CN109087041A - Logistics big data intelligent acquisition method in storing bin - Google Patents
Logistics big data intelligent acquisition method in storing bin Download PDFInfo
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- CN109087041A CN109087041A CN201810797678.5A CN201810797678A CN109087041A CN 109087041 A CN109087041 A CN 109087041A CN 201810797678 A CN201810797678 A CN 201810797678A CN 109087041 A CN109087041 A CN 109087041A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
- G06Q10/087—Inventory or stock management, e.g. order filling, procurement or balancing against orders
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
- G01D21/00—Measuring or testing not otherwise provided for
- G01D21/02—Measuring two or more variables by means not covered by a single other subclass
Abstract
In order to promote the logistic efficiency in the storing bin for placing and sorting out automatically cargo with manipulator, the present invention provides a kind of logistics big data intelligent acquisition methods in storing bin.The present invention according to the placement to a kind of cargo and can sort out information and establish efficient storing bin and place and sorting model, and then instruct the placement of other kinds of cargo and sort out, and promote various cargos sorts out efficiency.In addition, through testing, the present invention can improve sort out efficiency while compared with the prior art in similar techniques reduce power consumption 45%-55%.
Description
Technical field
The present invention relates to logistics technology, and the technology in logistics basin is applied to more particularly, to artificial intelligence technology
Scheme, more particularly, to a kind of logistics big data intelligent acquisition method in storing bin.
Background technique
In traditional logistics system, that storage (Warehousing) is mainly played the part of is the role of " storage " Yu " keeping ".
However, change, under a large amount of and complicated data and market competition environment pressure in consumer demand, related picking personnel's
Pressure is growing day by day.For example, under the support of common picking process and the system that matches, picking operation, see it is single etc. at most
One week can be skilled, but understands the approximate location of inventory, and oneself reasonable arrangement picking path, is not but two days one day energy
Enough complete.The most of time in these three middle of the month is to be familiar with kinds of goods for being familiar with storehouse, be familiar with goods yard.Enterprise's pipe
The computerization of reason is the necessary means that current enterprise increases competitiveness, and logistic storage company can will be ordered by computerization
Single processing, row's vehicle plan, the transport of cargo, store management etc. bring the management track of science into, raise the management level and daily
The treatment effeciency of business, and the acquisition of data is made to reach timely, accurate with statistics.So development is a set of to be suitable for different logistics storehouses
The computer management system for storing up business event development need, has been the task of top priority of each enterprise.
In order to cooperate the demand and timeliness of a small amount of multiplicity in market, the disengaging variation of material is quickly and multiple in warehousing system
It is miscellaneous, therefore " dynamically managing " (" dynamic pipe ") function of warehousing system, it has outmatched on the bare custody function that tradition is stored in a warehouse.And
This is combined traditional warehousing system and data warehousing (Data Warehouse) popular now once " dynamic pipe " function,
Just be desirable to be directed to the planning and management of storage space by the combination of the two, effectively to control kinds of goods come locate, whereabouts and flow,
To reach the target of " dynamic pipe ".
Application No. is the Chinese invention patent applications of CN201610654962.8 to disclose a kind of danger based on Internet of Things
Industrial chemicals logistic storage management system and method, including storage information monitoring system connect data information transfer, data information
Link information management system is transmitted, information management system connects peril pretreatment system, when storage, to dangerous industrial chemicals
Electronic tag mark is carried out, and is monitored in real time by intelligence sensors all kinds of in storehouse and camera by preset value, is delivered
When, video monitoring and alarm is arranged in significant points, by setting on haulage vehicle in automatically updated inventory information and real-time tracing
The BEI-DOU position system set, travel route show in fact when in monitoring system, meanwhile, install temperature and humidity, pressure additional on haulage vehicle
Sensors and the cameras such as power, vibration, tilt angle, pernicious gas detection, monitor shipped goods in real time, it can be achieved that being endangered
The real time monitoring and management of the entire logistics chain of dangerous industrial chemicals, the retrospect of information whole process, haulage vehicle complete monitoring, transport,
The integration of warehousing system information unification, the automatic Realtime Alerts of system, to ensure the safety storage and transport of hazardous chemical.So
And this " dynamic pipe " relevant prior art can not carry out intelligentized Logistic Scheduling according to physical state when storing in a warehouse, it is unfavorable
In reduction cost of labor and artificial data analysis load.
Summary of the invention
In order to promote the logistic efficiency in the storing bin for placing and sorting out automatically cargo with manipulator, the present invention provides
A kind of logistics big data intelligent acquisition method in storing bin, comprising:
(1) electronic mark is carried out to the first cargo wait enter in storing bin and carries out weight mark, acquire weight information
G_tobestr；
(2) electronic mark is carried out to the first cargo wait enter in storing bin and carries out volume mark, acquire volume information
V_tobestr；
(3) pressure that is generated to weight-bearing surface of cargo in storing bin described first wait enter in storing bin carry out detection with
Acquisition, obtains pressure set P_pos, and wherein pos is to indicate that the described first cargo wait enter in storing bin is locating after being placed
The vector of three-dimensional space position；
(4) the practical placement process of cargo to described first wait enter in storing bin is spent total time T_fact1 and
Each manipulator place the above-mentioned first the sum of the length absolute value in cargo path experienced R_fact1 wait enter in storing bin with
And consumed electric power E_fact1 is acquired；
(5) to described first wait enter storing bin in cargo it is practical sort out process cost total time T_fact2 and
Each manipulator sort out the above-mentioned first the sum of the length absolute value in cargo path experienced R_fact2 wait enter in storing bin with
And consumed electric power E_fact2 is acquired；
(6) interior to the weight information, the body that include NUM above-mentioned first cargo wait enter in storing bin by certain time
Product information, pressure set, the sum of the length absolute value in path, time and electric power sample training set are trained, and obtain object
Big data smart allocation model is flowed, NUM is the positive integer greater than 1；
(7) it is based on the logistics big data smart allocation model, to different from the described first goods wait enter in storing bin
Weight information, volume information, pressure set, time and power information of second cargo of object during sorting out are examined
It is disconnected, when the sum of the length absolute value in path, time and electric power do not meet predetermined threshold, to the placement parameter of the second cargo into
Row resurveys and implements placement operation again to the second cargo.
Further, the described first cargo wait enter in storing bin is divided into two classes: the first kind according to its weight information: tool
There are the cargo and the second class wait enter in storing bin of original packing: the cargo wait enter in storing bin with secondary package；
And for the first kind, the weight information is G_tobestr1, volume information V_tobestr1, and pressure collection is combined into P_
pos1；For the second class, the weight information is G_tobestr2, volume information V_tobestr2, and pressure collection is combined into P_
pos2。
Further, the electronic mark is RFID.
Further, the storing bin includes that manipulator sorts out device, for being put cargo by least one manipulator
It sets wherein or from wherein sorting out, the three-dimensional space position range that cargo can be placed or be sorted out to the manipulator is A_fact.
Further, multiple pressure sensors are arranged simultaneously by the weight-bearing surface in lowest level support construction in the pressure set
It is obtained after calculating the average value of the sum of pressure.
Further, the step (6) includes:
If sample training collection is combined into TRAIN={ (R_fact1i/R_fact2i, G_tobestr1i/G_tobestr2i, V_
Tobestr1i/V_tobestr2i) | R_fact1i/R_fact2i, G_tobestr1i/G_tobestr2i, V_tobestr1i/
The equal ∈ Rn of V_tobestr2i }, N is the natural number greater than 1；Sample training collection intersection, which is closed, shares NUM data object in TRAIN；
The matrix of the pos vector composition of these NUM data object is calculated separately according to the first kind and the second class
Characteristic value CH1 and CH2；If the number of iterations Iter1 is the upper integer of the geometrical mean of (CH1+CH2), in A_fact1i range
It is interior with initial solution (R_fact11+R_fact21)/2 to ((T_fact1i*CH1+T_fact2i*CH2)/(T_fact1i*CH2+
T_fact2i*CH1)) * (R_fact1i/R_fact2i) is iterated, and takes upper integer M to obtained final iterative value m, by M weight
It newly is set as the number of iterations Iter2, with initial solution (R_fact1b+R_fact2b)/2 to ((T_ within the scope of A_fact2i
fact1i*CH1+T_fact2i*CH2)/(T_fact1i*CH2+T_fact2i*CH1))^2*(R_fact1i/R_fact2i)^2
It is iterated, takes upper integer to obtain R obtained final iterative value r；Wherein absolute value of the difference of the b between Iter1 and Iter2
Divided by the obtained remainder of Iter1 units and be 0 when b take 1；These NUM are calculated separately according to the first kind and the second class
The characteristic value CH3 and CH4 of the matrix of the V_tobestr vector composition of a data object；If the number of iterations Iter3 is (CH3+
CH4 the upper integer of geometrical mean) adds R, with initial solution (R_fact11+R_fact21)/2 pair within the scope of A_fact1i
((E_fact1i*CH3+E_fact2i*CH4)/(E_fact1i*CH4+E_fact2i*CH3))*(R_fact1i/R_fact2i)
It is iterated, upper integer P is taken to obtained final iterative value p, P is re-set as the number of iterations Iter4, in A_fact2i model
With initial solution (R_fact1c+R_fact2c)/2 to ((E_fact1i*CH3+E_fact2i*CH4)/(E_fact1i*CH4 in enclosing
+ E_fact2i*CH3)) ^2* (R_fact1i/R_fact2i) ^2 is iterated, and take upper integer to obtain obtained final iterative value q
To Q；Wherein c be absolute value of the difference between Iter3 and Iter4 divided by the units of the obtained remainder of Iter4 and when being 0 c takes
1；
SVM training is carried out to TRAIN, obtains the logistics big data smart allocation model of svm classifier type, above-mentioned i=
1,...,N。
The present invention according to the placement to a kind of cargo and can sort out information and establish that efficient storing bin is placed and sorting is used
Model, and then instruct the placement of other kinds of cargo and sort out, promote various cargos sorts out efficiency.In addition, through testing, this
Invention can improve sort out efficiency while compared with the prior art in similar techniques reduce power consumption 45%-55%.
Specific embodiment
The present invention provides a kind of logistics big data intelligent acquisition methods in storing bin, comprising:
(1) electronic mark is carried out to the first cargo wait enter in storing bin and carries out weight mark, acquire weight information
G_tobestr；
(2) electronic mark is carried out to the first cargo wait enter in storing bin and carries out volume mark, acquire volume information
V_tobestr；
(3) pressure that is generated to weight-bearing surface of cargo in storing bin described first wait enter in storing bin carry out detection with
Acquisition, obtains pressure set P_pos, and wherein pos is to indicate that the described first cargo wait enter in storing bin is locating after being placed
The vector of three-dimensional space position；
(4) the practical placement process of cargo to described first wait enter in storing bin is spent total time T_fact1 and
Each manipulator place the above-mentioned first the sum of the length absolute value in cargo path experienced R_fact1 wait enter in storing bin with
And consumed electric power E_fact1 is acquired；
(5) to described first wait enter storing bin in cargo it is practical sort out process cost total time T_fact2 and
Each manipulator sort out the above-mentioned first the sum of the length absolute value in cargo path experienced R_fact2 wait enter in storing bin with
And consumed electric power E_fact2 is acquired；
(6) interior to the weight information, the body that include NUM above-mentioned first cargo wait enter in storing bin by certain time
Product information, pressure set, the sum of the length absolute value in path, time and electric power sample training set are trained, and obtain object
Big data smart allocation model is flowed, NUM is the positive integer greater than 1；
(7) it is based on the logistics big data smart allocation model, to different from the described first goods wait enter in storing bin
Weight information, volume information, pressure set, time and power information of second cargo of object during sorting out are examined
It is disconnected, when the sum of the length absolute value in path, time and electric power do not meet predetermined threshold, to the placement parameter of the second cargo into
Row resurveys and implements placement operation again to the second cargo.
Preferably, the described first cargo wait enter in storing bin is divided into two classes: the first kind according to its weight information: having
The cargo and the second class wait enter in storing bin of original packing: the cargo wait enter in storing bin with secondary package；And
For the first kind, the weight information is G_tobestr1, volume information V_tobestr1, and pressure collection is combined into P_
pos1；For the second class, the weight information is G_tobestr2, volume information V_tobestr2, and pressure collection is combined into P_
pos2。
Preferably, the electronic mark is RFID.
Preferably, the storing bin includes that manipulator sorts out device, for being placed cargo by least one manipulator
To wherein or from wherein sorting out, the three-dimensional space position range that cargo can be placed or be sorted out to the manipulator is A_fact.
Preferably, the pressure set is arranged multiple pressure sensors by the weight-bearing surface in lowest level support construction and counts
It is obtained after calculating the average value of the sum of pressure.
Preferably, the step (6) includes:
If sample training collection is combined into TRAIN={ (R_fact1i/R_fact2i, G_tobestr1i/G_tobestr2i, V_
Tobestr1i/V_tobestr2i) | R_fact1i/R_fact2i, G_tobestr1i/G_tobestr2i, V_tobestr1i/
The equal ∈ Rn of V_tobestr2i }, N is the natural number greater than 1；Sample training collection intersection, which is closed, shares NUM data object in TRAIN；
The matrix of the pos vector composition of these NUM data object is calculated separately according to the first kind and the second class
Characteristic value CH1 and CH2；If the number of iterations Iter1 is the upper integer of the geometrical mean of (CH1+CH2), in A_fact1i range
It is interior with initial solution (R_fact11+R_fact21)/2 to ((T_fact1i*CH1+T_fact2i*CH2)/(T_fact1i*CH2+
T_fact2i*CH1)) * (R_fact1i/R_fact2i) is iterated, and takes upper integer M to obtained final iterative value m, by M weight
It newly is set as the number of iterations Iter2, with initial solution (R_fact1b+R_fact2b)/2 to ((T_ within the scope of A_fact2i
fact1i*CH1+T_fact2i*CH2)/(T_fact1i*CH2+T_fact2i*CH1))^2*(R_fact1i/R_fact2i)^2
It is iterated, takes upper integer to obtain R obtained final iterative value r；Wherein absolute value of the difference of the b between Iter1 and Iter2
Divided by the obtained remainder of Iter1 units and be 0 when b take 1；These NUM are calculated separately according to the first kind and the second class
The characteristic value CH3 and CH4 of the matrix of the V_tobestr vector composition of a data object；If the number of iterations Iter3 is (CH3+
CH4 the upper integer of geometrical mean) adds R, with initial solution (R_fact11+R_fact21)/2 pair within the scope of A_fact1i
((E_fact1i*CH3+E_fact2i*CH4)/(E_fact1i*CH4+E_fact2i*CH3))*(R_fact1i/R_fact2i)
It is iterated, upper integer P is taken to obtained final iterative value p, P is re-set as the number of iterations Iter4, in A_fact2i model
With initial solution (R_fact1c+R_fact2c)/2 to ((E_fact1i*CH3+E_fact2i*CH4)/(E_fact1i*CH4 in enclosing
+ E_fact2i*CH3)) ^2* (R_fact1i/R_fact2i) ^2 is iterated, and take upper integer to obtain obtained final iterative value q
To Q；Wherein c be absolute value of the difference between Iter3 and Iter4 divided by the units of the obtained remainder of Iter4 and when being 0 c takes
1；
SVM training is carried out to TRAIN, obtains the logistics big data smart allocation model of svm classifier type, above-mentioned i=
1,...,N。
Particular embodiments described above has carried out further in detail the purpose of the present invention, technical scheme and beneficial effects
It describes in detail bright, it should be understood that the above is only a specific embodiment of the present invention, is not intended to restrict the invention, it is all
Within the spirit and principles in the present invention, any modification, equivalent substitution, improvement and etc. done should be included in guarantor of the invention
Within the scope of shield.
Claims (6)
1. the logistics big data intelligent acquisition method in storing bin, comprising:
(1) electronic mark is carried out to the first cargo wait enter in storing bin and carries out weight mark, acquire weight information G_
tobestr；
(2) electronic mark is carried out to the first cargo wait enter in storing bin and carries out volume mark, acquire volume information V_
tobestr；
(3) the in storing bin described first cargo wait enter in storing bin is detected and is adopted to the pressure that weight-bearing surface generates
Collection, obtains pressure set P_pos, and wherein pos is locating three after cargo of the instruction described first wait enter in storing bin is placed
The vector of dimension space position；
(4) total time T_fact1 to the practical placement process cost of the described first cargo wait enter in storing bin and each machine
Tool hand places the sum of the above-mentioned first length absolute value in cargo path experienced wait enter in storing bin R_fact1 and institute
The electric power E_fact1 of consumption is acquired；
(5) total time T_fact2 for sorting out process cost practical to the described first cargo wait enter in storing bin and each machine
Tool hand-sort goes out the sum of the above-mentioned first length absolute value in cargo path experienced wait enter in storing bin R_fact2 and institute
The electric power E_fact2 of consumption is acquired；
(6) by believing in certain time the weight information including NUM above-mentioned first cargo wait enter in storing bin, volume
Breath, pressure set, the sum of the length absolute value in path, time and electric power sample training set are trained, and it is big to obtain logistics
Data intelligence distribution model, NUM are the positive integer greater than 1；
(7) it is based on the logistics big data smart allocation model, to different from the described first cargo wait enter in storing bin
Weight information, volume information, pressure set, time and power information of second cargo during sorting out are diagnosed, when
When the sum of the length absolute value in path, time and electric power do not meet predetermined threshold, weight is carried out to the placement parameter of the second cargo
It is new to acquire and placement operation is implemented again to the second cargo.
2. the method according to claim 1, wherein the described first cargo wait enter in storing bin is heavy according to its
Amount information is divided into two classes: the first kind: the cargo and the second class wait enter in storing bin with original packing: having secondary packet
The cargo wait enter in storing bin of dress；And for the first kind, the weight information is G_tobestr1, and volume information is
V_tobestr1, pressure collection are combined into P_pos1；For the second class, the weight information is G_tobestr2, volume information V_
Tobestr2, pressure collection are combined into P_pos2.
3. according to the method described in claim 2, it is characterized in that, the electronic mark is RFID.
4. according to the method described in claim 3, it is characterized in that, the storing bin includes that manipulator sorts out device, for leading to
It crosses at least one manipulator to place goods onto wherein or from wherein sorting out, the three of cargo can be placed or be sorted out to the manipulator
Dimension space position range is A_fact.
5. according to the method described in claim 4, it is characterized in that, the pressure set passes through holding in lowest level support construction
It is obtained after the average value that weight face the sum of is arranged multiple pressure sensors and calculates pressure.
6. according to the method described in claim 5, it is characterized in that, the step (6) includes:
If sample training collection is combined into TRAIN={ (R_fact1i/R_fact2i, G_tobestr1i/G_tobestr2i, V_
Tobestr1i/V_tobestr2i) | R_fact1i/R_fact2i, G_tobestr1i/G_tobestr2i, V_tobestr1i/
The equal ∈ Rn of V_tobestr2i }, N is the natural number greater than 1；Sample training collection intersection, which is closed, shares NUM data object in TRAIN；
The feature of the matrix of the pos vector composition of these NUM data object is calculated separately according to the first kind and the second class
Value CH1 and CH2；If the number of iterations Iter1 be (CH1+CH2) geometrical mean upper integer, within the scope of A_fact1i with
Initial solution (R_fact11+R_fact21)/2 is to ((T_fact1i*CH1+T_fact2i*CH2)/(T_fact1i*CH2+T_
Fact2i*CH1)) * (R_fact1i/R_fact2i) is iterated, and takes upper integer M to obtained final iterative value m, again by M
It is set as the number of iterations Iter2, with initial solution (R_fact1b+R_fact2b)/2 to ((T_ within the scope of A_fact2i
fact1i*CH1+T_fact2i*CH2)/(T_fact1i*CH2+T_fact2i*CH1))^2*(R_fact1i/R_fact2i)^2
It is iterated, takes upper integer to obtain R obtained final iterative value r；Wherein absolute value of the difference of the b between Iter1 and Iter2
Divided by the obtained remainder of Iter1 units and be 0 when b take 1；These NUM are calculated separately according to the first kind and the second class
The characteristic value CH3 and CH4 of the matrix of the V_tobestr vector composition of a data object；If the number of iterations Iter3 is (CH3+
CH4 the upper integer of geometrical mean) adds R, with initial solution (R_fact11+R_fact21)/2 pair within the scope of A_fact1i
((E_fact1i*CH3+E_fact2i*CH4)/(E_fact1i*CH4+E_fact2i*CH3))*(R_fact1i/R_fact2i)
It is iterated, upper integer P is taken to obtained final iterative value p, P is re-set as the number of iterations Iter4, in A_fact2i model
With initial solution (R_fact1c+R_fact2c)/2 to ((E_fact1i*CH3+E_fact2i*CH4)/(E_fact1i*CH4 in enclosing
+ E_fact2i*CH3)) ^2* (R_fact1i/R_fact2i) ^2 is iterated, and take upper integer to obtain obtained final iterative value q
To Q；Wherein c be absolute value of the difference between Iter3 and Iter4 divided by the units of the obtained remainder of Iter4 and when being 0 c takes
1；
SVM training is carried out to TRAIN, obtains the logistics big data smart allocation model of svm classifier type, above-mentioned i=1 ...,
N。
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| KR20090127790A (en) * | 2008-06-09 | 2009-12-14 | 김혜연 | The business model of goods transport system by internet |
| US9488947B2 (en) * | 2009-04-23 | 2016-11-08 | Xerox Corporation | Method and system for managing field convertible customer replaceable components |
| CN105243530A (en) * | 2015-09-26 | 2016-01-13 | 苏州研博环保节能科技有限公司 | RFID technology based warehouse logistics management system |
| CN107025495A (en) * | 2015-12-17 | 2017-08-08 | Sap欧洲公司 | The complexity for determining the route for carrying containers is reduced based on user's selection |
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