CN113934217B - Intelligent scheduling processing system based on 5G - Google Patents

Intelligent scheduling processing system based on 5G Download PDF

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CN113934217B
CN113934217B CN202111532862.5A CN202111532862A CN113934217B CN 113934217 B CN113934217 B CN 113934217B CN 202111532862 A CN202111532862 A CN 202111532862A CN 113934217 B CN113934217 B CN 113934217B
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CN113934217A (en
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彭志君
曹青兰
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Nanjing Jiangmen Information Technology Co ltd
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Nanjing Redoor Information Technology Co ltd
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    • G—PHYSICS
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    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
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    • G05D1/02—Control of position or course in two dimensions
    • G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
    • G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
    • G05D1/0225—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory involving docking at a fixed facility, e.g. base station or loading bay
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Abstract

The invention discloses a 5G-based intelligent scheduling processing system, and belongs to the technical field of intelligent warehousing. The system comprises a 5G communication module, a newly-added scheduling prediction module, a driving distance adjusting module, an acquisition module, a path planning module and a warning module; the output end of the 5G communication module is connected with the input end of the newly increased scheduling prediction module; the output end of the newly-added scheduling prediction module is connected with the input end of the driving distance adjusting module; the output end of the driving distance adjusting module is connected with the input end of the acquisition module; the output end of the acquisition module is connected with the input end of the path planning module; the output end of the path planning module is connected with the input end of the warning module. The method can fill a plurality of blanks in the field of domestic AGV, solves the trouble caused by the requirement of sudden addition, can adjust and analyze the operation of the AGV, and has the marker post significance for the digital transformation of the storage industry.

Description

Intelligent scheduling processing system based on 5G
Technical Field
The invention relates to the technical field of intelligent warehousing, in particular to an intelligent scheduling processing system based on 5G.
Background
Wisdom storage, it is a logistics storage management system platform, can realize the digitization and the intellectuality at the storage in-process, in wisdom storage, there are numerous AGV (automatic handling dolly), in present traditional technological means, generally adopt the wiFi network to control AGV transport and unload, but the wiFi network signal interference is big, poor stability, if 200 AGV are connected in parallel, will shut down 10 times every day on average, and resume about 2 minutes consuming time at every turn, it is extremely inconvenient, seriously influence work efficiency.
In addition, in the smart storage process, a certain customer new demand is usually generated, for example, a certain customer suddenly carries out goods picking in advance, or under the influence of some inelegant force factors, for example, weather causes a plurality of customers to carry goods after delay, when a series of new demands occur, the prior art does not have a certain solution, and usually, the new demands are manually adjusted, or the new demands are directly carried by manual work, so that the high-efficiency capacity of the smart storage is lost. Meanwhile, in the prior art, influence factors in the process of AGV advancing are not analyzed, so that the traveling speed of the AGV is low, and the working efficiency is low.
Disclosure of Invention
The invention aims to provide a 5G-based intelligent scheduling processing system to solve the problems in the background technology.
In order to solve the technical problems, the invention provides the following technical scheme: A5G-based intelligent scheduling processing system comprises a 5G communication module, a newly-added scheduling prediction module, a driving distance adjusting module, an acquisition module, a path planning module and a warning module;
the 5G communication module is used for constructing a 5G communication network and reducing the system delay; the new scheduling prediction module is used for establishing a prediction model according to the historical requirements of the planning client and predicting the generation probability of the new scheduling; the travel distance adjusting module is used for determining the travel distance of the AGV in each batch according to the prediction result of the newly-added scheduling prediction module; the acquisition module is used for acquiring the driving information of the AGV, including the position and the speed; the path planning module is used for planning the AGV path when a new demand appears, and acquiring an optimal path; the warning module is used for adding warning marks to the AGV which participates in the newly added task and reminding a manager of checking in time;
the output end of the 5G communication module is connected with the input end of the newly increased scheduling prediction module; the output end of the newly-added scheduling prediction module is connected with the input end of the driving distance adjusting module; the output end of the driving distance adjusting module is connected with the input end of the acquisition module; the output end of the acquisition module is connected with the input end of the path planning module; the output end of the path planning module is connected with the input end of the warning module.
According to the technical scheme, the 5G communication module comprises a 5G network establishing unit and a delay speed measuring unit;
the 5G network establishing unit is used for establishing a 5G transmission network in the system, and the application of the AGV in intelligent storage and material distribution is realized by utilizing a 5G module and a 5G private network; the delay speed measurement unit is used for detecting the delay speed of the 5G network and recording the delay speed;
the output end of the 5G network establishing unit is connected with the input end of the delay speed measuring unit; and the output end of the delay speed measurement unit is connected with the input end of the newly added scheduling prediction module.
According to the technical scheme, the newly added scheduling prediction module comprises a prediction model establishing unit and an output unit;
the prediction model establishing unit is used for establishing a prediction model according to the historical requirements of the planned customers, predicting the newly added requirements of the users and establishing the prediction model; the output unit is used for outputting the prediction result to the driving distance adjusting module;
the output end of the prediction model establishing unit is connected with the input end of the output unit; and the output end of the output unit is connected with the input end of the driving distance adjusting module.
In the prediction model building unit, the present application uses a grey prediction model, since grey system theory considers that predicting a system containing both known and unknown or non-deterministic information is a prediction of time-dependent grey processes that vary within a certain orientation. In the application, the phenomenon displayed by the newly added requirement data of the client is random and disorderly, but is ordered and bounded after all, so that the data set has a potential rule, and belongs to a gray system, so that the gray prediction is utilized to identify the degree of dissimilarity of development trends among system factors, namely, correlation analysis of historical data and predicted data is carried out, the original data is generated and processed to find out the rule of system change, a data sequence with strong regularity is generated, and then a corresponding differential equation model is established to predict the future development trend condition of the newly added requirement of the client.
According to the above technical solution, the prediction model establishing unit includes:
s4-1, obtaining the historical demand data of the planning client, wherein the historical demand data comprises the times and the quantity of newly added demands of each client on the basis of the original demands, calling the historical demand data to establish an original data structure column S0，S0={X0（1），X0（2），X0（3），……，X0（n）}；
S4-2, pair S0Performing gray accumulation generation to generate S1In which S is1Is S0The AGO sequence of (a); namely S1={X1（1），X1（2），X1（3），……，X1（n）}；
Satisfies the following conditions:
Figure DEST_PATH_IMAGE001
wherein k =1, 2, … …, n;
s4-3, to S1Calculating the adjacent mean value to obtain S1Has the MEAN sequence of (1), denoted as Z1I.e. Z1={Z1（1），Z1（2），Z1（3），……，Z1（n）}；
Satisfies the following conditions:
Figure 950060DEST_PATH_IMAGE002
the calculation results are entered into a matrix B, Y;
wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE003
；
Figure 401770DEST_PATH_IMAGE004
；
s4-4, establishing S1The whitening differential equation of (a) is:
Figure 482858DEST_PATH_IMAGE005
wherein a is the developing ash number; b is endogenous control ash number;
s4-5, setting
Figure 629806DEST_PATH_IMAGE006
For the parameter vector to be estimated, it can be known that:
Figure DEST_PATH_IMAGE007
by using the least square method, it can be known
Figure 861067DEST_PATH_IMAGE008
；
S4-6, establishing
Figure 698442DEST_PATH_IMAGE009
And S0Defining a correlation coefficient, wherein the correlation coefficient is as follows:
Figure 419273DEST_PATH_IMAGE010
wherein the content of the first and second substances,
Figure 53517DEST_PATH_IMAGE009
for outputting sequences for predictive models, i.e.
Figure 291731DEST_PATH_IMAGE009
=
Figure 124558DEST_PATH_IMAGE011
；
Figure 219553DEST_PATH_IMAGE012
Is S0And
Figure 528044DEST_PATH_IMAGE009
absolute error of kth point;
Figure 429003DEST_PATH_IMAGE013
is a two-level minimum difference;
Figure 726124DEST_PATH_IMAGE014
is the two-stage maximum difference;
Figure 257599DEST_PATH_IMAGE015
taking 0.5 as resolution;
s4-7, calculating to obtain the output of the prediction model:
Figure 397594DEST_PATH_IMAGE016
the final prediction result of the newly added requirement of the client is recorded as J.
According to the scheme, the prediction model weakens the randomness of the system, the disordered original sequence can show a certain rule, the rule is not obvious and becomes obvious, residual error identification can be carried out after modeling, and high prediction precision can be obtained even if less historical data and random distribution exist.
Due to the influence of a plurality of objective factors, the predicted values really have practical significance, the predicted values with higher prediction accuracy are only the first predicted value and the second predicted value in the whole prediction sequence, and the more distant predicted values only reflect the trend of future development.
According to the technical scheme, the driving distance adjusting module comprises a judging unit and an adjusting unit;
the judging unit is used for judging whether the driving distance needs to be adjusted or not according to the prediction result output by the output unit and inputting the final result into the adjusting unit; the adjusting unit is used for sending an instruction and requesting the system to adjust the driving distance of the AGV;
the output end of the judging unit is connected with the input end of the adjusting unit; the output end of the adjusting unit is connected with the input end of the path planning module;
the driving distance is adjusted mainly because the AGV generally advances according to the driving distance set by the system to ensure that no collision occurs, and once a new requirement occurs, the AGV may be advanced to reverse the vehicle after passing through a turning point of a driving channel and cannot change a path to turn under the condition of turning, so that collision is easy to occur, therefore, when the new requirement occurs at a high probability after prediction of a prediction model, the driving distance needs to be adjusted, and specific adjustment is specifically analyzed according to the set driving speed and the set time.
The judging unit further includes:
establishing a judgment threshold, denoted as Jmax；
If J exceeds JmaxAdjusting the traveling distance of the AGV; if not, not adjusting;
the adjusting comprises:
acquiring set driving distance of AGV and recording the distance as H0；
Acquiring set driving speed v of AGV0；
Acquiring set reversing speed v of AGV1；
Establishing a maximum turn time T1，T1The turning demand time is the turning demand time when the distance between the middle points of the front AGV trolley and the driving channel is smaller than the distance between the middle points of the rear AGV trolley and the driving channel, at the moment, the front AGV trolley needs to back the car to the middle point of the driving channel firstly, then turns, and the backing time is recorded as t2；
Calculating the maximum driving distance H1：
H1=v1*t2+v0*（T1+t2）
At H1Over H0When the running distance is adjusted to be H1(ii) a At H1Does not exceed H0While keeping the running distance at H0。
According to the technical scheme, the acquisition module comprises a position information acquisition unit and a speed information acquisition unit;
the position information acquisition unit is used for acquiring the travelling position information of the AGV; the speed information acquisition unit is used for acquiring the driving speed information of the AGV;
the output end of the position information acquisition unit is connected with the input end of the path planning module; the output end of the speed information acquisition unit is connected with the input end of the path planning module.
According to the technical scheme, the path planning module comprises a selection unit and a path planning unit;
the selection unit is used for establishing a selection mark and adding and deleting inconsistent trolley paths; the path planning unit is used for planning the path of the AGV according to the collected information and the driving distance information of the AGV, and acquiring all feasible paths;
the output end of the selection unit is connected with the input end of the path planning unit; the output end of the path planning unit is connected with the input end of the warning module.
According to the above technical solution, the selecting unit further includes:
acquiring initial AGV trolley path information;
acquiring a pickup position of a newly added demand, and deleting the AGV trolleys which do not pass through the pickup position in a return path;
after deletion, sequencing the rest AGV trolleys from small to large according to the distance between the AGV trolleys and the pick-up position of the newly added requirement, and recording the sequence as a set C = { y =1，y2，……，yi}。
According to the above technical solution, the path planning unit further includes:
acquiring the current load of the AGV in the set C;
selecting any one of the trolleys in C, and marking as y0；
Obtaining y0Planning all turning points on the path, and acquiring turning angles of the turning points;
classifying the turning points into turning points before the picking position of the newly increased demand and turning points after the picking position of the newly increased demand;
establishing a turning angle
Figure 238597DEST_PATH_IMAGE017
Bearing capacity
Figure 46016DEST_PATH_IMAGE018
And take time
Figure 748393DEST_PATH_IMAGE019
Functional relationship of (a):
Figure 251049DEST_PATH_IMAGE020
wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE021
for the influence of the load on the turning angle, the conversion factor is
Figure 415183DEST_PATH_IMAGE017
When the angle is larger than 90 degrees, the angle is changed,
Figure 14792DEST_PATH_IMAGE022
(ii) a In that
Figure 684808DEST_PATH_IMAGE017
When the angle is less than 90 degrees, the angle is adjusted,
Figure 940340DEST_PATH_IMAGE023
(ii) a Wherein the content of the first and second substances,
Figure 924476DEST_PATH_IMAGE024
、
Figure 100002_DEST_PATH_IMAGE025
all can be set numerical values;
Figure 362280DEST_PATH_IMAGE026
is the turning angle influence coefficient;
acquiring the bearing capacity of all turning points under a planned path;
increasing the load capacity of the newly-increased required pickup at a turning point behind the newly-increased required pickup position;
based on y0The turning angles of all turning points on the planned path are calculatedTime spent on planned paths;
and taking the path with the shortest time as the optimal path, and if two or more paths with the same shortest time exist, selecting the planned path of the AGV trolley which is farthest away from the pickup position of the newly added requirement at present as the optimal path on the basis of the sequence in the set C.
In the scheme, because the AGV car belongs to a mobile robot, the direction and the calculation angle are distinguished within a certain time when the AGV car turns, so that the AGV car can cause great influence on the AGV car under different bearing capacities and different turning angles, and because the warehouse has many corners, in an actual situation, turning is difficult to occur and is the turning angle of 90 degrees, and more situations are irregular terrains, so that the time consumption is analyzed, the time is selected as the optimal path, and the working efficiency can be effectively improved.
According to the technical scheme, the warning module comprises a warning mark unit and a light unit;
the warning mark unit is used for warning and marking the AGV participating in the newly added task in the system; the light unit is used for lightening light on the marked AGV and flickering to ensure that a manager can clearly know the light;
the output end of the warning mark unit is connected with the input end of the light unit.
Compared with the prior art, the invention has the following beneficial effects:
1. the invention can analyze from an irregular data sequence based on the demand data of historical clients, predict the newly increased demands of the clients in a period of time, ensure that the newly increased pick-up demands of the clients are met in the scheduling process, adapt to the scene of a single channel (namely only one vehicle can be walked on a driving channel) in a logistics warehouse, realize the intensive AGV operation and the cooperative multiple AGV operation, fill up the multiple blanks in the field of AGV in China, and have the benchmarking significance for the digital transformation of the warehousing industry;
2. according to the invention, the application of the AGV in intelligent storage and material distribution is realized by using the 5G module and the 5G private network, the defects of large signal interference, poor stability and insufficient coverage existing in the traditional AGV adopting WIFI network control are solved, and the operation efficiency is greatly improved;
3. the method is based on the idea that the AGV trolley can carry goods out of the warehouse while delivering the goods into the warehouse, so that the working efficiency is effectively improved, meanwhile, analysis of the bearing capacity and the turning angle is integrated, the planned path is summarized, the optimal path is selected, the time is prevented from being long, the waiting time of a client is reduced, and the satisfaction degree of the client is effectively improved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
FIG. 1 is a schematic diagram of an intelligent warehouse identifier of a 5G-based intelligent scheduling processing system according to the present invention;
FIG. 2 is a schematic diagram of a prediction model building procedure of the intelligent 5G-based scheduling processing system according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1-2, the present invention provides the following technical solutions:
A5G-based intelligent scheduling processing system comprises a 5G communication module, a newly-added scheduling prediction module, a driving distance adjusting module, an acquisition module, a path planning module and a warning module;
the 5G communication module is used for constructing a 5G communication network and reducing the system delay; the new scheduling prediction module is used for establishing a prediction model according to the historical requirements of the planning client and predicting the generation probability of the new scheduling; the travel distance adjusting module is used for determining the travel distance of the AGV in each batch according to the prediction result of the newly-added scheduling prediction module; the acquisition module is used for acquiring the driving information of the AGV, including the position and the speed; the path planning module is used for planning the AGV path when a new demand appears, and acquiring an optimal path; the warning module is used for adding warning marks to the AGV which participates in the newly added task and reminding a manager of checking in time;
the output end of the 5G communication module is connected with the input end of the newly increased scheduling prediction module; the output end of the newly-added scheduling prediction module is connected with the input end of the driving distance adjusting module; the output end of the driving distance adjusting module is connected with the input end of the acquisition module; the output end of the acquisition module is connected with the input end of the path planning module; the output end of the path planning module is connected with the input end of the warning module.
The 5G communication module comprises a 5G network establishing unit and a delay speed measuring unit;
the 5G network establishing unit is used for establishing a 5G transmission network in the system, and the application of the AGV in intelligent storage and material distribution is realized by utilizing a 5G module and a 5G private network; the delay speed measurement unit is used for detecting the delay speed of the 5G network and recording the delay speed;
the output end of the 5G network establishing unit is connected with the input end of the delay speed measuring unit; and the output end of the delay speed measurement unit is connected with the input end of the newly added scheduling prediction module.
The newly increased scheduling prediction module comprises a prediction model establishing unit and an output unit;
the prediction model establishing unit is used for establishing a prediction model according to the historical requirements of the planned customers, predicting the newly added requirements of the users and establishing the prediction model; the output unit is used for outputting the prediction result to the driving distance adjusting module;
the output end of the prediction model establishing unit is connected with the input end of the output unit; and the output end of the output unit is connected with the input end of the driving distance adjusting module.
The prediction model building unit includes:
s4-1, obtaining the historical demand data of the planning client, wherein the historical demand data comprises the times and the quantity of newly added demands of each client on the basis of the original demands, calling the historical demand data to establish an original data structure column S0，S0={X0（1），X0（2），X0（3），……，X0（n）}；
S4-2, pair S0Performing gray accumulation generation to generate S1In which S is1Is S0The AGO sequence of (a); namely S1={X1（1），X1（2），X1（3），……，X1（n）}；
Satisfies the following conditions:
Figure 203197DEST_PATH_IMAGE001
wherein k =1, 2, … …, n;
s4-3, to S1Calculating the adjacent mean value to obtain S1Has the MEAN sequence of (1), denoted as Z1I.e. Z1={Z1（1），Z1（2），Z1（3），……，Z1（n）}；
Satisfies the following conditions:
Figure 8342DEST_PATH_IMAGE002
the calculation results are entered into a matrix B, Y;
wherein the content of the first and second substances,
Figure 733852DEST_PATH_IMAGE003
；
Figure 104791DEST_PATH_IMAGE004
；
s4-4, establishing S1The whitening differential equation of (a) is:
Figure 319871DEST_PATH_IMAGE005
wherein a is the developing ash number; b is endogenous control ash number;
s4-5, setting
Figure 533684DEST_PATH_IMAGE006
For the parameter vector to be estimated, it can be known that:
Figure 921940DEST_PATH_IMAGE007
by using the least square method, it can be known
Figure 757172DEST_PATH_IMAGE008
；
S4-6, establishing
Figure 205471DEST_PATH_IMAGE009
And S0Defining a correlation coefficient, wherein the correlation coefficient is as follows:
Figure 985208DEST_PATH_IMAGE010
wherein the content of the first and second substances,
Figure 36209DEST_PATH_IMAGE009
for outputting sequences for predictive models, i.e.
Figure 381740DEST_PATH_IMAGE009
=
Figure 204203DEST_PATH_IMAGE011
；
Figure 877760DEST_PATH_IMAGE012
Is S0And
Figure 873398DEST_PATH_IMAGE009
absolute error of kth point;
Figure 11119DEST_PATH_IMAGE013
is a two-level minimum difference;
Figure 925854DEST_PATH_IMAGE014
is the two-stage maximum difference;
Figure 476921DEST_PATH_IMAGE015
taking 0.5 as resolution;
s4-7, calculating to obtain the output of the prediction model:
Figure 948354DEST_PATH_IMAGE016
the final prediction result of the newly added requirement of the client is recorded as J.
The driving distance adjusting module comprises a judging unit and an adjusting unit;
the judging unit is used for judging whether the driving distance needs to be adjusted or not according to the prediction result output by the output unit and inputting the final result into the adjusting unit; the adjusting unit is used for sending an instruction and requesting the system to adjust the driving distance of the AGV;
the output end of the judging unit is connected with the input end of the adjusting unit; the output end of the adjusting unit is connected with the input end of the path planning module;
the judging unit further includes:
establishing a judgment threshold, denoted as Jmax；
If J exceeds JmaxAdjusting the traveling distance of the AGV; if not, not adjusting;
the adjusting comprises:
acquiring set driving distance of AGV and recording the distance as H0；
Acquiring set driving speed v of AGV0；
Acquiring set reversing speed v of AGV1；
Establishing maximum turn timeT1，T1The turning demand time is the turning demand time when the distance between the middle points of the front AGV trolley and the driving channel is smaller than the distance between the middle points of the rear AGV trolley and the driving channel, at the moment, the front AGV trolley needs to back the car to the middle point of the driving channel firstly, then turns, and the backing time is recorded as t2；
Calculating the maximum driving distance H1：
H1=v1*t2+v0*（T1+t2）
At H1Over H0When the running distance is adjusted to be H1(ii) a At H1Does not exceed H0While keeping the running distance at H0。
The acquisition module comprises a position information acquisition unit and a speed information acquisition unit;
the position information acquisition unit is used for acquiring the travelling position information of the AGV; the speed information acquisition unit is used for acquiring the driving speed information of the AGV;
the output end of the position information acquisition unit is connected with the input end of the path planning module; the output end of the speed information acquisition unit is connected with the input end of the path planning module.
The path planning module comprises a selection unit and a path planning unit;
the selection unit is used for establishing a selection mark and adding and deleting inconsistent trolley paths; the path planning unit is used for planning the path of the AGV according to the collected information and the driving distance information of the AGV, and acquiring all feasible paths;
the output end of the selection unit is connected with the input end of the path planning unit; the output end of the path planning unit is connected with the input end of the warning module.
The selection unit further comprises:
acquiring initial AGV trolley path information;
acquiring a pickup position of a newly added demand, and deleting the AGV trolleys which do not pass through the pickup position in a return path;
after deletion, sequencing the rest AGV trolleys from small to large according to the distance between the AGV trolleys and the pick-up position of the newly added requirement, and recording the sequence as a set C = { y =1，y2，……，yi}。
The path planning unit further includes:
acquiring the current load of the AGV in the set C;
selecting any one of the trolleys in C, and marking as y0；
Obtaining y0Planning all turning points on the path, and acquiring turning angles of the turning points;
classifying the turning points into turning points before the picking position of the newly increased demand and turning points after the picking position of the newly increased demand;
establishing a turning angle
Figure 878263DEST_PATH_IMAGE017
Bearing capacity
Figure 104845DEST_PATH_IMAGE018
And take time
Figure 815312DEST_PATH_IMAGE019
Functional relationship of (a):
Figure 11807DEST_PATH_IMAGE020
wherein the content of the first and second substances,
Figure 452016DEST_PATH_IMAGE021
for the influence of the load on the turning angle, the conversion factor is
Figure 521603DEST_PATH_IMAGE017
When the angle is larger than 90 degrees, the angle is changed,
Figure 657049DEST_PATH_IMAGE022
(ii) a In that
Figure 532602DEST_PATH_IMAGE017
When the angle is less than 90 degrees, the angle is adjusted,
Figure 765000DEST_PATH_IMAGE023
(ii) a Wherein the content of the first and second substances,
Figure 926860DEST_PATH_IMAGE024
、
Figure 939815DEST_PATH_IMAGE025
all can be set numerical values;
Figure 556741DEST_PATH_IMAGE026
is the turning angle influence coefficient;
acquiring the bearing capacity of all turning points under a planned path;
increasing the load capacity of the newly-increased required pickup at a turning point behind the newly-increased required pickup position;
based on y0The turning angles of all turning points on the planning paths are calculated, and the time spent on each planning path is calculated;
and taking the path with the shortest time as the optimal path, and if two or more paths with the same shortest time exist, selecting the planned path of the AGV trolley which is farthest away from the pickup position of the newly added requirement at present as the optimal path on the basis of the sequence in the set C.
The warning module comprises a warning mark unit and a light unit;
the warning mark unit is used for warning and marking the AGV participating in the newly added task in the system; the light unit is used for lightening light on the marked AGV and flickering to ensure that a manager can clearly know the light;
the output end of the warning mark unit is connected with the input end of the light unit.
In this embodiment:
setting a new requirement proposed by a client, and requiring that the AGV trolley takes a part Mkg in the time of returning to the era;
acquiring initial AGV trolley path information;
acquiring a pickup position of a newly added demand, and deleting the AGV trolleys which do not pass through the pickup position in a return path;
after deletion, sequencing the rest AGV trolleys from small to large according to the distance between the AGV trolleys and the pick-up position of the newly added requirement, and recording the sequence as a set C = { y =1，y2，……，yi}。
The path planning unit further includes:
selecting any one of the trolleys in C, and marking as y0；
Obtaining y0Planning all turning points on the path, and acquiring turning angles of the turning points;
classifying the turning points into turning points before the picking position of the newly increased demand and turning points after the picking position of the newly increased demand;
establishing a turning angle
Figure 315750DEST_PATH_IMAGE017
Bearing capacity
Figure 789456DEST_PATH_IMAGE018
And take time
Figure 883183DEST_PATH_IMAGE019
Functional relationship of (a):
Figure 100538DEST_PATH_IMAGE020
wherein the content of the first and second substances,
Figure 979632DEST_PATH_IMAGE021
for the influence of the load on the turning angle, the conversion factor is
Figure 561923DEST_PATH_IMAGE017
When the angle is larger than 90 degrees, the angle is changed,
Figure 18313DEST_PATH_IMAGE022
(ii) a In that
Figure 898413DEST_PATH_IMAGE017
When the angle is less than 90 degrees, the angle is adjusted,
Figure 22227DEST_PATH_IMAGE023
(ii) a Wherein the content of the first and second substances,
Figure 40998DEST_PATH_IMAGE024
、
Figure 860050DEST_PATH_IMAGE025
all can be set numerical values;
Figure 419207DEST_PATH_IMAGE026
is the turning angle influence coefficient;
acquiring a pickup position of the newly added requirement Mkg, deleting AGV trolleys which do not pass through the pickup position in a return path, and remaining two trolleys after deletion;
after deletion, sequencing the rest AGV trolleys from small to large according to the distance between the AGV trolleys and the pick-up position of the newly added requirement, and recording the sequence as a set C = { y =1，y2}；
Respectively acquiring the number of turning points of the two trolleys under the planned path, wherein y13 in number; y is24 in number;
acquiring the bearing capacity of all turning points under a planned path;
increasing the load capacity of the newly-increased required pickup at a turning point behind the newly-increased required pickup position;
respectively denoted as w1, w2, w 3;
w11、w22、w33、w44；
respectively calculate y1，y2The time taken for (2);
in which y is found1Takes less time than y2(ii) a Thus with y1As an optimal path, and arrange y1Bringing back the newly added demand piece.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Finally, it should be noted that: although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that changes may be made in the embodiments and/or equivalents thereof without departing from the spirit and scope of the invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (8)

1. The utility model provides an intelligent scheduling processing system based on 5G which characterized in that: the system comprises a 5G communication module, a newly-added scheduling prediction module, a driving distance adjusting module, an acquisition module, a path planning module and a warning module;
the 5G communication module is used for constructing a 5G communication network and reducing the system delay; the new scheduling prediction module is used for establishing a prediction model according to the historical requirements of the planning client and predicting the generation probability of the new scheduling; the travel distance adjusting module is used for determining the travel distance of the AGV in each batch according to the prediction result of the newly-added scheduling prediction module; the acquisition module is used for acquiring the driving information of the AGV, including the position and the speed; the path planning module is used for planning the AGV path when a new demand appears, and acquiring an optimal path; the warning module is used for adding warning marks to the AGV which participates in the newly added task and reminding a manager of checking in time;
the output end of the 5G communication module is connected with the input end of the newly increased scheduling prediction module; the output end of the newly-added scheduling prediction module is connected with the input end of the driving distance adjusting module; the output end of the driving distance adjusting module is connected with the input end of the acquisition module; the output end of the acquisition module is connected with the input end of the path planning module; the output end of the path planning module is connected with the input end of the warning module;
the newly increased scheduling prediction module comprises a prediction model establishing unit and an output unit;
the prediction model establishing unit is used for establishing a prediction model according to the historical requirements of the planned customers, predicting the newly added requirements of the users and establishing the prediction model; the output unit is used for outputting the prediction result to the driving distance adjusting module;
the output end of the prediction model establishing unit is connected with the input end of the output unit; the output end of the output unit is connected with the input end of the driving distance adjusting module;
the prediction model building unit includes:
s4-1, obtaining the historical demand data of the planning client, wherein the historical demand data comprises the times and the quantity of newly added demands of each client on the basis of the original demands, calling the historical demand data to establish an original data structure column S0，S0={X0（1），X0（2），X0（3），……，X0（n）}；
S4-2, pair S0Performing gray accumulation generation to generate S1In which S is1Is S0The AGO sequence of (a); namely S1={X1（1），X1（2），X1（3），……，X1（n）}；
Satisfies the following conditions:
Figure 43753DEST_PATH_IMAGE002
wherein k =1, 2, … …, n;
s4-3, to S1Calculating the adjacent mean value to obtain S1Has the MEAN sequence of (1), denoted as Z1I.e. Z1={Z1（1），Z1（2），Z1（3），……，Z1（n）}；
Satisfies the following conditions:
Figure 684687DEST_PATH_IMAGE004
the calculation results are entered into a matrix B, Y;
wherein the content of the first and second substances,
Figure 804959DEST_PATH_IMAGE005
；
Figure 769110DEST_PATH_IMAGE006
；
s4-4, establishing S1The whitening differential equation of (a) is:
Figure 683846DEST_PATH_IMAGE008
wherein a is the developing ash number; b is endogenous control ash number;
s4-5, setting
Figure DEST_PATH_IMAGE009
For the parameter vector to be estimated, it can be known that:
Figure DEST_PATH_IMAGE011
by using the least square method, it can be known
Figure 546497DEST_PATH_IMAGE012
；
S4-6, establishing
Figure DEST_PATH_IMAGE013
And S0Defining a correlation coefficient, wherein the correlation coefficient is as follows:
Figure DEST_PATH_IMAGE015
wherein the content of the first and second substances,
Figure 669129DEST_PATH_IMAGE013
for outputting sequences for predictive models, i.e.
Figure 317148DEST_PATH_IMAGE013
=
Figure 871626DEST_PATH_IMAGE016
；
Figure DEST_PATH_IMAGE017
Is S0And
Figure 159257DEST_PATH_IMAGE013
absolute error of kth point;
Figure 559014DEST_PATH_IMAGE018
is a two-level minimum difference;
Figure DEST_PATH_IMAGE019
is the two-stage maximum difference;
Figure 451753DEST_PATH_IMAGE020
taking 0.5 as resolution;
s4-7, calculating to obtain the output of the prediction model:
Figure 911553DEST_PATH_IMAGE022
the final prediction result of the newly added requirement of the client is recorded as J.
2. The intelligent scheduling processing system based on 5G, according to claim 1, wherein: the 5G communication module comprises a 5G network establishing unit and a delay speed measuring unit;
the 5G network establishing unit is used for establishing a 5G transmission network in the system, and the application of the AGV in intelligent storage and material distribution is realized by utilizing a 5G module and a 5G private network; the delay speed measurement unit is used for detecting the delay speed of the 5G network and recording the delay speed;
the output end of the 5G network establishing unit is connected with the input end of the delay speed measuring unit; and the output end of the delay speed measurement unit is connected with the input end of the newly added scheduling prediction module.
3. The intelligent 5G-based scheduling processing system according to claim 2, wherein: the driving distance adjusting module comprises a judging unit and an adjusting unit;
the judging unit is used for judging whether the driving distance needs to be adjusted or not according to the prediction result output by the output unit and inputting the final result into the adjusting unit; the adjusting unit is used for sending an instruction and requesting the system to adjust the driving distance of the AGV;
the output end of the judging unit is connected with the input end of the adjusting unit; the output end of the adjusting unit is connected with the input end of the path planning module;
the judging unit further includes:
establishing a judgment threshold, denoted as Jmax；
If J exceeds JmaxAdjusting the traveling distance of the AGV; if not, not adjusting;
the adjusting comprises:
acquiring set driving distance of AGV and recording the distance as H0；
Acquiring set driving speed v of AGV0；
Acquiring set reversing speed v of AGV1；
Establishing a maximum turn time T1，T1The turning is carried out when the distance between the front AGV trolley and the middle point of the driving channel is smaller than the distance between the rear AGV trolley and the middle point of the driving channelThe required time is that the AGV needs to back to the middle point of the driving channel firstly and then turns, and the backing time is recorded as t2；
Calculating the maximum driving distance H1：
H1=v1*t2+v0*（T1+t2）
At H1Over H0When the running distance is adjusted to be H1(ii) a At H1Does not exceed H0While keeping the running distance at H0。
4. The intelligent scheduling processing system based on 5G of claim 3, wherein: the acquisition module comprises a position information acquisition unit and a speed information acquisition unit;
the position information acquisition unit is used for acquiring the travelling position information of the AGV; the speed information acquisition unit is used for acquiring the driving speed information of the AGV;
the output end of the position information acquisition unit is connected with the input end of the path planning module; the output end of the speed information acquisition unit is connected with the input end of the path planning module.
5. The intelligent scheduling processing system based on 5G of claim 4, wherein: the path planning module comprises a selection unit and a path planning unit;
the selection unit is used for establishing a selection mark and adding and deleting inconsistent trolley paths; the path planning unit is used for planning the path of the AGV according to the collected information and the driving distance information of the AGV, and acquiring all feasible paths;
the output end of the selection unit is connected with the input end of the path planning unit; the output end of the path planning unit is connected with the input end of the warning module.
6. The intelligent scheduling processing system based on 5G of claim 5, wherein: the selection unit further comprises:
acquiring initial AGV trolley path information;
acquiring a pickup position of a newly added demand, and deleting the AGV trolleys which do not pass through the pickup position in a return path;
after deletion, sequencing the rest AGV trolleys from small to large according to the distance between the AGV trolleys and the pick-up position of the newly added requirement, and recording the sequence as a set C = { y =1，y2，……，yi}。
7. The intelligent scheduling processing system based on 5G of claim 6, wherein: the path planning unit further includes:
acquiring the current load of the AGV in the set C;
selecting any one of the trolleys in C, and marking as y0；
Obtaining y0Planning all turning points on the path, and acquiring turning angles of the turning points;
classifying the turning points into turning points before the picking position of the newly increased demand and turning points after the picking position of the newly increased demand;
establishing a turning angle
Figure DEST_PATH_IMAGE023
Bearing capacity
Figure 624163DEST_PATH_IMAGE024
And take time
Figure DEST_PATH_IMAGE025
Functional relationship of (a):
Figure DEST_PATH_IMAGE027
wherein the content of the first and second substances,
Figure 76879DEST_PATH_IMAGE028
for bearing capacity versus turning angleInfluence of degree is on conversion coefficient
Figure 699490DEST_PATH_IMAGE023
When the angle is larger than 90 degrees, the angle is changed,
Figure DEST_PATH_IMAGE029
(ii) a In that
Figure 528861DEST_PATH_IMAGE023
When the angle is less than 90 degrees, the angle is adjusted,
Figure 604133DEST_PATH_IMAGE030
(ii) a Wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE031
、
Figure 735906DEST_PATH_IMAGE032
all can be set numerical values;
Figure DEST_PATH_IMAGE033
is the turning angle influence coefficient;
acquiring the bearing capacity of all turning points under a planned path;
increasing the load capacity of the newly-increased required pickup at a turning point behind the newly-increased required pickup position;
based on y0The turning angles of all turning points on the planning paths are calculated, and the time spent on each planning path is calculated;
and taking the path with the shortest time as the optimal path, and if two or more paths with the same shortest time exist, selecting the planned path of the AGV trolley which is farthest away from the pickup position of the newly added requirement at present as the optimal path on the basis of the sequence in the set C.
8. The intelligent scheduling processing system based on 5G, according to claim 1, wherein: the warning module comprises a warning mark unit and a light unit;
the warning mark unit is used for warning and marking the AGV participating in the newly added task in the system; the light unit is used for lightening light on the marked AGV and flickering to ensure that a manager can clearly know the light;
the output end of the warning mark unit is connected with the input end of the light unit.
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