CN117114534B - Block chain-based product factory internal logistics management method and system - Google Patents
Block chain-based product factory internal logistics management method and system Download PDFInfo
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Abstract
The invention relates to the technical field of logistics management, in particular to a method and a system for managing logistics in a product factory based on a block chain, which comprise the following steps: the logistics monitoring management module, the product information acquisition module, the target screening module to be selected and the target selection management module are used for monitoring the logistics vehicles in and out of the factory in real time, the product information acquisition module is used for acquiring the historical data of the products produced in the places in the factory, the acquired data are uploaded to the blockchain, the target screening module to be selected is used for analyzing the historical data, the places to be selected for unloading the vehicles are screened out, the final unloading place for storing the vehicle materials is selected through the target selection management module, the balance degree of the material amount for producing the products stored in different places in the factory is maintained, the occurrence of the phenomenon of stock accumulation in part of places is reduced, the vehicles are helped to finish unloading as soon as possible, and the condition of congestion of the logistics vehicles in the factory is relieved.
Description
Technical Field
The invention relates to the technical field of logistics management, in particular to a method and a system for managing logistics in a product factory based on a block chain.
Background
In the production process of enterprises, materials such as materials and auxiliary materials are required to be purchased for product production, the purchased materials are required to be transported into a factory by vehicles, and after the vehicles for loading the materials enter the factory, the whole flow monitoring and management are carried out on the logistics in the factory, so that the consequences such as logistics congestion and potential safety hazards can be reduced;
however, there are still some problems with existing in-plant logistics management: firstly, due to the problem of unbalanced warehouse-in material quantity, the phenomenon of stock accumulation easily occurs in a site of a part in a factory, and in the prior art, after a vehicle for loading materials enters the factory, the site is randomly selected for unloading, so that the problem of unbalanced storage material quantity in the factory is easily caused; secondly, in the process of selecting the unloading place, the problems of congestion of the surrounding environment of the place and time spent in arriving at the place cannot be considered at the same time, so that the unloading speed of the vehicle is increased while the proper place is selected for unloading, and the phenomenon of logistic vehicle congestion in the factory is easily caused.
Therefore, there is a need for a method and system for managing logistics in a product factory based on blockchain to solve the above problems.
Disclosure of Invention
The invention aims to provide a method and a system for managing logistics in a product factory based on a block chain, which are used for solving the problems in the background technology.
In order to solve the technical problems, the invention provides the following technical scheme: a blockchain-based product in-plant logistics management system, the system comprising: the system comprises a logistics monitoring management module, a product information acquisition module, a target screening module to be selected and a target selection management module;
the output end of the logistics monitoring management module is connected with the input end of the target screening module to be selected, the output end of the product information acquisition module is connected with the input ends of the target screening module to be selected and the target selection management module, and the output end of the target screening module to be selected is connected with the input end of the target selection management module;
the logistics vehicles in the factory are monitored in real time by the logistics monitoring management module;
the historical data of product production at all places in a factory are collected through the product information collection module, and the collected data are uploaded to a blockchain;
analyzing historical data through the target screening module to be selected, and screening out a place to be selected for unloading the vehicle;
and analyzing the environmental information of the places to be selected by the target selection management module, and selecting the final unloading place for storing the vehicle materials.
Further, the logistics monitoring management module comprises a vehicle access monitoring unit, a vehicle registration management unit and a vehicle information acquisition unit;
the output end of the vehicle in-out monitoring unit is connected with the input end of the vehicle registration management unit, and the output end of the vehicle registration management unit is connected with the input end of the vehicle information acquisition unit;
the vehicle access monitoring unit is used for monitoring vehicles in and out of the factory in real time;
the vehicle registration management unit is used for sending reminding information of vehicle information registration when the unloading vehicle is monitored to enter the factory;
the vehicle information acquisition unit is used for acquiring the quantity information of materials to be unloaded in the vehicle after the vehicle is subjected to information registration, wherein the materials refer to materials used for product production;
the method has the advantages that the vehicles entering and exiting are monitored in real time, the unloading vehicles are reminded to register filling information, and potential safety hazards in factories are reduced.
Further, the product information acquisition module comprises a place information acquisition unit and a production information acquisition unit;
the place information acquisition unit is used for acquiring place position information for product production and number information of vehicles parked outside the place in the factory;
the production information acquisition unit is used for acquiring material information consumed by all places in the factory when the production of the products is carried out and the stored material quantity information in the places, uploading the acquired information of different places into the blockchain, and storing the information of different places in different blockchain nodes;
the information is stored in the blockchain, so that the production information of products in different places can be comprehensively acquired, and the production progress of the products can be comprehensively mastered.
Further, the target screening module to be selected comprises a production information analysis unit, a completion duration prediction unit and a place screening unit to be selected;
the input end of the production information analysis unit is connected with the output end of the production information acquisition unit, the output ends of the production information analysis unit and the vehicle information acquisition unit are connected with the input end of the completion time length prediction unit, and the output end of the completion time length prediction unit is connected with the input end of the to-be-selected place screening unit;
the production information analysis unit is used for calling material information consumed in different places when the production of products is carried out, and building material consumption completion time prediction models of different places according to the material information, wherein the material is the same as the material loaded by the unloading vehicle;
the completion time length prediction unit is used for substituting the sum of the loading material quantity of the unloading vehicle and the existing material quantity in the place into a material consumption completion time length prediction model to predict the time length required by different places to consume and complete all the materials;
the to-be-selected place screening unit is used for comparing the predicted time length and screening out the to-be-selected target place.
Further, the target selection management module comprises a place information calling unit, a fitness degree analysis unit and a discharge place selection unit;
the input end of the place information calling unit is connected with the output ends of the place screening unit and the place information collecting unit to be selected, the output end of the place information calling unit is connected with the input end of the suitability degree analyzing unit, and the output end of the suitability degree analyzing unit is connected with the input end of the unloading place selecting unit;
the place information calling unit is used for calling all place position information for product production and the number information of vehicles parked outside the place in the factory to the fitness analysis unit;
the suitability degree analysis unit is used for analyzing the suitability degree of loading materials of the storage unloading vehicles at different places;
the unloading place selecting unit is used for selecting a place with the highest suitability degree as an unloading place to store goods loaded by the unloading vehicle, and sending unloading place position information to an unloading vehicle driver terminal.
A logistics management method in a product factory based on a block chain comprises the following steps:
s1: real-time monitoring is carried out on the entry and exit of logistics vehicles in the factory;
s2: collecting historical data of product production at all places in a factory, and uploading the collected data to a blockchain;
s3: analyzing historical data and establishing a material consumption completion duration prediction model of different places;
s4: predicting the time length required by different places to finish all materials, and screening the places to be selected for unloading the vehicles;
s5: and analyzing the environmental information of the places to be selected, and selecting the final unloading place for storing the vehicle materials.
Further, in step S1: and sending reminding information of vehicle information registration when the unloading vehicle is monitored to enter the factory, and collecting the quantity of materials needing to be unloaded in the vehicle as K after the vehicle is subjected to information registration.
Further, in step S2: collecting material information consumed by all places in the factory when the product is produced m times, and acquiring the material quantity set consumed by randomly carrying out the product production m times in the factory, wherein the material quantity set consumed by randomly carrying out the product production m times in the factory is F= { F 1 ，F 2 ，…，F m The time duration set spent for completing the corresponding material is t= { t } 1 ，t 2 ，…，t m The collection of the number of the stored materials which are the same as the loading materials of the unloading vehicles in the place is C= { C 1 ，C 2 ，…，C n Collecting position information of all places used for product production in a factory, collecting the number set of vehicles parked in a circular area with different places as circle centers and radius r as A= { A when unloading vehicles enter the factory 1 ，A 2 ，…，A n And n represents the number of sites for product production within the factory floor.
Further, in step S3: call training sample { (F) 1 ，t 1 ），（F 2 ，t 2 ），…，（F m ，t m ) Performing straight line fitting on the training samples, and establishing a material consumption completion duration prediction model of a random place as follows: y=α×x+β, wherein α representsBias, beta represents intercept, and a final material consumption completion duration prediction model is obtained by solving alpha and beta:
α=[m∑ m i=1 (F i ×t i )-∑ m i=1 (F i )∑ m i=1 (t i )]/m∑ m i=1 (F i ) 2 -(∑ m i=1 (F i )) 2 ；
β=[∑ m i=1 (t i )-α∑ m i=1 (F i )]/m；
wherein F is i Representing the quantity of materials consumed by a random place in a factory when the product is produced for the ith time before, t i Indicating the length of time it takes to complete the corresponding supplies the ith time the product is produced.
Further, in step S4: the number of the stored goods and materials which are the same as the goods and materials loaded by the unloading vehicle in the corresponding place is obtained to be C j C is carried out by j And substituting K into a material consumption completion duration prediction model of the corresponding place: let x=c j +K, the time length required for the consumption of all materials after the loading of the unloading vehicle in the corresponding place is predicted to be T j ，T j =α*(C j +K) +beta, and the duration set for consuming and completing all materials after the unloading vehicle and the loading materials are continuously stored in n places is predicted to be T= { T 1 ，T 2 ，…，T j ，…，T n Arranging the time lengths in order from small to large, dividing n places into g groups according to the time sizes, wherein the number of places in each group is not less than 1, the time length required by all places in the former group to finish all the goods is less than that of the latter group after the goods are loaded by the unloading vehicles are continuously stored, and the average time length set required by all the goods in each g groups to finish all the goods after the goods are continuously stored in the unloading vehicles is V= { V in the random grouping result 1 ，V 2 ，…，V g According to the formula p= [ (Σ) g e=1 (V e -(∑ g e=1 V e )/g) 2 )/g] 1/2 Selection ofOptimal grouping result, wherein P represents the degree of dispersion of g inter-group parameters in a random grouping result, V e The method comprises the steps of representing the average time length required by finishing all materials after the e-th group of places in g groups continuously store the unloading vehicles to load the materials in a random grouping result, selecting the grouping result with the largest discrete degree as the optimal grouping result, and screening a first group of places from the optimal grouping result as places to be selected for unloading the vehicles;
the method comprises the steps of collecting and analyzing historical data of materials consumed during product production at different places in a factory area through a big data technology, establishing a material consumption completion duration prediction model, predicting the duration required by the fact that all materials are consumed after loading materials by a loading vehicle are stored at different places, judging that the faster the corresponding places are used for storing the loaded materials, the lower the probability of material accumulation at the corresponding places is, establishing the prediction model for the different places, predicting, screening unloading places to be selected for the second time through comparing predicted duration parameters, considering that the duration required by the fact that all materials are consumed at the plurality of places is similar, selecting places with shortest duration in the future, selecting places with partial shorter duration in a grouping and selecting optimal grouping result mode, and selecting unloading places for the second time through considering other factors.
Further, in step S5: screening q places to be selected for unloading the vehicles, predicting that the duration set required by the q places to be selected for unloading the vehicles to consume all materials after continuously storing the unloaded vehicles and loading the materials is T ’ ={T 1 ’ ，T 2 ’ ，…，T q ’ }，T ’ ⊂ T, the position information of q places is called, and the set of the distances required for the unloading vehicle to reach the q places from the factory entrance is obtained to be D= { D 1 ，D 2 ，…，D q And calling the number of vehicles parked in the circular area where the q fields are located when the unloading vehicles enter the factory areaAggregate b= { B 1 ，B 2 ，…，B q }, B ⊂ A, according to the formula W v =1/[(T v ’ /∑ q v=1 (T v ’ ))+(D v /∑ q v=1 (D v ))+(B v /∑ q v=1 (B v ))]Calculating the suitability W of a random place for storing goods and materials of the unloading vehicle v Wherein T is v ’ Representing the time period required for completing all materials after continuously storing the materials loaded by the unloading vehicle in the v-th place among q places to be selected for unloading the vehicles, D v Indicating the distance required by the unloading vehicle to reach the v-th place from the entrance of the factory, B v Representing the number of vehicles parked in the circular area where the v-th field of the unloading vehicles enters the factory area, and obtaining the suitability degree set of the q places for storing the loading materials of the unloading vehicles as W= { W 1 ，W 2 ，…，W v ，…，W q Selecting a place with the highest suitability as a discharge place to store materials loaded by the discharge vehicle;
after screening out some places with short duration, carrying out secondary selection of the best place as a discharge place, combining the environmental information around the screened places, including the distance of vehicles reaching the screened places and the information of the number of parked vehicles around the places, carrying out secondary selection, wherein the shorter the distance is, the less the vehicles are parked, the faster the vehicles reach the corresponding places are judged, the difficulty of parking and discharging is lower, combining the three parameters to select the most suitable places as discharge places, thereby being beneficial to maintaining the balance degree of the material quantity for product production stored in different places in a factory, reducing the occurrence of the phenomenon of stock accumulation of part places, helping the vehicles to finish discharging as soon as possible, and relieving the situation of logistical vehicle jam in the factory.
Compared with the prior art, the invention has the following beneficial effects:
according to the invention, by monitoring the vehicles entering and exiting in the factory in real time and reminding the unloading vehicles to register and fill in information, the potential safety hazard in the factory is reduced; the method comprises the steps of collecting and analyzing historical data of materials consumed during product production at different places in a factory by a big data technology, establishing a material consumption completion time length prediction model, predicting time lengths required by all materials to be completed when the materials are loaded by a loading vehicle at the different places, establishing a prediction model for the different places and predicting, screening out unloading places to be selected for the second time by comparing predicted time length parameters, considering that the time lengths required by all materials to be completed at the plurality of places are similar, and the probability of material accumulation in the future of the corresponding places is similar, not only screening out places with the shortest time length, but also screening out places with partial short time lengths by grouping and selecting the best grouping result, and then selecting unloading places for the second time by taking other factors into consideration, thereby improving the accuracy of selection results;
after screening out the places with short time length, carrying out secondary selection on the optimal places as unloading places, combining the environment information around the screened places, including the distance of vehicles reaching the screened places and the information of the number of vehicles parked around the places, carrying out secondary selection on the optimal places as unloading places, combining the three parameters, and being beneficial to maintaining the balance degree of the material quantity for product production stored in different places in a factory, reducing the occurrence of the stock accumulation phenomenon of part of places, helping vehicles to finish unloading as soon as possible, and relieving the condition of logistical vehicle congestion in the factory.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings:
FIG. 1 is a block chain based architecture diagram of a product in-plant logistics management system of the present invention;
FIG. 2 is a flow chart of a method for managing logistics in a product factory based on blockchain in accordance with the present invention.
The specific embodiment is as follows: the preferred embodiments of the present invention will be described below with reference to the accompanying drawings, it being understood that the preferred embodiments described herein are for illustration and explanation of the present invention only, and are not intended to limit the present invention.
The invention is further described below with reference to fig. 1-2 and the specific embodiments.
Example 1: as shown in fig. 1, the present embodiment provides a product factory logistics management system based on block chain, the system includes: the logistics monitoring management module is used for monitoring the entry and exit of logistics vehicles in a factory, collecting historical data of product production in all places in the factory through the product information collection module, uploading the collected data to a blockchain, analyzing the historical data through the target screening module to be selected, screening out places to be selected for unloading the vehicles, analyzing environmental information of the places to be selected through the target selection management module, and selecting out final unloading places for storing the vehicle materials.
The logistics monitoring management module comprises a vehicle access monitoring unit, a vehicle registration management unit and a vehicle information acquisition unit, wherein the vehicle access monitoring unit is used for monitoring vehicles in and out of a factory in real time, the vehicle registration management unit is used for sending reminding information of vehicle information registration when a discharged vehicle is monitored to enter the factory, the vehicle information acquisition unit is used for acquiring information of the quantity of materials to be discharged in the vehicle after the vehicle is subjected to information registration, and the materials refer to materials for product production.
The product information acquisition module comprises a place information acquisition unit and a production information acquisition unit, wherein the place information acquisition unit is used for acquiring place position information for product production and number information of vehicles parked outside a place in a factory, the production information acquisition unit is used for acquiring material information consumed in the process of product production and material number information stored in the place in all places in the factory, the acquired information of different places is uploaded to a blockchain, and the information of different places is stored in different blockchain nodes.
The to-be-selected target screening module comprises a production information analysis unit, a finishing time length prediction unit and a to-be-selected place screening unit, wherein the production information analysis unit is used for calling material information consumed in different places when products are produced, a material consumption finishing time length prediction model of different places is built according to the material information, the material is the same as the loading material of the unloading vehicle, the finishing time length prediction unit is used for substituting the sum of the loading material quantity of the unloading vehicle and the existing material quantity in the places into the material consumption finishing time length prediction model, predicting time lengths required by all the materials consumed by different places, and the to-be-selected place screening unit is used for comparing the predicted time lengths and screening out target places to be selected.
The target selection management module comprises a place information calling unit, a suitability degree analysis unit and a discharge place selection unit, wherein the place information calling unit is used for calling place position information for product production and information of the number of vehicles parked outside the place in a factory area to the suitability degree analysis unit, the suitability degree analysis unit is used for analyzing the suitability degree of the goods loaded by the discharge vehicles stored in different places, the discharge place selection unit is used for comparing the suitability degree, the place with the highest suitability degree is selected as the goods loaded by the discharge vehicles stored in the discharge place, and the discharge place position information is sent to a discharge vehicle driver terminal.
Example 2: as shown in fig. 2, the present embodiment provides a method for managing logistics in a product factory based on a blockchain, which is implemented based on the logistics management system in the embodiment, and specifically includes the following steps:
s1: real-time monitoring is carried out on the entry and exit of logistics vehicles in the factory, reminding information of vehicle information registration is sent when the entry of unloading vehicles into the factory is monitored, and after the information registration is carried out on the vehicles, the quantity of materials needing to be unloaded in the vehicles is collected to be K;
for example: the quantity of materials to be unloaded in the vehicle is 180;
s2: collecting historical data of product production at all places in a factory, uploading the collected data to a blockchain, collecting material information consumed by all places in the factory when the product production is carried out for m times, and obtaining the material quantity set consumed by random places in the factory when the product production is carried out for m times, wherein the material quantity set consumed by the random places in the factory is F= { F 1 ，F 2 ，…，F m The time duration set spent for completing the corresponding material is t= { t } 1 ，t 2 ，…，t m The collection of the number of the stored materials which are the same as the loading materials of the unloading vehicles in the place is C= { C 1 ，C 2 ，…，C n Collecting position information of all places used for product production in a factory, collecting the number set of vehicles parked in a circular area with different places as circle centers and radius r as A= { A when unloading vehicles enter the factory 1 ，A 2 ，…，A n -wherein n represents the number of sites for product production within the factory floor;
for example: when a random field is collected and the production of products is carried out for 3 times, the quantity of consumed materials is collected as F= { F 1 ，F 2 ，F 3 The time duration set spent completing the corresponding supplies is t= { t } = {100, 58, 70} 1 ，t 2 ，t 3 = {15,7, 10}, unit is: on the day, the collection of the quantity of the stored materials which are the same as the loading materials of the unloading vehicles in the place is C= { C 1 ，C 2 ，C 3 ，C 4 ，C 5 ，C 6 ，C 7 The method comprises the steps of collecting position information of all places used for product production in a factory area, collecting the number set of vehicles parked in a circular area with different places as circle centers and a radius of 15 meters when unloading vehicles enter the factory area, wherein the number set is A= { A 1 ，A 2 ，A 3 ，A 4 ，A 5 ，A 6 ，A 7 }={5，4，2，8，7，12，10}，
S3: analyzing historical data, establishing a material consumption completion duration prediction model of different places, and calling a training sample to be { (F) 1 ，t 1 ），（F 2 ，t 2 ），（F 3 ，t 3 ) Performing straight line fitting on the training samples, and establishing a material consumption completion duration prediction model of a random place as follows: y=α×x+β, where α represents bias and β represents intercept, and the final material consumption completion duration prediction model is obtained by solving α and β: alpha= [ m ] m i=1 (F i ×t i )-∑ m i=1 (F i )∑ m i=1 (t i )]/m∑ m i=1 (F i ) 2 -(∑ m i=1 (F i )) 2 ≈-1.67，β=[∑ m i=1 (t i )-α∑ m i=1 (F i )]M.apprxeq. 412.76 gives y= -1.67x+412.76, where F i Representing the quantity of materials consumed by a random place in a factory when the product is produced for the ith time before, t i Representing the time spent on completing the corresponding material when the product production is carried out for the ith time;
s4: predicting the time for completing the consumption of all materials in different places, screening the places to be selected for unloading the vehicles, and obtaining the number of materials stored in the corresponding places and the same as the number of the materials loaded by the unloading vehicles as C 1 =50, mix C j And substituting K into a material consumption completion duration prediction model of the corresponding place: let x=c 1 +k=230, and the duration of time required for completing all materials after the corresponding place continues to store the unloading vehicle and load the materials is predicted to be T 1 ，T 1 =α*(C j +K) +beta.about.29, the duration set for the consumption of all materials after the loading materials of the unloading vehicles are continuously stored in 7 places is predicted to be T= { T 1 ，T 2 ，T 3 ，T 4 ，T 5 ，T 6 ，T 7 The method comprises the steps of (1) arranging time lengths from small to large in a sequence of (i) } = {29, 12, 15, 23, 30, 18, 25}, dividing 7 places into 3 groups according to time sizes, wherein the number of places in each group is not less than 1, the time length required for consuming all materials after loading materials of unloading vehicles are continuously stored in all places in the former group is less than that in the latter group, and a random grouping result is obtained: the corresponding time duration sets of each group of places are {12, 15}, {18, 23, 25}, and {29, 30}, respectively, and in the corresponding grouping result, the average time duration set required by each group of places in 3 groups to consume all materials after continuing to store unloading vehicle loading materials is V= { V 1 ，V 2 ，V 3 } = {13.5, 22, 29.5}, according to formula p= [ (Σ) g e=1 (V e -(∑ g e=1 V e )/g) 2 )/g] 1/2 Selecting the optimal grouping result to obtain P (approximately 6.5), wherein P represents the degree of dispersion of g inter-group parameters in a random grouping result, V e The method comprises the steps of representing the average time length required by finishing all materials after the e-th group place in g groups continuously stores unloading vehicles to load the materials in the random grouping results, selecting the grouping result with the largest discrete degree as the optimal grouping result, and obtaining the optimal grouping result as follows: the corresponding duration sets of each group of places are {12, 15, 18}, {23, 25} and {29, 30}, and the first group of places are selected as places to be selected for unloading the vehicle;
s5: analyzing environmental information of places to be selected, selecting a final unloading place for storing vehicle materials, screening out 3 places to be selected for unloading the vehicles, and predicting that the duration set required by the 3 places to be selected for unloading the vehicles to consume all materials after continuously storing unloading vehicle loading materials is T ’ ={T 1 ’ ，T 2 ’ ，T 3 ’ }={12，15，18}，T ’ ⊂ T, the position information of 3 places is called, and the set of the distances required for the unloading vehicle to reach q places from the factory entrance is obtained to be D= { D 1 ，D 2 ，D 3 The number of vehicles parked in the circular area where q fields are located when the unloaded vehicle enters the factory area is called b= { B } = {100, 134, 125} 1 ，B 2 ，B 3 } = {4,2, 12}, B ⊂ a, according to formula W v =1/[(T v ’ /∑ q v=1 (T v ’ ))+(D v /∑ q v=1 (D v ))+(B v /∑ q v=1 (B v ))]Calculating the suitability W of a random place for storing goods and materials of the unloading vehicle v Wherein T is v ’ Representing the time period required for completing all materials after continuously storing the materials loaded by the unloading vehicle in the v-th place among q places to be selected for unloading the vehicles, D v Indicating the distance required by the unloading vehicle to reach the v-th place from the entrance of the factory, B v Representing the number of vehicles parked in the circular area where the v-th field of the unloading vehicles enters the factory area, and obtaining the suitability degree set of the q places for storing the loading materials of the unloading vehicles as W= { W 1 ，W 2 ，W 3 The most suitable place, i.e., W, is selected from the range of = {1.30,1.22,0.71} 1 The corresponding place is used as a discharging place for storing materials loaded by the discharging vehicle.
Finally, it should be noted that: the foregoing is merely a preferred example of the present invention, and the present invention is not limited thereto, but it is to be understood that modifications and equivalents of some of the technical features described in the foregoing embodiments may be made by those skilled in the art, although the present invention has been described in detail with reference to the foregoing embodiments. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (7)
1. A logistics management method in a product factory based on a block chain is characterized by comprising the following steps of: the method comprises the following steps:
s1: real-time monitoring is carried out on the entry and exit of logistics vehicles in the factory;
s2: collecting historical data of product production at all places in a factory, and uploading the collected data to a blockchain;
s3: analyzing historical data and establishing a material consumption completion duration prediction model of different places;
s4: predicting the time length required by different places to finish all materials, and screening the places to be selected for unloading the vehicles;
s5: analyzing environmental information of a place to be selected, and selecting a final unloading place for storing vehicle materials;
in step S1: the method comprises the steps that reminding information of vehicle information registration is sent when a discharged vehicle is monitored to enter a factory, and after the vehicle is subjected to information registration, the quantity of materials needing to be discharged in the vehicle is collected to be K;
in step S2: collecting all fields in a factory area for m timesThe material information consumed during the production of the product is obtained, and when the product is produced m times by randomly one field in the factory, the quantity of the consumed material is collected as F= { F 1 ，F 2 ，…，F m The time duration set spent for completing the corresponding material is t= { t } 1 ，t 2 ，…，t m The collection of the number of the stored materials which are the same as the loading materials of the unloading vehicles in the place is C= { C 1 ，C 2 ，…，C n Collecting position information of all places used for product production in a factory, collecting the number set of vehicles parked in a circular area with different places as circle centers and radius r as A= { A when unloading vehicles enter the factory 1 ，A 2 ，…，A n -wherein n represents the number of sites for product production within the factory floor;
in step S3: call training sample { (F) 1 ，t 1 )，(F 2 ，t 2 )，…，(F m ，t m ) Performing straight line fitting on the training samples, and establishing a material consumption completion duration prediction model of a random place as follows: y=α×x+β, where α represents bias and β represents intercept, and the final material consumption completion duration prediction model is obtained by solving α and β:
α＝[m∑ m i＝1 (F i ×t i )-∑ m i＝1 (F i )∑ m i＝1 (t i )]/m∑ m i＝1 (F i ) 2 -(∑ m i＝1 (F i )) 2 ；
β＝[∑ m i＝1 (t i )-α∑ m i＝1 (F i )]/m；
wherein F is i Representing the quantity of materials consumed by a random place in a factory when the product is produced for the ith time before, t i Representing the time spent on completing the corresponding material when the product production is carried out for the ith time;
in step S4: the number of the stored goods and materials which are the same as the goods and materials loaded by the unloading vehicle in the corresponding place is obtained to be C j C is carried out by j And substituting K into a material consumption completion duration prediction model of the corresponding place: let x=c j +K, the time length required for the consumption of all materials after the loading of the unloading vehicle in the corresponding place is predicted to be T j ，T j ＝α*(C j +K) +beta, and the duration set for consuming and completing all materials after the unloading vehicle and the loading materials are continuously stored in n places is predicted to be T= { T 1 ，T 2 ，…，T j ，…，T n Arranging the time lengths in order from small to large, dividing n places into g groups according to the time sizes, wherein the number of places in each group is not less than 1, and obtaining an average time length set V= { V required by finishing all materials after each group of places in the g groups continuously store unloading vehicles and load the materials in a random grouping result 1 ，V 2 ，…，V g According to the formula p= [ (Σ) g e＝1 (V e -(∑ g e＝1 V e )/g) 2 )/g] 1/2 Selecting the optimal grouping result, wherein P represents the degree of dispersion of g inter-group parameters in a random grouping result, V e And (3) representing the average time length required for finishing all materials after the e-th group of places in the g groups continuously store the unloading vehicles to load the materials in the random grouping results, selecting the grouping result with the largest discrete degree as the optimal grouping result, and screening the first group of places from the optimal grouping result as the places to be selected for unloading the vehicles.
2. The blockchain-based product in-plant logistics management method of claim 1, wherein: in step S5: screening q places to be selected for unloading the vehicles, predicting that the duration set required by the q places to be selected for unloading the vehicles to consume all materials after continuously storing the unloaded vehicles and loading the materials is T ’ ＝{T 1 ’ ，T 2 ’ ，…，T q ’ }，The position information of q places is called, and a path set required by the unloading vehicle to reach the q places from the factory entrance is obtained to be D= { D 1 ，D 2 ，…，D q And calling the number set of vehicles parked in the round area where the q fields are located when the unloading vehicles enter the factory area to be B= { B 1 ，B 2 ，…，B q }，/>According to formula W v ＝1/[(T v ’ /∑ q v＝1 (T v ’ ))+(D v /∑ q v＝1 (D v ))+(B v /∑ q v＝1 (B v ))]Calculating the suitability W of a random place for storing goods and materials of the unloading vehicle v Wherein T is v ’ Representing the time period required for completing all materials after continuously storing the materials loaded by the unloading vehicle in the v-th place among q places to be selected for unloading the vehicles, D v Indicating the distance required by the unloading vehicle to reach the v-th place from the entrance of the factory, B v Representing the number of vehicles parked in the circular area where the v-th field of the unloading vehicles enters the factory area, and obtaining the suitability degree set of the q places for storing the loading materials of the unloading vehicles as W= { W 1 ，W 2 ，…，W v ，…，W q And selecting the place with the highest suitability as a discharge place to store the materials loaded by the discharge vehicle.
3. A blockchain-based product in-plant logistics management system, applied to the blockchain-based product in-plant logistics management method as set forth in claim 1, characterized in that: the system comprises: the system comprises a logistics monitoring management module, a product information acquisition module, a target screening module to be selected and a target selection management module;
the output end of the logistics monitoring management module is connected with the input end of the target screening module to be selected, the output end of the product information acquisition module is connected with the input ends of the target screening module to be selected and the target selection management module, and the output end of the target screening module to be selected is connected with the input end of the target selection management module;
the logistics vehicles in the factory are monitored in real time by the logistics monitoring management module;
the historical data of product production at all places in a factory are collected through the product information collection module, and the collected data are uploaded to a blockchain;
analyzing historical data through the target screening module to be selected, and screening out a place to be selected for unloading the vehicle;
and analyzing the environmental information of the places to be selected by the target selection management module, and selecting the final unloading place for storing the vehicle materials.
4. A blockchain-based product in-plant logistics management system of claim 3, wherein: the logistics monitoring management module comprises a vehicle entrance and exit monitoring unit, a vehicle registration management unit and a vehicle information acquisition unit;
the output end of the vehicle in-out monitoring unit is connected with the input end of the vehicle registration management unit, and the output end of the vehicle registration management unit is connected with the input end of the vehicle information acquisition unit;
the vehicle access monitoring unit is used for monitoring vehicles in and out of the factory in real time;
the vehicle registration management unit is used for sending reminding information of vehicle information registration when the unloading vehicle is monitored to enter the factory;
the vehicle information acquisition unit is used for acquiring the information of the quantity of materials to be unloaded in the vehicle after the vehicle is subjected to information registration.
5. The blockchain-based product in-plant logistics management system of claim 4, wherein: the product information acquisition module comprises a place information acquisition unit and a production information acquisition unit;
the place information acquisition unit is used for acquiring place position information for product production and number information of vehicles parked outside the place in the factory;
the production information acquisition unit is used for acquiring material information consumed by all places in the factory during production of products and the stored material quantity information in the places, and uploading the acquired information of different places to the blockchain.
6. The blockchain-based product in-plant logistics management system of claim 5, wherein: the target screening module to be selected comprises a production information analysis unit, a completion duration prediction unit and a place screening unit to be selected;
the input end of the production information analysis unit is connected with the output end of the production information acquisition unit, the output ends of the production information analysis unit and the vehicle information acquisition unit are connected with the input end of the completion time length prediction unit, and the output end of the completion time length prediction unit is connected with the input end of the to-be-selected place screening unit;
the production information analysis unit is used for calling material information consumed in different places when the production of products is carried out, and building material consumption completion time prediction models of different places according to the material information, wherein the material is the same as the material loaded by the unloading vehicle;
the completion time length prediction unit is used for substituting the sum of the loading material quantity of the unloading vehicle and the existing material quantity in the place into a material consumption completion time length prediction model to predict the time length required by different places to consume and complete all the materials;
the to-be-selected place screening unit is used for comparing the predicted time length and screening out the to-be-selected target place.
7. The blockchain-based product in-plant logistics management system of claim 6, wherein: the target selection management module comprises a place information calling unit, a fitness degree analysis unit and a discharge place selection unit;
the input end of the place information calling unit is connected with the output ends of the place screening unit and the place information collecting unit to be selected, the output end of the place information calling unit is connected with the input end of the suitability degree analyzing unit, and the output end of the suitability degree analyzing unit is connected with the input end of the unloading place selecting unit;
the place information calling unit is used for calling all place position information for product production and the number information of vehicles parked outside the place in the factory to the fitness analysis unit;
the suitability degree analysis unit is used for analyzing the suitability degree of loading materials of the storage unloading vehicles at different places;
the unloading place selecting unit is used for selecting a place with the highest suitability degree as an unloading place to store goods loaded by the unloading vehicle, and sending unloading place position information to an unloading vehicle driver terminal.
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Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2016057882A (en) * | 2014-09-10 | 2016-04-21 | 前田建設工業株式会社 | Material transportation history administering system in civil engineering work |
| JP2017187854A (en) * | 2016-04-01 | 2017-10-12 | Ｊｆｅスチール株式会社 | Ship assignment plan creation method, iron mill operation method, and ship assignment plan creation device |
| KR20190013415A (en) * | 2017-07-28 | 2019-02-11 | 인하대학교 산학협력단 | Appartus for selection of shipbuilding block based on decision tree learning and method thereof |
| CN109377047A (en) * | 2018-10-22 | 2019-02-22 | 广州汽车集团乘用车（杭州）有限公司 | Intelligent logistic Monitoring and managing system |
| CN111105084A (en) * | 2019-12-10 | 2020-05-05 | 珠海格力电器股份有限公司 | Logistics information processing method, device and equipment and computer storage medium |
| CN113706085A (en) * | 2021-10-29 | 2021-11-26 | 南京我乐家居智能制造有限公司 | Intelligent manufacturing factory product warehouse logistics informatization management method and cloud management system |
| CN114154931A (en) * | 2021-12-06 | 2022-03-08 | 吴旻 | Storage management system and method based on Internet of things and big data |
| WO2022185616A1 (en) * | 2021-03-05 | 2022-09-09 | 株式会社日立製作所 | Warehousing management system, and control device for cargo-handling vehicles |
| CN115293390A (en) * | 2021-04-15 | 2022-11-04 | 山东金钟科技集团股份有限公司 | System and method for planning logistics outside plant |
| CN115630673A (en) * | 2022-10-28 | 2023-01-20 | 中国矿业大学 | Coal mine material intelligent distribution method based on Internet of things |
| CN115699050A (en) * | 2019-11-05 | 2023-02-03 | 强力价值链网络投资组合2019有限公司 | Value chain network control tower and enterprise management platform |
-
2023
- 2023-08-28 CN CN202311090369.1A patent/CN117114534B/en active Active
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2016057882A (en) * | 2014-09-10 | 2016-04-21 | 前田建設工業株式会社 | Material transportation history administering system in civil engineering work |
| JP2017187854A (en) * | 2016-04-01 | 2017-10-12 | Ｊｆｅスチール株式会社 | Ship assignment plan creation method, iron mill operation method, and ship assignment plan creation device |
| KR20190013415A (en) * | 2017-07-28 | 2019-02-11 | 인하대학교 산학협력단 | Appartus for selection of shipbuilding block based on decision tree learning and method thereof |
| CN109377047A (en) * | 2018-10-22 | 2019-02-22 | 广州汽车集团乘用车（杭州）有限公司 | Intelligent logistic Monitoring and managing system |
| CN115699050A (en) * | 2019-11-05 | 2023-02-03 | 强力价值链网络投资组合2019有限公司 | Value chain network control tower and enterprise management platform |
| CN111105084A (en) * | 2019-12-10 | 2020-05-05 | 珠海格力电器股份有限公司 | Logistics information processing method, device and equipment and computer storage medium |
| WO2022185616A1 (en) * | 2021-03-05 | 2022-09-09 | 株式会社日立製作所 | Warehousing management system, and control device for cargo-handling vehicles |
| CN115293390A (en) * | 2021-04-15 | 2022-11-04 | 山东金钟科技集团股份有限公司 | System and method for planning logistics outside plant |
| CN113706085A (en) * | 2021-10-29 | 2021-11-26 | 南京我乐家居智能制造有限公司 | Intelligent manufacturing factory product warehouse logistics informatization management method and cloud management system |
| CN114154931A (en) * | 2021-12-06 | 2022-03-08 | 吴旻 | Storage management system and method based on Internet of things and big data |
| CN115630673A (en) * | 2022-10-28 | 2023-01-20 | 中国矿业大学 | Coal mine material intelligent distribution method based on Internet of things |
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