference values, and then, based on the abnormal difference value, an adjacent unbalanced outlet is selected from the multiple adjacent business outlets. Specifically, the calculation formula for the multiple balance difference values ​​is as follows: ; in, For adjacent business outlets With business outlets The equilibrium difference value, For business outlets The comprehensive equilibrium index.

[0045] The optimization, reconstruction, and update module 104 is used to collaboratively reconstruct fences and reassign orders to adjacent unbalanced network points, construct multiple optimized virtual fences, and update and generate optimized assignment data.

[0046] In this embodiment of the invention, the optimization reconstruction and update module 104 calculates the boundary adjustment amount based on the abnormal difference value, and determines the adjustment increase points and adjustment decrease points among adjacent unbalanced network points. Then, according to the boundary adjustment amount, it expands the boundaries of the adjustment increase points among adjacent unbalanced network points and shrinks the boundaries of the adjustment decrease points among adjacent unbalanced network points, thereby realizing the collaborative fence reconstruction of adjacent unbalanced network points and obtaining multiple optimized virtual fences. Based on the multiple optimized virtual fences, the corresponding orders are reassigned, and the optimized assignment data is updated and generated. Specifically, the calculation formula for the boundary adjustment amount is as follows: ; in, Representing adjacent unbalanced points and uneven network points , For boundary adjustment amount, These are abnormal difference values. This is the preset adjustment coefficient.

[0047] The delivery route planning module 105 is used to plan order delivery routes in multiple optimized virtual fences according to the optimized attribution data.

[0048] In this embodiment of the invention, the delivery route planning module 105 extracts the order data of multiple business outlets at the lowest level from the optimized attribution data. By performing address identification on the order data of multiple business outlets at the lowest level, multiple order addresses are determined. At the same time, the module obtains the virtual fence map of the optimized virtual fence of multiple business outlets at the lowest level. Then, in the multiple virtual fence maps, delivery routes are planned according to the multiple order addresses to generate delivery routes for multiple business outlets at the lowest level. After that, automatic control of unmanned delivery or route navigation for manual delivery can be performed according to the multiple order delivery routes.

[0049] The above-described embodiments are merely examples of several implementations of the present invention, and their descriptions are relatively specific and detailed. However, they should not be construed as limiting the scope of the present invention. For those skilled in the art, various modifications and improvements can be made without departing from the concept of the present invention, and these modifications and improvements all fall within the protection scope of the present invention.

Claims

1. A method for order attribution based on multi-level network collaboration, characterized in that, The method specifically includes the following steps: Acquire business structure data and business order data, divide business outlets into multiple levels, construct virtual fences for multiple business outlets, and plan the order attribution data for multiple business outlets; Analyze the virtual fences and order attribution data of multiple business outlets at the same level, and calculate multiple comprehensive equilibrium indices; Compare the overall balance index of adjacent business outlets to determine whether there is a local imbalance, and select the adjacent unbalanced outlets when there is a local imbalance. Collaboratively reconstruct fences and reassign orders to adjacent unbalanced network points, construct multiple optimized virtual fences, and update and generate optimized assignment data; Based on the optimized attribution data, plan order delivery routes within multiple optimized virtual fences.

2. The order attribution method based on multi-level network collaboration according to claim 1, characterized in that, The process of acquiring business structure data and business order data, performing multi-level segmentation of business outlets, constructing virtual fences for multiple business outlets, and planning the order attribution data for multiple business outlets specifically includes the following steps: Obtain business structure data; Based on the aforementioned business structure data, business outlets are divided into multiple levels. Obtain the initial area of ​​multiple business outlets; Based on the multiple initial areas, construct virtual fences for multiple business outlets; Obtain business order data; Based on multiple virtual fences of the business outlets, the business order data is matched for attribution, and the order attribution data of multiple business outlets is planned.

3. The order attribution method based on multi-level network collaboration according to claim 1, characterized in that, The analysis of virtual fences and order attribution data of multiple business outlets at the same level, and the calculation of multiple comprehensive equilibrium indices, specifically includes the following steps: Analyze the virtual fences and order attribution data of multiple business outlets at the same level to determine the number of orders and the area of ​​fences at the same level. The quantities of multiple orders at the same level and the areas of multiple fences at the same level are normalized to calculate multiple normalized quantity values ​​and multiple normalized area values; Based on multiple normalized quantity values ​​and multiple normalized area values, a comprehensive equilibrium index is calculated for multiple business outlets at the same level.

4. The order attribution method based on multi-level network collaboration according to claim 3, characterized in that, The formulas for calculating the multiple normalized quantity values ​​are as follows: ； in, For business outlets The normalized quantity value, For business outlets The number of orders at the same level, For business outlets The number of orders at the same level, For the first A collection of business outlets at different levels; The formulas for calculating the normalized area values ​​are as follows: ； in, For business outlets The normalized area value, For business outlets The area of ​​the same level of fence, For business outlets The area of ​​the same level of fence; The formulas for calculating the various comprehensive equilibrium indices are as follows: ； ； in, For business outlets The comprehensive equilibrium index, and These are the preset weighting coefficients.

5. The order attribution method based on multi-level network collaboration according to claim 4, characterized in that, The process of comparing the comprehensive balance index of adjacent business outlets to determine whether there is a local imbalance, and selecting adjacent imbalanced outlets when a local imbalance exists, specifically includes the following steps: Compare the overall equilibrium index of adjacent business outlets and calculate multiple equilibrium difference values; The multiple equilibrium difference values ​​are compared with the preset standard difference values ​​to determine whether there is a local adjacent imbalance. When there is local adjacent imbalance, select the abnormal difference value from the multiple balance difference values; Based on the abnormal difference values, adjacent unbalanced network points are identified.

6. The order attribution method based on multi-level network collaboration according to claim 5, characterized in that, The formulas for calculating the multiple equilibrium difference values ​​are as follows: ； in, For adjacent business outlets With business outlets The equilibrium difference value, For business outlets The comprehensive equilibrium index.

7. The order attribution method based on multi-level network collaboration according to claim 6, characterized in that, The process of collaboratively reconstructing fences and reassigning orders to adjacent unbalanced network points, constructing multiple optimized virtual fences, and updating the generated optimized assignment data specifically includes the following steps: Calculate the boundary adjustment amount based on the abnormal difference value; According to the boundary adjustment amount, the adjacent unbalanced points are reconstructed collaboratively to obtain multiple optimized virtual fences. Based on the multiple optimized virtual fences, corresponding orders are reassigned, and optimized assignment data is updated and generated.

8. The order attribution method based on multi-level network collaboration according to claim 7, characterized in that, The formula for calculating the boundary adjustment amount is: ； in, Representing adjacent unbalanced points and uneven network points , For boundary adjustment amount, These are abnormal difference values. This is the preset adjustment coefficient.

9. The order attribution method based on multi-level network collaboration according to claim 1, characterized in that, The step of planning order delivery routes in multiple optimized virtual fences based on the optimized attribution data specifically includes the following steps: From the optimized attribution data, extract the branch order data of multiple business outlets at the lowest level; Identify multiple order data from various outlets to determine multiple order addresses; Obtain the optimized virtual fence map of multiple business outlets at the lowest level; In multiple virtual fence maps, delivery routes are planned according to multiple order addresses to generate delivery routes for multiple business outlets at the lowest level.

10. An order attribution system based on multi-level branch network collaboration for executing the order attribution method based on multi-level branch network collaboration as described in any one of claims 1-9, characterized in that, The system includes a data initialization and processing module, an equilibrium index calculation module, an adjacent imbalance comparison module, an optimization, reconstruction, and update module, and a delivery route planning module, wherein: The data initialization processing module is used to acquire business structure data and business order data, perform multi-level division of business outlets, construct virtual fences for multiple business outlets, and plan the order attribution data for multiple business outlets. The equilibrium index calculation module is used to analyze the virtual fences and order attribution data of multiple business outlets at the same level and calculate multiple comprehensive equilibrium indices. The adjacent imbalance comparison module is used to compare the comprehensive balance index of adjacent business outlets, determine whether there is a local adjacent imbalance, and select the adjacent unbalanced outlet when a local adjacent imbalance exists. The optimization and reconstruction update module is used to collaboratively reconstruct fences and reassign orders to adjacent unbalanced network points, build multiple optimized virtual fences, and update and generate optimized assignment data. The delivery route planning module is used to plan order delivery routes in multiple optimized virtual fences based on the optimized attribution data.

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