CN115907600B - Reverse logistics transportation method and system based on Internet of things - Google Patents
Reverse logistics transportation method and system based on Internet of things Download PDFInfo
- Publication number
- CN115907600B CN115907600B CN202211717709.4A CN202211717709A CN115907600B CN 115907600 B CN115907600 B CN 115907600B CN 202211717709 A CN202211717709 A CN 202211717709A CN 115907600 B CN115907600 B CN 115907600B
- Authority
- CN
- China
- Prior art keywords
- transportation
- goods
- information
- vehicle
- volume
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims description 56
- 238000004806 packaging method and process Methods 0.000 claims abstract description 8
- 238000004458 analytical method Methods 0.000 claims abstract description 6
- 230000008569 process Effects 0.000 claims description 36
- 238000011156 evaluation Methods 0.000 claims description 14
- 238000004364 calculation method Methods 0.000 claims description 6
- 238000007726 management method Methods 0.000 claims description 5
- 230000006870 function Effects 0.000 description 14
- 230000008901 benefit Effects 0.000 description 2
- 238000004519 manufacturing process Methods 0.000 description 2
- 230000009471 action Effects 0.000 description 1
- 238000013528 artificial neural network Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000001788 irregular Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012858 packaging process Methods 0.000 description 1
- 238000004064 recycling Methods 0.000 description 1
Landscapes
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention relates to the technical field of reverse logistics transportation, and particularly discloses a reverse logistics transportation system based on the Internet of things, which comprises the following steps: the goods image acquisition module is used for acquiring image information of returned goods; the identification module is used for identifying the type of the returned goods according to the image information; the analysis module is used for predicting packaged volume information according to the acquired image information and the category information and determining a vehicle dispatching strategy according to the packaged volume information of all returned goods; the vehicle management module is used for executing a vehicle scheduling strategy; the system predicts the packaging volume of the parts of the transportation vehicle in an image acquisition mode, analyzes and judges the predicted packaged volume, can better assist a logistics party to manage the transportation of goods, and further generates a vehicle dispatching strategy to manage the vehicle, so that the transportation cost is reduced while the vehicle meets the transportation requirement.
Description
Technical Field
The invention relates to the technical field of reverse logistics transportation, in particular to a reverse logistics transportation method and system based on the Internet of things.
Background
The reverse logistics refers to a transportation mode that a merchant client entrusts a third party logistics company to send the cross-linked objects to the location of the merchant client from the location designated by the user, taking the automobile manufacturing industry as an example, and the most difficult logistics link, when the reverse logistics is performed for the automobile recycling parts, the automobile parts have the advantages of numerous types, irregular package and even no package, the distribution area of each service station is wide, the delivery scale is not fixed, the transportation is mainly supported by the spare part transportation channel, the spare part logistics market is subdivided, and the logistics transportation cost is high.
In the prior art, the automobile manufacturing factories cooperate with each other in a mode of orienting a cooperation logistics company to finish the reverse logistics process of automobile parts, the logistics company is connected with a 4S shop, the processes of goods picking and data handover are finished according to the specific conditions of the 4S shop, and the reclaimed parts are transported back to a host factory in batches through a transit warehouse, so that the reverse transportation process is finished.
In the reverse transportation of the automobile parts, the judgment of the transportation volume of the automobile parts is difficult to determine due to the irregularity of the structure of the automobile parts, and the deviation of the whole transportation volume is caused, so that the management of the transportation is affected, and the transportation efficiency and the transportation cost are affected.
Disclosure of Invention
The invention aims to provide a reverse logistics transportation method and system based on the Internet of things, which solve the following technical problems:
how to achieve an accurate determination of the overall transport volume.
The aim of the invention can be achieved by the following technical scheme:
a reverse logistics transportation system based on the internet of things, the system comprising:
the goods image acquisition module is used for acquiring image information of returned goods;
the identification module is used for identifying the type of the returned goods according to the image information;
the analysis module is used for predicting packaged volume information according to the acquired image information and the category information and determining a vehicle dispatching strategy according to the packaged volume information of all returned goods;
and the vehicle management module is used for executing the vehicle scheduling strategy.
In one embodiment, the process of post-packaging volume information prediction is:
obtaining a preset basic image corresponding to the goods according to the types of the goods;
comparing the image of the returned goods with the basic image, and obtaining the packaged volume V of the returned goods according to the comparison result and the protection grade and the transportation information corresponding to the returned goods types pd ；
According to the predicted packaged volume V of all returned goods pd Determining the total volume V of net point pickup sum ；
According to the total volume V of the forecast net point sum To determine the vehicle type assignment for pick-up.
In one embodiment, the predicted post-package volume V pd The calculation process is as follows:
by the formulaCalculating the predicted packaged volume V pd ；
Wherein S is 0 The area of the goods in the basic image; v (V) 0 The volume corresponding to the goods in the basic image; s is S f The specific area of the goods in the returned goods image information is used for the specific area of the goods; level (Level) p The protection grade corresponds to the cargo type; delta is a protection level increment function; l is the transport distance; gamma is a transport distance increment piecewise function; sigma (sigma) 1 、σ 2 For presetting an adjustment coefficient, and sigma 1 +σ 2 ＝1。
In one embodiment, the predicted total volume V sum The acquisition process of (1) is as follows:
by formula V sum ＝∑V pd +V cl (n，∑V pd ) Calculating the predicted total volume V sum ；
Wherein n is the number of returned goods; v (V) cl A volume increment function associated with the sum of n and all return cargo forecast packaged volumes.
In one embodiment, the process of vehicle type allocation is:
acquisition of transport vehicle type A 1 、A 2 、…、A n ；
Constructing a transportation cost model
Where n is the number of vehicle types; i epsilon [1, n ]]；M i Input quantity for the ith transport vehicle type; c (C) i Transportation costs for the ith transportation vehicle type;
according toEstablishing constraint conditions;
wherein V is i Cargo volume for the i-th transport vehicle type;
according to aboutBeam conditions and based on iterative algorithm pair transportation cost model Solving to obtain M 1 、M 2 、…、M n And (5) an optimal solution.
In one embodiment, the system further comprises a transportation data acquisition module and a transportation evaluation module;
the transportation data acquisition module is used for acquiring transportation information, and the transportation information comprises transportation route data, transportation speed data and corresponding time data;
and the transportation evaluation module is used for evaluating the transportation process according to the transportation information.
In one embodiment, the process of evaluating the transportation process by the transportation evaluation module is as follows:
dividing a transport route into sections Q 1 、Q 2 、…、Q m The method comprises the steps of carrying out a first treatment on the surface of the Setting a standard speed v for each section 1 、v 2 、…、v m ；
By the formulaObtaining an evaluation coefficient E v ；
Will evaluate coefficient E v And a preset threshold E th And (3) performing comparison:
if E v ＜E th Judging that the transportation process is unqualified;
otherwise, judging that the transportation process is qualified;
wherein j is E [1, m]；v j Standard speed for the j-th zone; v fj The actual speed of the jth zone; delta is a piecewise judgment function whenWhen (I)>Otherwise, go (L)>W rest In order to judge whether the transportation personnel have a rest according to the preset interval time, when the transportation personnel have a rest according to the interval time, W rest =1, otherwise, W rest ＝0；W arrive To determine whether the transport vehicle arrives at the coefficient on time, W is calculated when arriving at time arrive =1, otherwise, W arrive ＝0；τ 1 、τ 2 τ 3 Is a preset coefficient.
In one embodiment, the method comprises:
s1, acquiring image information of returned goods;
s2, identifying the type of the returned goods according to the image information;
s3, predicting packaged volume information according to the acquired image information and the category information, and determining a vehicle dispatching strategy according to the packaged volume information of all returned goods;
s4, managing the transport vehicle according to the vehicle dispatching strategy.
The invention has the beneficial effects that:
(1) According to the invention, the prediction of the packaging volume of the parts of the transportation vehicle is realized in an image acquisition mode, and the analysis and judgment are carried out through the predicted packaged volume, so that the logistics party can be better assisted to manage the transportation of goods, and then a vehicle dispatching strategy is generated to manage the vehicle, so that the transportation cost is reduced as far as possible while the vehicle meets the transportation requirement.
Drawings
The invention is further described below with reference to the accompanying drawings.
FIG. 1 is a schematic block diagram of a reverse logistics transportation system based on the Internet of things of the present invention;
fig. 2 is a flow chart of steps of the reverse logistics transportation method based on the internet of things.
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, in one embodiment, a reverse logistics transportation system based on internet of things is provided, the system includes:
the goods image acquisition module is used for acquiring image information of returned goods;
the identification module is used for identifying the type of the returned goods according to the image information;
the analysis module is used for predicting packaged volume information according to the acquired image information and the category information and determining a vehicle dispatching strategy according to the packaged volume information of all returned goods;
and the vehicle management module is used for executing the vehicle scheduling strategy.
Through the above technical scheme, the embodiment predicts the packaging volume of the parts of the transportation vehicle through the mode of image acquisition, specifically, the acquired part images are identified based on the neural network identification model, the specific types of the parts of the vehicle are obtained, the packaged volume of the parts of the vehicle is predicted according to the preset information of the corresponding types, the predicted packaged volume is analyzed and judged, the logistics party can be better assisted to manage the transportation of goods, and then the vehicle scheduling strategy is generated to manage the vehicle, so that the transportation cost is reduced as much as possible while the transportation requirement is met.
It should be noted that, in the above scheme, the recognition model of the part image may be implemented through the prior art, and the model is trained by using the images of different parts as learning samples, so that the recognition model may be obtained, and the specific training process is not further described in the embodiment.
As one embodiment of the present invention, the process of predicting the volume information after packaging is:
obtaining a preset basic image corresponding to the goods according to the types of the goods;
comparing the image of the returned goods with the basic image, and obtaining the packaged volume V of the returned goods according to the comparison result and the protection grade and the transportation information corresponding to the returned goods types pd ；
According to the predicted packaged volume V of all returned goods pd Determining the total volume V of net point pickup sum ；
According to the total volume V of the forecast net point sum To determine the vehicle type assignment for pick-up.
Through the above technical solution, the present embodiment provides a method for predicting the packaged volume information, specifically, since the determination of the object volume cannot be directly implemented through the image information, the present embodiment firstly obtains a preset basic image corresponding to the goods according to the types of the goods, and determines by comparing the basic image with the collected image, and meanwhile, determines when predicting the packaged volume in combination with the goods protection grade and the transportation information, thereby more accurately obtaining the total volume V of the goods obtained from the net points of all returned goods sum Accurate judgment is carried out, and then the type of the vehicle for picking up the goods is distributed according to the judgment result, so that the transportation requirement can be met and the transportation cost can be reduced.
As one embodiment of the present invention, the predicted post-package volume V pd The calculation process is as follows:
by the formulaCalculating the predicted packaged volume V pd ；
Wherein S is 0 The area of the goods in the basic image; v (V) 0 The volume corresponding to the goods in the basic image; s is S f The specific area of the goods in the returned goods image information is used for the specific area of the goods; level (Level) p The protection grade corresponds to the cargo type; delta is a protection level increment function; l is the transport distance; gamma is a transport distance increment piecewise function; sigma (sigma) 1 、σ 2 For presetting an adjustment coefficient, and sigma 1 +σ 2 ＝1。
Through the technical scheme, the volume V after calculation and prediction package is provided pd By the formulaJudging, wherein L is a transportation distance, gamma is a transportation distance increment piecewise function, and different proportions are preset according to different ranges of the transportation distance; level (Level) p The control relation of the protection grade corresponding to the cargo type is set according to the characteristics of the part type, and the protection grade increment function delta is preset with different ratio values according to the specific grade; thus passing through sigma 1 *γ(L)+σ 2 *δ(Level p ) Can judge the influence of the transportation distance factor and the safety protection grade factor on the package, and is based on +.>Judging the basic occupied volume of the transported goods, and then enabling the formula +.> Obtain more accurate predicted packaged volume V pd 。
In the above technical solution, the transportation distance increment piecewise function γ, the protection level increment function δ, and the preset adjustment coefficient σ 1 、σ 2 Selectively setting according to the experience data; level (Level) p The specific grading of the parts is also judged based on the types of the parts; not described in detail herein.
It should be noted that, in the actual packaging process, there is a problem that the packaging size and the actual size deviate greatly, which affects the actual judgment result, but in this embodiment, the assurance of the parts is uniformly guaranteed by the 4S store, so that the deviation of the predicted data is small.
As one embodiment of the present invention, the predicted total volume V sum The acquisition process of (1) is as follows:
by formula V sum ＝∑V pd +V cl (n，∑V pd ) Calculating the predicted total volume V sum ；
Wherein n is the number of returned goods; v (V) cl A volume increment function associated with the sum of n and all return cargo forecast packaged volumes.
By the technical proposal, the embodiment provides the calculation and prediction of the total volume V sum By the method of formula V sum ＝∑V pd +V cl (n，∑V pd ) To obtain the total volume V sum Wherein Σv pd Predicting the sum of the packaged volumes for all returned items, V cl As an incremental function, which is determined from the sum of the number of returned items n and the predicted packaged volume of all returned items, and therefore by the formula V sum ＝∑V pd +V cl (n，∑V pd ) The gap factors during loading of cargoes can be integrated, and the total volume V is more accurately calculated sum And (5) predicting.
The volume increment function V cl Based on the historical data, the data is obtained by analysis of the data relating to the different cargo quantities and the total volume of the cargo, and is not described in detail herein.
As one embodiment of the present invention, the process of vehicle type allocation is as follows:
acquisition of transport vehicle type A 1 、A 2 、…、A n ；
Constructing a transportation cost model
Where n is the number of vehicle types; i epsilon [1, n ]]；M i Input quantity for the ith transport vehicle type; c (C) j Transportation costs for the ith transportation vehicle type;
according toEstablishing constraint conditions;
wherein V is i Cargo volume for the i-th transport vehicle type;
modeling transportation costs according to constraints and based on iterative algorithms Solving to obtain M 1 、M 2 、…、M n And (5) an optimal solution.
Through the above technical solution, the present embodiment provides a method for distributing transport vehicles, specifically, constructing a transport cost model according to the type of transport vehicle Wherein C is i For the transport costs of the ith transport vehicle type, tc thus represents the total cost of transport, while the transport costs are modeled according to the number limitsMaking constraints, i.e.)>Ensuring that the vehicle meets the minimum transport space requirements, the transport cost model is therefore +_ checked by means of an iterative algorithm>Solving to obtain the optimal solution M 1 、M 2 、…、M n The cost of transportation is minimized and the transportation requirement is satisfied.
It should be noted that, the above iterative algorithm selects a limited value according to the constraint conditionCombining, substituting the numerical combination into the transportation cost model for calculation, so that the optimal solution M can be obtained according to the Tc minimum value 1 、M 2 、…、M n The specific process is based on mathematical basic knowledge and is not described in detail here.
As one embodiment of the invention, the system further comprises a transportation data acquisition module and a transportation evaluation module;
the transportation data acquisition module is used for acquiring transportation information, and the transportation information comprises transportation route data, transportation speed data and corresponding time data;
and the transportation evaluation module is used for evaluating the transportation process according to the transportation information.
Through above-mentioned technical scheme, this embodiment is in order to realize the management and control to the transportation, gathers transportation information through setting up transportation data acquisition module, transportation information vehicle transportation route data, transportation speed data and corresponding time data, through transportation information to the transportation evaluation, judge for the single factor of directly sending through accuracy, combine transportation information vehicle transportation route data, transportation speed data and corresponding time data analysis, more can judge the action of transportation personnel in the transportation, guaranteed transportation personnel's safe operation, more do benefit to simultaneously and judge transportation personnel's operating condition.
As one embodiment of the present invention, the process of evaluating the transportation process by the transportation evaluation module is as follows:
dividing a transport route into sections Q 1 、Q 2 、…、Q m The method comprises the steps of carrying out a first treatment on the surface of the Setting a standard speed v for each section 1 、v 2 、…、v m ；
By the formulaObtaining an evaluation coefficient E v ；
Will evaluate coefficient E v And a preset threshold E th And (3) performing comparison:
if E v ＜E th Judging that the transportation process is unqualified;
otherwise, judging that the transportation process is qualified;
wherein j is E [1, m]；v j Standard speed for the j-th zone; v fj The actual speed of the jth zone; delta is a piecewise judgment function whenWhen (I)>Otherwise, go (L)>W rest In order to judge whether the transportation personnel have a rest according to the preset interval time, when the transportation personnel have a rest according to the interval time, W rest =1, otherwise, W rest ＝0；W arrive To determine whether the transport vehicle arrives at the coefficient on time, W is calculated when arriving at time arrive =1, otherwise, W arrive ＝0；τ 1 、τ 2 τ 3 Is a preset coefficient.
Through the technical scheme, the embodiment provides a method for evaluating the transportation process specifically, and the method comprises the following steps ofObtaining an evaluation coefficient E v The method comprises the steps of carrying out a first treatment on the surface of the Will evaluate coefficient E v And a preset threshold E th And (3) performing comparison: if E v ＜E th Judging that the transportation process is unqualified; otherwise, judging that the transportation process is qualified; the comprehensive judgment of the transportation personnel is carried out according to the driving state of the transportation vehicle on each road section, whether the transportation vehicle is in fatigue driving, whether the transportation vehicle arrives on time and other factors, so that the safety of the transportation process of the transportation personnel of the vehicle is ensured.
In the above technical solution, the division of the transportation route into sections and the setting of the standard speed of each section are obtained by connecting the Api ports of the map, which is not described in detail herein; in the above scheme, the coefficient τ is preset 1 、τ 2 、τ 3 E and E v The selective setting is based on empirical data and is not described in detail herein.
Referring to fig. 2, in one embodiment, a reverse logistics transportation method based on the internet of things is provided, where the method includes:
s1, acquiring image information of returned goods;
s2, identifying the type of the returned goods according to the image information;
s3, predicting packaged volume information according to the acquired image information and the category information, and determining a vehicle dispatching strategy according to the packaged volume information of all returned goods;
s4, managing the transport vehicle according to the vehicle dispatching strategy.
Through the above technical scheme, the embodiment predicts the packaging volume of the parts of the transportation vehicle through the mode of image acquisition, and the specific category of the parts of the transportation vehicle is obtained by identifying the acquired part images, the packaged volume of the parts of the transportation vehicle is predicted according to the preset information of the corresponding category, and the predicted packaged volume is analyzed and judged, so that the logistics party can be better assisted to manage the transportation of goods, and then a vehicle dispatching strategy is generated to manage the vehicle, so that the transportation cost of the vehicle is reduced as much as possible while the transportation requirement is met.
The foregoing describes one embodiment of the present invention in detail, but the description is only a preferred embodiment of the present invention and should not be construed as limiting the scope of the invention. All equivalent changes and modifications within the scope of the present invention are intended to be covered by the present invention.
Claims (4)
1. Reverse logistics transportation system based on the internet of things, characterized in that the system comprises:
the goods image acquisition module is used for acquiring image information of returned goods;
the identification module is used for identifying the type of the returned goods according to the image information;
the analysis module is used for predicting packaged volume information according to the acquired image information and the category information and determining a vehicle dispatching strategy according to the packaged volume information of all returned goods;
the vehicle management module is used for executing a vehicle scheduling strategy;
the process of the volume information prediction after packaging is as follows:
obtaining a preset basic image corresponding to the goods according to the types of the goods;
comparing the image of the returned goods with the basic image, and obtaining the packaged volume V of the returned goods according to the comparison result and the protection grade and the transportation information corresponding to the returned goods types pd ；
According to the predicted packaged volume V of all returned goods pd Determining the total volume V of net point pickup sum ；
According to the total volume V of the forecast net point sum To determine a vehicle type allocation for the pick;
the predicted post-package volume V pd The calculation process is as follows:
by the formulaCalculating the predicted packaged volume V pd ；
Wherein S is 0 The area of the goods in the basic image; v (V) 0 The volume corresponding to the goods in the basic image; s is S f The specific area of the goods in the returned goods image information is used for the specific area of the goods; level (Level) p The protection grade corresponds to the cargo type; delta is a protection level increment function; l is the transport distance; gamma is a transport distance increment piecewise function; sigma (sigma) 1 、σ 2 For presetting an adjustment coefficient, and sigma 1 +σ 2 ＝1；
The predicted total volume V sum The acquisition process of (1) is as follows:
by formula V sum ＝∑V pd +V cl (k，∑V pd ) Calculating the predicted total volume V sum ；
Wherein k is the number of returned goodsAn amount of; v (V) cl Predicting a volume increment function associated with the sum of the packaged volumes for k and all returned items;
the vehicle type distribution process comprises the following steps:
acquisition of transport vehicle type A 1 、A 2 、…、A n ；
Constructing a transportation cost model
Where n is the number of vehicle types; i epsilon [1, n ]]；M i Input quantity for the ith transport vehicle type; c (C) i Transportation costs for the ith transportation vehicle type;
according toEstablishing constraint conditions;
wherein V is i Cargo volume for the i-th transport vehicle type;
modeling transportation costs according to constraints and based on iterative algorithms Solving to obtain M 1 、M 2 、…、M n And (5) an optimal solution.
2. The reverse logistics transportation system based on the internet of things of claim 1, further comprising a transportation data acquisition module and a transportation evaluation module;
the transportation data acquisition module is used for acquiring transportation information, and the transportation information comprises transportation route data, transportation speed data and corresponding time data;
and the transportation evaluation module is used for evaluating the transportation process according to the transportation information.
3. The reverse logistics transportation system based on the internet of things of claim 2, wherein the transportation evaluation module evaluates the transportation process by:
dividing a transport route into sections Q 1 、Q 2 、…、Q m The method comprises the steps of carrying out a first treatment on the surface of the Setting a standard speed v for each section 1 、v 2 、…、v m ；
By the formulaObtaining an evaluation coefficient E v ；
Will evaluate coefficient E v And a preset threshold E th And (3) performing comparison:
if E v ＜E th Judging that the transportation process is unqualified;
otherwise, judging that the transportation process is qualified;
wherein m is the number of sections divided by the transportation route, j E [1, m]；v j Standard speed for the j-th zone; v fj The actual speed of the jth zone; delta is a piecewise judgment function whenWhen the standard threshold value is in the interval, the user is->Otherwise, go (L)>W rest In order to judge whether the transportation personnel have a rest according to the preset interval time, when the transportation personnel have a rest according to the interval time, W rest =1, otherwise, W rest ＝0；W arrive To determine whether the transport vehicle arrives at the coefficient on time, W is calculated when arriving at time arrive =1, otherwise, W arrive ＝0；τ 1 、τ 2 τ 3 Is a preset coefficient.
4. A reverse logistics transportation method based on the internet of things, characterized in that the method adopts the reverse logistics transportation system based on the internet of things as set forth in any one of claims 1-3 to manage the transportation process, and the method comprises:
s1, acquiring image information of returned goods;
s2, identifying the type of the returned goods according to the image information;
s3, predicting packaged volume information according to the acquired image information and the category information, and determining a vehicle dispatching strategy according to the packaged volume information of all returned goods;
s4, managing the transport vehicle according to the vehicle dispatching strategy.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211717709.4A CN115907600B (en) | 2022-12-29 | 2022-12-29 | Reverse logistics transportation method and system based on Internet of things |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211717709.4A CN115907600B (en) | 2022-12-29 | 2022-12-29 | Reverse logistics transportation method and system based on Internet of things |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN115907600A CN115907600A (en) | 2023-04-04 |
| CN115907600B true CN115907600B (en) | 2023-08-25 |
Family
ID=86472947
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211717709.4A Active CN115907600B (en) | 2022-12-29 | 2022-12-29 | Reverse logistics transportation method and system based on Internet of things |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115907600B (en) |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20170101555A (en) * | 2016-02-29 | 2017-09-06 | 성균관대학교산학협력단 | Goods loading system using 3d camera and bigdata system, and thereof method |
| CN107730068A (en) * | 2017-08-29 | 2018-02-23 | 陈飞 | The allocator of freight orders packet recombining is carried out according to cargo service evaluation result |
| CN108734459A (en) * | 2018-08-23 | 2018-11-02 | 深圳码隆科技有限公司 | A kind of 3D supermarkets commodity verifying system |
| CN110411530A (en) * | 2019-03-21 | 2019-11-05 | 重庆大学 | A kind of intelligent identification Method of container residual volume |
| CN111738570A (en) * | 2020-06-05 | 2020-10-02 | 苏州华易通物流有限公司 | Logistics management system based on internet |
| CN114187502A (en) * | 2020-09-14 | 2022-03-15 | 顺丰科技有限公司 | Vehicle loading rate identification method and device, electronic equipment and storage medium |
| CN114298198A (en) * | 2021-12-23 | 2022-04-08 | 国网河北省电力有限公司营销服务中心 | Intelligent goods storage warehouse |
| CN114462905A (en) * | 2020-11-09 | 2022-05-10 | 江苏玥配物流有限公司 | Logistics distribution route optimization system |
| US11429991B1 (en) * | 2019-09-18 | 2022-08-30 | Cenports Inc. | Systems and methods for production and logistics management |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10579949B2 (en) * | 2016-11-16 | 2020-03-03 | Wipro Limited | Methods and systems for facilitating transportation of articles in warehouses using video analytics |
-
2022
- 2022-12-29 CN CN202211717709.4A patent/CN115907600B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20170101555A (en) * | 2016-02-29 | 2017-09-06 | 성균관대학교산학협력단 | Goods loading system using 3d camera and bigdata system, and thereof method |
| CN107730068A (en) * | 2017-08-29 | 2018-02-23 | 陈飞 | The allocator of freight orders packet recombining is carried out according to cargo service evaluation result |
| CN108734459A (en) * | 2018-08-23 | 2018-11-02 | 深圳码隆科技有限公司 | A kind of 3D supermarkets commodity verifying system |
| CN110411530A (en) * | 2019-03-21 | 2019-11-05 | 重庆大学 | A kind of intelligent identification Method of container residual volume |
| US11429991B1 (en) * | 2019-09-18 | 2022-08-30 | Cenports Inc. | Systems and methods for production and logistics management |
| CN111738570A (en) * | 2020-06-05 | 2020-10-02 | 苏州华易通物流有限公司 | Logistics management system based on internet |
| CN114187502A (en) * | 2020-09-14 | 2022-03-15 | 顺丰科技有限公司 | Vehicle loading rate identification method and device, electronic equipment and storage medium |
| CN114462905A (en) * | 2020-11-09 | 2022-05-10 | 江苏玥配物流有限公司 | Logistics distribution route optimization system |
| CN114298198A (en) * | 2021-12-23 | 2022-04-08 | 国网河北省电力有限公司营销服务中心 | Intelligent goods storage warehouse |
Non-Patent Citations (1)
| Title |
|---|
| 公路货运车辆动态配装优化方案研究;张赫;王炜;陆丹;;武汉理工大学学报(交通科学与工程版)(06);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN115907600A (en) | 2023-04-04 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110097234B (en) | Intelligent dispatching method and system for industrial cigarette transportation | |
| US7509228B2 (en) | Systems and methods for forecasting container density | |
| CN102542395B (en) | A kind of emergency materials dispatching system and computing method | |
| Poliak et al. | Competitiveness of price in international road freight transport | |
| CN109685411A (en) | A kind of share-car matching process of logistics shipping platform | |
| CN111784088A (en) | Order matching method, order matching device, server and storage medium | |
| CN116402309B (en) | Port collection and distribution vehicle scheduling matching method and system | |
| CN114372758A (en) | Production raw material storage method and system | |
| CN113554387A (en) | Driver preference-based e-commerce logistics order allocation method, device, equipment and storage medium | |
| Safiullin et al. | The software adaptive system for managing the heavy cargo transportation process based on the automated vehicle weight and size control system | |
| CN115907600B (en) | Reverse logistics transportation method and system based on Internet of things | |
| CN109117989B (en) | Prediction method and device during task matching | |
| CN112950091B (en) | Vehicle scheduling method, device and storage medium | |
| CN108921326A (en) | A kind of intelligent cultural gene Logistics Distribution Method based on similarity study | |
| Mazurenko et al. | Development of a simulation model of a cargo customs complex operation as a link of a logistic supply chain | |
| Samchuk et al. | Freight fleet management problem: evaluation of a truck utilization rate based on agent modeling | |
| Oklander et al. | Minimization of transportation risks in logistics by choosing a cargo delivery route with the minimal projected number of road accidents | |
| EP2242011A1 (en) | Method for managing the distribution of products or goods. | |
| CN115907374A (en) | Intelligent dispatching method, device and equipment for vehicle of distribution center and storage medium | |
| Rahimi et al. | Inventory routing problem for perishable products by considering social issue | |
| EP3861287B1 (en) | A method for operating a vehicle | |
| Trofimova | System planning the motor transport enterprise functioning in freighting in interurban communication | |
| Hu et al. | Dynamic Pricing Method of Non-Truck Operating Common Carrier under Cost-Core Multivariate Conditions | |
| CN115730879B (en) | Commodity logistics transportation distribution method based on zero storage | |
| CN116862339A (en) | Logistics cargo management and control method, system, storage medium and intelligent terminal |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| CP03 | Change of name, title or address | ||
| CP03 | Change of name, title or address |
Address after: Room 405, No. 6, Jinqiao Road, Guangzhou Free Trade Zone, Guangzhou, Guangdong 510000 Patentee after: Guangzhou Jieshitong Supply Chain Co.,Ltd. Country or region after: China Address before: Room 405, No. 6, Jinqiao Road, Guangzhou Free Trade Zone, Guangzhou, Guangdong 510000 Patentee before: Guangzhou Jieshitong Logistics Co.,Ltd. Country or region before: China |