Intelligent logistics optimization management system and method based on intelligent supply chain

Abstract

The invention belongs to the field of logistics optimization, relates to a data analysis technology, is used for solving the problem that an existing logistics management optimization system cannot carry out processing decision analysis when the overall logistics distribution efficiency is unqualified, and particularly relates to a logistics intelligent optimization management system and method based on an intelligent supply chain. The optimization management platform is in communication connection with a distribution analysis module, a resource management module and a storage module; the distribution analysis module is used for monitoring and analyzing the distribution efficiency of the logistics distribution area; a monitoring period is generated, and the logistics distribution area is divided into a plurality of analysis areas; according to the method, the distribution efficiency of the logistics distribution area can be monitored and analyzed, the efficiency coefficient is obtained through comprehensive analysis and calculation of each piece of distribution data of the analysis area, the analysis area is marked through the efficiency coefficient, and then the overall distribution efficiency state is fed back through the proportion of the effective and different areas in the analysis area.

Description

Technical Field

[0001] The present invention belongs to the field of logistics optimization and relates to data analysis technology, specifically to a logistics intelligent optimization management system and method based on a smart supply chain. Background Art

[0002] A logistics system refers to an organic aggregate composed of two or more logistics functional units for the purpose of completing logistics services. The "input" of a logistics system refers to the labor, equipment, materials, resources and other elements required for logistics links such as procurement, transportation, storage, circulation processing, loading and unloading, handling, packaging, sales, and logistics information processing, which are provided by the external environment to the system.

[0003] The existing logistics management optimization system is unable to monitor and analyze the distribution efficiency and resource allocation of the logistics distribution area, and thus cannot perform processing decision analysis when the overall logistics distribution efficiency is unsatisfactory, resulting in low efficiency of logistics management optimization.

[0004] In response to the above technical problems, this application proposes a solution. Summary of the Invention

[0005] The purpose of the present invention is to provide a logistics intelligent optimization management system and method based on a smart supply chain, which is used to solve the problem that the existing logistics management optimization system is unable to perform processing decision analysis when the overall logistics distribution efficiency is unsatisfactory; The technical problem to be solved by the present invention is: how to provide a logistics intelligent optimization management system and method based on a smart supply chain that can perform processing decision analysis when the overall logistics distribution efficiency is unsatisfactory.

[0006] The purpose of the present invention can be achieved through the following technical solutions: A logistics intelligent optimization management system based on a smart supply chain includes an optimization management platform, which is communicatively connected to a distribution analysis module, a resource management module, and a storage module; The distribution analysis module is used to monitor and analyze the distribution efficiency of the logistics distribution area: generate a monitoring cycle, divide the logistics distribution area into several analysis areas, obtain the order distribution data PD, time distribution data PS and loop distribution data PH of the analysis area within the monitoring cycle; obtain the efficiency coefficient XL of the analysis area within the monitoring cycle by numerically calculating the order distribution data PD, time distribution data PS and loop distribution data PH; mark the analysis area as a positive efficiency area or a negative efficiency area according to the efficiency coefficient XL; mark the ratio of the number of negative efficiency areas to the number of analysis areas as the positive efficiency coefficient, obtain the positive efficiency threshold through the storage module, compare the positive efficiency coefficient with the positive efficiency threshold, and judge whether the overall distribution efficiency of the logistics distribution area meets the requirements based on the comparison result; The resource management module is used to monitor and analyze the distribution resources in the logistics distribution area: obtain the personnel data RY, vehicle data CL, coverage data FG and order data PD of the analysis area and perform numerical calculations to obtain the resource coefficient ZY of the analysis area; arrange all the analysis objects in the logistics distribution area in order from small to large according to the efficiency coefficient XL to obtain an efficiency sequence; arrange all the analysis objects in the logistics distribution area in order from large to small according to the resource coefficient ZY to obtain a resource sequence, mark the absolute value of the difference between the sequence number of the analysis object in the efficiency sequence and the sequence number in the resource sequence as the matching value of the analysis object, sum and average the matching values ​​of all analysis objects to obtain the matching coefficient, obtain the matching threshold through the storage module, compare the matching coefficient with the matching threshold, and generate a resource configuration signal or a distribution training signal based on the comparison result.

[0007] As a preferred embodiment of the present invention, the order distribution data PD is the number of delivery orders completed in the analysis area during the monitoring period, the time distribution data PS is the total time consumed by the analysis area to complete the delivery of all orders during the monitoring period, and the loop distribution data PH is the number of delivery days with bad weather in the analysis area during the monitoring period. Bad weather includes foggy weather with visibility less than 500 meters, heavy rain weather with rainfall of not less than 25 mm, and strong wind weather with wind force level not less than level 6.

[0008] As a preferred embodiment of the present invention, the specific process of marking the analysis area as a positive area or a negative area includes: obtaining the efficiency threshold XLmax through the storage module, and comparing the efficiency coefficient XL of the analysis area during the monitoring period with the efficiency threshold XLmax: if the efficiency coefficient XL is less than the efficiency threshold XLmax, it is determined that the distribution efficiency of the analysis area during the management period meets the requirements, and the corresponding analysis area is marked as a positive area; if the efficiency coefficient XL is greater than or equal to the efficiency threshold XLmax, it is determined that the distribution efficiency of the analysis area during the management period does not meet the requirements, and the corresponding analysis area is marked as a negative area.

[0009] As a preferred embodiment of the present invention, the specific process of comparing the efficiency difference coefficient with the efficiency difference threshold includes: if the efficiency difference coefficient is less than the efficiency difference threshold, it is determined that the overall distribution efficiency of the logistics distribution area meets the requirements; if the efficiency difference coefficient is greater than or equal to the efficiency difference threshold, it is determined that the overall distribution efficiency of the logistics distribution area does not meet the requirements, a resource management signal is generated and the resource management signal is sent to the optimization management platform, and after receiving the resource management signal, the optimization management platform sends the resource management signal to the resource management module.

[0010] As a preferred embodiment of the present invention, the number of personnel RY is the number of personnel responsible for logistics distribution in the analysis area, the vehicle data CL is the number of vehicles responsible for logistics distribution in the analysis area, and the coverage data FG is the area value of the distribution range covered by the analysis area.

[0011] As a preferred embodiment of the present invention, the specific process of comparing the matching coefficient with the matching threshold includes: if the matching coefficient is less than the matching threshold, a resource configuration signal is generated and the resource configuration signal is sent to the optimization management platform, and the optimization management platform sends the resource configuration signal to the manager's mobile phone terminal after receiving the resource configuration signal; if the matching coefficient is greater than or equal to the matching threshold, a distribution training signal is generated and the distribution training signal is sent to the optimization management platform, and the optimization management platform sends the distribution training signal to the manager's mobile phone terminal after receiving the distribution training signal.

[0012] The logistics intelligent optimization management method based on the smart supply chain includes the following steps: Step 1: Monitor and analyze the distribution efficiency of the logistics distribution area: Generate a monitoring cycle, divide the logistics distribution area into several analysis areas, obtain the order distribution data PD, time distribution data PS, and loop distribution data PH of the analysis area within the monitoring cycle, and perform numerical calculations to obtain the efficiency coefficient XL of the analysis area; Step 2: Use the efficiency coefficient XL to mark the analysis area as a positive or negative efficiency area. The ratio of the number of negative efficiency areas to the number of analysis areas is marked as the positive efficiency coefficient. The positive efficiency coefficient is used to determine whether the overall distribution efficiency of the logistics distribution area meets the requirements. Step 3: Monitor and analyze the distribution resources in the logistics distribution area: Obtain the personnel data RY, vehicle data CL, coverage data FG, and order data PD in the analysis area and perform numerical calculations to obtain the resource coefficient. Generate a resource allocation signal or distribution training signal based on the resource coefficient ZY and the efficiency coefficient XL and send it to the optimization management platform.

[0013] The present invention has the following beneficial effects: The distribution analysis module can monitor and analyze the distribution efficiency of the logistics distribution area. By comprehensively analyzing and calculating the distribution data of the analysis area, the efficiency coefficient is obtained. The analysis area is marked by the efficiency coefficient, and the overall distribution efficiency status is fed back by the proportion of the abnormal area in the analysis area. When anomalies occur, timely optimization is carried out. The resource management module can be used to monitor and analyze the distribution resources in the logistics distribution area. The resource coefficient is obtained by numerically calculating the various resource data in the analysis area. Then, the efficiency sequence and resource sequence are combined. Then, the efficiency anomaly handling measures are screened by analyzing the degree of sequence deviation of the area in the efficiency sequence and resource sequence, thereby improving the efficiency of handling distribution efficiency anomalies. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. 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 any creative work.

[0015] Figure 1 This is a system block diagram of Embodiment 1 of the present invention; Figure 2 This is a flow chart of the method of embodiment 2 of the present invention. DETAILED DESCRIPTION

[0016] The technical solutions of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0017] Example 1 like Figure 1 As shown, the logistics intelligent optimization management system based on the smart supply chain includes an optimization management platform, which is connected to the distribution analysis module, resource management module and storage module.

[0018] The distribution analysis module is used to monitor and analyze the distribution efficiency of the logistics distribution area: generate a monitoring cycle, divide the logistics distribution area into several analysis areas, and obtain the order distribution data PD, timing data PS and distribution loop data PH of the analysis area during the monitoring cycle. The order distribution data PD is the number of delivery orders completed by the analysis area during the monitoring cycle, the timing data PS is the total time consumed by the analysis area to complete all order distributions during the monitoring cycle, and the distribution loop data PH is the number of delivery days with bad weather in the analysis area during the monitoring cycle. Bad weather includes foggy weather with visibility less than 500 meters, heavy rain with rainfall of not less than 25 mm, and strong winds with wind force of not less than level 6. The efficiency coefficient XL of the analysis area during the monitoring cycle is obtained by the formula XL=(α1*PS-α2*PH) / (α3*PD), where α1, α2 and α3 are all proportional coefficients, and α1>α2>α3>1. The efficiency threshold XLmax is obtained through the storage module, and the efficiency coefficient XL of the analysis area during the monitoring cycle is compared with the efficiency threshold XLmax: if the efficiency coefficient XL is less than the efficiency threshold XLmax, the analysis area is determined to be If the distribution efficiency of a domain meets the requirements within the management cycle, the corresponding analysis area is marked as a positive efficiency area. If the efficiency coefficient XL is greater than or equal to the efficiency threshold XLmax, the distribution efficiency of the analysis area within the management cycle does not meet the requirements, and the corresponding analysis area is marked as a negative efficiency area. The ratio of the number of negative efficiency areas to the number of analysis areas is marked as the negative efficiency coefficient. The negative efficiency threshold is obtained through the storage module, and the negative efficiency coefficient is compared with the negative efficiency threshold. If the negative efficiency coefficient is less than the negative efficiency threshold, the overall distribution efficiency of the logistics distribution area is determined to meet the requirements. If the negative efficiency coefficient is greater than or equal to the negative efficiency threshold, the overall distribution efficiency of the logistics distribution area is determined to meet the requirements. After receiving the resource management signal, the optimization management platform sends the resource management signal to the resource management module. The distribution efficiency of the logistics distribution area is monitored and analyzed. The efficiency coefficient is obtained by comprehensively analyzing and calculating various distribution data in the analysis area. The analysis area is marked according to the efficiency coefficient. The overall distribution efficiency status is then fed back based on the proportion of negative efficiency areas in the analysis area. When anomalies occur, timely optimization is performed.

[0019] The resource management module is used to monitor and analyze the distribution resources in the logistics distribution area: obtain the personnel data RY, vehicle data CL, coverage data FG and order data PD of the analysis area; the number of personnel RY is the number of personnel responsible for logistics distribution in the analysis area, the vehicle data CL is the number of vehicles responsible for logistics distribution in the analysis area, and the coverage data FG is the area value of the distribution range covered by the analysis area. The resource coefficient ZY of the analysis area is obtained by the formula ZY=(β1*RY+β2*CL) / (β3*FG+β4*PD), where β1, β2, β3 and β4 are all proportional coefficients, and β1>β2>β3>β4>1; arrange all the analysis objects in the logistics distribution area in order of efficiency coefficient XL from small to large to obtain an efficiency sequence; arrange all the analysis objects in the logistics distribution area in order of resource coefficient ZY from large to small to obtain a resource sequence, and the absolute difference between the sequence number of the analysis object in the efficiency sequence and the sequence number in the resource sequence is calculated. The value is marked as the matching value of the analysis object, and the matching values ​​of all analysis objects are summed and averaged to obtain the matching coefficient. The matching threshold is obtained through the storage module, and the matching coefficient is compared with the matching threshold: if the matching coefficient is less than the matching threshold, a resource configuration signal is generated and sent to the optimization management platform, and the optimization management platform sends the resource configuration signal to the manager's mobile terminal after receiving the resource configuration signal; if the matching coefficient is greater than or equal to the matching threshold, a distribution training signal is generated and sent to the optimization management platform, and the optimization management platform sends the distribution training signal to the manager's mobile terminal after receiving the distribution training signal; the distribution resources in the logistics distribution area are monitored and analyzed, and the resource coefficient is obtained by numerically calculating the various resource data in the analysis area, and then the efficiency sequence and resource sequence are combined, and then the efficiency anomaly handling measures are screened by analyzing the degree of sequence deviation of the area in the efficiency sequence and the resource sequence, so as to improve the efficiency of handling distribution efficiency anomalies.

[0020] Example 2 like Figure 2 As shown in FIG, the logistics intelligent optimization management method based on the smart supply chain includes the following steps: Step 1: Monitor and analyze the distribution efficiency of the logistics distribution area: Generate a monitoring cycle, divide the logistics distribution area into several analysis areas, obtain the order distribution data PD, time distribution data PS, and loop distribution data PH of the analysis area within the monitoring cycle, and perform numerical calculations to obtain the efficiency coefficient XL of the analysis area; Step 2: Use the efficiency coefficient XL to mark the analysis area as a positive or negative efficiency area. The ratio of the number of negative efficiency areas to the number of analysis areas is marked as the positive efficiency coefficient. The positive efficiency coefficient is used to determine whether the overall distribution efficiency of the logistics distribution area meets the requirements. Step 3: Monitor and analyze the distribution resources in the logistics distribution area: Obtain the personnel data RY, vehicle data CL, coverage data FG, and order data PD in the analysis area and perform numerical calculations to obtain the resource coefficient. Generate a resource allocation signal or distribution training signal based on the resource coefficient ZY and the efficiency coefficient XL and send it to the optimization management platform.

[0021] The intelligent logistics optimization management system and method based on the smart supply chain generates a monitoring cycle during operation, divides the logistics distribution area into several analysis areas, obtains the order distribution data PD, time distribution data PS and ring distribution data PH of the analysis area within the monitoring cycle, and performs numerical calculations to obtain the efficiency coefficient XL of the analysis area; marks the analysis area as a positive efficiency area or a negative efficiency area through the efficiency coefficient XL, and marks the ratio of the number of negative efficiency areas to the number of analysis areas as the positive efficiency coefficient, and judges whether the overall distribution efficiency of the logistics distribution area meets the requirements through the positive efficiency coefficient; obtains the personnel data RY, vehicle data CL, coverage data FG and order distribution data PD of the analysis area and performs numerical calculations to obtain the resource coefficient, and generates a resource allocation signal or a distribution training signal through the resource coefficient ZY and the efficiency coefficient XL and sends it to the optimization management platform.

[0022] The above content is merely an example and explanation of the structure of the present invention. Those skilled in the art may make various modifications or additions to the described specific embodiments 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 scope of protection of the present invention.

[0023] The above formulas are all derived by collecting a large amount of data and performing software simulation to select a formula that is close to the actual value. The coefficients in the formula are set by those skilled in the art based on actual conditions; for example, the formula XL = (α1*PS-α2*PH) / (α3*PD); those skilled in the art collect multiple sets of sample data and set a corresponding efficiency coefficient for each set of sample data; the set efficiency coefficient and the collected sample data are substituted into the formula, and any three formulas form a three-variable linear equation system. The calculated coefficients are screened and averaged, resulting in the values ​​of α1, α2, and α3 being 3.74, 2.97, and 2.65, respectively; The size of the coefficient is to quantify each parameter to obtain a specific value, which is convenient for subsequent comparison. The size of the coefficient depends on the amount of sample data and the initial setting of the corresponding efficiency 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 quantized value, such as the efficiency coefficient is proportional to the value of the timing data.

[0024] Throughout this specification, references to terms such as "one embodiment," "example," or "specific example" indicate that the specific features, structures, materials, or characteristics described in conjunction with that embodiment or example are included in at least one embodiment or example of the present invention. In this specification, schematic representations of these terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0025] The preferred embodiments of the present invention disclosed above are intended only to help illustrate the present invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the present invention to specific embodiments. Obviously, many modifications and variations are possible based on the contents of this specification. These embodiments are selected and described in detail in this specification to better explain the principles and practical applications of the present invention, thereby enabling those skilled in the art to better understand and utilize the present invention. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. Logistics intelligent optimization management system based on smart supply chain, characterized by: It includes an optimization management platform, which is communicatively connected to a distribution analysis module, a resource management module, and a storage module; The distribution analysis module is used to monitor and analyze the distribution efficiency of the logistics distribution area: generate a monitoring cycle, divide the logistics distribution area into several analysis areas, obtain the order distribution data PD, time distribution data PS and loop distribution data PH of the analysis area within the monitoring cycle; obtain the efficiency coefficient XL of the analysis area within the monitoring cycle by numerically calculating the order distribution data PD, time distribution data PS and loop distribution data PH; mark the analysis area as a positive efficiency area or a negative efficiency area based on the efficiency coefficient XL; The ratio of the number of efficiency-deviation areas to the number of analysis areas is marked as the efficiency-deviation coefficient. The efficiency-deviation threshold is obtained through the storage module. The efficiency-deviation coefficient is compared with the efficiency-deviation threshold, and the comparison result is used to determine whether the overall distribution efficiency of the logistics distribution area meets the requirements. The resource management module is used to monitor and analyze the distribution resources in the logistics distribution area: obtain the personnel data RY, vehicle data CL, coverage data FG and order data PD of the analysis area and perform numerical calculations to obtain the resource coefficient ZY of the analysis area; arrange all the analysis objects in the logistics distribution area in order from small to large according to the efficiency coefficient XL to obtain an efficiency sequence; arrange all the analysis objects in the logistics distribution area in order from large to small according to the resource coefficient ZY to obtain a resource sequence, mark the absolute value of the difference between the sequence number of the analysis object in the efficiency sequence and the sequence number in the resource sequence as the matching value of the analysis object, sum and average the matching values ​​of all analysis objects to obtain the matching coefficient, obtain the matching threshold through the storage module, compare the matching coefficient with the matching threshold, and generate a resource configuration signal or a distribution training signal based on the comparison result.

2. The logistics intelligent optimization management system based on the smart supply chain according to claim 1 is characterized in that: The order distribution data PD is the number of delivery orders completed in the analysis area during the monitoring period. The time distribution data PS is the total time consumed by the analysis area to complete the delivery of all orders during the monitoring period. The loop distribution data PH is the number of delivery days with bad weather in the analysis area during the monitoring period. Bad weather includes foggy weather with visibility less than 500 meters, heavy rain with rainfall of not less than 25 mm, and strong wind with wind force level not less than level 6.

3. The logistics intelligent optimization management system based on the smart supply chain according to claim 2 is characterized in that: The specific process of marking the analysis area as a positive area or a negative area includes: obtaining the efficiency threshold XLmax through the storage module, and comparing the efficiency coefficient XL of the analysis area during the monitoring period with the efficiency threshold XLmax: if the efficiency coefficient XL is less than the efficiency threshold XLmax, it is determined that the distribution efficiency of the analysis area during the management period meets the requirements, and the corresponding analysis area is marked as a positive area; if the efficiency coefficient XL is greater than or equal to the efficiency threshold XLmax, it is determined that the distribution efficiency of the analysis area during the management period does not meet the requirements, and the corresponding analysis area is marked as a negative area.

4. The logistics intelligent optimization management system based on the smart supply chain according to claim 3 is characterized in that: The specific process of comparing the efficiency difference coefficient with the efficiency difference threshold includes: if the efficiency difference coefficient is less than the efficiency difference threshold, it is determined that the overall distribution efficiency of the logistics distribution area meets the requirements; if the efficiency difference coefficient is greater than or equal to the efficiency difference threshold, it is determined that the overall distribution efficiency of the logistics distribution area does not meet the requirements, a resource management signal is generated and the resource management signal is sent to the optimization management platform, and after receiving the resource management signal, the optimization management platform sends the resource management signal to the resource management module.

5. The logistics intelligent optimization management system based on the smart supply chain according to claim 4 is characterized in that: The number of personnel RY is the number of personnel responsible for logistics distribution in the analysis area, the vehicle data CL is the number of vehicles responsible for logistics distribution in the analysis area, and the coverage data FG is the area value of the distribution range covered by the analysis area.

6. The logistics intelligent optimization management system based on the smart supply chain according to claim 5 is characterized in that: The specific process of comparing the matching coefficient with the matching threshold includes: if the matching coefficient is less than the matching threshold, a resource configuration signal is generated and sent to the optimization management platform, and the optimization management platform sends the resource configuration signal to the manager's mobile terminal after receiving the resource configuration signal; if the matching coefficient is greater than or equal to the matching threshold, a distribution training signal is generated and sent to the optimization management platform, and the optimization management platform sends the distribution training signal to the manager's mobile terminal after receiving the distribution training signal.

7. The logistics intelligent optimization management method based on the smart supply chain is characterized by: The following steps are involved: Step 1: Monitor and analyze the distribution efficiency of the logistics distribution area: Generate a monitoring cycle, divide the logistics distribution area into several analysis areas, obtain the order distribution data PD, time distribution data PS, and loop distribution data PH of the analysis area within the monitoring cycle, and perform numerical calculations to obtain the efficiency coefficient XL of the analysis area; Step 2: Use the efficiency coefficient XL to mark the analysis area as a positive or negative efficiency area. The ratio of the number of negative efficiency areas to the number of analysis areas is marked as the positive efficiency coefficient. The positive efficiency coefficient is used to determine whether the overall distribution efficiency of the logistics distribution area meets the requirements. Step 3: Monitor and analyze the distribution resources in the logistics distribution area: Obtain the personnel data RY, vehicle data CL, coverage data FG, and order data PD in the analysis area and perform numerical calculations to obtain the resource coefficient. Generate a resource allocation signal or distribution training signal based on the resource coefficient ZY and the efficiency coefficient XL and send it to the optimization management platform.

Images