CN116051004B - Intelligent management method, system and medium based on big data - Google Patents
Intelligent management method, system and medium based on big data Download PDFInfo
- Publication number
- CN116051004B CN116051004B CN202310302292.3A CN202310302292A CN116051004B CN 116051004 B CN116051004 B CN 116051004B CN 202310302292 A CN202310302292 A CN 202310302292A CN 116051004 B CN116051004 B CN 116051004B
- Authority
- CN
- China
- Prior art keywords
- warehouse
- storage
- logistics
- efficiency
- bin
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000007726 management method Methods 0.000 title claims abstract description 57
- 238000003860 storage Methods 0.000 claims abstract description 157
- 238000000034 method Methods 0.000 claims abstract description 58
- 238000009826 distribution Methods 0.000 claims abstract description 25
- 230000006870 function Effects 0.000 claims description 81
- 210000000349 chromosome Anatomy 0.000 claims description 25
- 230000035772 mutation Effects 0.000 claims description 17
- 238000004364 calculation method Methods 0.000 claims description 16
- 230000008569 process Effects 0.000 claims description 16
- 230000000694 effects Effects 0.000 claims description 12
- 230000002068 genetic effect Effects 0.000 claims description 12
- 239000011159 matrix material Substances 0.000 claims description 12
- 238000011156 evaluation Methods 0.000 claims description 10
- 238000012163 sequencing technique Methods 0.000 claims description 8
- 238000010606 normalization Methods 0.000 claims description 4
- 230000009286 beneficial effect Effects 0.000 abstract description 4
- 230000003993 interaction Effects 0.000 description 7
- 238000004458 analytical method Methods 0.000 description 4
- 238000010586 diagram Methods 0.000 description 4
- 238000011161 development Methods 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- 239000000126 substance Substances 0.000 description 3
- 230000008901 benefit Effects 0.000 description 2
- 230000007547 defect Effects 0.000 description 2
- 230000007613 environmental effect Effects 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 238000006243 chemical reaction Methods 0.000 description 1
- 239000003795 chemical substances by application Substances 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000013507 mapping Methods 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 230000001737 promoting effect Effects 0.000 description 1
- 238000006479 redox reaction Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000001502 supplementing effect Effects 0.000 description 1
Images
Classifications
-
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/12—Computing arrangements based on biological models using genetic models
- G06N3/126—Evolutionary algorithms, e.g. genetic algorithms or genetic programming
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/30—Computing systems specially adapted for manufacturing
Abstract
The invention relates to an intelligent management method, a system and a medium based on big data, which belong to the technical field of logistics management. According to the method, the optimal cargo warehousing distribution results within the preset period can be solved according to the warehousing efficiency and the ex-warehouse efficiency of different cargo types of the logistics wharf, the logistics storage utilization rate is improved to the greatest extent, the logistics wharf can be in a dynamic balance state, logistics work of the logistics wharf can be normally operated, and the logistics wharf is beneficial to storage resource distribution of the logistics wharf.
Description
Technical Field
The invention relates to the technical field of logistics management, in particular to an intelligent management method, system and medium based on big data.
Background
Modern logistics industry is a modern service industry which integrates industries such as transportation, storage, freight agent, information and the like, relates to a plurality of fields, and plays an important role in promoting production and pulling internal needs. With the rapid expansion of the industry foundation and the continuous rise of the consumer market in China, the logistics industry in China is in a rapid development stage. As a critical part of the economic development, the logistics have gained a high attention from the decision-making sector. The internet of things technology and big data technology bring informatization, automation and comprehensive logistics management and flow monitoring technical means to the logistics industry, so that benefits such as improvement of logistics efficiency and enhancement of logistics cost control capability can be brought to enterprises, informatization level of the enterprises and related fields is integrally improved, and the purpose of driving the development of the whole industry is achieved. However, in the process of logistics management, particularly in some busy logistics wharfs, the logistics in warehouse entry and the logistics out of warehouse need to be monitored, so that the logistics wharfs can be in a dynamic balance condition, when the logistics wharfs cannot be in a dynamic balance condition, the logistics wharfs warehouse entry management and warehouse out management can generate contradiction, if the articles to be warehoused do not have residual space for warehouse entry, and the normal operation of the logistics wharfs is facilitated. While warehousing and ex-warehouse are usually performed simultaneously, how to maximize the utilization of the storage space of the logistics dock is an important technical problem that needs to be solved by logistics.
Disclosure of Invention
The invention overcomes the defects of the prior art and provides an intelligent management method, system and medium based on big data.
In order to achieve the above purpose, the invention adopts the following technical scheme:
the first aspect of the invention provides an intelligent management method based on big data, which comprises the following steps:
acquiring order information to be delivered of each cargo type of the current logistics warehouse, and estimating according to the order information to be delivered of each cargo type of the current logistics warehouse so as to acquire delivery efficiency of each cargo type of the current logistics warehouse;
acquiring influence factor data influencing warehousing efficiency through a big data network, and analyzing the influence factor data through an analytic hierarchy process to acquire weight vector information of each influence factor data;
acquiring order information to be allocated and put in storage in a logistics management platform, and estimating the put-in efficiency of each cargo type in current logistics and storage based on the weight vector information of each influence factor data and the order information to be allocated and put in storage;
and constructing a residual bin target function set according to the ex-warehouse efficiency of each cargo type in the current logistics warehouse, constructing a warehouse-in bin target function set based on the warehouse-in efficiency of each cargo type in the current logistics warehouse, and solving the residual bin target function set and the warehouse-in estimated bin target function set based on a genetic algorithm to obtain an optimal cargo warehouse-in distribution result within a preset period.
Further, in a preferred embodiment of the present invention, information of an order to be delivered from a warehouse of each cargo type in a current logistics warehouse is obtained, and prediction is performed according to the information of the order to be delivered from the warehouse of each cargo type in the current logistics warehouse, so as to obtain delivery efficiency of each cargo type in the current logistics warehouse, and the method specifically includes the following steps:
setting keyword information according to the ex-warehouse efficiency of each cargo type, performing data retrieval according to the keyword information through a big data network to obtain the ex-warehouse efficiency of each cargo type of each grade of logistics warehouse, and constructing an information database according to the ex-warehouse efficiency of each cargo type of each grade of logistics warehouse;
the method comprises the steps of obtaining the storage grade of a current logistics storage and obtaining to-be-stored order information of each goods type of the current logistics storage, inputting the storage grade of the current logistics storage into an information database for one-time matching to obtain the logistics storage with the matching degree larger than a preset matching degree, and obtaining the ex-storage efficiency of each goods type corresponding to the logistics storage with the matching degree larger than the preset matching degree;
acquiring order information to be delivered of each cargo type of the current logistics warehouse, and classifying according to the order information to be delivered of each cargo type of the current logistics warehouse so as to acquire the order information to be delivered of each cargo type;
And acquiring the delivery efficiency of each cargo type of the current logistics warehouse based on the delivery efficiency of each cargo type corresponding to the logistics warehouse with the matching degree larger than the preset matching degree and the to-be-delivered order information of each cargo type.
Further, in a preferred embodiment of the present invention, influence factor data influencing warehousing efficiency is obtained through a big data network, and the influence factor data is analyzed through a hierarchical analysis method to obtain weight vector information of each influence factor data, which specifically includes the following steps:
acquiring influence factor data influencing warehousing efficiency through a big data network, determining an evaluation hierarchical structure system, and dividing the evaluation hierarchical structure system into a target layer, a criterion layer and a scheme layer;
inputting the warehousing efficiency into a criterion layer, inputting the influence factor data influencing the warehousing efficiency into a scheme layer, presetting a plurality of influence effects, inputting the influence effects into a target layer, and generating an evaluation index system;
comparing the factors in the criterion layer and the scheme layer group with each other, quantifying the relative importance according to a preset quantitative scale, generating a scale value, and constructing a judgment matrix according to the scale value;
And carrying out normalization calculation on the judgment matrix by a method root to obtain a feature vector, obtaining the maximum value of the feature vector in the judgment matrix, and obtaining weight vector information of each influence factor data according to the feature vector and the maximum value.
Further, in a preferred embodiment of the present invention, order information to be allocated and put in storage in a logistics management platform is obtained, and based on weight vector information of each influence factor data and the order information to be allocated and put in storage, the storage efficiency of each cargo type in current logistics storage is estimated, which specifically includes the following steps:
acquiring order information to be allocated and put in storage in a logistics management platform, and classifying data of the order information to be allocated and put in storage in the logistics management platform according to the types of goods so as to acquire the order information to be allocated and put in storage corresponding to the types of goods;
constructing a warehouse-in efficiency estimation model based on the weight vector information of each influence factor data, and acquiring the influence factor data of the current logistics warehouse;
adjusting the warehouse-in efficiency estimation model according to the influence factor data of the current logistics warehouse so as to obtain an adjusted warehouse-in efficiency estimation model;
And obtaining the warehousing efficiency of each cargo type in the current logistics warehouse based on the adjusted warehousing efficiency estimation model and the order information to be allocated and warehoused corresponding to each cargo type.
Further, in a preferred embodiment of the present invention, a remaining bin target function set is constructed according to the delivery efficiency of each cargo type in the current logistics warehouse, and the method specifically includes the following steps:
acquiring the data information of the residual bin of the logistics storage at the current moment, and initializing the bin information to be stored of the logistics storage according to the data information of the residual bin of the logistics storage at the current moment;
constructing a residual bin target function set based on the bin information to be stored of the initialized logistics storage and the ex-warehouse efficiency of each cargo type of the current logistics storage, wherein the bin target function set meets the following conditions:
wherein ,/>
For real-time bin data information, n is the number of shipment types, and +.>
And (3) the delivery efficiency of the ith cargo type is achieved, T is time, and Q is the information of the warehouse to be in warehouse of the initialized logistics warehouse.
Further, in a preferred embodiment of the present invention, a warehouse entry position target function set is constructed based on the warehouse entry efficiency of each cargo type in the current logistics warehouse, and specifically includes the following steps:
Acquiring real-time bin data information at the current moment, and constructing a bin-storage bin target function set according to the bin-storage efficiency of each cargo type in the current logistics storage and the real-time bin data information at the current moment, wherein the bin-storage bin target function set specifically meets the following relation:
wherein ,/>
For real-time bin data information, m is the number of warehouse-in goods types, < >>
And the warehousing efficiency of the jth cargo type of the cargoes to be distributed is the time T. Further, in a preferred embodiment of the present invention, the remaining bin target function set is based on a genetic algorithm toSolving a warehouse entry estimated warehouse entry target function group to obtain an optimal warehouse entry distribution result of goods within a preset range, and specifically comprising the following steps:
uniformly generating a plurality of chromosome numbers in a solution space, presetting the dimension of each chromosome to generate a first generation population, determining the population scale and the maximum evolution algebra, and initializing the selection proportion, the crossover probability and the mutation probability;
taking order information to be allocated and put in storage in the logistics management platform as allocation basis, based on calculated values of a residual bin target function set and a put in storage estimated bin target function set, carrying out rapid non-dominant sequencing and crowding calculation on each chromosome individual in the primary population, and carrying out selection, crossing and mutation operation on the primary population to obtain a next generation population;
Combining the primary population with the next generation population to obtain a new population, taking order information to be allocated and put in storage in the logistics management platform as allocation basis, and carrying out rapid non-dominant sorting and crowding calculation on each chromosome individual in the new population based on calculated values of a residual bin target function group and a put estimated bin target function group;
selecting individuals meeting the conditions for the new population to form a new primary population, determining the evolution algebra of an evolution process, adding one to the number of the evolution algebra if the evolution algebra is smaller than the maximum evolution algebra, and switching to the primary population for selecting, crossing and mutation operation to obtain a next generation population, and stopping iteration and outputting an optimal cargo warehousing distribution result within a preset period if the evolution algebra is larger than the maximum evolution algebra.
The second aspect of the present invention provides an intelligent management system based on big data, the system comprising a memory and a processor, wherein the memory comprises an intelligent management method program based on big data, and when the intelligent management method program based on big data is executed by the processor, the following steps are implemented:
Acquiring order information to be delivered of each cargo type of the current logistics warehouse, and estimating according to the order information to be delivered of each cargo type of the current logistics warehouse so as to acquire delivery efficiency of each cargo type of the current logistics warehouse;
acquiring influence factor data influencing warehousing efficiency through a big data network, and analyzing the influence factor data through an analytic hierarchy process to acquire weight vector information of each influence factor data;
acquiring order information to be allocated and put in storage in a logistics management platform, and estimating the put-in efficiency of each cargo type in current logistics and storage based on the weight vector information of each influence factor data and the order information to be allocated and put in storage;
and constructing a residual bin target function set according to the ex-warehouse efficiency of each cargo type in the current logistics warehouse, constructing a warehouse-in bin target function set based on the warehouse-in efficiency of each cargo type in the current logistics warehouse, and solving the residual bin target function set and the warehouse-in estimated bin target function set based on a genetic algorithm to obtain an optimal cargo warehouse-in distribution result within a preset period.
In a preferred embodiment of the system, the solution is performed based on the residual bin target function set and the warehouse entry estimated bin target function set of the genetic algorithm, so as to obtain an optimal cargo warehouse entry allocation result within a preset range, and the method specifically comprises the following steps:
Uniformly generating a plurality of chromosome numbers in a solution space, presetting the dimension of each chromosome to generate a first generation population, determining the population scale and the maximum evolution algebra, and initializing the selection proportion, the crossover probability and the mutation probability;
taking order information to be allocated and put in storage in the logistics management platform as allocation basis, based on calculated values of a residual bin target function set and a put in storage estimated bin target function set, carrying out rapid non-dominant sequencing and crowding calculation on each chromosome individual in the primary population, and carrying out selection, crossing and mutation operation on the primary population to obtain a next generation population;
combining the primary population with the next generation population to obtain a new population, taking order information to be allocated and put in storage in the logistics management platform as allocation basis, and carrying out rapid non-dominant sorting and crowding calculation on each chromosome individual in the new population based on calculated values of a residual bin target function group and a put estimated bin target function group;
selecting individuals meeting the conditions for the new population to form a new primary population, determining the evolution algebra of an evolution process, adding one to the number of the evolution algebra if the evolution algebra is smaller than the maximum evolution algebra, and switching to the first-generation population for selection, crossing and mutation operation so as to obtain a next-generation population, and stopping iteration and outputting an optimal cargo warehousing distribution result within a preset period if the evolution algebra is larger than the maximum evolution algebra.
A third aspect of the present invention provides a computer-readable storage medium containing a big data based intelligent management method program which, when executed by a processor, implements the steps of any one of the big data based intelligent management methods.
The invention solves the defects existing in the background technology, and has the following beneficial effects:
according to the method, the order information to be delivered of each cargo type of the current logistics warehouse is obtained, the delivery efficiency of each cargo type of the current logistics warehouse is obtained through pre-estimation according to the order information to be delivered of each cargo type of the current logistics warehouse, influence factor data influencing the delivery efficiency is further obtained through a big data network, the influence factor data is analyzed through a hierarchical analysis method to obtain weight vector information of each influence factor data, further the order information to be distributed and delivered in a logistics management platform is obtained, the delivery efficiency of each cargo type of the current logistics warehouse is estimated based on the weight vector information of each influence factor data and the order information to be distributed and delivered in the warehouse, finally a residual bin target function group is built according to the delivery efficiency of each cargo type of the current logistics warehouse, the warehouse entry target function group is built based on the warehouse entry efficiency of each cargo type of the current logistics warehouse, and the warehouse entry target function group is solved based on the genetic algorithm to obtain the optimal cargo warehouse delivery results between pre-estimation. According to the method, the optimal cargo warehousing distribution results within the preset period can be solved according to the warehousing efficiency and the ex-warehouse efficiency of different cargo types of the logistics wharf, the logistics storage utilization rate is improved to the greatest extent, the logistics wharf can be in a dynamic balance state, logistics work of the logistics wharf can be normally operated, and the logistics wharf is beneficial to storage resource distribution of the logistics wharf.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other embodiments of the drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 shows an overall method flow diagram of a big data based intelligent management method;
FIG. 2 shows a first method flow diagram of a big data based intelligent management method;
FIG. 3 shows a second method flow diagram of a big data based intelligent management method;
fig. 4 shows a system block diagram of a big data based intelligent management system.
Description of the embodiments
In order that the above-recited objects, features and advantages of the present invention will be more clearly understood, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description. It should be noted that, in the case of no conflict, the embodiments of the present application and the features in the embodiments may be combined with each other.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, however, the present invention may be practiced in other ways than those described herein, and therefore the scope of the present invention is not limited to the specific embodiments disclosed below.
As shown in fig. 1, the first aspect of the present invention provides an intelligent management method based on big data, which includes the following steps:
s102, acquiring to-be-ex-warehouse order information of each cargo type of the current logistics warehouse, and estimating according to the to-be-ex-warehouse order information of each cargo type of the current logistics warehouse to acquire ex-warehouse efficiency of each cargo type of the current logistics warehouse;
s104, acquiring influence factor data influencing warehousing efficiency through a big data network, and analyzing the influence factor data through a hierarchical analysis method to acquire weight vector information of each influence factor data;
s106, acquiring order information to be allocated and put in storage in the logistics management platform, and estimating the put-in efficiency of each cargo type in the current logistics warehouse based on the weight vector information of each influence factor data and the order information to be allocated and put in storage;
s108, constructing a residual bin target function set according to the ex-warehouse efficiency of each cargo type in the current logistics warehouse, constructing a warehouse-in bin target function set based on the warehouse-in efficiency of each cargo type in the current logistics warehouse, and solving based on the residual bin target function set and the warehouse-in estimated bin target function set of the genetic algorithm to obtain an optimal cargo warehouse-in distribution result within a preset range.
By the method, the optimal cargo warehousing distribution result within the preset period can be solved according to the warehousing efficiency and the ex-warehouse efficiency of different cargo types of the logistics wharf, the logistics storage utilization rate is improved to the greatest extent, the logistics wharf can be in a dynamic balance state, logistics work of the logistics wharf can be normally operated, and the logistics wharf is beneficial to storage resource distribution of the logistics wharf. The warehouse-in efficiency is the required inventory space in unit time, and the warehouse-out efficiency is the generated inventory space in unit time. The order information contains data such as the type of goods, the weight of the goods, the volume of the goods, the quantity of the goods, and the like.
Further, in a preferred embodiment of the present invention, information of an order to be delivered from a warehouse of each cargo type in a current logistics warehouse is obtained, and prediction is performed according to the information of the order to be delivered from the warehouse of each cargo type in the current logistics warehouse, so as to obtain delivery efficiency of each cargo type in the current logistics warehouse, and the method specifically includes the following steps:
setting keyword information according to the ex-warehouse efficiency of each cargo type, carrying out data retrieval according to the keyword information through a big data network to obtain the ex-warehouse efficiency of each cargo type of each grade of logistics warehouse, and constructing an information database according to the ex-warehouse efficiency of each cargo type of each grade of logistics warehouse;
The method comprises the steps of obtaining the storage grade of a current logistics storage and obtaining to-be-stored order information of each goods type of the current logistics storage, inputting the storage grade of the current logistics storage into an information database for one-time matching to obtain the logistics storage with the matching degree larger than a preset matching degree, and obtaining the ex-storage efficiency of each goods type corresponding to the logistics storage with the matching degree larger than the preset matching degree;
acquiring order information to be delivered of each cargo type of the current logistics warehouse, and classifying according to the order information to be delivered of each cargo type of the current logistics warehouse so as to acquire the order information to be delivered of each cargo type;
and acquiring the delivery efficiency of each cargo type of the current logistics warehouse based on the delivery efficiency of each cargo type corresponding to the logistics warehouse with the matching degree larger than the preset matching degree and the to-be-delivered order information of each cargo type.
It should be noted that, the time of warehousing different types of goods is generally different, such as 2kg of goods and 100kg of goods, in fact, the warehousing efficiency of 100kg is obviously lower than that of 2kg of goods; secondly, the storage grade of the logistics storage is limited, such as logistics storage with the grade of 50 mu of land occupation and logistics storage with the grade of 200 mu of land occupation, and other conditions are relatively the same, and the logistics storage with the grade of 200 mu of land occupation and the grade of 50 mu of land occupation are inconsistent. Therefore, the method can screen the warehousing efficiency and the ex-warehouse efficiency of various cargo types in the current logistics warehouse.
Further, in a preferred embodiment of the present invention, influence factor data influencing warehousing efficiency is obtained through a big data network, and the influence factor data is analyzed through a hierarchical analysis method to obtain weight vector information of each influence factor data, which specifically includes the following steps:
acquiring influence factor data influencing warehousing efficiency through a big data network, determining an evaluated hierarchical structure system, and dividing the evaluated hierarchical structure system into a target layer, a criterion layer and a scheme layer;
inputting the warehousing efficiency into a criterion layer, inputting influence factor data influencing the warehousing efficiency into a scheme layer, presetting a plurality of influence effects, inputting the influence effects into a target layer, and generating an evaluation index system;
comparing the factors in the criterion layer and the scheme layer group with each other, quantifying the relative importance according to a preset quantitative scale, generating a scale value, and constructing a judgment matrix according to the scale value;
and carrying out normalization calculation on the judgment matrix by a method root to obtain a feature vector, obtaining the maximum value of the feature vector in the judgment matrix, and obtaining the weight vector information of each influence factor data according to the feature vector and the maximum value.
It should be noted that, the influence factor data is weather data, the environmental factor data may be temperature, humidity, etc., and the weather data may be sunny, rainy, foggy, etc.; since the ex-warehouse efficiency is usually in the warehouse, when other data are normal, the weather data are difficult to influence the ex-warehouse efficiency. In fact, during the warehouse entry process, the warehouse entry is mainly affected by transportation, and the process from transportation and carrying to warehouse entry needs to be calculated before warehouse entry, such as a logistics transport vehicle to be warehouse entry in the transportation in rainy days; in the aspect of the delivery efficiency, the delivery is usually carried out in a warehouse, and delivery success can be indicated when the warehouse is moved to an original position, and the delivery is basically not influenced by the data and can be ignored. Wherein the effect is a serious effect, a moderate effect, a mild effect, etc.
As shown in fig. 2, in a further preferred embodiment of the present invention, order information to be allocated and put in storage in a logistics management platform is obtained, and based on weight vector information of each influencing factor data and the order information to be allocated and put in storage, the storage efficiency of each cargo type in current logistics storage is estimated, which specifically includes the following steps:
s202, acquiring order information to be allocated and put in storage in a logistics management platform, and classifying data of the order information to be allocated and put in storage in the logistics management platform according to the types of goods so as to acquire the order information to be allocated and put in storage corresponding to the types of goods;
S204, constructing a warehouse-in efficiency estimation model based on weight vector information of each influence factor data, and acquiring the influence factor data of the current logistics warehouse;
s206, adjusting the warehouse-in efficiency estimation model according to the influence factor data of the current logistics warehouse so as to obtain an adjusted warehouse-in efficiency estimation model;
and S208, based on the adjusted warehousing efficiency estimation model and the to-be-allocated warehousing order information corresponding to each cargo type, the warehousing efficiency of each cargo type in the current logistics warehouse is obtained.
It should be noted that, due to the influence of weather data and environmental factor data, the goods are also affected correspondingly, for example, the warehouse-in efficiency is necessarily reduced by bad weather, wherein the warehouse-in efficiency estimation model specifically satisfies the following relation:
wherein k is real-time warehousing efficiency; />
The warehousing efficiency of each cargo type at the beginning; y is the number of influencing factors; />
The influence weight value of the xth weather condition is that e is the base number of natural logarithm; />
The probability value of the occurrence of the xth weather condition within the preset time period is calculated from the weatherAnd obtaining in forecast.
It should be noted that, the influence factor data of the current logistics warehouse in the preset time period can be input into the warehouse-in efficiency estimation model through the above relation, so as to obtain the adjusted warehouse-in efficiency estimation model, for example, the number of y is 3 when the user needs to go through a sunny day, a rainy day and a foggy day in a certain time period, and so on. The method can obtain the warehousing efficiency of each cargo type in the current logistics warehouse based on the adjusted warehousing efficiency estimation model and the order information to be allocated and warehoused corresponding to each cargo type.
Further, in a preferred embodiment of the present invention, a remaining bin target function set is constructed according to the delivery efficiency of each cargo type of the current logistics warehouse, and specifically includes the following steps:
acquiring the data information of the residual bin of the logistics storage at the current moment, and initializing the bin information to be stored of the logistics storage according to the data information of the residual bin of the logistics storage at the current moment;
constructing a residual bin target function set based on the to-be-warehouse bin information of the initialized logistics warehouse and the ex-warehouse efficiency of each cargo type of the current logistics warehouse, wherein the bin target function set meets the following conditions:
wherein ,/>
For real-time bin data information, n is the number of shipment types, and +.>
And (3) the delivery efficiency of the ith cargo type is achieved, T is time, and Q is the information of the warehouse to be in warehouse of the initialized logistics warehouse.
In the logistics dock where the cargo is frequently transferred, the warehousing and the ex-warehouse are often performed simultaneously, and the real-time bin data after the ex-warehouse is also changed, so that the real-time bin data (available inventory space) can be obtained by the above relation. Wherein bin data may be understood as an inventory space.
Further, in a preferred embodiment of the present invention, a warehouse entry position target function set is constructed based on the warehouse entry efficiency of each cargo type in the current logistics warehouse, and specifically includes the following steps:
Acquiring real-time bin data information at the current moment, and constructing a bin-storage bin target function set according to the bin-storage efficiency of each cargo type of the current logistics storage and the real-time bin data information at the current moment, wherein the bin-storage bin target function set specifically meets the following relation:
wherein ,/>
For real-time bin data information, m is the number of warehouse-in cargo types,
and the warehousing efficiency of the jth cargo type of the cargoes to be distributed is the time T.
When the real-time bin data changes, the order information to be distributed in the internet of things platform needs to be distributed, and because the real-time bin data changes, when one space is available for supplementing data, the total warehousing efficiency needs to be considered, the warehousing efficiency and the ex-warehouse efficiency need to be balanced all the time, namely the warehousing plan cannot be larger than the remaining space, and the warehouse-in and warehouse-out bin target function group can be constructed through the relational expression.
As shown in fig. 3, in a preferred embodiment of the present invention, the solution is performed based on the remaining bin target function set and the warehouse entry estimated bin target function set of the genetic algorithm to obtain the optimal cargo warehouse entry allocation result within the preset range, which specifically includes the following steps:
S302, uniformly generating a plurality of chromosome numbers in a solution space, presetting the dimension of each chromosome to generate a first generation population, determining the population scale and the maximum evolution algebra, and initializing the selection proportion, the crossover probability and the mutation probability;
s304, taking order information to be allocated and put in storage in a logistics management platform as allocation basis, based on calculated values of a residual bin target function set and a put in storage estimated bin target function set, carrying out rapid non-dominant sequencing and crowding calculation on each chromosome individual in the primary population, and carrying out selection, crossing and mutation operation on the primary population to obtain a next-generation population;
s306, merging the primary population with the next population to obtain a new population, taking order information to be allocated and put in storage in the logistics management platform as allocation basis, and carrying out rapid non-dominant sequencing and congestion degree calculation on each chromosome individual in the new population based on calculated values of the residual bin target function groups and the put in storage estimated bin target function groups;
s308, selecting individuals meeting the conditions for the new population to form a new primary population, determining the evolution algebra in the evolution process, adding one to the number of the evolution algebra if the evolution algebra is smaller than the maximum evolution algebra, and switching to the operation of selecting, crossing and mutating the primary population to obtain the next generation population, and stopping iteration and outputting the optimal cargo warehousing distribution result within the preset period if the evolution algebra is larger than the maximum evolution algebra.
It should be noted that, the dimension of the chromosome is selected to be 12, the population scale is 200, the maximum evolution algebra is 150, and the order information to be distributed and put in storage in the logistics management platform can be used as a distribution basis for distribution through a genetic algorithm, so that the optimal goods put in storage distribution result in the preset period is output, the logistics scheduling plan of the logistics wharf is more reasonable in busy period, the resource utilization rate of the logistics wharf is improved, and further the operation and maintenance cost of the logistics wharf is reduced.
Furthermore, the method comprises the following steps:
acquiring position information of the empty stock in the current logistics warehouse and unloading position information of a logistics dock, and acquiring a distance value based on the position information of the empty stock in the current logistics warehouse and the unloading position information of the logistics dock by taking the unloading position information of the logistics dock as a reference;
sorting the distance values from small to large to obtain a distance value priority sorting table, obtaining order information to be distributed and put in storage in a logistics management platform, and extracting the shipment time characteristics of the order information to be distributed and put in storage in the logistics management platform to obtain shipment time of the order information to be distributed and put in storage;
Sorting the shipment time of the order information to be distributed and put in storage from big to small, and mapping the shipment time of the order information to be distributed and put in storage after sorting from big to small into a distance value priority sorting table one by one according to the highest priority position to the lowest priority position of the distance value priority sorting table so as to obtain a priority distribution table of the order information to be distributed and put in storage;
and orderly distributing and warehousing the current order to be distributed and warehoused based on the priority distribution table of the order information to be distributed and warehoused so as to obtain an inventory space distribution result.
It should be noted that, by the method, the inventory space can be allocated according to the order of the shipment time, that is, the distance value between the position information of the empty inventory in the current logistics warehouse and the unloading position information of the logistics dock is allocated, for example, the goods which are most rapidly shipped are allocated in the inventory space corresponding to the minimum distance value between the position information of the empty inventory in the current logistics warehouse and the unloading position information of the logistics dock, and for example, the goods which are most slowly shipped are allocated in the inventory space corresponding to the maximum distance value.
In addition, the invention can also comprise the following steps:
acquiring corresponding goods order information in each inventory space, and extracting goods name characteristics of the corresponding goods order information in each inventory space to acquire corresponding goods name information in each inventory space;
carrying out data retrieval on the relationship of whether interaction exists between the corresponding cargo name information in each inventory space through a large data network;
when the search result shows that the corresponding cargo name information in each inventory space has an interaction relationship, acquiring the cargo inventory position with the interaction relationship;
and judging whether the goods stock positions with the interaction relations are adjacent, and if the goods stock positions with the interaction relations are adjacent, adjusting the stock positions of the goods with the interaction relations to generate an adjustment result.
It should be noted that, the interaction means that there is a chemical reaction between goods, such as a redox reaction between a chemical and another chemical, and such a substance is not suitable for being stored too close. The method can effectively improve the rationality of the goods in warehouse entry.
As shown in fig. 4, the second aspect of the present invention provides a big data based intelligent management system, the system includes a memory 41 and a processor 62, the memory 41 includes a big data based intelligent management method program, and when the big data based intelligent management method program is executed by the processor 62, the following steps are implemented:
acquiring order information to be delivered of each cargo type of the current logistics warehouse, and estimating according to the order information to be delivered of each cargo type of the current logistics warehouse so as to acquire delivery efficiency of each cargo type of the current logistics warehouse;
acquiring influence factor data influencing warehousing efficiency through a big data network, and analyzing the influence factor data through an analytic hierarchy process to acquire weight vector information of each influence factor data;
acquiring order information to be allocated and put in storage in a logistics management platform, and estimating the put-in efficiency of each cargo type of the current logistics warehouse based on weight vector information of each influence factor data and the order information to be allocated and put in storage;
the method comprises the steps of constructing a residual bin target function set according to the ex-warehouse efficiency of each cargo type in current logistics warehouse, constructing a warehouse-in bin target function set based on the warehouse-in efficiency of each cargo type in current logistics warehouse, and solving based on the residual bin target function set and the warehouse-in estimated bin target function set of a genetic algorithm to obtain an optimal cargo warehouse-in distribution result within a preset range.
In a preferred embodiment of the system, the solution is performed based on the genetic algorithm residual bin target function set and the warehouse entry estimated bin target function set to obtain an optimal cargo warehouse entry allocation result within a preset range, and the method specifically comprises the following steps:
uniformly generating a plurality of chromosome numbers in a solution space, presetting the dimension of each chromosome to generate a first generation population, determining the population scale and the maximum evolution algebra, and initializing the selection proportion, the crossover probability and the mutation probability;
taking order information to be allocated and put in storage in the logistics management platform as allocation basis, based on calculated values of the residual bin target function groups and the put-in estimated bin target function groups, carrying out rapid non-dominant sequencing and crowding calculation on each chromosome individual in the primary population, and carrying out selection, crossing and mutation operation on the primary population to obtain the next-generation population;
combining the primary population with the next-generation population to obtain a new population, taking order information to be allocated and put in storage in the logistics management platform as allocation basis, and carrying out rapid non-dominant sequencing and congestion degree calculation on each chromosome individual in the new population based on calculated values of the residual bin target function groups and the put-in-storage estimated bin target function groups;
Selecting individuals meeting the conditions for the new population to form a new primary population, determining the evolution algebra in the evolution process, adding one to the number of the evolution algebra if the evolution algebra is smaller than the maximum evolution algebra, and switching to the primary population for selecting, crossing and mutation operation to obtain the next generation population, and stopping iteration and outputting the optimal cargo warehousing distribution result within the preset period if the evolution algebra is larger than the maximum evolution algebra.
In the several embodiments provided in this application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above described device embodiments are only illustrative, e.g. the division of the units is only one logical function division, and there may be other divisions in practice, such as: multiple units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed. In addition, the various components shown or discussed may be coupled or directly coupled or communicatively coupled to each other via some interface, whether indirectly coupled or communicatively coupled to devices or units, whether electrically, mechanically, or otherwise.
The units described above as separate components may or may not be physically separate, and components shown as units may or may not be physical units; can be located in one place or distributed to a plurality of network units; some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in each embodiment of the present invention may be integrated in one processing unit, or each unit may be separately used as one unit, or two or more units may be integrated in one unit; the integrated units may be implemented in hardware or in hardware plus software functional units.
Those of ordinary skill in the art will appreciate that: all or part of the steps for implementing the above method embodiments may be implemented by hardware related to program instructions, and the foregoing program may be stored in a computer readable storage medium, where the program, when executed, performs steps including the above method embodiments; and the aforementioned storage medium includes: a mobile storage device, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk or an optical disk, or the like, which can store program codes.
Alternatively, the above-described integrated units of the present invention may be stored in a computer-readable storage medium if implemented in the form of software functional modules and sold or used as separate products. Based on such understanding, the technical solutions of the embodiments of the present invention may be embodied in essence or a part contributing to the prior art in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the methods of the embodiments of the present invention. And the aforementioned storage medium includes: a removable storage device, ROM, RAM, magnetic or optical disk, or other medium capable of storing program code.
The foregoing is merely illustrative embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily think about variations or substitutions within the technical scope of the present invention, and the invention should be covered. Therefore, the protection scope of the invention is subject to the protection scope of the claims.
Claims (3)
1. The intelligent management method based on big data is characterized by comprising the following steps:
acquiring order information to be delivered of each cargo type of the current logistics warehouse, and estimating according to the order information to be delivered of each cargo type of the current logistics warehouse so as to acquire delivery efficiency of each cargo type of the current logistics warehouse;
acquiring influence factor data influencing warehousing efficiency through a big data network, and analyzing the influence factor data through an analytic hierarchy process to acquire weight vector information of each influence factor data;
acquiring order information to be allocated and put in storage in a logistics management platform, and estimating the put-in efficiency of each cargo type in current logistics and storage based on the weight vector information of each influence factor data and the order information to be allocated and put in storage;
constructing a residual bin target function set according to the ex-warehouse efficiency of each cargo type in the current logistics warehouse, constructing a warehouse-in bin target function set based on the warehouse-in efficiency of each cargo type in the current logistics warehouse, and solving the residual bin target function set and the warehouse-in estimated bin target function set based on a genetic algorithm to obtain an optimal cargo warehouse-in distribution result within a preset time;
The method comprises the steps of obtaining to-be-ex-warehouse order information of each cargo type of current logistics warehouse, estimating according to the to-be-ex-warehouse order information of each cargo type of the current logistics warehouse to obtain ex-warehouse efficiency of each cargo type of the current logistics warehouse, and specifically comprises the following steps:
setting keyword information according to the ex-warehouse efficiency of each cargo type, performing data retrieval according to the keyword information through a big data network to obtain the ex-warehouse efficiency of each cargo type of each grade of logistics warehouse, and constructing an information database according to the ex-warehouse efficiency of each cargo type of each grade of logistics warehouse;
the method comprises the steps of obtaining the storage grade of a current logistics storage and obtaining to-be-stored order information of each goods type of the current logistics storage, inputting the storage grade of the current logistics storage into an information database for one-time matching to obtain the logistics storage with the matching degree larger than a preset matching degree, and obtaining the ex-storage efficiency of each goods type corresponding to the logistics storage with the matching degree larger than the preset matching degree;
acquiring order information to be delivered of each cargo type of the current logistics warehouse, and classifying according to the order information to be delivered of each cargo type of the current logistics warehouse so as to acquire the order information to be delivered of each cargo type;
Acquiring the delivery efficiency of each cargo type of the current logistics warehouse based on the delivery efficiency of each cargo type corresponding to the logistics warehouse with the matching degree larger than the preset matching degree and the to-be-delivered order information of each cargo type;
the method comprises the steps of acquiring influence factor data influencing warehousing efficiency through a big data network, analyzing the influence factor data through an analytic hierarchy process to acquire weight vector information of each influence factor data, and specifically comprising the following steps:
acquiring influence factor data influencing warehousing efficiency through a big data network, determining an evaluation hierarchical structure system, and dividing the evaluation hierarchical structure system into a target layer, a criterion layer and a scheme layer;
inputting the warehousing efficiency into a criterion layer, inputting the influence factor data influencing the warehousing efficiency into a scheme layer, presetting a plurality of influence effects, inputting the influence effects into a target layer, and generating an evaluation index system;
comparing the factors in the criterion layer and the scheme layer group with each other, quantifying the relative importance according to a preset quantitative scale, generating a scale value, and constructing a judgment matrix according to the scale value;
carrying out normalization calculation on the judgment matrix by a method root to obtain a feature vector, obtaining the maximum value of the feature vector in the judgment matrix, and obtaining weight vector information of each influence factor data according to the feature vector and the maximum value;
The method comprises the steps of acquiring order information to be distributed and put in storage in a logistics management platform, and estimating the put-in efficiency of each cargo type in the current logistics warehouse based on the weight vector information of each influence factor data and the order information to be distributed and put in storage, and specifically comprises the following steps:
acquiring order information to be allocated and put in storage in a logistics management platform, and classifying data of the order information to be allocated and put in storage in the logistics management platform according to the types of goods so as to acquire the order information to be allocated and put in storage corresponding to the types of goods;
constructing a warehouse-in efficiency estimation model based on the weight vector information of each influence factor data, and acquiring the influence factor data of the current logistics warehouse;
adjusting the warehouse-in efficiency estimation model according to the influence factor data of the current logistics warehouse so as to obtain an adjusted warehouse-in efficiency estimation model;
based on the adjusted warehousing efficiency estimation model and the to-be-allocated warehousing order information corresponding to each cargo type, the warehousing efficiency of each cargo type in the current logistics warehouse is obtained;
the warehouse-in efficiency estimation model specifically meets the following relation:
wherein k is the real-time warehousing efficiency ；
The warehousing efficiency of each cargo type at the beginning; y is the number of influencing factors; />
The influence weight value of the xth weather condition is that e is the base number of natural logarithm; />
The probability value of the occurrence of the xth weather condition within a preset time period is set;
the method comprises the following steps of:
acquiring the data information of the residual bin of the logistics storage at the current moment, and initializing the bin information to be stored of the logistics storage according to the data information of the residual bin of the logistics storage at the current moment;
constructing a residual bin target function set based on the bin information to be stored of the initialized logistics storage and the ex-warehouse efficiency of each cargo type of the current logistics storage, wherein the bin target function set meets the following conditions:
wherein ,
for real-time bin data information, n is the number of shipment types, and +.>
For the delivery efficiency of the ith cargo type, < +.>
For time, Q is the warehouse to-be-warehouse position information of the initialized logistics warehouse;
the method comprises the following steps of:
Acquiring real-time bin data information at the current moment, and constructing a bin-storage bin target function set according to the bin-storage efficiency of each cargo type in the current logistics storage and the real-time bin data information at the current moment, wherein the bin-storage bin target function set specifically meets the following relation:
wherein ,
for real-time bin data information, m is the number of warehouse-in goods types, < >>
Warehouse-in efficiency for the j-th cargo type of cargo to be allocated,/-, for the cargo to be allocated>
Time is;
the method comprises the following steps of:
uniformly generating a plurality of chromosome numbers in a solution space, presetting the dimension of each chromosome to generate a first generation population, determining the population scale and the maximum evolution algebra, and initializing the selection proportion, the crossover probability and the mutation probability;
taking order information to be allocated and put in storage in the logistics management platform as allocation basis, based on calculated values of a residual bin target function set and a put in storage estimated bin target function set, carrying out rapid non-dominant sequencing and crowding calculation on each chromosome individual in the primary population, and carrying out selection, crossing and mutation operation on the primary population to obtain a next generation population;
Combining the primary population with the next generation population to obtain a new population, taking order information to be allocated and put in storage in the logistics management platform as allocation basis, and carrying out rapid non-dominant sorting and congestion degree calculation on each chromosome individual in the new population based on the calculated value of the residual bin target function group and the put in storage estimated bin target function group;
selecting individuals meeting the conditions for the new population to form a new primary population, determining the evolution algebra of an evolution process, adding one to the number of the evolution algebra if the evolution algebra is smaller than the maximum evolution algebra, and switching to the primary population for selecting, crossing and mutation operation to obtain a next generation population, and stopping iteration and outputting an optimal cargo warehousing distribution result within a preset period if the evolution algebra is larger than the maximum evolution algebra.
2. The intelligent management system based on big data is characterized by comprising a memory and a processor, wherein the memory comprises an intelligent management method program based on big data, and when the intelligent management method program based on big data is executed by the processor, the following steps are realized:
Acquiring order information to be delivered of each cargo type of the current logistics warehouse, and estimating according to the order information to be delivered of each cargo type of the current logistics warehouse so as to acquire delivery efficiency of each cargo type of the current logistics warehouse;
acquiring influence factor data influencing warehousing efficiency through a big data network, and analyzing the influence factor data through an analytic hierarchy process to acquire weight vector information of each influence factor data;
acquiring order information to be allocated and put in storage in a logistics management platform, and estimating the put-in efficiency of each cargo type in current logistics and storage based on the weight vector information of each influence factor data and the order information to be allocated and put in storage;
constructing a residual bin target function set according to the ex-warehouse efficiency of each cargo type in the current logistics warehouse, constructing a warehouse-in bin target function set based on the warehouse-in efficiency of each cargo type in the current logistics warehouse, and solving the residual bin target function set and the warehouse-in estimated bin target function set based on a genetic algorithm to obtain an optimal cargo warehouse-in distribution result within a preset time;
the method comprises the steps of obtaining to-be-ex-warehouse order information of each cargo type of current logistics warehouse, estimating according to the to-be-ex-warehouse order information of each cargo type of the current logistics warehouse to obtain ex-warehouse efficiency of each cargo type of the current logistics warehouse, and specifically comprises the following steps:
Setting keyword information according to the ex-warehouse efficiency of each cargo type, performing data retrieval according to the keyword information through a big data network to obtain the ex-warehouse efficiency of each cargo type of each grade of logistics warehouse, and constructing an information database according to the ex-warehouse efficiency of each cargo type of each grade of logistics warehouse;
the method comprises the steps of obtaining the storage grade of a current logistics storage and obtaining to-be-stored order information of each goods type of the current logistics storage, inputting the storage grade of the current logistics storage into an information database for one-time matching to obtain the logistics storage with the matching degree larger than a preset matching degree, and obtaining the ex-storage efficiency of each goods type corresponding to the logistics storage with the matching degree larger than the preset matching degree;
acquiring order information to be delivered of each cargo type of the current logistics warehouse, and classifying according to the order information to be delivered of each cargo type of the current logistics warehouse so as to acquire the order information to be delivered of each cargo type;
acquiring the delivery efficiency of each cargo type of the current logistics warehouse based on the delivery efficiency of each cargo type corresponding to the logistics warehouse with the matching degree larger than the preset matching degree and the to-be-delivered order information of each cargo type;
The method comprises the steps of acquiring influence factor data influencing warehousing efficiency through a big data network, analyzing the influence factor data through an analytic hierarchy process to acquire weight vector information of each influence factor data, and specifically comprising the following steps:
acquiring influence factor data influencing warehousing efficiency through a big data network, determining an evaluation hierarchical structure system, and dividing the evaluation hierarchical structure system into a target layer, a criterion layer and a scheme layer;
inputting the warehousing efficiency into a criterion layer, inputting the influence factor data influencing the warehousing efficiency into a scheme layer, presetting a plurality of influence effects, inputting the influence effects into a target layer, and generating an evaluation index system;
comparing the factors in the criterion layer and the scheme layer group with each other, quantifying the relative importance according to a preset quantitative scale, generating a scale value, and constructing a judgment matrix according to the scale value;
carrying out normalization calculation on the judgment matrix by a method root to obtain a feature vector, obtaining the maximum value of the feature vector in the judgment matrix, and obtaining weight vector information of each influence factor data according to the feature vector and the maximum value;
The method comprises the steps of acquiring order information to be distributed and put in storage in a logistics management platform, and estimating the put-in efficiency of each cargo type in the current logistics warehouse based on the weight vector information of each influence factor data and the order information to be distributed and put in storage, and specifically comprises the following steps:
acquiring order information to be allocated and put in storage in a logistics management platform, and classifying data of the order information to be allocated and put in storage in the logistics management platform according to the types of goods so as to acquire the order information to be allocated and put in storage corresponding to the types of goods;
constructing a warehouse-in efficiency estimation model based on the weight vector information of each influence factor data, and acquiring the influence factor data of the current logistics warehouse;
adjusting the warehouse-in efficiency estimation model according to the influence factor data of the current logistics warehouse so as to obtain an adjusted warehouse-in efficiency estimation model;
based on the adjusted warehousing efficiency estimation model and the to-be-allocated warehousing order information corresponding to each cargo type, the warehousing efficiency of each cargo type in the current logistics warehouse is obtained;
the warehouse-in efficiency estimation model specifically meets the following relation:
wherein k is real-time warehousing efficiency; 
The warehousing efficiency of each cargo type at the beginning; y is the number of influencing factors; />
The influence weight value of the xth weather condition is that e is the base number of natural logarithm; />
The probability value of the occurrence of the xth weather condition within a preset time period is set;
the method comprises the following steps of:
acquiring the data information of the residual bin of the logistics storage at the current moment, and initializing the bin information to be stored of the logistics storage according to the data information of the residual bin of the logistics storage at the current moment;
constructing a residual bin target function set based on the bin information to be stored of the initialized logistics storage and the ex-warehouse efficiency of each cargo type of the current logistics storage, wherein the bin target function set meets the following conditions:
wherein ,
for real-time bin data information, n is the number of shipment types, and +.>
For the delivery efficiency of the ith cargo type, < +.>
For time, Q is the warehouse to-be-warehouse position information of the initialized logistics warehouse;
the method comprises the following steps of:
Acquiring real-time bin data information at the current moment, and constructing a bin-storage bin target function set according to the bin-storage efficiency of each cargo type in the current logistics storage and the real-time bin data information at the current moment, wherein the bin-storage bin target function set specifically meets the following relation:
wherein ,
for real-time bin data information, m is the number of warehouse-in goods types, < >>
Warehouse-in efficiency for the j-th cargo type of cargo to be allocated,/-, for the cargo to be allocated>
Time is;
the method comprises the following steps of:
uniformly generating a plurality of chromosome numbers in a solution space, presetting the dimension of each chromosome to generate a first generation population, determining the population scale and the maximum evolution algebra, and initializing the selection proportion, the crossover probability and the mutation probability;
taking order information to be allocated and put in storage in the logistics management platform as allocation basis, based on calculated values of a residual bin target function set and a put in storage estimated bin target function set, carrying out rapid non-dominant sequencing and crowding calculation on each chromosome individual in the primary population, and carrying out selection, crossing and mutation operation on the primary population to obtain a next generation population;
Combining the primary population with the next generation population to obtain a new population, taking order information to be allocated and put in storage in the logistics management platform as allocation basis, and carrying out rapid non-dominant sorting and congestion degree calculation on each chromosome individual in the new population based on the calculated value of the residual bin target function group and the put in storage estimated bin target function group;
selecting individuals meeting the conditions for the new population to form a new primary population, determining the evolution algebra of an evolution process, adding one to the number of the evolution algebra if the evolution algebra is smaller than the maximum evolution algebra, and switching to the primary population for selecting, crossing and mutation operation to obtain a next generation population, and stopping iteration and outputting an optimal cargo warehousing distribution result within a preset period if the evolution algebra is larger than the maximum evolution algebra.
3. A computer readable storage medium containing a big data based intelligent management method program which, when executed by a processor, implements the big data based intelligent management method steps of claim 1.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310302292.3A CN116051004B (en) | 2023-03-27 | 2023-03-27 | Intelligent management method, system and medium based on big data |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310302292.3A CN116051004B (en) | 2023-03-27 | 2023-03-27 | Intelligent management method, system and medium based on big data |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN116051004A CN116051004A (en) | 2023-05-02 |
| CN116051004B true CN116051004B (en) | 2023-06-13 |
Family
ID=86127595
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310302292.3A Active CN116051004B (en) | 2023-03-27 | 2023-03-27 | Intelligent management method, system and medium based on big data |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116051004B (en) |
Families Citing this family (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117236854B (en) * | 2023-11-10 | 2024-02-02 | 山西艾特嘉科技有限公司 | Wisdom commodity circulation warehouse system |
| CN117391593B (en) * | 2023-12-13 | 2024-05-03 | 广东精工智能***有限公司 | Intelligent warehouse article scheduling management method and system based on Internet of things |
| CN117495065B (en) * | 2024-01-03 | 2024-04-02 | 百工汇智(苏州)智能科技有限公司 | Intelligent dispatching method for equipment for vision recognition of storage robot |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105046468A (en) * | 2015-07-06 | 2015-11-11 | 苏州华拓信息技术有限公司 | Method for intelligent storage based on internet-of things |
| CN114417696A (en) * | 2021-12-07 | 2022-04-29 | 长春工业大学 | Automatic stereoscopic warehouse goods space allocation optimization method based on genetic algorithm |
| CN115115256A (en) * | 2022-07-15 | 2022-09-27 | 南京邮电大学 | Medicine warehouse goods space distribution method |
| CN115689439A (en) * | 2022-12-21 | 2023-02-03 | 浙江工业大学 | Warehouse material storage position distributing and selecting system based on genetic algorithm |
Family Cites Families (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108122082A (en) * | 2016-11-29 | 2018-06-05 | 杭州海康机器人技术有限公司 | A kind of warehouse-out method and device |
| MX2020013044A (en) * | 2018-06-01 | 2021-02-26 | Beijing Geekplus Tech Co Ltd | Management method, apparatus, system applied to goods-to-person system, and server and computer storage medium. |
| CN109685475A (en) * | 2018-12-27 | 2019-04-26 | 成都电科智联科技有限公司 | Intelligent warehouse management system based on SOA and genetic algorithm |
| CN111476413A (en) * | 2020-04-03 | 2020-07-31 | 上海明略人工智能(集团)有限公司 | Warehouse storage position distribution method and system based on big data |
| CN113487259B (en) * | 2021-07-06 | 2022-02-18 | 深圳市通拓信息技术网络有限公司 | Ex-warehouse delivery method for e-commerce intelligent warehousing |
| CN113962617B (en) * | 2021-12-22 | 2022-03-08 | 华清科盛(北京)信息技术有限公司 | Method and device for dispatching logistics in factory, electronic equipment and storage medium |
| CN114936830A (en) * | 2022-07-27 | 2022-08-23 | 四川金叶生物防治有限公司 | Warehouse management method and system based on Internet of things technology |
| CN115330312A (en) * | 2022-08-18 | 2022-11-11 | 山东港口陆海国际物流集团有限公司 | Bulk goods warehousing stock space planning system and method based on genetic algorithm |
| CN115829469A (en) * | 2022-12-06 | 2023-03-21 | 江苏杰瑞科技集团有限责任公司 | Warehouse logistics transportation method |
| CN115577995B (en) * | 2022-12-09 | 2023-04-07 | 中交第四航务工程勘察设计院有限公司 | Intelligent port PIM platform management method and system based on digital twin |
-
2023
- 2023-03-27 CN CN202310302292.3A patent/CN116051004B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105046468A (en) * | 2015-07-06 | 2015-11-11 | 苏州华拓信息技术有限公司 | Method for intelligent storage based on internet-of things |
| CN114417696A (en) * | 2021-12-07 | 2022-04-29 | 长春工业大学 | Automatic stereoscopic warehouse goods space allocation optimization method based on genetic algorithm |
| CN115115256A (en) * | 2022-07-15 | 2022-09-27 | 南京邮电大学 | Medicine warehouse goods space distribution method |
| CN115689439A (en) * | 2022-12-21 | 2023-02-03 | 浙江工业大学 | Warehouse material storage position distributing and selecting system based on genetic algorithm |
Also Published As
| Publication number | Publication date |
|---|---|
| CN116051004A (en) | 2023-05-02 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN116051004B (en) | Intelligent management method, system and medium based on big data | |
| CN109685583B (en) | Supply chain demand prediction method based on big data | |
| Camargo et al. | A knapsack problem as a tool to solve the production planning problem in small foundries | |
| Jamrus et al. | Multistage production distribution under uncertain demands with integrated discrete particle swarm optimization and extended priority-based hybrid genetic algorithm | |
| CN112150237B (en) | Multi-model fused order overdue early warning method, device, equipment and storage medium | |
| Heidari et al. | Modeling truck scheduling problem at a cross-dock facility through a bi-objective bi-level optimization approach | |
| CN111476677B (en) | Electricity consumption type electricity sales amount analysis and prediction method and system based on big data | |
| CN115860645B (en) | Logistics storage management method and system based on big data | |
| CN114187120A (en) | Vehicle insurance claim settlement fraud risk identification method and device | |
| Raju et al. | An approach for demand forecasting in steel industries using ensemble learning | |
| Shinkevich et al. | Computer analysis of energy and resource efficiency in the context of transformation of petrochemical supply chains | |
| CN112860917A (en) | Method, device and equipment for processing data of goods to be warehoused and storage medium | |
| Zhou et al. | Research on intelligent solution of service industry supply chain network optimization based on genetic algorithm | |
| Babaei et al. | A new branch and efficiency algorithm for an optimal design of the supply chain network in view of resilience, inequity and traffic congestion | |
| CN113656691A (en) | Data prediction method, device and storage medium | |
| Wang et al. | Improved harmony search algorithm for truck scheduling problem in multiple-door cross-docking systems | |
| CN113962416A (en) | Engineering machinery part stock prediction method, management method and system | |
| CN112949885A (en) | Method and device for predicting delivery mode, storage medium and electronic equipment | |
| US20230096633A1 (en) | Optimized tree ensemble based demand model | |
| Yousefi et al. | A decision support framework for sustainable production planning of paper recycling systems | |
| CN114971688A (en) | Supply chain production prediction method based on combined model | |
| CN113077108A (en) | Data prediction system for power material configuration requirements | |
| CN113762842A (en) | Warehouse scheduling method, server and system | |
| CN113822301B (en) | Sorting center sorting method and device, storage medium and electronic equipment | |
| CN116664052B (en) | Global digitalized operation management method and system based on artificial intelligence |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| TR01 | Transfer of patent right | ||
| TR01 | Transfer of patent right |
Effective date of registration: 20240326 Address after: 518000 Room 301, building B, floor 3, coastal future incubation center, Longyuan community, Longhua street, Longhua District, Shenzhen, Guangdong Province Patentee after: Shenzhen Zhiyang culture media Co.,Ltd. Country or region after: China Address before: 518000 Room 2705, Baofa Building, Shatoujiao Bonded Zone, Yantian District, Shenzhen, Guangdong Province Patentee before: SHENZHEN HONGDA SUPPLY CHAIN SERVICE Co.,Ltd. Country or region before: China |