CN116703275A - Logistics vehicle scheduling method based on waybill data analysis - Google Patents
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Abstract
The invention relates to the technical field of logistics vehicle dispatching analysis, and particularly discloses a logistics vehicle dispatching method based on waybill data analysis, which comprises the steps of waybill data acquisition, vehicle information acquisition, driver information acquisition, available vehicle screening, vehicle bearing suitability analysis, vehicle health index analysis, driver experience coincidence analysis and vehicle dispatching analysis; according to the invention, the matching evaluation of each available vehicle is carried out from three layers of vehicle bearing, vehicle health and driver experience, so that the multi-dimensional matching evaluation of the available vehicles is realized, the accuracy and suitability of the confirmation of the transportation vehicles are improved, the reliability of the current logistics vehicle dispatching is ensured, the experience coincidence analysis is carried out by combining the transportation experience of each logistics vehicle corresponding to the driver, the coverage of the logistics vehicle dispatching analysis is improved, the rationality of the logistics vehicle dispatching analysis is further improved, and a reliable basis is provided for the confirmation of the follow-up suitable transportation vehicles.
Description
Technical Field
The invention relates to the technical field of logistics vehicle dispatching, in particular to a logistics vehicle dispatching method based on waybill data analysis.
Background
In the modern logistics industry, logistics vehicle dispatching is an important link for ensuring that goods can be timely and accurately delivered to a destination, and meanwhile, logistics vehicle dispatching is a key link for helping logistics enterprises to realize logistics efficiency and ensuring high-quality logistics service to the greatest extent, so that logistics vehicles are required to be dispatched and analyzed.
The existing logistics vehicle dispatching is mainly used for carrying out logistics vehicle dispatching analysis according to the basic information of the waybill data and the basic information of the logistics vehicles, and obviously, the dispatching analysis mode has the following problems: 1. and judging whether the goods to be conveyed can be loaded or not only according to the volume and the volume load capacity of the logistics vehicles at the vehicle selection level, and carrying out loading adaptation analysis is not carried out, so that the space resources of the logistics vehicles are not fully utilized, and the utilization rate of the space resources of the logistics vehicles is reduced.
2. The vehicle availability level is mainly based on subjective monitoring of personnel, and data analysis is not performed on the health condition of the vehicle, so that the evaluation result of the health condition of the vehicle has great error, risk hidden danger in the cargo transportation process is increased, and the life safety of drivers corresponding to all logistics vehicles is not guaranteed.
3. In the aspect of vehicle dispatching confirmation, the situation of a driver is not considered, namely, dispatching analysis of the logistics vehicles is not carried out by combining the transportation experience of drivers corresponding to all logistics vehicles, so that the coverage of dispatching analysis of the logistics vehicles is insufficient, the reliability and the rationality of dispatching analysis of the logistics vehicles are further reduced, and a reliable basis cannot be provided for confirmation of the follow-up suitable transportation vehicles.
Disclosure of Invention
In view of this, in order to solve the problems set forth in the background art, a logistics vehicle scheduling method based on waybill data analysis is now proposed.
The aim of the invention can be achieved by the following technical scheme: the invention provides a logistics vehicle dispatching method based on waybill data analysis, which comprises the following steps: s1, waybill data acquisition: the transportation path of the current waybill and the type, volume and weight of the current goods to be transported are collected.
S2, vehicle information acquisition: and collecting the transportation state, the additional load volume, the additional load capacity and the health information of the target logistics company corresponding to each logistics vehicle, and numbering each logistics vehicle.
S3, driver information acquisition, namely acquiring the total number of times of departure of the drivers corresponding to all logistics vehicles in the target logistics company, and the types and the transportation paths of transportation goods corresponding to all times of departure.
S4, screening by using a vehicle: and screening all the current available logistics vehicles from all the logistics vehicles corresponding to the target logistics company, taking the current available logistics vehicles as all the available vehicles, and extracting the numbers of all the available vehicles.
S5, vehicle bearing suitability analysis: analyzing the bearing fitness rho of each available vehicle according to the volume and the weight of the goods to be transported and the volume and the weight of each logistics vehicle corresponding to the target logistics company i Where i represents the available vehicle number, i=1, 2,..n.
S6, vehicle health index analysis: according to the health information of the target logistics company corresponding to each logistics vehicle, analyzing the health index of each available vehicle
S7, driver experience conformity analysis: analyzing the experience coincidence omega of drivers corresponding to each available vehicle i 。
S8, vehicle scheduling analysis: calculating a matching evaluation coefficient psi of each available vehicle according to the vehicle bearing suitability, the vehicle health index and the driver experience coincidence degree i And taking the available vehicle with the largest matching evaluation coefficient as the transportation vehicle of the current waybill.
Specifically, the health information includes a service life, a total number of repairs, a repair grade of each repair, a number of mileage traveled, vehicle body health information, and tire health information.
The maintenance level includes, among other things, a high level of difficulty and a general difficulty.
The vehicle body health information comprises the number of vehicle body concave positions, the concave volumes corresponding to the concave positions, the number of vehicle body paint dropping positions, the paint dropping area corresponding to the paint dropping positions, the number of vehicle body corrosion positions, the corrosion area corresponding to the corrosion positions, the number of vehicle body scratch positions and the scratch length corresponding to the scratch positions.
Wherein the tire health information includes a wear area and a wear thickness of each tire.
Specifically, the screening process of screening each currently available logistics vehicle from all logistics vehicles corresponding to the target logistics company includes: a1, screening out all logistics vehicles waiting for transportation according to the transportation state of all logistics vehicles of the target logistics company.
And A2, extracting the volume and the volume load capacity of each logistics vehicle waiting for transportation.
A3, correspondingly comparing the volume and the weight of the goods to be transported with the sum carrier and the sum load weight of each logistics vehicle waiting for transportation, if the volume and the weight of the goods to be transported are smaller than the sum carrier and the sum load weight of a logistics vehicle waiting for transportation, taking the logistics vehicle waiting for transportation as a logistics vehicle currently available, otherwise taking the logistics vehicle waiting for transportation as a logistics vehicle currently unavailable, and obtaining each available vehicle.
Specifically, the load-bearing suitability of each available vehicle is analyzed, and the specific analysis process is as follows: b1, respectively marking the volume and the weight of the goods to be transported at present as V Goods (e.g. a cargo) And beta Goods (e.g. a cargo) 。
B2, extracting the volume and the volume load capacity of each available vehicle and respectively marking asAnd->
B3, calculating the bearing fitness rho of each available vehicle i ，
Wherein DeltaV and Deltabeta respectively represent the volume difference and the weight difference of the set reference, a 1 And a 2 Respectively representing the set load-bearing fitness duty ratio weights corresponding to the volume difference and the weight difference, gamma 1 Indicating the set bearer suitability correction factor.
In particularThe health index of each available vehicle is analyzed, and the specific analysis process is as follows: c1, extracting the number of the vehicle body concave positions and the corresponding concave volumes of the concave positions from the vehicle body health information of each available vehicle, and respectively marking as M i And V i j Where j represents the recess number, j=1, 2,..m.
C2, calculating the sinking degree of the vehicle body of each available vehicle Wherein M 'and V' respectively represent the number of the set reference vehicle body concave positions and the concave volume, a 3 And a 4 The set number of the vehicle body concave positions and the corresponding vehicle body concave degree occupancy weight of the concave volume are respectively represented, and e represents a natural constant.
And C3, extracting the number of paint dropping positions of the vehicle body, the corresponding paint dropping area of the paint dropping positions of the vehicle body, the number of rust positions of the vehicle body, the corresponding rust area of the rust positions, the number of scratch positions of the vehicle body and the corresponding scratch lengths of the scratch positions of the vehicle body from the body health information of each available vehicle.
C4, calculating the paint dropping degree, the rust degree and the scratch degree of the vehicle body of each available vehicle according to the same calculation mode of the dent degree of the vehicle body of each available vehicle, and respectively marking asAnd->
C5, calculating the health index of the corresponding vehicle body layer of each available vehicle Wherein a is 5 、a 6 、a 7 And a 8 Respectively are provided withThe health index corresponding to the set car body dent degree, car body paint falling degree, car body rust degree and car body scratch degree is expressed as the weight of the ratio of the health index to the car body layer, gamma 2 The health index correction factor indicating the set vehicle body level.
C6, extracting the abrasion area and the abrasion thickness of each tire from the tire health information of each available vehicle, and calculating the health index of the corresponding tire layer of each available vehicle
And C7, extracting service life, total maintenance times, maintenance grades of each maintenance and the number of the mileage from the health information of each available vehicle.
C8, calculating the health index of each available vehicle corresponding to the use level
C9, calculating the health index of each available vehicle
Specifically, the health index of each available vehicle corresponding to the use level is calculated by the following steps: d1, the service life, the total maintenance times and the number of travelled mileage of each available vehicle are respectively marked as epsilon i 、Sum mu i 。
D2, counting the number of high-grade difficulty maintenance grades and general difficulty maintenance grades of each available vehicle, and respectively recording asAnd->
D3, calculating the health index of each available vehicle corresponding to the use level
Wherein ε ', μ'And DeltaM respectively represent the service life of the set reference, the number of the travelled mileage, the number of times of the higher difficulty maintenance level and the difference of the number of times of the maintenance level, b 1 、b 2 、b 3 And b 4 Respectively representing the set service life, the number of the travelled mileage, the number of times of the higher difficulty maintenance level and the health coincidence duty ratio weight of the corresponding use level with the number of times difference of the maintenance level, gamma 3 The health indicating the set usage level corresponds to the correction factor.
Specifically, the health index calculation formula of each available vehicle is as follows:wherein b 5 、b 6 And b 7 Respectively representing the set health coincidence duty ratio weights of the vehicle body layer, the tire layer and the using layer corresponding to the available vehicles 4 Indicating that the health of the set available vehicle meets the correction factor.
Specifically, the method analyzes the experience coincidence degree of the corresponding drivers of the available vehicles, and the specific analysis process is as follows: e1, recording the total number of the departure times of the drivers corresponding to all logistics vehicles in the target logistics company as
And E2, comparing the types of the transported goods corresponding to the various outgoing vehicles of the drivers corresponding to the available vehicles, taking the same type of the transported goods as the type of the integrated transported goods, and counting the number of the types of the integrated transported goods of the drivers corresponding to the available vehicles and the number of outgoing vehicles corresponding to the types of the integrated transported goods.
E3 from eachThe number of times of delivery of the goods category to be delivered can be screened out from the number of times of delivery of the goods category to be delivered corresponding to the driver of the vehicle
And E4, comparing the transport paths corresponding to the drivers of the available vehicles with each other, taking the same transport path as a comprehensive transport path, and counting the number of the comprehensive transport paths corresponding to the drivers of the available vehicles and the number of the departure times corresponding to the comprehensive transport paths.
And E5, marking the transport path of the current waybill as a target transport path, comparing the target transport path with the comprehensive transport paths of the drivers corresponding to the available vehicles, and calculating the coincidence degree of the comprehensive transport paths and the target transport paths in the drivers corresponding to the available vehicles.
E6, extracting the maximum coincidence degree theta of the comprehensive transportation path corresponding to the drivers corresponding to the available vehicles and the target transportation path i Number of departure times corresponding to comprehensive transportation path with maximum overlap ratio
E7, calculating the experience coincidence degree omega of the corresponding drivers of the available vehicles i ，
Wherein lambda is Seed species θ' and λ Transport and transport C, respectively representing the ratio of the number of times of departure, the maximum overlapping degree and the ratio of the number of times of departure of the transportation path of the goods with the set reference 1 、c 2 And c 3 Corresponding experience coincidence assessment duty ratio weight of the number of times of departure of the set goods category, maximum coincidence degree and number of times of departure of the transportation path 5 Experience representing the settings conforms to the evaluation correction factor.
Specifically, the calculation formula of the matching evaluation coefficient of each available vehicle is as follows:wherein d 1 、d 2 And d 3 Matching evaluation coincidence duty ratio weight gamma of available vehicles corresponding to the set bearing suitability, health index and driver experience coincidence degree of available vehicles 6 The matching evaluation indicating the set available vehicles conforms to the correction factor.
Compared with the prior art, the embodiment of the invention has at least the following advantages or beneficial effects: (1) According to the invention, the matching evaluation of each available vehicle is carried out from three layers of vehicle bearing, vehicle health and driver experience, so that the multi-dimensional matching evaluation of the available vehicles is realized, the problem of limitation in the current logistics vehicle dispatching analysis is effectively solved, the accuracy and suitability of the transportation vehicle confirmation are improved, and the reliability of the current logistics vehicle dispatching is ensured.
(2) According to the invention, the carrying suitability analysis of each available vehicle is carried out by combining the volume and the weight of the goods to be transported and the carrying volume and carrying capacity of each logistics vehicle, so that the space resources of each logistics vehicle are more fully utilized, and the utilization rate of the space resources of each logistics vehicle is further improved.
(3) According to the invention, the health index analysis of each available vehicle is carried out from the three layers of the vehicle body layer, the tire layer and the use layer, so that the data analysis of the health state of each available vehicle is realized, the health state of each available vehicle is intuitively displayed, and the error of the assessment result of the health condition of the vehicle is reduced, thereby reducing the risk hidden trouble in the cargo transportation process and ensuring the life safety of drivers corresponding to all logistics vehicles.
(4) According to the invention, the experience coincidence degree analysis of the drivers corresponding to the available vehicles is carried out by combining the goods type transportation experience of the drivers corresponding to the logistics vehicles and the transportation experience of the waybill transportation route, so that the coverage of the logistics vehicle dispatching analysis is improved, the reliability and the rationality of the logistics vehicle dispatching analysis are further improved, and a reliable basis is provided for the confirmation of the follow-up suitable transportation vehicles.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed for the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of the steps of the method of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, the invention provides a logistics vehicle scheduling method based on waybill data analysis, which comprises the following steps: s1, waybill data acquisition: the transportation path of the current waybill and the type, volume and weight of the current goods to be transported are collected.
It should be noted that the types of goods to be transported currently include, but are not limited to, agricultural products, living goods, and industrial products.
The volume of the goods to be transported currently is obtained through monitoring by a three-dimensional camera arranged above the goods placing platform, and the weight of the goods to be transported currently is obtained through measuring by a gravity sensor arranged below the goods placing platform.
The transportation route of the current waybill and the transportation state, the volume, the weight, the service life, the total number of maintenance, the maintenance level and the number of mileage of each maintenance corresponding to each logistics vehicle, the total number of departure times of drivers corresponding to each logistics vehicle, the type of transported goods corresponding to each departure and the transportation route are extracted from the leveling platform.
S2, vehicle information acquisition: and collecting the transportation state, the additional load volume, the additional load capacity and the health information of the target logistics company corresponding to each logistics vehicle, and numbering each logistics vehicle.
In a specific embodiment of the present invention, the health information includes age, total number of repairs, repair level of each repair, number of mileage traveled, body health information, and tire health information.
The maintenance level includes, among other things, a high level of difficulty and a general difficulty.
The vehicle body health information comprises the number of vehicle body concave positions, the concave volumes corresponding to the concave positions, the number of vehicle body paint dropping positions, the paint dropping area corresponding to the paint dropping positions, the number of vehicle body corrosion positions, the corrosion area corresponding to the corrosion positions, the number of vehicle body scratch positions and the scratch length corresponding to the scratch positions.
Wherein the tire health information includes a wear area and a wear thickness of each tire.
The vehicle body health information and the tire health information are acquired through cameras arranged around all logistics vehicle stopping points in the target logistics company, and are obtained through positioning from the acquired image outlines.
S3, driver information acquisition, namely acquiring the total number of times of departure of the drivers corresponding to all logistics vehicles in the target logistics company, and the types and the transportation paths of transportation goods corresponding to all times of departure.
S4, screening by using a vehicle: and screening all the current available logistics vehicles from all the logistics vehicles corresponding to the target logistics company, taking the current available logistics vehicles as all the available vehicles, and extracting the numbers of all the available vehicles.
In a specific embodiment of the present invention, the screening process for screening each currently available logistics vehicle from each logistics vehicle corresponding to the target logistics company includes: a1, screening out all logistics vehicles waiting for transportation according to the transportation state of all logistics vehicles of the target logistics company.
And A2, extracting the volume and the volume load capacity of each logistics vehicle waiting for transportation.
A3, correspondingly comparing the volume and the weight of the goods to be transported with the sum carrier and the sum load weight of each logistics vehicle waiting for transportation, if the volume and the weight of the goods to be transported are smaller than the sum carrier and the sum load weight of a logistics vehicle waiting for transportation, taking the logistics vehicle waiting for transportation as a logistics vehicle currently available, otherwise taking the logistics vehicle waiting for transportation as a logistics vehicle currently unavailable, and obtaining each available vehicle.
S5, vehicle bearing suitability analysis: analyzing the bearing fitness rho of each available vehicle according to the volume and the weight of the goods to be transported and the volume and the weight of each logistics vehicle corresponding to the target logistics company i Where i represents the available vehicle number, i=1, 2,..n.
In a specific embodiment of the present invention, the analyzing the carrying suitability of each available vehicle includes: b1, respectively marking the volume and the weight of the goods to be transported at present as V Goods (e.g. a cargo) And beta Goods (e.g. a cargo) 。
B2, extracting the volume and the volume load capacity of each available vehicle and respectively marking asAnd->
B3, calculating the bearing fitness rho of each available vehicle i ，
Wherein DeltaV and Deltabeta respectively represent the volume difference and the weight difference of the set reference, a 1 And a 2 Respectively representing the set load-bearing fitness duty ratio weights corresponding to the volume difference and the weight difference, gamma 1 Indicating the set bearer suitability correction factor.
According to the embodiment of the invention, the carrying suitability of each available vehicle is analyzed by combining the volume and the weight of the goods to be transported and the carrying volume and carrying capacity of each logistics vehicle, so that the space resources of each logistics vehicle are more fully utilized, and the utilization rate of the space resources of each logistics vehicle is further improved.
S6, vehicle health index analysis: according to the health information of the target logistics company corresponding to each logistics vehicle, analyzing the health index of each available vehicle
In a specific embodiment of the present invention, the analyzing the health index of each available vehicle includes the following specific analysis processes: c1, extracting the number of the vehicle body concave positions and the corresponding concave volumes of the concave positions from the vehicle body health information of each available vehicle, and respectively marking as M i And V i j Where j represents the recess number, j=1, 2,..m.
C2, calculating the sinking degree of the vehicle body of each available vehicle Wherein M 'and V' respectively represent the number of the set reference vehicle body concave positions and the concave volume, a 3 And a 4 The set number of the vehicle body concave positions and the corresponding vehicle body concave degree occupancy weight of the concave volume are respectively represented, and e represents a natural constant.
And C3, extracting the number of paint dropping positions of the vehicle body, the corresponding paint dropping area of the paint dropping positions of the vehicle body, the number of rust positions of the vehicle body, the corresponding rust area of the rust positions, the number of scratch positions of the vehicle body and the corresponding scratch lengths of the scratch positions of the vehicle body from the body health information of each available vehicle.
C4, calculating the paint dropping degree, the rust degree and the scratch degree of the vehicle body of each available vehicle according to the same calculation mode of the dent degree of the vehicle body of each available vehicle, and respectively marking asAnd->
C5, calculating the health index of the corresponding vehicle body layer of each available vehicle Wherein a is 5 、a 6 、a 7 And a 8 Respectively representing the health index duty ratio weight of the set car body dishing degree, car body paint dropping degree, car body rust degree and car body scratch degree corresponding to the car body layer 2 The health index correction factor indicating the set vehicle body level.
According to the embodiment of the invention, the health index analysis of the vehicle body layer is carried out from four layers of the vehicle body dishing degree, the vehicle body paint falling degree, the vehicle body rust degree and the vehicle body scratch degree, so that the health state of the vehicle body layer is intuitively displayed, the sufficiency of the health analysis of the vehicle body layer is improved, and the accuracy of the health analysis result of the vehicle body is ensured.
C6, extracting the abrasion area and the abrasion thickness of each tire from the tire health information of each available vehicle, and calculating the health index of the corresponding tire layer of each available vehicle
It should be noted that, the specific calculation process of the health index of each available vehicle corresponding to the tire layer is as follows: f1, the wear area and the wear thickness of each tire of each usable vehicle are respectively expressed asAnd->Where k represents the tire number, k=1, 2,..i.
F2, calculating the health index of the corresponding tire layer of each available vehicle Wherein S' and->Respectively representing the tire wear area and the wear thickness of the set reference, d 4 And d 5 Respectively represent the health index duty ratio weight, lambda of the tire layer corresponding to the set tire wear area and wear thickness 1 The health index correction factor indicating the set tire level.
And C7, extracting service life, total maintenance times, maintenance grades of each maintenance and the number of the mileage from the health information of each available vehicle.
C8, calculating the health index of each available vehicle corresponding to the use level
In a specific embodiment of the present invention, the health index of each available vehicle corresponding to the usage level is specifically calculated as follows: d1, the service life, the total maintenance times and the number of travelled mileage of each available vehicle are respectively marked as epsilon i 、Sum mu i 。
D2, counting the number of high-grade difficulty maintenance grades and general difficulty maintenance grades of each available vehicle, and respectively recording asAnd->
D3, calculating the health index of each available vehicle corresponding to the use level
Wherein ε ', μ'And DeltaM respectively represent the service life of the set reference, the number of the travelled mileage, the number of times of the higher difficulty maintenance level and the difference of the number of times of the maintenance level, b 1 、b 2 、b 3 And b 4 Respectively representing the set service life, the number of the travelled mileage, the number of times of the higher difficulty maintenance level and the health coincidence duty ratio weight of the corresponding use level with the number of times difference of the maintenance level, gamma 3 The health indicating the set usage level corresponds to the correction factor.
C9, calculating the health index of each available vehicle
In a specific embodiment of the present invention, the health index calculation formula of each available vehicle is:wherein b 5 、b 6 And b 7 Respectively representing the set health coincidence duty ratio weights of the vehicle body layer, the tire layer and the using layer corresponding to the available vehicles 4 Indicating that the health of the set available vehicle meets the correction factor.
According to the embodiment of the invention, the health index analysis of each available vehicle is carried out from the three layers of the vehicle body layer, the tire layer and the use layer, so that the data analysis of the health state of each available vehicle is realized, the health state of each available vehicle is intuitively displayed, and the error of the assessment result of the health condition of the vehicle is reduced, thereby reducing the risk hidden trouble in the cargo transportation process and ensuring the life safety of the corresponding driver of each logistics vehicle.
S7, driver experience conformity analysis: the experience coincidence degree omega i of the corresponding driver of each available vehicle is analyzed.
In the embodiment of the invention, the method analyzes the experience coincidence degree of the corresponding drivers of the available vehicles, and the specific analysis process is that: e1, recording the total number of the departure times of the drivers corresponding to all logistics vehicles in the target logistics company as
And E2, comparing the types of the transported goods corresponding to the various outgoing vehicles of the drivers corresponding to the available vehicles, taking the same type of the transported goods as the type of the integrated transported goods, and counting the number of the types of the integrated transported goods of the drivers corresponding to the available vehicles and the number of outgoing vehicles corresponding to the types of the integrated transported goods.
E3, screening out the number of times of delivery of the goods category to be delivered from the number of times of delivery of the goods category to be delivered of the comprehensive transportation of the drivers corresponding to the available vehicles
And E4, comparing the transport paths corresponding to the drivers of the available vehicles with each other, taking the same transport path as a comprehensive transport path, and counting the number of the comprehensive transport paths corresponding to the drivers of the available vehicles and the number of the departure times corresponding to the comprehensive transport paths.
And E5, marking the transport path of the current waybill as a target transport path, comparing the target transport path with the comprehensive transport paths of the drivers corresponding to the available vehicles, and calculating the coincidence degree of the comprehensive transport paths and the target transport paths in the drivers corresponding to the available vehicles.
The specific calculation process of the overlap ratio of the comprehensive transportation paths and the target transportation paths in the drivers corresponding to the available vehicles comprises G1, extracting the path cities from the comprehensive transportation paths of the drivers corresponding to the available vehicles, counting the number of the path cities of the comprehensive transportation paths, and recording asWhere g represents the integrated transport path number, g=1, 2.
And G2, extracting each path city of the target transportation path from the target transportation path.
G3, comparing each path city of each comprehensive transportation path corresponding to the driver of each available vehicle with each path city of the target transportation path, counting the number of path cities of each comprehensive transportation path which are the same as the target transportation path, and recording as
G4, calculating the overlap ratio of each comprehensive transportation path and the target transportation path in the drivers corresponding to the available vehicles Wherein ζ' represents the set number of integrated pathway cities.
It should be noted that, the city is a characteristic feature on the transportation path, so that the city of the approach is selected as an important parameter for calculating the coincidence degree of each comprehensive transportation path and the target transportation path in the corresponding drivers of each available vehicle.
E6, extracting the maximum coincidence degree theta of the comprehensive transportation path corresponding to the drivers corresponding to the available vehicles and the target transportation path i Number of departure times corresponding to comprehensive transportation path with maximum overlap ratio
E7, calculating the experience coincidence degree omega of the corresponding drivers of the available vehicles i ，
Wherein lambda is Seed species θ' and λ Transport and transport C, respectively representing the ratio of the number of times of departure, the maximum overlapping degree and the ratio of the number of times of departure of the transportation path of the goods with the set reference 1 、c 2 And c 3 Corresponding experience coincidence assessment duty ratio weight of the number of times of departure of the set goods category, maximum coincidence degree and number of times of departure of the transportation path 5 Experience representing the settings conforms to the evaluation correction factor.
According to the embodiment of the invention, the experience coincidence degree analysis of the drivers corresponding to the available vehicles is carried out by combining the cargo type transportation experience of the drivers corresponding to the logistics vehicles and the transportation experience of the transportation route of the freight list, so that the coverage of logistics vehicle dispatching analysis is improved, the reliability and the rationality of logistics vehicle dispatching analysis are further improved, and a reliable basis is provided for the confirmation of the follow-up suitable transportation vehicles.
S8, vehicle scheduling analysis: calculating a matching evaluation coefficient psi of each available vehicle according to the vehicle bearing suitability, the vehicle health index and the driver experience coincidence degree i And taking the available vehicle with the largest matching evaluation coefficient as the transportation vehicle of the current waybill.
In a specific embodiment of the present invention, the calculation formula of the matching evaluation coefficient of each available vehicle is:wherein d 1 、d 2 And d 3 Matching evaluation coincidence duty ratio weight gamma of available vehicles corresponding to the set bearing suitability, health index and driver experience coincidence degree of available vehicles 6 The matching evaluation indicating the set available vehicles conforms to the correction factor.
According to the embodiment of the invention, the matching evaluation of each available vehicle is carried out from three layers of vehicle bearing, vehicle health and driver experience, so that the multi-dimensional matching evaluation of the available vehicles is realized, the problem of limitation in the current logistics vehicle dispatching analysis is effectively solved, the accuracy and the suitability of the transportation vehicle confirmation are improved, and the reliability of the current logistics vehicle dispatching is ensured.
The foregoing is merely illustrative and explanatory of the principles of this invention, as various modifications and additions may be made to the specific embodiments described, or similar arrangements may be substituted by those skilled in the art, without departing from the principles of this invention or beyond the scope of this invention as defined in the claims.
Claims (9)
1. The logistics vehicle scheduling method based on the waybill data analysis is characterized by comprising the following steps of:
s1, waybill data acquisition: collecting the type, volume and weight of the goods to be transported currently and the transportation path of the current waybill;
s2, vehicle information acquisition: the method comprises the steps of collecting transportation state, additional load volume, additional load capacity and health information of each logistics vehicle corresponding to a target logistics company, and numbering each logistics vehicle;
s3, driver information acquisition, namely acquiring the total number of times of departure of drivers corresponding to all logistics vehicles in a target logistics company, and the types and transportation paths of transportation goods corresponding to all times of departure;
s4, screening by using a vehicle: screening all current available logistics vehicles from all logistics vehicles corresponding to a target logistics company, taking the current available logistics vehicles as all available vehicles, and extracting the numbers of all the available vehicles;
s5, vehicle bearing suitability analysis: analyzing the bearing fitness rho of each available vehicle according to the volume and the weight of the goods to be transported and the volume and the weight of each logistics vehicle corresponding to the target logistics company i Where i represents the available vehicle number, i=1, 2, n;
s6, vehicle health index analysis: according to the health information of the target logistics company corresponding to each logistics vehicle, analyzing the health index of each available vehicle
S7, driver experience conformity analysis: analyzing the experience coincidence omega of drivers corresponding to each available vehicle i ；
S8, vehicle scheduling analysis: calculating a matching evaluation coefficient psi of each available vehicle according to the vehicle bearing suitability, the vehicle health index and the driver experience coincidence degree i And taking the available vehicle with the largest matching evaluation coefficient as the transportation vehicle of the current waybill.
2. The logistics vehicle scheduling method based on the waybill data analysis of claim 1, wherein: the health information comprises service life, total maintenance times, maintenance grades of each maintenance, number of mileage travelled, vehicle body health information and tire health information;
wherein, the maintenance level comprises a high-grade difficulty and a general difficulty;
the vehicle body health information comprises the number of vehicle body concave positions, the corresponding concave volumes of all concave positions, the number of vehicle body paint dropping positions, the corresponding paint dropping areas of all paint dropping positions, the number of vehicle body corrosion positions, the corresponding corrosion areas of all corrosion positions, the number of vehicle body scratch positions and the corresponding scratch lengths of all scratch positions;
wherein the tire health information includes a wear area and a wear thickness of each tire.
3. The logistics vehicle scheduling method based on the waybill data analysis of claim 1, wherein: the method for screening the logistics vehicles from the logistics vehicles corresponding to the target logistics company comprises the following steps of:
a1, screening out all logistics vehicles waiting for transportation according to the transportation state of all logistics vehicles of a target logistics company;
a2, extracting the volume and the volume load capacity of each logistics vehicle waiting for transportation;
a3, correspondingly comparing the volume and the weight of the goods to be transported with the sum carrier and the sum load weight of each logistics vehicle waiting for transportation, if the volume and the weight of the goods to be transported are smaller than the sum carrier and the sum load weight of a logistics vehicle waiting for transportation, taking the logistics vehicle waiting for transportation as a logistics vehicle currently available, otherwise taking the logistics vehicle waiting for transportation as a logistics vehicle currently unavailable, and obtaining each available vehicle.
4. The logistics vehicle scheduling method based on the waybill data analysis of claim 1, wherein: the method for analyzing the bearing suitability of each available vehicle comprises the following specific analysis processes:
b1, the current goods to be transported are transportedThe volume and weight of the material are respectively recorded as V Goods (e.g. a cargo) And beta Goods (e.g. a cargo) ；
B2, extracting the volume and the volume load capacity of each available vehicle and respectively marking asAnd->
B3, calculating the bearing fitness rho of each available vehicle i ，
Wherein DeltaV and Deltabeta respectively represent the volume difference and the weight difference of the set reference, a 1 And a 2 Respectively representing the set load-bearing fitness duty ratio weights corresponding to the volume difference and the weight difference, gamma 1 Indicating the set bearer suitability correction factor.
5. A method for logistics vehicular dispatch based on waybill data analysis as defined in claim 2, wherein: the health index of each available vehicle is analyzed, and the specific analysis process is as follows:
c1, extracting the number of the vehicle body concave positions and the corresponding concave volumes of the concave positions from the vehicle body health information of each available vehicle, and respectively marking as M i And V i j Wherein j represents the number of the concave part, j=1, 2, m;
c2, calculating the sinking degree of the vehicle body of each available vehicle Wherein M 'and V' respectively represent the number of the set reference vehicle body concave positions and the concave volume, a 3 And a 4 Respectively representing the set number of the vehicle body concave positions and the vehicle body concave degree occupying weight corresponding to the concave volume, wherein e represents a natural constant;
c3, extracting the number of paint dropping positions of the vehicle body, the corresponding paint dropping area of the paint dropping positions of the vehicle body, the number of rust positions of the vehicle body, the corresponding rust area of the rust positions, the number of scratch positions of the vehicle body and the corresponding scratch lengths of the scratch positions of the vehicle body from the body health information of each available vehicle;
c4, calculating the paint dropping degree, the rust degree and the scratch degree of the vehicle body of each available vehicle according to the same calculation mode of the dent degree of the vehicle body of each available vehicle, and respectively marking asAnd->
C5, calculating the health index of the corresponding vehicle body layer of each available vehicle Wherein a is 5 、a 6 、a 7 And a 8 Respectively representing the health index duty ratio weight of the set car body dishing degree, car body paint dropping degree, car body rust degree and car body scratch degree corresponding to the car body layer 2 A health index correction factor indicating a set vehicle body level;
c6, extracting the abrasion area and the abrasion thickness of each tire from the tire health information of each available vehicle, and calculating the health index of the corresponding tire layer of each available vehicle
C7, extracting service life, total maintenance times, maintenance grades of each maintenance and the number of mileage from the health information of each available vehicle;
c8, calculating the health index of each available vehicle corresponding to the use level
C9, calculating the health index of each available vehicle
6. The logistics vehicle scheduling method based on the waybill data analysis of claim 5, wherein: the health index of each available vehicle corresponding to the use level comprises the following specific calculation process:
d1, the service life, the total maintenance times and the number of travelled mileage of each available vehicle are respectively marked as epsilon i 、Sum mu i ；
D2, counting the number of high-grade difficulty maintenance grades and general difficulty maintenance grades of each available vehicle, and respectively recording asAnd->
D3, calculating the health index of each available vehicle corresponding to the use level
Wherein ε ', μ'And DeltaM respectively represent the service life of the set reference, the number of the travelled mileage, the number of times of the higher difficulty maintenance level and the difference of the number of times of the maintenance level, b 1 、b 2 、b 3 And b 4 Respectively representing the set service life, the number of the travelled mileage, the number of times of the higher difficulty maintenance level and the health coincidence duty ratio weight of the corresponding use level with the number of times difference of the maintenance level, gamma 3 The health indicating the set usage level corresponds to the correction factor.
7. The logistics vehicle scheduling method based on the waybill data analysis of claim 6, wherein: the health index calculation formula of each available vehicle is as follows:wherein b 5 、b 6 And b 7 Respectively representing the set health coincidence duty ratio weights of the vehicle body layer, the tire layer and the using layer corresponding to the available vehicles 4 Indicating that the health of the set available vehicle meets the correction factor.
8. The logistics vehicle scheduling method based on the waybill data analysis of claim 1, wherein: the method for analyzing the experience coincidence degree of the drivers corresponding to the available vehicles comprises the following specific analysis processes:
e1, recording the total number of the departure times of the drivers corresponding to all logistics vehicles in the target logistics company as
E2, comparing the types of the transported goods corresponding to the drivers of the available vehicles with each other, taking the same type of the transported goods as the type of the integrated transported goods, and counting the number of the types of the integrated transported goods of the drivers of the available vehicles and the number of times of the outgoing vehicles corresponding to the types of the integrated transported goods;
e3, screening the number of times of departure of each comprehensive transportation cargo type corresponding to each driver of each available vehicleNumber of departure times for selecting the type of goods to be transported
E4, comparing the transport paths corresponding to the drivers of the available vehicles with each other, taking the same transport path as a comprehensive transport path, and counting the number of the comprehensive transport paths of the drivers corresponding to the available vehicles and the number of the departure times corresponding to the comprehensive transport paths;
e5, marking the transport path of the current waybill as a target transport path, comparing the target transport path with the comprehensive transport paths of drivers corresponding to the available vehicles, and calculating the coincidence ratio of the comprehensive transport paths and the target transport paths in the drivers corresponding to the available vehicles;
e6, extracting the maximum coincidence degree theta of the comprehensive transportation path corresponding to the drivers corresponding to the available vehicles and the target transportation path i Number of departure times corresponding to comprehensive transportation path with maximum overlap ratio
E7, calculating the experience coincidence degree omega of the corresponding drivers of the available vehicles i ，
Wherein lambda is Seed species θ' and λ Transport and transport C, respectively representing the ratio of the number of times of departure, the maximum overlapping degree and the ratio of the number of times of departure of the transportation path of the goods with the set reference 1 、c 2 And c 3 Corresponding experience coincidence assessment duty ratio weight of the number of times of departure of the set goods category, maximum coincidence degree and number of times of departure of the transportation path 5 Experience representing the settings conforms to the evaluation correction factor.
9. The logistics vehicle scheduling method based on the waybill data analysis of claim 1, wherein: matching of the available vehiclesThe calculation formula of the evaluation coefficient is as follows:wherein d 1 、d 2 And d 3 Matching evaluation coincidence duty ratio weight gamma of available vehicles corresponding to the set bearing suitability, health index and driver experience coincidence degree of available vehicles 6 The matching evaluation indicating the set available vehicles conforms to the correction factor.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116930196A (en) * | 2023-09-18 | 2023-10-24 | 山东卓越精工集团有限公司 | Machine vision-based aluminum profile production defect analysis processing method |
| CN117436777A (en) * | 2023-12-20 | 2024-01-23 | 江苏运满满同城信息科技有限公司 | Multi-type intermodal method and system for intelligent logistics freight transportation |
| CN117933672A (en) * | 2024-03-22 | 2024-04-26 | 长春理工大学 | Park logistics vehicle dispatching management system based on artificial intelligence |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116930196A (en) * | 2023-09-18 | 2023-10-24 | 山东卓越精工集团有限公司 | Machine vision-based aluminum profile production defect analysis processing method |
| CN116930196B (en) * | 2023-09-18 | 2023-12-22 | 山东卓越精工集团有限公司 | Machine vision-based aluminum profile production defect analysis processing method |
| CN117436777A (en) * | 2023-12-20 | 2024-01-23 | 江苏运满满同城信息科技有限公司 | Multi-type intermodal method and system for intelligent logistics freight transportation |
| CN117436777B (en) * | 2023-12-20 | 2024-03-08 | 江苏运满满同城信息科技有限公司 | Multi-type intermodal method and system for intelligent logistics freight transportation |
| CN117933672A (en) * | 2024-03-22 | 2024-04-26 | 长春理工大学 | Park logistics vehicle dispatching management system based on artificial intelligence |
| CN117933672B (en) * | 2024-03-22 | 2024-05-24 | 长春理工大学 | Park logistics vehicle dispatching management system based on artificial intelligence |
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