Supply chain operation intelligent real-time scheduling system adaptive to e-commerce platform

Abstract

The invention belongs to the field of supply chain operation, relates to a data analysis technology, is used for solving the problem that in the prior art, a plurality of feasible parameters cannot be combined for analysis to obtain an optimal scheduling scheme, and particularly relates to a supply chain operation intelligent real-time scheduling system adaptive to an e-commerce platform. The real-time scheduling platform is in communication connection with a saturation analysis module, a scheduling management module, a configuration optimization module and a database; the saturation analysis module is used for carrying out transportation saturation analysis on the supply chain coverage area of the e-commerce platform: dividing the supply chain coverage area into a plurality of analysis areas, and judging whether the overall saturation of the supply chain coverage area meets the requirement or not through the scheduling demand value; according to the method, transport capacity scheduling analysis can be carried out on the supply chain coverage area of the e-commerce platform, the priority performance values of all the generated scheduling schemes are analyzed, the optimal scheduling schemes are screened according to the priority performance values, and the scheduling matching efficiency is improved.

Description

Technical Field

[0001] The present invention belongs to the field of supply chain operation and relates to data analysis technology, specifically to an intelligent real-time scheduling system for supply chain operation adapted to an e-commerce platform. Background Art

[0002] The e-commerce supply chain operation model refers to the way e-commerce companies use the Internet and information technology to coordinate and integrate the procurement, production, transportation, and sales of goods or services in order to meet customer needs, thereby achieving efficiency, convenience, and low cost.

[0003] The invention patent with announcement number CN117350624B discloses an intelligent early warning system for logistics supply chain based on the Internet of Things. The system provides risk warning for the supply chain and determines whether there is a risk after the type of supply chain is determined, thereby ensuring the operational stability of the supply chain and improving the efficiency of intelligent early warning of the supply chain; it shares and analyzes transportation information of multiple supply chains in the current sales area, so that when there are abnormalities in the routes during the logistics transportation process, the supply chain information can be shared in time to reduce the impact of transportation risks on the supply chain during the logistics transportation process; however, when the system needs to dispatch transportation capacity, it is unable to combine multiple feasibility parameters for analysis to obtain the best dispatching plan, which leads to poor transportation dispatching effect, and it is also unable to periodically evaluate the rationality of the overall transportation capacity configuration, resulting in the inability to scientifically and rationally allocate the transportation capacity resources of the supply chain.

[0004] In view of the above technical problems, this application proposes a solution. Summary of the invention

[0005] The purpose of the present invention is to provide an intelligent real-time scheduling system for supply chain operation adapted to an e-commerce platform, which is used to solve the problem that the prior art cannot combine multiple feasibility parameters to analyze and obtain the best scheduling solution;

[0006] The technical problem to be solved by the present invention is: how to provide an intelligent real-time scheduling system for supply chain operation adapted to e-commerce platforms that can analyze multiple feasibility parameters to obtain the best scheduling solution.

[0007] The purpose of the present invention can be achieved through the following technical solutions:

[0008] An intelligent real-time scheduling system for supply chain operation adapted to an e-commerce platform includes a real-time scheduling platform, wherein the real-time scheduling platform is communicatively connected to a saturation analysis module, a scheduling management module, a configuration optimization module, and a database;

[0009] The saturation analysis module is used to perform transportation saturation analysis on the supply chain coverage area of ​​the e-commerce platform: divide the supply chain coverage area into several analysis areas, generate an analysis cycle, and obtain the saturation coefficient BH of the analysis area at regular intervals within the analysis cycle; mark the analysis area as a saturated area or a loose area according to the saturation coefficient BH; mark the ratio of the number of saturated areas to the number of all analysis areas as a scheduling demand value, and determine whether the overall saturation of the supply chain coverage area meets the requirements according to the scheduling demand value;

[0010] The scheduling management module is used to perform capacity scheduling analysis on the supply chain coverage area of ​​the e-commerce platform: all analysis areas are arranged in descending order according to the saturation coefficient BH value to obtain a saturation sequence, the number of saturated areas is marked as H1, the H1 analysis areas ranked at the end of the saturation sequence are intercepted and marked as burden areas, the saturated areas are randomly matched with the burden areas to obtain several scheduling schemes, the priority performance value of the scheduling scheme is obtained, the scheduling scheme with the smallest priority performance value is marked as a matching scheme, and all scheduling combinations in the matching scheme are sent to the mobile terminal of the manager through the real-time scheduling platform;

[0011] The configuration optimization module is used to optimize the transportation configuration of the supply chain coverage area of ​​the e-commerce platform.

[0012] Furthermore, the process of obtaining the saturation coefficient BH of the analysis area includes: the order data DD, mileage data LC and vehicle data CL of the analysis area, the order data DD is the number of unprocessed orders of the transport unit in the analysis area, the mileage data LC is the sum of the transport mileage of all unprocessed orders of the transport unit in the analysis area; the vehicle data CL is the number of available vehicles of the transport unit in the analysis area, through the formula The saturation coefficient BH of the analysis area is obtained, where c1, c2 and c3 are all proportional coefficients, and c1>c2>c3>1.

[0013] Furthermore, the specific process of marking the analysis area as a saturated area or a loose area includes: obtaining the saturation threshold BHmax through the database, and comparing the saturation coefficient BH of the analysis area with the saturation threshold BHmax: if the saturation coefficient BH is less than the saturation threshold BHmax, it is determined that the transport saturation of the analysis area meets the requirements, and the corresponding analysis area is marked as a loose area; if the saturation coefficient BH is greater than or equal to the saturation threshold BHmax, it is determined that the transport saturation of the analysis area does not meet the requirements, and the corresponding analysis area is marked as a saturated area.

[0014] Furthermore, the specific process of determining whether the overall saturation of the supply chain coverage area meets the requirements includes: comparing the scheduling demand value with a preset scheduling demand threshold: if the scheduling demand value is less than the scheduling demand threshold, then it is determined that the overall transportation saturation of the current transportation unit in the supply chain coverage area meets the requirements; if the scheduling demand value is greater than or equal to the scheduling demand threshold, then it is determined that the overall saturation of the current transportation unit in the supply chain coverage area does not meet the requirements, and a scheduling analysis signal is generated and sent to the scheduling management module through the real-time scheduling platform.

[0015] Furthermore, the process of obtaining the priority performance value of the scheduling scheme includes: each scheduling scheme corresponds to H1 group scheduling combinations, each scheduling combination includes a saturated area and a burden area, the scheduling data DS and the direct distance data ZJ of the scheduling combination in the scheduling scheme are obtained, the scheduling data DS is the sum of the saturation coefficient BH of the saturated area and the saturation coefficient BH of the burden area in the scheduling combination, and the direct distance data ZJ is the straight-line distance value between the center point of the saturated area and the center point of the burden area in the scheduling combination; the priority coefficient YX of the scheduling combination is obtained by the formula YX=k1*DS+k2*ZJ, the priority coefficients YX of the H1 scheduling combinations in the scheduling scheme are summed and averaged to obtain the priority performance value of the scheduling scheme.

[0016] Furthermore, the specific process of the configuration optimization module optimizing the transportation configuration of the supply chain coverage area of ​​the e-commerce platform includes: generating a configuration cycle, obtaining the overall configuration coefficient and the local configuration coefficient of the supply chain coverage area at the end of the configuration cycle; obtaining the overall configuration threshold and the local configuration threshold through the storage module, and comparing the overall configuration coefficient and the local configuration coefficient with the overall configuration threshold and the local configuration threshold respectively: if the local configuration coefficient is greater than or equal to the local configuration threshold, it is determined that the capacity configuration of the supply chain coverage area does not meet the requirements, and a local configuration optimization signal is generated and sent to the mobile phone terminal of the manager through the real-time scheduling platform; if the local configuration coefficient is less than the local configuration threshold and the overall configuration coefficient is less than the overall configuration threshold, it is determined that the capacity configuration of the supply chain coverage area meets the requirements; if the local configuration coefficient is less than the local configuration threshold and the overall configuration coefficient is greater than or equal to the overall configuration threshold, it is determined that the capacity configuration of the supply chain coverage area does not meet the requirements, and an overall configuration optimization signal is generated and sent to the mobile phone terminal of the manager through the real-time scheduling platform.

[0017] Furthermore, the process of obtaining the overall configuration coefficient and the local configuration coefficient of the supply chain coverage area includes: obtaining the number of times the analysis area is marked as a saturated area and marking it as the configuration requirement value of the analysis area, summing and averaging the configuration requirement values ​​of all analysis areas to obtain the overall configuration coefficient, and performing variance calculation on the configuration requirement values ​​of the analysis area to obtain the local configuration coefficient.

[0018] Furthermore, the working method of the supply chain operation intelligent real-time scheduling system adapted to the e-commerce platform includes the following steps:

[0019] Step 1: Conduct transportation saturation analysis on the supply chain coverage areas of the e-commerce platform;

[0020] Step 2: Process the saturation coefficient BH and determine whether the overall saturation of the supply chain coverage area meets the requirements based on the processing results;

[0021] Step 3: Conduct capacity dispatch analysis on the supply chain coverage areas of the e-commerce platform;

[0022] Step 4: Optimize and analyze the transportation configuration in the areas covered by the e-commerce platform’s supply chain.

[0023] The present invention has the following beneficial effects:

[0024] 1. The saturation analysis module can be used to conduct regional capacity saturation analysis on the supply chain coverage area of ​​the e-commerce platform. The saturation coefficient is obtained by comprehensive analysis and calculation of multiple capacity parameters in the analysis area, and then the analysis area is differentiated by the saturation coefficient. The overall capacity saturation status of the supply chain coverage area is evaluated based on the differentiated marking results;

[0025] 2. The dispatch management module can be used to analyze the capacity dispatch of the supply chain coverage area of ​​the e-commerce platform, intercept the saturated sequence and then perform dispatch matching, analyze the priority performance values ​​of all generated dispatch plans and select the best dispatch plan based on the priority performance value, so as to improve the efficiency of dispatch matching;

[0026] 3. The configuration optimization module can be used to optimize and analyze the transportation configuration of the supply chain coverage area of ​​the e-commerce platform, conduct statistics and analysis on the overall transportation saturation status of all analysis areas within the configuration cycle, provide feedback on the overall transportation configuration status based on the analysis results, and generate targeted configuration optimization signals to improve the efficiency of configuration optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0028] Figure 1 is a system block diagram of Embodiment 1 of the present invention;

[0029] Figure 2This is a flow chart of the method of Embodiment 2 of the present invention. DETAILED DESCRIPTION

[0030] The technical scheme of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0031] Embodiment 1: Figure 1 As shown, the intelligent real-time scheduling system for supply chain operation adapted to the e-commerce platform includes a real-time scheduling platform, which is communicatively connected to a saturation analysis module, a scheduling management module, a configuration optimization module and a database.

[0032] The saturation analysis module is used to analyze the transportation saturation of the supply chain coverage area of ​​the e-commerce platform: the supply chain coverage area is divided into several analysis areas, an analysis cycle is generated, and the order data DD, mileage data LC, and vehicle data CL of the analysis area are obtained regularly during the analysis cycle. The order data DD is the number of unprocessed orders of the transport unit in the analysis area, and the mileage data LC is the sum of the transportation mileage of all unprocessed orders of the transport unit in the analysis area; the vehicle data CL is the number of disposable vehicles of the transport unit in the analysis area. The saturation coefficient BH of the analysis area is obtained, where c1, c2 and c3 are all proportional coefficients, and c1>c2>c3>1; the saturation threshold BHmax is obtained through the database, and the saturation coefficient BH of the analysis area is compared with the saturation threshold BHmax: if the saturation coefficient BH is less than the saturation threshold BHmax, it is determined that the transportation saturation of the analysis area meets the requirements, and the corresponding analysis area is marked as a loose area; if the saturation coefficient BH is greater than or equal to the saturation threshold BHmax, it is determined that the transportation saturation of the analysis area does not meet the requirements, and the corresponding analysis area is marked as a saturated area; the ratio of the number of saturated areas to the number of all analysis areas is marked as the scheduling demand value, and the scheduling demand value is compared with the preset scheduling demand threshold. The dispatch demand value is compared: if the dispatch demand value is less than the dispatch demand threshold, it is determined that the overall transport saturation of the current transport unit in the supply chain coverage area meets the requirements; if the dispatch demand value is greater than or equal to the dispatch demand threshold, it is determined that the overall saturation of the current transport unit in the supply chain coverage area does not meet the requirements, and a dispatch analysis signal is generated and sent to the dispatch management module through the real-time dispatch platform; a regional capacity saturation analysis is conducted on the supply chain coverage area of ​​the e-commerce platform, and a saturation coefficient is obtained after comprehensive analysis and calculation of multiple capacity parameters in the analysis area, and then the analysis area is differentiated by the saturation coefficient, and the overall capacity saturation status of the supply chain coverage area is evaluated according to the differentiated marking results.

[0033] The scheduling management module is used to perform capacity scheduling analysis on the supply chain coverage area of ​​the e-commerce platform: all analysis areas are arranged in descending order according to the saturation coefficient BH value to obtain a saturated sequence, the number of saturated areas is marked as H1, the H1 analysis areas ranked at the end of the saturated sequence are intercepted and marked as burden areas, and the saturated areas are randomly matched with the burden areas to obtain several scheduling schemes. Each scheduling scheme corresponds to H1 group scheduling combinations, and each scheduling combination contains a saturated area and a burden area. The scheduling data DS and direct distance data ZJ of the scheduling combination in the scheduling scheme are obtained. The scheduling data DS is the sum of the saturation coefficient BH of the saturated area and the saturation coefficient BH of the burden area in the scheduling combination, and the direct distance data ZJ is the scheduling. The straight-line distance between the center point of the saturated area and the center point of the burdened area in the combination; the priority coefficient YX of the scheduling combination is obtained by the formula YX=k1*DS+k2*ZJ, the priority coefficients YX of the H1 scheduling combinations in the scheduling scheme are summed and averaged to obtain the priority performance value of the scheduling scheme, the scheduling scheme with the smallest priority performance value is marked as the matching scheme, and all scheduling combinations in the matching scheme are sent to the mobile terminal of the manager through the real-time scheduling platform; the capacity scheduling analysis is carried out on the supply chain coverage area of ​​the e-commerce platform, the saturated sequence is intercepted and then the scheduling matching is carried out, the priority performance values ​​of all the generated scheduling schemes are analyzed, and the best scheduling scheme is screened according to the priority performance value to improve the efficiency of scheduling matching.

[0034] The configuration optimization module is used to optimize and analyze the transportation configuration of the supply chain coverage area of ​​the e-commerce platform: generate a configuration cycle, obtain the number of times the analysis area is marked as a saturated area at the end of the configuration cycle and mark it as the configuration demand value of the analysis area, sum and average the configuration demand values ​​of all analysis areas to obtain the overall configuration coefficient, and perform variance calculation on the configuration demand values ​​of the analysis area to obtain the local configuration coefficient; obtain the overall configuration threshold and the local configuration threshold through the storage module, and compare the overall configuration coefficient and the local configuration coefficient with the overall configuration threshold and the local configuration threshold respectively: if the local configuration coefficient is greater than or equal to the local configuration threshold, it is determined that the transportation capacity configuration in the supply chain coverage area does not meet the requirements, generate a local configuration optimization signal and send the local configuration optimization signal through real-time adjustment The scheduling platform sends the information to the mobile phone terminal of the manager; if the local configuration coefficient is less than the local configuration threshold and the overall configuration coefficient is less than the overall configuration threshold, it is determined that the capacity configuration of the supply chain coverage area meets the requirements; if the local configuration coefficient is less than the local configuration threshold and the overall configuration coefficient is greater than or equal to the overall configuration threshold, it is determined that the capacity configuration of the supply chain coverage area does not meet the requirements, and an overall configuration optimization signal is generated and sent to the mobile phone terminal of the manager through the real-time scheduling platform; the transportation configuration of the supply chain coverage area of ​​the e-commerce platform is optimized and analyzed, the overall transportation saturation status of all analysis areas within the configuration period is statistically analyzed, the overall transportation configuration status is fed back according to the analysis results, and targeted configuration optimization signals are generated to improve the configuration optimization efficiency.

[0035] Embodiment 2: Figure 2 As shown, the intelligent real-time scheduling method for supply chain operation adapted to the e-commerce platform includes the following steps:

[0036] Step 1: Conduct transportation saturation analysis on the supply chain coverage area of ​​the e-commerce platform: divide the supply chain coverage area into several analysis areas, generate an analysis cycle, and obtain the order data DD, mileage data LC, and vehicle data CL of the analysis area at regular intervals during the analysis cycle. Perform numerical calculations on the order data DD, mileage data LC, and vehicle data CL to obtain the saturation coefficient BH of the analysis area.

[0037] Step 2: Mark the analysis area as a saturated area or a loose area through the saturation coefficient BH, mark the ratio of the number of saturated areas to the number of all analysis areas as the scheduling demand value, and use the scheduling demand value to determine whether the overall saturation of the supply chain coverage area meets the requirements;

[0038] Step 3: Conduct capacity dispatch analysis on the supply chain coverage area of ​​the e-commerce platform and generate several dispatch plans, obtain the priority coefficients YX of all dispatch combinations in the dispatch plan, sum and average the priority coefficients YX of H1 dispatch combinations in the dispatch plan to obtain the priority performance value of the dispatch plan, and mark the matching plan by the priority performance value;

[0039] Step 4: Optimize and analyze the transportation configuration of the supply chain coverage area of ​​the e-commerce platform and obtain the overall configuration coefficient and the local configuration coefficient. Use the overall configuration coefficient and the local configuration coefficient to determine whether the transportation capacity configuration of the supply chain area meets the requirements.

[0040] The intelligent real-time scheduling system for supply chain operation adapted to the e-commerce platform divides the supply chain coverage area into several analysis areas when working, generates an analysis cycle, and obtains the order data DD, mileage data LC and vehicle data CL of the analysis area at regular intervals during the analysis cycle, and performs numerical calculations on the order data DD, mileage data LC and vehicle data CL to obtain the saturation coefficient BH of the analysis area; the analysis area is marked as a saturated area or a loose area by the saturation coefficient BH, and the ratio of the number of saturated areas to the number of all analysis areas is marked as the scheduling demand value, and whether the overall saturation of the supply chain coverage area meets the requirements is determined by the scheduling demand value; the supply chain coverage area of ​​the e-commerce platform is analyzed for capacity scheduling and several scheduling plans are generated, the priority coefficients YX of all scheduling combinations in the scheduling plan are obtained, the priority coefficients YX of H1 scheduling combinations in the scheduling plan are summed and averaged to obtain the priority performance value of the scheduling plan, and the matching plan is marked by the priority performance value; the transportation configuration of the supply chain coverage area of ​​the e-commerce platform is optimized and analyzed to obtain the overall configuration coefficient and the local configuration coefficient, and whether the capacity configuration of the supply chain area meets the requirements is determined by the overall configuration coefficient and the local configuration coefficient.

[0041] The above contents are merely examples and explanations of the structure of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the structure of the invention or exceed the scope defined by the claims, they should all fall within the protection scope of the present invention.

[0042] The above formulas are obtained by collecting a large amount of data and performing software simulation to select a formula close to the actual value. The coefficients in the formula are set by technicians in this field according to actual conditions; for example: Formula A technician in this field collects multiple groups of sample data and sets a corresponding saturation coefficient for each group of sample data; substitutes the set saturation coefficient and the collected sample data into the formula, any three formulas constitute a three-variable linear equation system, and the calculated coefficients are screened and averaged to obtain the values ​​of c1, c2 and c3, which are 3.74, 2.97 and 2.65 respectively;

[0043] The size of the coefficient is to quantify each parameter to obtain a specific value for subsequent comparison. The size of the coefficient depends on the amount of sample data and the initial setting of the corresponding saturation coefficient for each set of sample data by technical personnel in this field; as long as it does not affect the proportional relationship between the parameter and the quantified value, such as the saturation coefficient is proportional to the value of the order data.

[0044] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0045] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. An intelligent real-time scheduling system for supply chain operation adapted to e-commerce platforms, characterized by: It includes a real-time scheduling platform, which is communicatively connected with a saturation analysis module, a scheduling management module, a configuration optimization module and a database; The saturation analysis module is used to perform transportation saturation analysis on the supply chain coverage area of ​​the e-commerce platform: the supply chain coverage area is divided into a number of analysis areas, an analysis cycle is generated, and the saturation coefficient BH of the analysis area is obtained regularly within the analysis cycle; The analysis area is marked as a saturated area or a loose area through the saturation coefficient BH; the ratio of the number of saturated areas to the number of all analysis areas is marked as the scheduling demand value, and the scheduling demand value is used to determine whether the overall saturation of the supply chain coverage area meets the requirements; The scheduling management module is used to perform capacity scheduling analysis on the supply chain coverage area of ​​the e-commerce platform: all analysis areas are arranged in descending order according to the saturation coefficient BH value to obtain a saturation sequence, the number of saturated areas is marked as H1, the H1 analysis areas ranked at the end of the saturation sequence are intercepted and marked as burden areas, the saturated areas are randomly matched with the burden areas to obtain several scheduling schemes, the priority performance value of the scheduling scheme is obtained, the scheduling scheme with the smallest priority performance value is marked as a matching scheme, and all scheduling combinations in the matching scheme are sent to the mobile terminal of the manager through the real-time scheduling platform; The configuration optimization module is used to optimize the transportation configuration of the supply chain coverage area of ​​the e-commerce platform.

2. According to claim 1, the intelligent real-time scheduling system for supply chain operation adapted to the e-commerce platform is characterized in that: The process of obtaining the saturation coefficient BH of the analysis area includes: the order data DD, mileage data LC and vehicle data CL of the analysis area. The order data DD is the number of unprocessed orders of the transport unit in the analysis area, and the mileage data LC is the sum of the transportation mileage of all unprocessed orders of the transport unit in the analysis area; the vehicle data CL is the number of disposable vehicles of the transport unit in the analysis area, which is obtained by the formula The saturation coefficient BH of the analysis area is obtained, where c1, c2 and c3 are all proportional coefficients, and c1>c2>c3>1.

3. The intelligent real-time scheduling system for supply chain operation adapted to the e-commerce platform according to claim 2 is characterized in that: The specific process of marking the analysis area as a saturated area or a loose area includes: obtaining the saturation threshold BHmax through the database, and comparing the saturation coefficient BH of the analysis area with the saturation threshold BHmax: if the saturation coefficient BH is less than the saturation threshold BHmax, it is determined that the transport saturation of the analysis area meets the requirements, and the corresponding analysis area is marked as a loose area; if the saturation coefficient BH is greater than or equal to the saturation threshold BHmax, it is determined that the transport saturation of the analysis area does not meet the requirements, and the corresponding analysis area is marked as a saturated area.

4. The intelligent real-time scheduling system for supply chain operation adapted to the e-commerce platform according to claim 3 is characterized in that: The specific process of determining whether the overall saturation of the supply chain coverage area meets the requirements includes: comparing the scheduling demand value with the preset scheduling demand threshold: if the scheduling demand value is less than the scheduling demand threshold, it is determined that the overall transportation saturation of the current transportation unit in the supply chain coverage area meets the requirements; if the scheduling demand value is greater than or equal to the scheduling demand threshold, it is determined that the overall saturation of the current transportation unit in the supply chain coverage area does not meet the requirements, generating a scheduling analysis signal and sending the scheduling analysis signal to the scheduling management module through the real-time scheduling platform.

5. The intelligent real-time scheduling system for supply chain operation adapted to the e-commerce platform according to claim 4 is characterized in that: The process of obtaining the priority performance value of the scheduling scheme includes: each scheduling scheme corresponds to H1 group scheduling combinations, each scheduling combination contains a saturated area and a burden area, the scheduling data DS and the direct distance data ZJ of the scheduling combination in the scheduling scheme are obtained, the scheduling data DS is the sum of the saturation coefficient BH of the saturated area and the saturation coefficient BH of the burden area in the scheduling combination, and the direct distance data ZJ is the straight-line distance value between the center point of the saturated area and the center point of the burden area in the scheduling combination; the priority coefficient YX of the scheduling combination is obtained by the formula YX=k1*DS+k2*ZJ, and the priority coefficients YX of the H1 scheduling combinations in the scheduling scheme are summed and averaged to obtain the priority performance value of the scheduling scheme.

6. The intelligent real-time scheduling system for supply chain operation adapted to the e-commerce platform according to claim 5 is characterized in that: The specific process of the configuration optimization module optimizing and analyzing the transportation configuration of the supply chain coverage area of ​​the e-commerce platform includes: generating a configuration cycle, obtaining the overall configuration coefficient and the local configuration coefficient of the supply chain coverage area at the end of the configuration cycle; obtaining the overall configuration threshold and the local configuration threshold through the storage module, and comparing the overall configuration coefficient and the local configuration coefficient with the overall configuration threshold and the local configuration threshold respectively: if the local configuration coefficient is greater than or equal to the local configuration threshold, it is determined that the capacity configuration of the supply chain coverage area does not meet the requirements, and a local configuration optimization signal is generated and sent to the mobile terminal of the manager through the real-time scheduling platform; if the local configuration coefficient is less than the local configuration threshold and the overall configuration coefficient is less than the overall configuration threshold, it is determined that the capacity configuration of the supply chain coverage area meets the requirements; if the local configuration coefficient is less than the local configuration threshold and the overall configuration coefficient is greater than or equal to the overall configuration threshold, it is determined that the capacity configuration of the supply chain coverage area does not meet the requirements, and an overall configuration optimization signal is generated and sent to the mobile terminal of the manager through the real-time scheduling platform.

7. The intelligent real-time scheduling system for supply chain operation adapted to the e-commerce platform according to claim 6 is characterized in that: The process of obtaining the overall configuration coefficient and the local configuration coefficient of the supply chain coverage area includes: obtaining the number of times the analysis area is marked as a saturated area and marking it as the configuration demand value of the analysis area, summing up and averaging the configuration demand values ​​of all analysis areas to obtain the overall configuration coefficient, and performing variance calculation on the configuration demand values ​​of the analysis area to obtain the local configuration coefficient.

8. The intelligent real-time scheduling system for supply chain operation adapted to an e-commerce platform according to any one of claims 1 to 7, characterized in that: The working method of the supply chain operation intelligent real-time scheduling system adapted to the e-commerce platform includes the following steps: Step 1: Conduct transportation saturation analysis on the supply chain coverage areas of the e-commerce platform; Step 2: Process the saturation coefficient BH and determine whether the overall saturation of the supply chain coverage area meets the requirements based on the processing results; Step 3: Conduct capacity dispatch analysis on the supply chain coverage areas of the e-commerce platform; Step 4: Optimize and analyze the transportation configuration in the areas covered by the e-commerce platform’s supply chain.

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