CN111415044A - Logistics distribution vehicle scheduling system and method based on big data - Google Patents
Logistics distribution vehicle scheduling system and method based on big data Download PDFInfo
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Abstract
The invention discloses a logistics distribution vehicle scheduling system and method based on big data, wherein the vehicle scheduling system comprises a logistics distribution vehicle basic information acquisition module, a goods condition estimation module and a scheduling condition judgment module, the logistics distribution vehicle basic information acquisition module is used for acquiring the number of stations on a conventional distribution route of a logistics distribution vehicle and the maximum load-bearing goods amount of the logistics distribution vehicle, the goods condition estimation module estimates the distribution goods condition and the consignee condition of a current time period according to the historical distribution goods condition and the historical consignee condition of each station, and the scheduling condition judgment module determines whether to schedule the vehicle according to the estimated distribution goods condition and the consignee condition of the current time period.
Description
Technical Field
The invention relates to the field of big data, in particular to a logistics distribution vehicle scheduling system and method based on big data.
Background
Due to the rapid development of the internet and traffic, online shopping is more and more popular with people, and meanwhile, the express industry is rapidly developed. In order to further increase the sales volume of online shopping, merchants often hold large-scale sales promotion activities, such as "618" and "twenty-one", and when the sales promotion activities are held, the number of the express items is increased rapidly, and the express items are often exploded, so that the express items are retained or the express items reach the destination for a long time.
Disclosure of Invention
The invention aims to provide a logistics distribution vehicle dispatching system and method based on big data, and aims to solve the problems in the prior art.
In order to achieve the purpose, the invention provides the following technical scheme:
a logistics distribution vehicle dispatching system based on big data comprises a logistics distribution vehicle basic information obtaining module, a goods condition estimating module and a dispatching condition judging module, wherein the logistics distribution vehicle basic information obtaining module is used for obtaining the number of stations on a conventional distribution route of a logistics distribution vehicle and the maximum load-bearing goods amount of the logistics distribution vehicle, the goods condition estimating module estimates the distribution goods condition and the receiving goods condition of a current time period according to the historical distribution goods condition and the historical receiving goods condition of each station, and the dispatching condition judging module determines whether to dispatch the vehicle according to the estimated distribution goods condition and the receiving goods condition of the current time period.
Preferably, the estimated cargo condition includes a historical cargo condition collection module, a network promotion judgment module, an increase calculation module, a first cargo quantity calculation module and a second cargo quantity acquisition module, the historical cargo condition collection module is used for collecting the average delivered cargo quantity j and the received cargo quantity k of each station every day in the month before the current time period, the average delivered cargo quantity jb and the received cargo quantity kb of each station every day in the month before the same year, the network promotion judgment module is used for judging whether the current time period is in the network promotion stage and acquiring the average delivered cargo quantity pb and the average received cargo quantity qb of each station in the network promotion stage in the same year when the current time period is in the network promotion stage, the increase calculation module calculates the delivered cargo increase a according to the average delivered cargo quantity j and the average delivered cargo quantity jb, the method comprises the steps that an consignee quantity increase amount b is calculated according to a consignee quantity k and the consignee quantity kb, a first delivery cargo quantity p1 is calculated by a first cargo quantity calculation module according to an average delivery cargo quantity pb and a delivery cargo increase amount a, and a first consignee quantity q1 is calculated according to an average consignee quantity qb and the consignee quantity increase amount b; the second cargo quantity obtaining module comprises a household registration permanent population counting module, a permanent population influence factor calculating module, a geographic position obtaining module, a factory influence factor calculating module, a student influence factor calculating module, a cargo total quantity counting module, a percentage calculating module, a distributed cargo quantity floating factor calculating module, a second distributed cargo quantity calculating module and a second received cargo quantity calculating module, the household registration permanent population counting module is used for counting the household registration permanent population condition of each station distribution area, the permanent population influence factor calculating module outputs the permanent population influence factor x according to the proportion of the household registration permanent population from 18 years old to 40 years old in the statistical result of the household registration permanent population counting module, the geographic position obtaining module is used for obtaining the geographic position disease of each station to judge whether the distribution area of the station is additionally provided with a factory or university school in the next year, the factory influence factor calculation module outputs a factory influence factor y1 according to the situation of a newly added factory and the situation of the total number of workers of the newly added factory, the student influence factor calculation module outputs a student influence factor y2 according to the situation of a newly added school and whether the current time period is in a cold or hot holiday situation, the total cargo quantity counting module is used for counting the total cargo quantity of each station in the last year, and the percentage calculation module outputs the percentage t of the total cargo quantity of each station in the sum of the total cargo quantities of n stations according to the counting result of the total cargo quantity counting module; the distribution cargo amount floating factor calculating module calculates a distribution cargo amount floating factor z according to a standing population influence factor x, a factory influence factor y1, a student influence factor y2 and a percentage t, the second distribution cargo amount calculating module calculates the estimated sum of the second distribution cargo amount according to the first distribution cargo amount p1 and the distribution cargo amount floating factor z, and the second received cargo amount calculating module calculates the estimated sum of the second received cargo amount according to the first received cargo amount q1, the percentage t and the factory influence factor y 1.
Preferably, the scheduling condition judging module comprises a vehicle position obtaining module, an actual cargo quantity counting module, a cargo quantity comparing module and a scheduling result output module, wherein the vehicle position obtaining module is used for obtaining the current station position of the logistics distribution vehicle, the actual cargo quantity counting module is used for counting the sum of the actual distribution cargo quantity and the sum of the actual consignee quantity of the current logistics distribution vehicle, and the cargo quantity comparing module is used for transmitting information to the scheduling result output module to output whether the logistics distribution vehicle needs to be scheduled for support or not according to the relation between the sum of the actual distribution cargo quantity and the estimated sum of the second distribution cargo quantity and the estimated sum of the actual consignee quantity and the estimated sum of the second consignee quantity.
A logistics distribution vehicle scheduling method based on big data comprises the following steps:
step S1: acquiring the number n of stations on a conventional distribution route of the logistics distribution vehicle, and the maximum load capacity m of the logistics distribution vehicle;
step S2: estimating the goods delivery condition and the goods receiving condition of the current time period according to the historical goods delivery condition and the historical goods receiving condition of each site;
step S3: and determining whether to dispatch the vehicle according to the estimated goods delivery condition and goods receiving condition of the current time period.
Preferably, the step S2 further includes:
step S21: acquiring an average delivery cargo quantity j and an average consignee quantity k of each station every day in the month before the current time period, acquiring an average delivery cargo quantity jb and an average consignee quantity kb of each station every day in the month before the same year, respectively calculating a delivery cargo increase amount a = (j-jb)/jb and a consignee quantity increase amount b = (k-kb)/kb of each station,
step S22: judging whether the current time period is in a network promotion stage, if the current time period is in the network promotion stage, obtaining the average delivery cargo quantity pb and the receiving cargo quantity qb of each station in the network promotion time period in the same period of the last year, then the first delivery cargo quantity p1= pb (1+ a) and the first receiving cargo quantity q1= qb (1+ b) in the current time period,
step S23: and calculating a second goods distribution amount and a second goods receiving amount according to the household registration population condition and the geographical position condition of each station.
Preferably, the step S23 further includes:
counting the permanent resident population condition of each site distribution area, wherein if the permanent resident population with the age of 18-40 in the site distribution area accounts for more than forty percent of the total permanent resident population, the permanent resident population influence factor x =10%, otherwise, the permanent resident population influence factor x = 0;
acquiring the geographical position of each station, judging whether a distribution area of the station is newly provided with a factory or a college school in the last year, if the distribution area of the station is newly provided with a factory in the last year and the total number of workers of the newly provided factory is greater than or equal to 5000, then the factory influence factor y1=10%, otherwise the factory influence factor y1=5%, if the distribution area of the station is newly provided with a college school in the last year, judging whether the current time period is in a cold holiday period or a summer holiday period, if the current time period is in the cold holiday period or the summer holiday period, then the student influence factor y2=0, and if the current time period is not in the cold holiday period or the summer holiday period, then the student influence factor y2= 10%;
counting the total goods of each station in the last year, wherein the total goods is the sum of the delivered goods amount and the received goods amount, and the total goods of each station accounts for the percentage t of the sum of the total goods of the n stations;
the delivery cargo amount floating factor z = t (x + y1+ y2), the second delivery cargo amount p2= p1 (1+ z) of each station for the current time period, and the second pickup amount q2= q1 (1+ t y 1) of each station are calculated.
Preferably, the step S3 includes:
respectively calculating the estimated sum of the second cargo delivery quantity of all the stations
The estimated sum of the second consignee quantity
、
Wherein i denotes the ith site, p2iQ2 indicating the second quantity of goods delivered at the ith stationiIndicating a second addressee at the ith siteThe amount of cargo. .
Preferably, the step S3 further includes:
acquiring the current station position of the logistics distribution vehicle, setting the logistics distribution vehicle to be positioned at the h station, and counting the sum ES of the actual cargo distribution quantity of the previous h stationshThe sum FS of the actual received goods amounthComparison of EShAnd E2, FShAnd the relationship between the first and second signals and F2,
when EShH is E2/m or more and FShLess than or equal to h x F2/m, determining that the logistics distribution vehicle temporarily does not need the assistance of the dispatching logistics distribution vehicle,
when EShLess than h × E2/m and FShAnd h is larger than F2/m, and the sum ES of the actual distributed cargo quantities of the previous h +1 stations is counted when the logistics distribution vehicle drives to the next stationh+1The sum FS of the actual received goods amounth+1Comparison of ESh+1And E2, FSh+1Relation to F2 if ESh+1Less than (h +1) E2/m and FShAnd if the vehicle speed is greater than (h +1) × F2/m, determining that the logistics distribution vehicle needs the support of the dispatching vehicle, otherwise determining that the logistics distribution vehicle temporarily does not need the support of the dispatching logistics distribution vehicle.
Compared with the prior art, the invention has the beneficial effects that: according to the invention, the delivery cargo quantity and the received cargo quantity are estimated according to the historical cargo condition, the cargo condition in the historical network sales promotion time period, the household and resident population condition of each site and the condition of a newly added factory or college school in the delivery area of each site, the actual delivery cargo quantity and the actual received cargo quantity of each site are compared with the estimated delivery cargo quantity and received cargo quantity in real time, and whether the vehicle needs to be scheduled or not is judged, so that the vehicle is scheduled in time, the logistics circulation rate is accelerated, the express use experience of a user is improved, and the probability of bin explosion is reduced.
Drawings
FIG. 1 is a block diagram of a big data based logistics distribution vehicle dispatching system of the present invention;
fig. 2 is a schematic flow chart of a logistics distribution vehicle scheduling method based on big data according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1-2, in the embodiment of the invention, a logistics distribution vehicle scheduling system and method based on big data are provided, the vehicle scheduling system includes a logistics distribution vehicle basic information obtaining module, a cargo condition estimating module and a scheduling condition judging module, the logistics distribution vehicle basic information obtaining module is used for obtaining the number of stations on a conventional distribution route of a logistics distribution vehicle and the maximum cargo carrying amount of the logistics distribution vehicle, the cargo condition estimating module estimates the distribution cargo condition and the consignee condition of a current time period according to the historical distribution cargo condition and the historical consignee condition of each station, and the scheduling condition judging module determines whether to schedule the vehicle according to the estimated distribution cargo condition and the consignee condition of the current time period.
The goods condition estimation condition comprises a historical goods condition acquisition module, a network promotion judgment module, an increase amount calculation module, a first goods amount calculation module and a second goods amount acquisition module, wherein the historical goods condition acquisition module is used for acquiring the average delivery goods amount j and the consignee amount k of each station every day in the month before the current time period, the average delivery goods amount jb and the consignee amount kb of each station every day in the month before the same year, the network promotion judgment module is used for judging whether the current time period is in a network promotion stage and acquiring the average delivery goods amount pb and the average consignee amount qb of each station in the network promotion stage in the same year under the condition that the current time period is in the network promotion stage, and the increase amount calculation module calculates the delivery goods increase amount a according to the average delivery goods amount j and the average delivery goods amount jb, the method comprises the steps that an consignee quantity increase amount b is calculated according to a consignee quantity k and the consignee quantity kb, a first delivery cargo quantity p1 is calculated by a first cargo quantity calculation module according to an average delivery cargo quantity pb and a delivery cargo increase amount a, and a first consignee quantity q1 is calculated according to an average consignee quantity qb and the consignee quantity increase amount b; the second cargo quantity obtaining module comprises a household registration permanent population counting module, a permanent population influence factor calculating module, a geographic position obtaining module, a factory influence factor calculating module, a student influence factor calculating module, a cargo total quantity counting module, a percentage calculating module, a distributed cargo quantity floating factor calculating module, a second distributed cargo quantity calculating module and a second received cargo quantity calculating module, the household registration permanent population counting module is used for counting the household registration permanent population condition of each distribution area of each site, the permanent population influence factor calculating module outputs the permanent population influence factor x according to the proportion of the household registration permanent population from 18 years old to 40 years old in the statistical result of the household registration permanent population counting module, the geographic position obtaining module is used for obtaining the geographic position disease of each site and judging whether the distribution area of the site is additionally provided with a factory or university school in the next year or not, the factory influence factor calculation module outputs a factory influence factor y1 according to the situation of a newly added factory and the situation of the total number of workers of the newly added factory, the student influence factor calculation module outputs a student influence factor y2 according to the situation of a newly added school and whether the current time period is in a cold or hot holiday situation, the total cargo quantity counting module is used for counting the total cargo quantity of each station in the last year, and the percentage calculation module outputs the percentage t of the total cargo quantity of each station in the sum of the total cargo quantities of n stations according to the counting result of the total cargo quantity counting module; the distribution cargo amount floating factor calculating module calculates a distribution cargo amount floating factor z according to a standing population influence factor x, a factory influence factor y1, a student influence factor y2 and a percentage t, the second distribution cargo amount calculating module calculates the estimated sum of the second distribution cargo amount according to the first distribution cargo amount p1 and the distribution cargo amount floating factor z, and the second received cargo amount calculating module calculates the estimated sum of the second received cargo amount according to the first received cargo amount q1, the percentage t and the factory influence factor y 1.
The dispatching condition judging module comprises a vehicle position obtaining module, an actual cargo quantity counting module, a cargo quantity comparing module and a dispatching result output module, wherein the vehicle position obtaining module is used for obtaining the current station position of the logistics distribution vehicle, the actual cargo quantity counting module is used for counting the sum of the actual distribution cargo quantity and the sum of the actual consignee quantity of the current logistics distribution vehicle, and the cargo quantity comparing module is used for transmitting information to the dispatching result output module to output whether the logistics distribution vehicle needs to be dispatched for support or not according to the relation between the sum of the actual distribution cargo quantity and the estimated sum of the second distribution cargo quantity and the estimated sum of the actual consignee quantity and the estimated sum of the second consignee quantity.
A logistics distribution vehicle scheduling method based on big data comprises the following steps:
step S1: acquiring the number n of stations on a conventional distribution route of the logistics distribution vehicle, and the maximum load capacity m of the logistics distribution vehicle;
step S2: estimating the goods delivery condition and the goods receiving condition of the current time period according to the historical goods delivery condition and the historical goods receiving condition of each site;
step S21: acquiring an average delivery cargo quantity j and an average consignee quantity k of each station every day in the month before the current time period, acquiring an average delivery cargo quantity jb and an average consignee quantity kb of each station every day in the month before the same year, respectively calculating a delivery cargo increase amount a = (j-jb)/jb and a consignee quantity increase amount b = (k-kb)/kb of each station,
step S22: judging whether the current time period is in a network promotion stage, if the current time period is in the network promotion stage, obtaining the average delivery cargo quantity pb and the receiving cargo quantity qb of each station in the network promotion time period in the same period of the last year, then the first delivery cargo quantity p1= pb (1+ a) and the first receiving cargo quantity q1= qb (1+ b) in the current time period,
step S23: and calculating a second goods distribution amount and a second goods receiving amount according to the household registration population condition and the geographical position condition of each station.
Counting the permanent resident population condition of each site distribution area, wherein if the permanent resident population with the age of 18-40 in the site distribution area accounts for more than forty percent of the total permanent resident population, the permanent resident population influence factor x =10%, otherwise, the permanent resident population influence factor x = 0;
acquiring the geographical position of each station, judging whether a distribution area of the station is newly provided with a factory or a college school in the last year, if the distribution area of the station is newly provided with a factory in the last year and the total number of workers of the newly provided factory is greater than or equal to 5000, then the factory influence factor y1=10%, otherwise the factory influence factor y1=5%, if the distribution area of the station is newly provided with a college school in the last year, judging whether the current time period is in a cold holiday period or a summer holiday period, if the current time period is in the cold holiday period or the summer holiday period, then the student influence factor y2=0, and if the current time period is not in the cold holiday period or the summer holiday period, then the student influence factor y2= 10%;
counting the total goods of each station in the last year, wherein the total goods is the sum of the delivered goods amount and the received goods amount, and the total goods of each station accounts for the percentage t of the sum of the total goods of the n stations;
calculating a delivery cargo amount floating factor z = t (x + y1+ y2), a second delivery cargo amount p2= p1 (1+ z) of each station for the current time period, and a second pickup cargo amount q2= q1 (1+ t y 1) of each station;
step S3: determining whether to dispatch the vehicle according to the estimated delivered goods condition and the received goods condition of the current time period:
respectively calculating the estimated sum of the second cargo delivery quantity of all the stations
The estimated sum of the second consignee quantity
、
Wherein i denotes the ith site, p2iQ2 indicating the second quantity of goods delivered at the ith stationiIndicating the second consignee amount of the ith station;
acquiring the current station position of the logistics distribution vehicle, setting the logistics distribution vehicle to be positioned at the h station, and counting the sum ES of the actual cargo distribution quantity of the previous h stationshThe sum FS of the actual received goods amounthComparison of EShAnd E2, FShAnd the relationship between the first and second signals and F2,
when EShH is E2/m or more and FShLess than or equal to h x F2/m, determining that the logistics distribution vehicle temporarily does not need the assistance of the dispatching logistics distribution vehicle,
when EShLess than h × E2/m and FShAnd h is larger than F2/m, and the sum ES of the actual distributed cargo quantities of the previous h +1 stations is counted when the logistics distribution vehicle drives to the next stationh+1The sum FS of the actual received goods amounth+1Comparison of ESh+1And E2, FSh+1Relation to F2 if ESh+1Less than (h +1) E2/m and FShAnd if the vehicle speed is greater than (h +1) × F2/m, determining that the logistics distribution vehicle needs the support of the dispatching vehicle, otherwise determining that the logistics distribution vehicle temporarily does not need the support of the dispatching logistics distribution vehicle.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Claims (8)
1. The logistics distribution vehicle dispatching system based on big data is characterized in that: the vehicle scheduling system comprises a logistics distribution vehicle basic information acquisition module, a cargo condition estimation module and a scheduling condition judgment module, wherein the logistics distribution vehicle basic information acquisition module is used for acquiring the number of stations on a conventional distribution route of a logistics distribution vehicle and the maximum cargo carrying amount of the logistics distribution vehicle, the cargo condition estimation module estimates the cargo distribution condition and the cargo receiving condition of a current time period according to the historical cargo distribution condition and the historical cargo receiving condition of each station, and the scheduling condition judgment module determines whether to schedule the vehicle according to the estimated cargo distribution condition and cargo receiving condition of the current time period.
2. The logistics distribution vehicle dispatching system based on big data as claimed in claim 1, wherein: the goods condition estimation condition comprises a historical goods condition acquisition module, a network promotion judgment module, an increase amount calculation module, a first goods amount calculation module and a second goods amount acquisition module, wherein the historical goods condition acquisition module is used for acquiring the average delivery goods amount j and the consignee amount k of each station every day in the month before the current time period, the average delivery goods amount jb and the consignee amount kb of each station every day in the month before the same year, the network promotion judgment module is used for judging whether the current time period is in a network promotion stage and acquiring the average delivery goods amount pb and the average consignee amount qb of each station in the network promotion stage in the same year under the condition that the current time period is in the network promotion stage, and the increase amount calculation module calculates the delivery goods increase amount a according to the average delivery goods amount j and the average delivery goods amount jb, the method comprises the steps that an consignee quantity increase amount b is calculated according to a consignee quantity k and the consignee quantity kb, a first delivery cargo quantity p1 is calculated by a first cargo quantity calculation module according to an average delivery cargo quantity pb and a delivery cargo increase amount a, and a first consignee quantity q1 is calculated according to an average consignee quantity qb and the consignee quantity increase amount b; the second cargo quantity obtaining module comprises a household registration permanent population counting module, a permanent population influence factor calculating module, a geographic position obtaining module, a factory influence factor calculating module, a student influence factor calculating module, a cargo total quantity counting module, a percentage calculating module, a distributed cargo quantity floating factor calculating module, a second distributed cargo quantity calculating module and a second received cargo quantity calculating module, the household registration permanent population counting module is used for counting the household registration permanent population condition of each distribution area of each site, the permanent population influence factor calculating module outputs the permanent population influence factor x according to the proportion of the household registration permanent population from 18 years old to 40 years old in the statistical result of the household registration permanent population counting module, the geographic position obtaining module is used for obtaining the geographic position disease of each site and judging whether the distribution area of the site is additionally provided with a factory or university school in the next year or not, the factory influence factor calculation module outputs a factory influence factor y1 according to the situation of a newly added factory and the situation of the total number of workers of the newly added factory, the student influence factor calculation module outputs a student influence factor y2 according to the situation of a newly added school and whether the current time period is in a cold or hot holiday situation, the total cargo quantity counting module is used for counting the total cargo quantity of each station in the last year, and the percentage calculation module outputs the percentage t of the total cargo quantity of each station in the sum of the total cargo quantities of n stations according to the counting result of the total cargo quantity counting module; the distribution cargo amount floating factor calculating module calculates a distribution cargo amount floating factor z according to a standing population influence factor x, a factory influence factor y1, a student influence factor y2 and a percentage t, the second distribution cargo amount calculating module calculates the estimated sum of the second distribution cargo amount according to the first distribution cargo amount p1 and the distribution cargo amount floating factor z, and the second received cargo amount calculating module calculates the estimated sum of the second received cargo amount according to the first received cargo amount q1, the percentage t and the factory influence factor y 1.
3. The logistics distribution vehicle dispatching system based on big data as claimed in claim 2, wherein: the dispatching condition judging module comprises a vehicle position obtaining module, an actual cargo quantity counting module, a cargo quantity comparing module and a dispatching result output module, wherein the vehicle position obtaining module is used for obtaining the current station position of the logistics distribution vehicle, the actual cargo quantity counting module is used for counting the sum of the actual distribution cargo quantity and the sum of the actual consignee quantity of the current logistics distribution vehicle, and the cargo quantity comparing module is used for transmitting information to the dispatching result output module to output whether the logistics distribution vehicle needs to be dispatched for support or not according to the relation between the sum of the actual distribution cargo quantity and the estimated sum of the second distribution cargo quantity and the estimated sum of the actual consignee quantity and the estimated sum of the second consignee quantity.
4. A logistics distribution vehicle scheduling method based on big data is characterized in that: the vehicle dispatching method comprises the following steps:
step S1: acquiring the number n of stations on a conventional distribution route of the logistics distribution vehicle, and the maximum load capacity m of the logistics distribution vehicle;
step S2: estimating the goods delivery condition and the goods receiving condition of the current time period according to the historical goods delivery condition and the historical goods receiving condition of each site;
step S3: and determining whether to dispatch the vehicle according to the estimated goods delivery condition and goods receiving condition of the current time period.
5. The logistics distribution vehicle scheduling method based on big data as claimed in claim 4, wherein: the step S2 further includes:
step S21: acquiring an average delivery cargo quantity j and an average consignee quantity k of each station every day in the month before the current time period, acquiring an average delivery cargo quantity jb and an average consignee quantity kb of each station every day in the month before the same year, respectively calculating a delivery cargo increase amount a = (j-jb)/jb and a consignee quantity increase amount b = (k-kb)/kb of each station,
step S22: judging whether the current time period is in a network promotion stage, if the current time period is in the network promotion stage, obtaining the average delivery cargo quantity pb and the receiving cargo quantity qb of each station in the network promotion time period in the same period of the last year, then the first delivery cargo quantity p1= pb (1+ a) and the first receiving cargo quantity q1= qb (1+ b) in the current time period,
step S23: and calculating a second goods distribution amount and a second goods receiving amount according to the household registration population condition and the geographical position condition of each station.
6. The logistics distribution vehicle scheduling method based on big data as claimed in claim 5, wherein: the step S23 further includes:
counting the permanent resident population condition of each site distribution area, wherein if the permanent resident population with the age of 18-40 in the site distribution area accounts for more than forty percent of the total permanent resident population, the permanent resident population influence factor x =10%, otherwise, the permanent resident population influence factor x = 0;
acquiring the geographical position of each station, judging whether a distribution area of the station is newly provided with a factory or a college school in the last year, if the distribution area of the station is newly provided with a factory in the last year and the total number of workers of the newly provided factory is greater than or equal to 5000, then the factory influence factor y1=10%, otherwise the factory influence factor y1=5%, if the distribution area of the station is newly provided with a college school in the last year, judging whether the current time period is in a cold holiday period or a summer holiday period, if the current time period is in the cold holiday period or the summer holiday period, then the student influence factor y2=0, and if the current time period is not in the cold holiday period or the summer holiday period, then the student influence factor y2= 10%;
counting the total goods of each station in the last year, wherein the total goods is the sum of the delivered goods amount and the received goods amount, and the total goods of each station accounts for the percentage t of the sum of the total goods of the n stations;
the delivery cargo amount floating factor z = t (x + y1+ y2), the second delivery cargo amount p2= p1 (1+ z) of each station for the current time period, and the second pickup amount q2= q1 (1+ t y 1) of each station are calculated.
7. The logistics distribution vehicle scheduling method based on big data as claimed in claim 6, wherein: the step S3 includes:
respectively calculating the estimated sum of the second cargo delivery quantity of all the stations
The estimated sum of the second consignee quantity
；
Wherein i denotes the ith site, p2iQ2 indicating the second quantity of goods delivered at the ith stationiIndicating the second consignee amount at the ith site.
8. The logistics distribution vehicle scheduling method based on big data as claimed in claim 7, wherein: the step S3 further includes:
acquiring the current station position of the logistics distribution vehicle, setting the logistics distribution vehicle to be positioned at the h station, and counting the sum ES of the actual cargo distribution quantity of the previous h stationshThe sum FS of the actual received goods amounthComparison of EShAnd E2, FShAnd the relationship between the first and second signals and F2,
when EShH is E2/m or more and FShLess than or equal to h x F2/m, determining that the logistics distribution vehicle temporarily does not need the assistance of the dispatching logistics distribution vehicle,
when EShLess than h × E2/m and FShAnd h is larger than F2/m, and the sum ES of the actual distributed cargo quantities of the previous h +1 stations is counted when the logistics distribution vehicle drives to the next stationh+1The sum FS of the actual received goods amounth+1Comparison of ESh+1And E2, FSh+1Relation to F2 if ESh+1Less than (h +1) E2/m and FShAnd if the vehicle speed is greater than (h +1) × F2/m, determining that the logistics distribution vehicle needs the support of the dispatching vehicle, otherwise determining that the logistics distribution vehicle temporarily does not need the support of the dispatching logistics distribution vehicle.
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