Logistics supply chain intelligent distribution scheduling system based on data analysis

Abstract

The invention relates to the technical field of logistics supply chains, in particular to a logistics supply chain intelligent distribution scheduling system based on data analysis, and aims to solve the problems that an existing logistics supply chain intelligent distribution scheduling system cannot accurately monitor inventory in real time and cannot reasonably select merchants and logistics distribution vehicles in a supply chain, and the logistics distribution efficiency is high. And the overall performance of the logistics supply chain is not good. The system comprises an inventory monitoring module, a supply monitoring module, a data analysis module, an analysis and judgment module and a distribution scheduling module. According to the system, through inventory monitoring, supply chain merchant intelligent screening and logistics vehicle data analysis, intelligent scheduling and optimization of logistics distribution can be realized, rapid response of market demands can be realized, the stability and reliability of a supply chain can be improved, the efficiency and accuracy of logistics distribution can be improved, the risk of the supply chain can be reduced, and the risk of logistics distribution can be reduced. The cost of logistics distribution is reduced, and the economic benefit of logistics distribution is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of logistics supply chain, and in particular to an intelligent distribution and scheduling system for a logistics supply chain based on data analysis. Background Art

[0002] With the rapid development of e-commerce, the logistics and distribution industry is also facing increasing challenges. In the modern logistics supply chain system, although many technologies and tools have been developed to improve logistics efficiency and reduce costs, there are still significant challenges and room for improvement. First, traditional inventory monitoring methods rely on manual operations or simple automation systems, which cannot provide real-time and accurate information, resulting in large fluctuations in inventory levels, which may cause overstocking or shortages, increasing operating costs and risks. Secondly, the selection of merchants and logistics distribution vehicles in the supply chain is usually based on limited information and experience values, lacking systematic intelligent evaluation tools, which not only limits the efficiency and flexibility of the supply chain, but may also lead to the selection of wrong partners and increased transportation costs, affecting the overall service quality.

[0003] Therefore, there is an urgent need for an intelligent distribution scheduling system for the logistics supply chain based on data analysis to optimize the overall performance of the logistics supply chain, thereby achieving more efficient and low-cost logistics distribution, which is of great significance. Summary of the invention

[0004] In order to overcome the above-mentioned technical problems, the purpose of the present invention is to provide a logistics supply chain intelligent distribution and scheduling system based on data analysis, which solves the problem that the existing logistics supply chain intelligent distribution and scheduling system is unable to monitor inventory in real time and accurately, and is unable to reasonably select merchants and logistics distribution vehicles in the supply chain, resulting in poor overall performance of the logistics supply chain.

[0005] The purpose of the present invention can be achieved by the following technical solutions:

[0006] The intelligent distribution and dispatching system for logistics supply chain based on data analysis includes:

[0007] The supply monitoring module is used to obtain the supply optimization parameters of the supply object j after receiving the replenishment notification instruction, and send the supply optimization parameters to the data analysis module; wherein the supply optimization parameters include supply price information GJ, transportation information YS, supply information GH and scoring information PF;

[0008] A data analysis module is used to obtain a supply optimization coefficient GYj according to supply optimization parameters, and send the supply optimization coefficient GYj to the analysis and judgment module;

[0009] The specific process of the data analysis module obtaining the supply optimization coefficient GYj is as follows:

[0010] The price information GJ, transportation information YS, supply information GH and scoring information PF are normalized, and the values ​​of the price information GJ, transportation information YS, supply information GH and scoring information PF are quantified according to a preset supply optimization analysis model to obtain a supply optimization coefficient GYj;

[0011] Among them, the supply optimization analysis model is as follows:

[0012]

[0013] Where:

[0014] λ is the preset error adjustment factor, and λ=1.058;

[0015] κ1, κ2, κ3 and κ4 are respectively the preset weight factors corresponding to the set supply price information GJ, transportation information YS, supply information GH and scoring information PF, κ1, κ2, κ3 and κ4 satisfy κ4>κ1>κ2>κ3>1.248, taking κ1=2.12, κ2=1.77, κ3=1.36, κ4=2.81;

[0016] Send the supply optimization coefficient GYj to the analysis and judgment module;

[0017] The analysis and judgment module is used to obtain the distribution scheduling supply object according to the supply optimization coefficient GYj, and send the distribution scheduling supply object to the distribution scheduling module;

[0018] The delivery scheduling module is used to match the delivery scheduling supply objects and the delivery scheduling logistics objects, and send the delivery scheduling supply objects to the mobile phone terminals of the drivers matched with the delivery scheduling logistics objects.

[0019] As a preferred implementation of the present invention, the specific process of the supply monitoring module obtaining the supply optimization parameters is as follows:

[0020] After receiving the replenishment notification instruction, obtain all suppliers of monitored item i and mark them as supply objects j in sequence, where j = 1, ..., u, u is a positive integer, j is the number of any supply object, and u is the total number of supply objects;

[0021] Obtain the supply price of the supplier j for the monitored item i, and mark it as the supply price information GJ;

[0022] Get the location of the supply object j and the location of the user's warehouse, obtain the preset logistics route between the two, obtain the distance and average transportation cost of the logistics route, and mark them as the distance value LJ and the transportation cost value YB respectively, quantify the distance value LJ and the transportation cost value YB, extract the values ​​of the distance value LJ and the transportation cost value YB, and substitute them into the formula for calculation. The transportation information YS is obtained; wherein, e is a mathematical constant, s1 and s2 are respectively the preset proportional coefficients corresponding to the set road distance value LJ and the transportation cost value YB, s1 and s2 satisfy s1+s2=1, 0<s1<s2<1, and s1=0.33, s2=0.67; wherein, the average transportation cost is the average value of the sum of the fuel costs and tolls generated in several transportation processes in the historical data;

[0023] Obtain the total number of supply customers of the supply object j for the monitored item i, and mark it as the customer value KS; obtain the total number of the monitored item i supplied by the supply object j per unit time, and mark it as the supply value GS; quantify the customer value KS and the supply value GS, extract the values ​​of the customer value KS and the supply value GS, and substitute them into the formula for calculation; obtain the supply information GH according to the formula GH = g1 × KS + g2 × GS; wherein g1 and g2 are the preset proportional coefficients corresponding to the set customer value KS and the supply value GS, respectively; g1 and g2 satisfy g1 + g2 = 1, 0 < g2 < g1 < 1, and g1 = 0.71 and g2 = 0.29;

[0024] Obtain the ratings of the supply object j by all the supply customers of the supply object j, where the rating is a five-point system, obtain the average of all ratings within three days, thirty days and three hundred and sixty-five days at the current time, and mark them as the first rating value YP, the second rating value EP and the third rating value SP respectively, quantify the first rating value YP, the second rating value EP and the third rating value SP, extract the values ​​of the first rating value YP, the second rating value EP and the third rating value SP, and substitute them into the formula for calculation, and obtain the rating information PF according to the formula PF=p1×YP+p2×EP+p3×SP; wherein p1, p2 and p3 are the preset proportional coefficients corresponding to the set first rating value YP, the second rating value EP and the third rating value SP respectively, p1, p2 and p3 satisfy p1+p2+p3=1, 0<p3<p2<p1<1, and take p1=0.41, p2=0.33, p3=0.26;

[0025] The price information GJ, transportation information YS, supply information GH and rating information PF are sent to the data analysis module.

[0026] As a preferred implementation of the present invention, the specific process of the analysis and judgment module obtaining the delivery scheduling supply object is as follows:

[0027] All supply objects j are sorted in descending order according to the supply preference coefficient GYj, and the supply object j at the first position is marked as the distribution scheduling supply object, and the distribution scheduling supply object is sent to the distribution scheduling module.

[0028] As a preferred embodiment of the present invention, the logistics supply chain intelligent distribution scheduling system based on data analysis also includes:

[0029] The inventory monitoring module is used to generate a replenishment notification instruction according to the inventory remaining value KYi and the inventory remaining monitoring value KJi of the monitored item i, and send the replenishment notification instruction to the supply monitoring module.

[0030] As a preferred implementation of the present invention, the specific process of the inventory monitoring module generating a replenishment notification instruction is as follows:

[0031] Get all items in the user's inventory and mark them as monitored items i, i = 1, ..., v, v is a positive integer, i is the number of any monitored item, and v is the total number of monitored items;

[0032] Get the remaining inventory quantity of monitored item i and mark it as the remaining inventory value KYi; get the preset remaining inventory monitoring quantity of monitored item i and mark it as the remaining inventory monitoring value KJi;

[0033] Compare the remaining inventory value KYi with the remaining inventory monitoring value KJ i, and the comparison results are as follows:

[0034] If the inventory balance value KYi≤the inventory balance monitoring value KJi, a replenishment notification instruction is generated and sent to the supply monitoring module.

[0035] As a preferred embodiment of the present invention, the distribution scheduling module is also used to obtain the logistics optimal parameters of the pre-selected logistics vehicle o, and send the logistics optimal parameters to the data analysis module; wherein the logistics optimal parameters include usage information SY, maintenance information WX and experience information JY.

[0036] As a preferred implementation of the present invention, the specific process of the distribution scheduling module obtaining the logistics optimization parameters is as follows:

[0037] Obtain all idle logistics vehicles and mark them as pre-selected logistics vehicles o in sequence, where o=1, ..., n, where n is a positive integer, o is the number of any pre-selected logistics vehicle, and n is the total number of pre-selected logistics vehicles;

[0038] The mileage and total number of starts of the pre-selected logistics vehicle o are obtained, and they are marked as the mileage value LC and the start value QD, respectively. A diamond shape is drawn with the values ​​of the mileage value LC and the start value QD as the two diagonal lines of the diamond shape, and the area of ​​the diamond shape is obtained and marked as the usage information SY.

[0039] Obtain the total number of maintenance times of the pre-selected logistics vehicle o and mark it as maintenance information WX;

[0040] Obtain the driving experience and working experience of the driver matched with the pre-selected logistics vehicle o, and mark them as driving experience value JL and working experience value GL respectively, quantify the driving experience value JL and working experience value GL, extract the values ​​of driving experience value JL and working experience value GL, and substitute them into the formula for calculation, and obtain the experience information JY according to the formula JY=y1×JL+y2×GL; wherein y1 and y2 are the preset proportional coefficients corresponding to the set driving experience value JL and working experience value GL respectively, y1 and y2 satisfy y1+y2=1, 0<y1<y2<1, and y1=0.42 and y2=0.58;

[0041] The usage information SY, maintenance information WX and experience information JY are sent to the data analysis module.

[0042] As a preferred embodiment of the present invention, the data analysis module is also used to obtain a logistics optimization coefficient WYo according to logistics optimization parameters, and send the logistics optimization coefficient WYo to the analysis and judgment module.

[0043] As a preferred embodiment of the present invention, the specific process of the data analysis module obtaining the logistics optimization coefficient WYo is as follows:

[0044] The usage information SY, maintenance information WX and experience information JY are normalized, and the values ​​of the usage information SY, maintenance information WX and experience information JY are quantified according to a preset logistics optimization analysis model to obtain a logistics optimization coefficient WYo;

[0045] Among them, the supply optimization analysis model is as follows:

[0046]

[0047] Where:

[0048] δ is the preset error adjustment factor, δ=0.962;

[0049] π and e are both mathematical constants;

[0050] α1, α2 and α3 are respectively the preset weight factors corresponding to the set usage information SY, maintenance information WX and experience information JY, α1, α2 and α3 satisfy α3>α2>α1>2.018, α1=2.36, α2=2.81, α3=3.39;

[0051] The logistics optimization coefficient WYo is sent to the analysis and judgment module.

[0052] As a preferred implementation mode of the present invention, the analysis and judgment module is also used to obtain a distribution scheduling logistics object according to the logistics optimization coefficient WYo, and send the distribution scheduling logistics object to the distribution scheduling module.

[0053] As a preferred implementation of the present invention, the specific process of the analysis and judgment module obtaining the distribution scheduling logistics object is as follows:

[0054] All pre-selected logistics vehicles o are sorted in descending order according to the logistics optimization coefficient WYo, and the pre-selected logistics vehicle o at the first place is marked as a distribution scheduling logistics object, and the distribution scheduling logistics object is sent to the distribution scheduling module.

[0055] Beneficial effects of the present invention:

[0056] The intelligent distribution and scheduling system of the logistics supply chain based on data analysis of the present invention first monitors the inventory status in real time, then collects data on suppliers to obtain supply optimization parameters, and then uses data analysis technology to obtain supply optimization coefficients based on supply optimization parameters to comprehensively measure the quality of suppliers, and the larger the supply optimization coefficient, the higher the quality, and finally selects suppliers with high quality for supply and replenishment, and then collects data on logistics vehicles to obtain logistics optimization parameters, and then uses data analysis technology to obtain logistics optimization coefficients based on logistics optimization parameters to comprehensively measure the quality of logistics vehicles, and the larger the logistics optimization coefficient, the higher the quality, and finally selects logistics vehicles with high quality for supply and logistics distribution, and finally realizes intelligent distribution and scheduling of the logistics supply chain;

[0057] The data analysis-based logistics supply chain intelligent distribution scheduling system of the present invention can realize intelligent scheduling and optimization of logistics distribution through inventory monitoring, intelligent screening of supply chain merchants and data analysis of logistics vehicles, achieve rapid response to market demand, improve the stability and reliability of the supply chain, improve the efficiency and accuracy of logistics distribution, reduce supply chain risks, reduce the cost of logistics distribution, improve the economic benefits of logistics distribution, improve user satisfaction, and enhance the competitiveness of enterprises in the market. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] In order to facilitate understanding by those skilled in the art, the present invention is further described below with reference to the accompanying drawings.

[0059] Figure 1 This is a principle block diagram of the logistics supply chain intelligent distribution scheduling system based on data analysis of the present invention.

[0060] Figure 2 The present invention is a flow chart of the working method of the logistics supply chain intelligent distribution scheduling system based on data analysis. DETAILED DESCRIPTION

[0061] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not 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.

[0062] Embodiment 1:

[0063] See also Figure 1 As shown, this embodiment is a logistics supply chain intelligent distribution scheduling system based on data analysis, including the following modules: inventory monitoring module, supply monitoring module, data analysis module, analysis and judgment module and distribution scheduling module;

[0064] The inventory monitoring module is used to generate a replenishment notification instruction according to the inventory value KYi and the inventory monitoring value KJi of the monitored item i, and send the replenishment notification instruction to the supply monitoring module;

[0065] The supply monitoring module is used to obtain the supply optimization parameters of the supply object j after receiving the replenishment notification instruction, and send the supply optimization parameters to the data analysis module; wherein the supply optimization parameters include supply price information GJ, transportation information YS, supply information GH and scoring information PF;

[0066] The data analysis module is used to obtain the supply optimization coefficient GYj according to the supply optimization parameters, and send the supply optimization coefficient GYj to the analysis and judgment module; it is also used to obtain the logistics optimization coefficient WYo according to the logistics optimization parameters, and send the logistics optimization coefficient WYo to the analysis and judgment module;

[0067] The analysis and judgment module is used to obtain the distribution scheduling supply object according to the supply optimization coefficient GYj, and send the distribution scheduling supply object to the distribution scheduling module; it is also used to obtain the distribution scheduling logistics object according to the logistics optimization coefficient WYo, and send the distribution scheduling logistics object to the distribution scheduling module;

[0068] Among them, the distribution scheduling module is used to obtain the logistics optimization parameters of the pre-selected logistics vehicle o, and send the logistics optimization parameters to the data analysis module; wherein, the logistics optimization parameters include usage information SY, maintenance information WX and experience information JY; it is also used to match the distribution scheduling supply object and the distribution scheduling logistics object, and send the distribution scheduling supply object to the mobile terminal of the driver matched with the distribution scheduling logistics object.

[0069] Embodiment 2:

[0070] See also Figure 2As shown, this embodiment is a working method of a logistics supply chain intelligent distribution scheduling system based on data analysis, comprising the following steps:

[0071] Step 1: The inventory monitoring module generates a replenishment notification instruction according to the inventory value KYi and the inventory monitoring value KJi of the monitored item i, and sends the replenishment notification instruction to the supply monitoring module;

[0072] Step 2: After receiving the replenishment notification instruction, the supply monitoring module obtains the supply optimization parameters of the supply object j, where the supply optimization parameters include supply price information GJ, transportation information YS, supply information GH and scoring information PF, and sends the supply optimization parameters to the data analysis module;

[0073] Step 3: The data analysis module obtains the supply optimization coefficient GYj according to the supply optimization parameters, and sends the supply optimization coefficient GYj to the analysis and judgment module;

[0074] Step 4: The analysis and judgment module obtains the distribution scheduling supply object according to the supply optimization coefficient GYj, and sends the distribution scheduling supply object to the distribution scheduling module;

[0075] Step 5: The distribution scheduling module obtains the logistics optimization parameters of the pre-selected logistics vehicle o, where the logistics optimization parameters include usage information SY, maintenance information WX and experience information JY, and sends the logistics optimization parameters to the data analysis module;

[0076] Step 6: The data analysis module obtains the logistics optimization coefficient WYo according to the logistics optimization parameters, and sends the logistics optimization coefficient WYo to the analysis and judgment module;

[0077] Step 7: The analysis and judgment module obtains the distribution scheduling logistics object according to the logistics optimization coefficient WYo, and sends the distribution scheduling logistics object to the distribution scheduling module;

[0078] Step 8: The delivery scheduling module matches the delivery scheduling supply object and the delivery scheduling logistics object, and sends the delivery scheduling supply object to the mobile phone terminal of the driver matched with the delivery scheduling logistics object.

[0079] Embodiment 3:

[0080] Based on any of the above embodiments, Embodiment 3 of the present invention is an inventory monitoring module. The function of the inventory monitoring module is to generate a replenishment notification instruction. The specific process is as follows:

[0081] The inventory monitoring module obtains all items in the user's inventory and marks them as monitored items i, i = 1, ..., v, v is a positive integer, i is the number of any monitored item, and v is the total number of monitored items;

[0082] The inventory monitoring module obtains the remaining inventory quantity of the monitored item i and marks it as the remaining inventory value KYi, obtains the preset remaining inventory monitoring quantity of the monitored item i, and marks it as the remaining inventory monitoring value KJi;

[0083] The inventory monitoring module compares the inventory remaining value KYi with the inventory remaining monitoring value KJ i. The comparison results are as follows:

[0084] If the inventory balance value KYi≤the inventory balance monitoring value KJi, a replenishment notification instruction is generated and sent to the supply monitoring module.

[0085] Embodiment 4:

[0086] Based on any of the above embodiments, Embodiment 4 of the present invention is a supply monitoring module. The function of the supply monitoring module is to obtain supply optimization parameters, wherein the supply optimization parameters include supply price information GJ, transportation information YS, supply information GH and scoring information PF. The specific process is as follows:

[0087] After receiving the replenishment notification instruction, the supply monitoring module obtains all suppliers of monitored item i and marks them as supply objects j in sequence, where j=1, ..., u, where u is a positive integer, j is the number of any supply object, and u is the total number of supply objects;

[0088] The supply monitoring module obtains the supply price of the supply object j for the monitored item i and marks it as the supply price information GJ;

[0089] The supply monitoring module obtains the location of the supply object j and the location of the user's warehouse, obtains the preset logistics route between the two, obtains the distance and average transportation cost of the logistics route, and marks them as the distance value LJ and the transportation cost value YB respectively, quantifies the distance value LJ and the transportation cost value YB, extracts the values ​​of the distance value LJ and the transportation cost value YB, and substitutes them into the formula for calculation. The transportation information YS is obtained; wherein, e is a mathematical constant, s1 and s2 are respectively the preset proportional coefficients corresponding to the set road distance value LJ and the transportation cost value YB, s1 and s2 satisfy s1+s2=1, 0<s1<s2<1, and s1=0.33, s2=0.67; wherein, the average transportation cost is the average value of the sum of the fuel costs and tolls generated in several transportation processes in the historical data;

[0090] The supply monitoring module obtains the total number of supply customers of the supply object j for the monitored item i, and marks it as the customer value KS, obtains the total number of the monitored item i supplied by the supply object j per unit time, and marks it as the supply value GS, quantifies the customer value KS and the supply value GS, extracts the values ​​of the customer value KS and the supply value GS, and substitutes them into the formula for calculation, and obtains the supply information GH according to the formula GH=g1×KS+g2×GS; wherein g1 and g2 are the preset proportional coefficients corresponding to the set customer value KS and the supply value GS, respectively, and g1 and g2 satisfy g1+g2=1, 0<g2<g1<1, and g1=0.71 and g2=0.29;

[0091] The supply monitoring module obtains the ratings of the supply object j by all the supply customers of the supply object j, wherein the ratings are of a five-point system, obtains the average values ​​of all the ratings within three days, thirty days and three hundred and sixty-five days at the current moment, and marks them as the first rating value YP, the second rating value EP and the third rating value SP respectively, quantifies the first rating value YP, the second rating value EP and the third rating value SP, extracts the values ​​of the first rating value YP, the second rating value EP and the third rating value SP, and substitutes them into the formula for calculation, and obtains the rating information PF according to the formula PF=p1×YP+p2×EP+p3×SP; wherein p1, p2 and p3 are the preset proportional coefficients corresponding to the set first rating value YP, the second rating value EP and the third rating value SP respectively, and p1, p2 and p3 satisfy p1+p2+p3=1, 0<p3<p2<p1<1, and p1=0.41, p2=0.33, and p3=0.26;

[0092] The supply monitoring module sends the supply price information GJ, transportation information YS, supply information GH and scoring information PF to the data analysis module.

[0093] Embodiment 5:

[0094] Based on any of the above embodiments, Embodiment 5 of the present invention is a data analysis module, and the data analysis module has two functions:

[0095] One of the functions is to obtain the supply optimization coefficient GYj. The specific process is as follows:

[0096] The data analysis module normalizes the price information GJ, transportation information YS, supply information GH and scoring information PF, and quantifies the values ​​of the price information GJ, transportation information YS, supply information GH and scoring information PF according to the preset supply optimization analysis model to obtain the supply optimization coefficient GYj;

[0097] Among them, the supply optimization analysis model is as follows:

[0098]

[0099] Where:

[0100] λ is the preset error adjustment factor, and λ=1.058;

[0101] κ1, κ2, κ3 and κ4 are respectively the preset weight factors corresponding to the set supply price information GJ, transportation information YS, supply information GH and scoring information PF, κ1, κ2, κ3 and κ4 satisfy κ4>κ1>κ2>κ3>1.248, taking κ1=2.12, κ2=1.77, κ3=1.36, κ4=2.81;

[0102] The data analysis module sends the supply optimization coefficient GYj to the analysis and judgment module;

[0103] The second function is to obtain the logistics optimization coefficient WYo. The specific process is as follows:

[0104] The data analysis module normalizes the usage information SY, maintenance information WX and experience information JY, and quantifies the values ​​of the usage information SY, maintenance information WX and experience information JY according to the preset logistics optimization analysis model to obtain the logistics optimization coefficient WYo;

[0105] Among them, the supply optimization analysis model is as follows:

[0106]

[0107] Where:

[0108] δ is the preset error adjustment factor, δ=0.962;

[0109] π and e are both mathematical constants;

[0110] α1, α2 and α3 are respectively the preset weight factors corresponding to the set usage information SY, maintenance information WX and experience information JY, α1, α2 and α3 satisfy α3>α2>α1>2.018, α1=2.36, α2=2.81, α3=3.39;

[0111] The data analysis module sends the logistics optimization coefficient WYo to the analysis and judgment module.

[0112] Embodiment 6:

[0113] Based on any of the above embodiments, Embodiment 6 of the present invention is an analysis and judgment module, which has two functions:

[0114] One of the functions is to obtain the distribution scheduling supply object. The specific process is as follows:

[0115] The analysis and judgment module sorts all the supply objects j according to the supply preference coefficient GYj from large to small, marks the supply object j at the first place as the distribution scheduling supply object, and sends the distribution scheduling supply object to the distribution scheduling module;

[0116] The second function is to obtain the distribution scheduling logistics object. The specific process is as follows:

[0117] The analysis and judgment module sorts all the pre-selected logistics vehicles o according to the logistics optimization coefficient WYo from large to small, marks the pre-selected logistics vehicle o at the first place as a distribution scheduling logistics object, and sends the distribution scheduling logistics object to the distribution scheduling module.

[0118] Embodiment 7:

[0119] Based on any of the above embodiments, Embodiment 7 of the present invention is a delivery scheduling module, which has two functions:

[0120] One of the functions is to obtain logistics optimization parameters, wherein the logistics optimization parameters include usage information SY, maintenance information WX and experience information JY. The specific process is as follows:

[0121] The distribution scheduling module obtains all idle logistics vehicles and marks them as pre-selected logistics vehicles o in sequence, where o=1, ..., n, n is a positive integer, o is the number of any pre-selected logistics vehicle, and n is the total number of pre-selected logistics vehicles;

[0122] The distribution scheduling module obtains the mileage and total number of starts of the pre-selected logistics vehicle o, and marks them as the mileage value LC and the start value QD respectively, draws a diamond figure with the values ​​of the mileage value LC and the start value QD as the two diagonals of the diamond, obtains the area of ​​the diamond figure, and marks it as the usage information SY;

[0123] The distribution scheduling module obtains the total number of maintenance times of the pre-selected logistics vehicle o and marks it as maintenance information WX;

[0124] The distribution scheduling module obtains the driving experience and working experience of the driver matched with the pre-selected logistics vehicle o, and marks them as driving experience value JL and working experience value GL respectively, quantifies the driving experience value JL and working experience value GL, extracts the values ​​of driving experience value JL and working experience value GL, and substitutes them into the formula for calculation, and obtains the experience information JY according to the formula JY=y1×JL+y2×GL; wherein y1 and y2 are the preset proportional coefficients corresponding to the set driving experience value JL and working experience value GL respectively, and y1 and y2 satisfy y1+y2=1, 0<y1<y2<1, and y1=0.42 and y2=0.58;

[0125] The distribution scheduling module sends the usage information SY, maintenance information WX and experience information JY to the data analysis module;

[0126] The second function is to match the distribution scheduling supply object and the distribution scheduling logistics object. The specific process is as follows:

[0127] The delivery scheduling module matches the delivery scheduling supply object and the delivery scheduling logistics object, and sends the delivery scheduling supply object to the mobile phone terminal of the driver matched with the delivery scheduling logistics object.

[0128] Based on the above embodiments 1-7, the working principle of the present invention is as follows:

[0129] The intelligent distribution scheduling system of the logistics supply chain based on data analysis of the present invention generates a replenishment notification instruction according to the inventory surplus value and inventory surplus monitoring value of the monitored items through the inventory monitoring module, obtains the supply optimization parameters of the supply object after receiving the replenishment notification instruction through the supply monitoring module, obtains the supply optimization coefficient according to the supply optimization parameters through the data analysis module, obtains the distribution scheduling supply object according to the supply optimization coefficient through the analysis and judgment module, obtains the logistics optimization parameters of the pre-selected logistics vehicles through the distribution scheduling module, obtains the logistics optimization coefficient according to the logistics optimization parameters through the data analysis module, obtains the distribution scheduling logistics object according to the logistics optimization coefficient through the analysis and judgment module, matches the distribution scheduling supply object and the distribution scheduling logistics object through the distribution scheduling module, and sends the distribution scheduling supply object to the mobile phone terminal of the driver matched with the distribution scheduling logistics object; the system first monitors the inventory status in real time, then collects data on the supplier to obtain the supply optimization parameters, and then uses the data analysis technology to calculate the distribution scheduling supply object according to the supply optimization parameters The supply optimization coefficient obtained by the cargo optimization parameters can comprehensively measure the quality of the supplier, and the larger the supply optimization coefficient is, the higher the quality is. Finally, the supplier with high quality is selected for supply and replenishment, and then the data of the logistics vehicles is collected to obtain the logistics optimization parameters. Then, the logistics optimization coefficient obtained according to the logistics optimization parameters using data analysis technology can comprehensively measure the quality of the logistics vehicles, and the larger the logistics optimization coefficient is, the higher the quality is. Finally, the logistics vehicles with high quality are selected for supply logistics distribution, and finally the intelligent distribution scheduling of the logistics supply chain is realized; the system can realize intelligent scheduling and optimization of logistics distribution through inventory monitoring, intelligent screening of supply chain merchants and data analysis of logistics vehicles, realize rapid response to market demand, improve the stability and reliability of the supply chain, improve the efficiency and accuracy of logistics distribution, reduce supply chain risks, reduce the cost of logistics distribution, improve the economic benefits of logistics distribution, improve user satisfaction, and enhance the competitiveness of enterprises in the market.

[0130] The above formulas are all obtained by collecting a large amount of data and performing software simulation, and a formula close to the actual value is selected. The coefficients in the formula are set by technicians in this field according to actual conditions.

[0131] 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.

[0132] The above contents are merely examples and explanations of the present invention. Those skilled in the art 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 invention or exceed the scope defined in this application, they shall all fall within the protection scope of the present invention.

Claims

1. The intelligent distribution and scheduling system for logistics supply chain based on data analysis is characterized by: include: The supply monitoring module is used to obtain the supply optimization parameters of the supply object j after receiving the replenishment notification instruction, and send the supply optimization parameters to the data analysis module; wherein the supply optimization parameters include supply price information GJ, transportation information YS, supply information GH and scoring information PF; A data analysis module is used to obtain a supply optimization coefficient GYj according to supply optimization parameters, and send the supply optimization coefficient GYj to the analysis and judgment module; The specific process of the data analysis module obtaining the supply optimization coefficient GYj is as follows: The values ​​of the supply price information GJ, the transportation information YS, the supply information GH and the scoring information PF are quantified according to the preset supply optimization analysis model to obtain the supply optimization coefficient GYj; Among them, the supply optimization analysis model is as follows: Where: λ is the preset error adjustment factor; κ1, κ2, κ3 and κ4 are respectively the preset weight factors corresponding to the set supply price information GJ, transportation information YS, supply information GH and scoring information PF; The analysis and judgment module is used to obtain the distribution scheduling supply object according to the supply optimization coefficient GYj, and send the distribution scheduling supply object to the distribution scheduling module; The delivery scheduling module is used to match the delivery scheduling supply objects and the delivery scheduling logistics objects, and send the delivery scheduling supply objects to the mobile phone terminals of the drivers matched with the delivery scheduling logistics objects.

2. The intelligent distribution and scheduling system for logistics supply chain based on data analysis according to claim 1 is characterized in that: The specific process of the supply monitoring module obtaining the supply optimization parameters is as follows: After receiving the replenishment notification instruction, obtain all suppliers of monitored item i and mark them as supply objects j in sequence, where j = 1, ..., u, u is a positive integer, j is the number of any supply object, and u is the total number of supply objects; Obtain the supply price of the supplier j for the monitored item i, and mark it as the supply price information GJ; Get the location of the supply object j and the location of the user's warehouse, obtain the preset logistics route between the two, obtain the distance and average transportation cost of the logistics route, and mark them as the distance value LJ and the transportation cost value YB respectively. Quantify the distance value LJ and the transportation cost value YB according to the formula The transportation information YS is obtained; wherein, e is a mathematical constant, s1 and s2 are preset proportional coefficients corresponding to the set road distance value LJ and the transportation cost value YB respectively; Obtain the total number of supply customers of the supply object j for the monitored item i, and mark it as the customer value KS; obtain the total number of the monitored item i supplied by the supply object j per unit time, and mark it as the supply value GS; quantify the customer value KS and the supply value GS, and obtain the supply information GH according to the formula GH=g1×KS+g2×GS; wherein g1 and g2 are the preset proportional coefficients corresponding to the set customer value KS and the supply value GS respectively; Obtain the ratings of the supplier j by all the supply customers of the supplier j, obtain the average of all ratings within three days, thirty days and three hundred and sixty-five days at the current moment, and mark them as the first rating value YP, the second rating value EP and the third rating value SP respectively, quantify the first rating value YP, the second rating value EP and the third rating value SP, and obtain the rating information PF according to the formula PF=p1×YP+p2×EP+p3×SP; wherein p1, p2 and p3 are the preset proportional coefficients corresponding to the set first rating value YP, the second rating value EP and the third rating value SP respectively; The price information GJ, transportation information YS, supply information GH and rating information PF are sent to the data analysis module.

3. The intelligent distribution and scheduling system for logistics supply chain based on data analysis according to claim 1 is characterized in that: The specific process of the analysis and judgment module obtaining the distribution scheduling supply object is as follows: All supply objects j are sorted in descending order according to the supply preference coefficient GYj, and the supply object j at the first position is marked as the distribution scheduling supply object, and the distribution scheduling supply object is sent to the distribution scheduling module.

4. The intelligent distribution and scheduling system for logistics supply chain based on data analysis according to claim 1 is characterized in that: Also includes: The inventory monitoring module is used to generate a replenishment notification instruction according to the inventory remaining value KYi and the inventory remaining monitoring value KJi of the monitored item i, and send the replenishment notification instruction to the supply monitoring module.

5. The intelligent distribution and scheduling system for logistics supply chain based on data analysis according to claim 4 is characterized in that: The specific process of the inventory monitoring module generating a replenishment notification instruction is as follows: Get all items in the user's inventory and mark them as monitored items i, i = 1, ..., v, v is a positive integer, i is the number of any monitored item, and v is the total number of monitored items; Get the remaining inventory quantity of monitored item i and mark it as the remaining inventory value KYi; get the preset remaining inventory monitoring quantity of monitored item i and mark it as the remaining inventory monitoring value KJi; Compare the remaining inventory value KYi with the remaining inventory monitoring value KJi. The comparison results are as follows: If the inventory balance value KYi≤the inventory balance monitoring value KJi, a replenishment notification instruction is generated and sent to the supply monitoring module.

6. The intelligent distribution and scheduling system for logistics supply chain based on data analysis according to claim 1 is characterized in that: The distribution scheduling module is also used to obtain the logistics optimization parameters of the pre-selected logistics vehicle o, and send the logistics optimization parameters to the data analysis module; wherein the logistics optimization parameters include usage information SY, maintenance information WX and experience information JY.

7. The intelligent distribution and scheduling system for logistics supply chain based on data analysis according to claim 6 is characterized in that: The specific process of the distribution scheduling module obtaining logistics optimization parameters is as follows: Obtain all idle logistics vehicles and mark them as pre-selected logistics vehicles o in sequence, where o=1, ..., n, where n is a positive integer, o is the number of any pre-selected logistics vehicle, and n is the total number of pre-selected logistics vehicles; The mileage and total number of starts of the pre-selected logistics vehicle o are obtained, and they are marked as the mileage value LC and the start value QD, respectively. A diamond shape is drawn with the values ​​of the mileage value LC and the start value QD as the two diagonal lines of the diamond shape, and the area of ​​the diamond shape is obtained and marked as the usage information SY. Obtain the total number of maintenance times of the pre-selected logistics vehicle o and mark it as maintenance information WX; Obtain the driving experience and working experience of the driver matched with the pre-selected logistics vehicle o, and mark them as driving experience value JL and working experience value GL respectively, quantify the driving experience value JL and working experience value GL, and obtain the experience information JY according to the formula JY=y1×JL+y2×GL; where y1 and y2 are the preset proportional coefficients corresponding to the set driving experience value JL and working experience value GL respectively; The usage information SY, maintenance information WX and experience information JY are sent to the data analysis module.

8. The intelligent distribution and scheduling system for logistics supply chain based on data analysis according to claim 1 is characterized in that: The data analysis module is also used to obtain a logistics optimization coefficient WYo according to the logistics optimization parameters, and send the logistics optimization coefficient WYo to the analysis and judgment module.

9. The intelligent distribution and scheduling system for logistics supply chain based on data analysis according to claim 8 is characterized in that: The specific process of the data analysis module obtaining the logistics optimization coefficient WYo is as follows: The values ​​of the usage information SY, maintenance information WX and experience information JY are quantified according to the preset logistics optimization analysis model to obtain the logistics optimization coefficient WYo; Among them, the supply optimization analysis model is as follows: Where: δ is the preset error adjustment factor; π and e are both mathematical constants; α1, α2 and α3 are preset weight factors corresponding to the set usage information SY, maintenance information WX and experience information JY respectively; The logistics optimization coefficient WYo is sent to the analysis and judgment module.

10. The intelligent distribution and scheduling system for logistics supply chain based on data analysis according to claim 1 is characterized in that: The analysis and judgment module is also used to obtain the distribution scheduling logistics object according to the logistics optimization coefficient WYo, and send the distribution scheduling logistics object to the distribution scheduling module; The specific process of the analysis and judgment module obtaining the distribution scheduling logistics object is as follows: All pre-selected logistics vehicles o are sorted in descending order according to the logistics optimization coefficient WYo, and the pre-selected logistics vehicle o at the first place is marked as a distribution scheduling logistics object, and the distribution scheduling logistics object is sent to the distribution scheduling module.

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