CN119228277A - Shelf return method, device, computing equipment and medium based on thermal analysis - Google Patents
Shelf return method, device, computing equipment and medium based on thermal analysis Download PDFInfo
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Abstract
The application discloses a goods shelf warehouse-returning method, device, computing equipment and medium based on thermal analysis, and relates to the technical field of warehouse. The method comprises the steps of respectively determining the heat of each goods according to the turnover rate of the goods, the time interval of delivery and the delivery quantity of the goods in a historical order, respectively determining the heat of each goods shelf according to the number of the goods shelves and the heat of the goods stored in each goods shelf, determining the heat distribution of storage positions according to the heat of all the goods shelves, determining the updated heat of the target goods shelf after the target goods shelf is delivered, determining the cost of each storage position according to the cost of the path from each storage position to a picking workstation and the cost of the other goods shelves, wherein the cost is the cost of the target goods shelf from the picking workstation to the storage position, and carrying out storage position distribution on the target goods shelf according to the cost of each storage position, the updated heat of the target goods shelf and the heat distribution of the storage positions. The application dynamically realizes the storage allocation of the goods shelves and effectively improves the picking and putting efficiency of a picking system.
Description
Technical Field
The application relates to the technical field of storage, in particular to a goods shelf returning method, a device, computing equipment and a medium based on thermal analysis.
Background
The rapid development of electronic commerce has led to the increasing interest in the industry in the picking efficiency of warehouses, and in this context, goods-to-person picking systems have grown. Along with popularization of the goods-to-person picking system, how to further improve the picking efficiency of the goods-to-person picking system becomes a new research direction, and shelf warehouse-returning storage allocation in a picking or shelf-loading environment is one research focus. In a goods-to-person picking system, the shelf storage location determines the distance and efficiency of the AGVs (Automated Guided Vehicle, automated guided vehicles) to handle.
At present, most warehouses in the goods-arrival person picking system adopt a static storage allocation technology, and the goods shelf storage allocation cannot be reasonably carried out in the mode, so that the carrying efficiency of the goods shelf to a working station is low, the waiting time of workers at the working station is prolonged, and the picking and loading efficiency of the goods-arrival person picking system is affected.
Disclosure of Invention
The embodiment of the application aims to provide a goods shelf returning method, a device, a computing device and a medium based on thermal analysis, which are used for solving the technical problems of low picking and loading efficiency of a goods-to-person picking system in the prior art.
In order to achieve the above object, a first aspect of the present application provides a shelf returning method based on thermal analysis, including:
according to the turnover rate of the goods, the time interval of delivery and the delivery quantity of the goods in the historical order, the heat of each goods is respectively determined;
according to the number of goods positions of the goods shelves and the heat of goods stored in each goods position, the heat of each goods shelf is respectively determined;
Determining heat distribution of storage positions according to the heat of all the shelves;
After the target goods shelf is taken out of the warehouse, determining the update heat of the target goods shelf;
determining the cost of each storage position according to the path cost from each storage position to the picking workstation and the cost of moving other storage racks, wherein the cost is the cost of moving a target storage rack from the picking workstation to the storage position;
and carrying out storage allocation on the target goods shelf according to the cost of each storage, the updated heat of the target goods shelf and the distribution of the heat of the storage.
In the embodiment of the application, the heat degree of each cargo is respectively determined according to the turnover rate, the time interval of the cargo delivery and the quantity of the cargo delivery in the historical order, and the method comprises the following steps:
According to the frequency of the goods in the historical orders, determining the turnover rate of the goods as a first heat of the goods;
determining the time interval of the shipment of the goods according to the target deadline and the last shipment time of the goods;
Multiplying the time attenuation coefficient by the time interval of the warehouse-out, and adding the sum of preset values to obtain the inverse of the sum to obtain the second heat of the goods;
Determining the picking number of the cargoes under the measurement according to the total ex-warehouse number of the cargoes in the historical order and the quotient of the measurement, wherein the measurement is the maximum picking ex-warehouse number of the cargoes;
determining a third heat of the good based on a quotient of the good and a sum of the pick numbers of all the good under the measure;
and carrying out weighted calculation on the first heat, the second heat and the third heat to obtain the heat of the goods.
In the embodiment of the application, the method for determining the heat distribution of the storage position according to the heat of all the shelves comprises the following steps:
Sampling according to the heat of each goods shelf to obtain a plurality of sampling distributions;
and performing nuclear density estimation on the plurality of sampling distributions to obtain the heat distribution of the storage position.
In the embodiment of the application, sampling is performed according to the heat of each shelf to obtain a plurality of sampling distributions, including:
step S1, resampling is carried out on all the shelves to obtain a sample set;
step S2, determining the heat of each goods shelf in the sample set;
And repeatedly executing the step S1 and the step S2 to obtain a plurality of sampling distributions.
In the embodiment of the application, the nuclear density estimation is performed on a plurality of sampling distributions to obtain the heat distribution of the storage position, which comprises the following steps:
determining a bandwidth parameter according to the distance between the data point in the sampling distribution and other data points with the nearest preset number of data points;
Determining a kernel density estimation function of each sampling distribution according to the bandwidth parameters;
and obtaining the heat distribution of the storage bit according to the calculation result of the nuclear density estimation function.
In the embodiment of the application, according to the cost of each storage position, the update heat of the target storage rack and the distribution of the heat of the storage positions, the storage position distribution is carried out on the target storage rack, and the method comprises the following steps:
Ordering the cost of each storage bit to obtain a storage bit cost sequence;
Dividing the storage cost sequence according to the storage heat distribution to obtain storage IDs corresponding to each storage heat level;
determining a corresponding target storage heat level according to the updated heat of the target goods shelf;
And selecting the spare storage ID with the minimum substitution value from all storage IDs corresponding to the target storage heat level so as to finish the storage allocation of the target goods shelf.
In the embodiment of the application, the Redis database is utilized to store and read the heat of the goods.
The second aspect of the present application provides a thermal analysis-based shelf returning device, comprising:
a memory configured to store instructions;
a processor configured to call instructions from the memory and when executing the instructions is capable of implementing a shelf-banking method based on thermal analysis according to the first aspect.
A third aspect of the application provides a computing device comprising:
The thermal analysis-based shelf warehousing device according to the second aspect.
A fourth aspect of the application provides a machine-readable storage medium having stored thereon instructions for causing a machine to perform a shelf-banking method based on thermal analysis according to the first aspect.
According to the goods shelf returning method based on thermal analysis, the goods heat calculating method based on the historical orders is provided, the influences of time and turnover rate on the goods heat can be more intuitively reflected due to factors such as the time interval of delivery and the turnover rate, after the goods shelf is delivered, the updated heat of the goods shelf is calculated in real time, the current storage position condition is considered for dynamic distribution, dynamic goods shelf storage position matching can be achieved, the overall picking and loading efficiency of a goods to person picking system can be effectively improved, and the digital and intelligent level of the goods to person picking system is improved.
Additional features and advantages of embodiments of the application will be set forth in the detailed description which follows.
Drawings
The accompanying drawings are included to provide a further understanding of embodiments of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain, without limitation, the embodiments of the application. In the drawings:
FIG. 1 schematically illustrates a flow diagram of a shelf-return method based on thermal analysis according to an embodiment of the application;
FIG. 2 schematically illustrates a diagram of a nuclear density estimation result according to an embodiment of the present application;
fig. 3 schematically shows a schematic structural diagram of a shelf storage device based on thermal analysis according to an embodiment of the application.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present application more apparent, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application, and it should be understood that the detailed description described herein is merely for illustrating and explaining the embodiments of the present application, and is not intended to limit the embodiments of the present application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
In addition, if there is a description of "first", "second", etc. in the embodiments of the present application, the description of "first", "second", etc. is for descriptive purposes only and is not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In addition, the technical solutions of the embodiments may be combined with each other, but it is necessary to base that the technical solutions can be realized by those skilled in the art, and when the technical solutions are contradictory or cannot be realized, the combination of the technical solutions should be considered to be absent and not within the scope of protection claimed in the present application.
Fig. 1 schematically shows a flow diagram of a shelf-returning method based on thermal analysis according to an embodiment of the application. As shown in fig. 1, an embodiment of the present application provides a shelf returning method based on thermal analysis, which may include the following steps.
And 110, respectively determining the heat of each cargo according to the turnover rate, the delivery time interval and the delivery quantity of the cargoes in the historical order.
The method for calculating the heat of the goods based on the historical order has the advantages that the heat of the goods is influenced by various factors such as seasons, policies, market demands, supply chain efficiency and the like, the accuracy of dynamic allocation of storage positions of a warehouse is directly influenced by accurate heat calculation of the goods, meanwhile, the influence factors such as turnover rate and delivery time interval of the goods are considered, and a calculation model of the heat of the goods is built, so that the accuracy of heat calculation of the goods can be improved.
In an alternative embodiment, step 110 includes:
According to the frequency of the goods in the historical orders, determining the turnover rate of the goods as a first heat of the goods;
determining the time interval of the shipment of the goods according to the target deadline and the last shipment time of the goods;
Multiplying the time attenuation coefficient by the time interval of the warehouse-out, and adding the sum of preset values to obtain the inverse of the sum to obtain the second heat of the goods;
Determining the picking number of the cargoes under the measurement according to the total ex-warehouse number of the cargoes in the historical order and the quotient of the measurement, wherein the measurement is the maximum picking ex-warehouse number of the cargoes;
determining a third heat of the good based on a quotient of the good and a sum of the pick numbers of all the good under the measure;
and carrying out weighted calculation on the first heat, the second heat and the third heat to obtain the heat of the goods.
Specifically, first, the turnover rate can be understood as the frequency of occurrence of the goods in the historical order, and the following formula (1) can be referred to:
(1)
Where p i represents the frequency of occurrence of the good in the historical order, N i represents the total number of occurrences of good i in the historical order, and N represents the total number of good rows in the historical order.
It will be appreciated that the first heat resulting from the turnover rate is between 0 and 1.
Secondly, the time also has an influence on the heat of the goods, and the embodiment of the application combines the time attenuation item of the goods to reflect the heat change condition of the goods along with the time. The target deadline can be understood as a deadline set based on actual requirements, for example, 5 points, and the difference between the target deadline and the last shipment time of the goods is the shipment time interval, that is, the time interval from the last shipment of the goods. And multiplying the time attenuation coefficient by the time interval of the warehouse-out, and adding the sum of preset values to obtain the inverse of the sum, so that the second heat degree can be obtained. Illustratively, the preset value may be 1. The second heat may be referred to the following formula (2):
(2)
In the formula, The second heat degree is represented by α, the time decay coefficient is represented by α, the larger the value of α is, the larger the influence of time on the heat decay of the goods is, T represents the target deadline, and T i represents the last time the goods i are delivered.
It is understood that in the embodiment of the present application, the maximum value of the second heat is 1.
And thirdly, the quantity of the cargos in the historical order has a certain influence on the heat degree of the cargos, but the accumulation effect exists on the heat degree of the cargos in the dimension, in order to reduce the influence of the heat degree accumulation effect on the heat degree of the cargos, the maximum picking and warehousing quantity of the cargos is adopted as the measurement of the cargos, and the maximum picking and warehousing quantity can be understood as the maximum value in the warehousing quantity of the cargos each time. Specifically, assuming that the goods i appear m times together, and the number of outgoing stores each time is s j, the measurement of the goods isI.e. the maximum pick-out number. Then, cargo i is measuredThe following sorting numbers are: i.e., the quotient of the total shipment number and the metric for the good in the historical order.
Whereby the third heat of the good is derived from the quotient of the good and the sum of the pick numbers of all the good under the metric, reference can be made to equation (3):
(3)
In the formula, A third degree of heat is shown as being,Representing the number of picks of the good under the metric, N representing the total number of the good in the historical order.
Finally, the heat of the cargo may be obtained by performing weighted calculation according to the first heat, the second heat and the third heat, and reference may be made to formula (4):
(4)
In the formula, The heat degree of the goods is represented,A first heat level of the cargo is indicated,A second heat level of the cargo is indicated,A third heat level of the cargo is indicated,、、Respectively represent the weights corresponding to the first heat degree, the second heat degree and the third heat degree, and satisfy。
In an alternative embodiment, the method further comprises:
and storing and reading the heat of the goods by using the Redis database.
Specifically, the Redis database, which is called Remote Dictionary Server in English, is a remote dictionary service, is an open-source log-type, key-Value (Key-Value pair) database written and supported by ANSI (American National Standards Institute ) C language, can be based on memory and can also be persistent, and provides multiple language APIs (Application Programming Interface, application programming interfaces). According to the embodiment of the application, the Redis database is used for storing and reading the heat of the goods, so that the calculation speed of the warehouse goods shelf warehouse returning method based on thermal analysis can be effectively improved.
And 120, respectively determining the heat of each goods shelf according to the number of the goods shelves and the heat of the goods stored in each goods shelf.
Specifically, in general, a shelf includes a plurality of cargo spaces, and the cargo is stored on the shelf according to a principle that one type of cargo is placed on one cargo space, that is, the cargo of each type is not mixed. Based on the heat of each cargo obtained in step 110, the heat of each cargo rack can be obtained according to the number of cargo positions of the cargo racks and the heat of the cargo stored in each cargo position. Let the number of shelves s contain a number of shelves C, one for each shelf, the heat of the shelves can be referenced by equation (5):
(5)
In the formula, The heat degree of the goods shelf is indicated,The heat degree of the goods stored in the goods space o is represented, and C represents the number of the goods space o.
Illustratively, one pallet has a total of 3*3 cargo areas, each of which stores cargo including a first cargo area to a third cargo area each storing cargo i 1, a fourth cargo area to a sixth cargo area each storing cargo i 2, and a seventh cargo area to a ninth cargo area each storing cargo i 3. Assume that the heat of the goods i 1, i 2, and i 3 are respectively、AndThen the heat of the shelf is。
And 130, determining the heat distribution of the storage positions according to the heat of all the shelves.
Typically, one shelf is in one storage location. In the embodiment of the application, the number of the shelves in the large warehouse is hundreds to thousands, and the heat division of the warehouse storage layout can be more reasonably performed based on the distribution of the whole heat condition of the shelves. It will be appreciated that the heat distribution of the storage location may be set according to actual requirements, which is not limited in this embodiment of the present application. Illustratively, the reservoir heat distribution may include three levels, high, medium and low.
In an alternative embodiment, step 130 includes:
step 131, sampling according to the heat of each shelf to obtain a plurality of sampling distributions;
and 132, performing nuclear density estimation on the plurality of sampling distributions to obtain the heat distribution of the storage bit.
Specifically, in the embodiment of the application, sampling is firstly performed according to the heat of each shelf to obtain a plurality of sampling distributions, and then the overall storage heat distribution is evaluated by adopting a nuclear density estimation mode based on the sampling distribution result. In order to improve the calculation efficiency of a shelf returning method based on thermal analysis, the embodiment of the application adopts a self-help sampling mode to estimate the overall distribution of the shelf in the warehouse, and the core idea is to simulate the distribution of the overall storage heat degree through the sample data of the existing shelf heat degree, thereby avoiding the dependence on all data and improving the representativeness of the overall sampling data.
In an alternative embodiment, step 131 includes:
Step 131a, resampling all the shelves to obtain a sample set;
step 131b, determining the heat of each shelf in the sample set;
Step 131a and step 131b are repeatedly performed to obtain a plurality of sampling distributions.
Illustratively, in embodiments of the present application, resampling is used to obtain the sample set. Resampling is a method that can repeatedly extract samples from a dataset, i.e., randomly extract a certain number of samples from all shelves with places back, resulting in a sample set. In this process, the same sample is allowed to be extracted multiple times, so that the extraction of each sample is independent, and the representativeness of the whole sample set is improved. And determining the heat of each shelf in the sample set. It will be appreciated that the heat of the shelves may be determined in step 131b in a similar manner to step 120. By repeating this, a large number of sampling distributions can be constructed. It should be noted that the number of repetitions may be determined according to the number of sampled shelves.
In an alternative embodiment, step 132 includes:
determining a bandwidth parameter according to the distance between the data point in the sampling distribution and other data points with the nearest preset number of data points;
Determining a kernel density estimation function of each sampling distribution according to the bandwidth parameters;
and obtaining the heat distribution of the storage bit according to the calculation result of the nuclear density estimation function.
Specifically, in order to improve the accuracy of the kernel density estimation result, the embodiment of the application adopts a self-adaptive bandwidth kernel density estimation mode. Let the sampled data points in the multiple sampling distributions in the embodiment of the application beThe data points represent the heat of the shelves and the kernel density estimation function can be expressed with reference to the following equation (6):
(6)
Where n represents the number of data points, The heat level of the data point i is indicated,The kernel function is represented by a function of the kernel,Data pointsThe associated bandwidth parameter, x, represents the input value.
In the embodiment of the application, the kernel function adopts a gaussian function, and the expression can refer to the following formula (7):
(7)
Where x represents the input value, μ represents the mean, σ represents the variance, and e represents the natural constant.
Wherein x is the heat of the shelf in the embodiment of the application.
In an embodiment of the application, the bandwidth parameters in the kernel functionBased on nearest neighbor. That is, each data point is calculated from the distance of its nearest neighbors. Specifically, data points can be selectedIs calculated by calculating the preset number of nearest neighbor data pointsThe average distance between the two is multiplied by a constant factorObtaining bandwidth parametersReference may be made to the following equation (8):
(8)
Where h i denotes the bandwidth parameter, x i denotes the data point, x j denotes the nearest neighbor data point, Representing a constant factor, k represents the number of nearest neighbor data points, i.e., a preset number.
It will be appreciated that the preset number may be set according to actual requirements, which is not limited in this embodiment of the present application.
Further, the result of the kernel density estimation is the distribution of all shelves in the warehouse. Referring to fig. 2 together, fig. 2 schematically illustrates a schematic diagram of a nuclear density estimation result according to an embodiment of the application. As shown in fig. 2, the red dotted line in fig. 2 is the estimated result of the nuclear density obtained by each sampling distribution, and the blue solid line is the distribution of all shelves in the warehouse. It will be appreciated that a blue solid line may be obtained by superimposing a plurality of red dashed lines.
The heat of the storage bits in the warehouse can be divided according to the result of the core density estimation. Let the core density estimation result beThe range of the nuclear density estimation result isThe dividing number m of the heat level of the storage position can be configured according to the actual demands of users, and dividing point values can be generated after the dividing number is setThe calculation mode of the proportion of the storage bit corresponding to the mth storage bit heat level to the whole storage bit can refer to the following formula (9):
(9)
In the formula, The scale is indicated by the terms of scale,The result of the kernel density estimation is represented,Represents the value of the m-th division point,、Representing the minimum and maximum values of the kernel density estimation result, respectively.
And 140, after the target goods shelf is taken out of the warehouse, determining the update heat of the target goods shelf.
In the embodiment of the application, after the target goods shelf is delivered out of the warehouse, the goods on the target goods shelf are changed, reduced or increased after the goods are subjected to picking or loading. Therefore, the update heat of the target goods shelf after the goods are changed needs to be determined, and a foundation is provided for dynamically realizing the storage allocation of the goods shelf. It will be appreciated that a similar method to step 120 may be employed herein as well.
And 150, determining the cost of each storage position according to the path cost of each storage position to the picking workstation and the cost of moving other shelves, wherein the cost is the cost of moving the target shelf from the picking workstation to the storage position.
In the embodiment of the application, the cost of the storage is the cost of the target goods shelf moving from the picking workstation to the storage, including the path cost of the storage to the picking workstation and the cost of moving other goods shelves. The path cost from the storage to the picking workstation needs to select the path with the minimum length from all paths obtained by path planning, so that the moving efficiency is improved, and the cost of the storage is reduced. The cost of moving other shelves is understood to be that one shelf is centered at nine Gong Gechu bits and eight shelves are surrounded by the surrounding, so that the shelf needs to be moved out of the storage, and at least one other shelf needs to be moved. Thus, the cost can be referenced by the following equation (10):
(10)
In the formula, Representing the cost of the storage location,Representing the lengths of multiple paths in the pick workstation to storage r path set,Indicating the cost of moving the other shelf,A mapping factor representing the path length mapping versus time,A mapping factor representing the number of shelf movements versus time.
And 160, distributing the storage positions to the target storage racks according to the cost of each storage position, the updated heat of the target storage racks and the heat distribution of the storage positions.
In the embodiment of the application, after the target goods shelf is taken out of the warehouse, the storage position of the target goods shelf for warehouse returning is determined based on the three information of the cost of each storage position obtained in the step 150, the update heat of the target goods shelf obtained in the step 140 and the storage position heat distribution obtained in the step 130, so that the storage position distribution of the target goods shelf is dynamically realized, the warehouse returning efficiency of the goods shelf can be effectively improved, and the overall picking and loading efficiency of a goods-to-person picking system can be further improved.
In an alternative embodiment, step 160 includes:
Ordering the cost of each storage bit to obtain a storage bit cost sequence;
Dividing the storage cost sequence according to the storage heat distribution to obtain storage IDs corresponding to each storage heat level;
determining a corresponding target storage heat level according to the updated heat of the target goods shelf;
And selecting the spare storage ID with the minimum substitution value from all storage IDs corresponding to the target storage heat level so as to finish the storage allocation of the target goods shelf.
In the embodiment of the application, the cost of each storage bit in the warehouse is firstly sequenced to generate the storage bit cost sequence, and the storage bit ID (Identity) corresponding to each storage bit heat level is obtained based on the storage bit cost sequence, thereby realizing the division of storage bits. Further, according to the updated heat of the target goods shelf after the goods shelf is delivered out of the warehouse, the updated heat is matched with the heat storage grade, and the replacement price is selected to be the smallest from the corresponding target heat storage grade.
Illustratively, there are 10 storage locations in the warehouse, each storage location having a storage location ID of 1 to 10, each storage location having a cost of A1, A2, a10, and (3) arranging the storage cost sequences from large to small to obtain storage cost sequences of A5, A8, A10, A3, A7, A2, A4, A1, A6 and A9. Assuming that the heat distribution of the storage bits comprises the three categories of high, medium and low in a ratio of 1:3:1, the storage bit IDs corresponding to the high heat level are 5 and 8, the storage bit IDs corresponding to the medium heat level are 10, 3, 7, 2, 4 and 1, and the storage bit IDs corresponding to the low heat level are 6 and 9. And if the updated heat of the target goods shelf corresponds to the heat level, selecting the storage ID with the minimum cost based on the cost of each storage from the six storage IDs 10, 3, 7, 2, 4 and 1 corresponding to the heat level so as to finish the storage allocation of the target goods shelf. And if the storage bit ID with the minimum cost and the idle storage bit ID is 3, returning the target goods shelf to the storage bit with the storage bit ID of 3.
According to the goods shelf returning method based on thermal analysis, the goods heat calculating method based on the historical orders is provided, and the influences of time and turnover rate on the goods heat can be more intuitively reflected by considering factors such as the time interval of delivery and the turnover rate, after the goods shelf is delivered, the updated heat of the goods shelf is calculated in real time, the current storage situation is considered for dynamic allocation, dynamic storage matching of the goods shelf can be achieved, the overall picking and loading efficiency of a goods to a person picking system can be effectively improved, and the digitization and intelligence level of the goods to the person picking system is improved.
Fig. 3 schematically shows a schematic structural diagram of a shelf storage device based on thermal analysis according to an embodiment of the application. As shown in fig. 3, an embodiment of the present application provides a shelf warehouse-returning device based on thermal analysis, which may include:
a memory 310 configured to store instructions;
processor 320 is configured to invoke instructions from memory 310 and when executing the instructions, to implement the method for controlling a boom described above.
Specifically, in an embodiment of the present application, processor 320 may be configured to:
according to the turnover rate of the goods, the time interval of delivery and the delivery quantity of the goods in the historical order, the heat of each goods is respectively determined;
according to the number of goods positions of the goods shelves and the heat of goods stored in each goods position, the heat of each goods shelf is respectively determined;
Determining heat distribution of storage positions according to the heat of all the shelves;
After the target goods shelf is taken out of the warehouse, determining the update heat of the target goods shelf;
determining the cost of each storage position according to the path cost from each storage position to the picking workstation and the cost of moving other storage racks, wherein the cost is the cost of moving a target storage rack from the picking workstation to the storage position;
and carrying out storage allocation on the target goods shelf according to the cost of each storage, the updated heat of the target goods shelf and the distribution of the heat of the storage.
The goods shelf returning device based on thermal analysis provided by the embodiment of the application can realize each process of the goods shelf returning method based on thermal analysis in the method embodiment, and can achieve the same technical effect, and in order to avoid repetition, the description is omitted.
Embodiments of the present application also provide a computing device, which may include:
the goods shelf warehouse returning device based on thermal analysis.
The embodiment of the application also provides a machine-readable storage medium, wherein the machine-readable storage medium is stored with instructions for causing a machine to execute the shelf returning method based on thermal analysis.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In one typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include volatile memory in a computer-readable medium, random Access Memory (RAM) and/or nonvolatile memory, etc., such as Read Only Memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.
Computer readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Disks (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium which can be used to store information that can be accessed by a computing device. Computer-readable media, as defined herein, does not include transitory computer-readable media (transmission media), such as modulated data signals and carrier waves.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises an element.
The foregoing is merely exemplary of the present application and is not intended to limit the present application. Various modifications and variations of the present application will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. which come within the spirit and principles of the application are to be included in the scope of the claims of the present application.
Claims (10)
1. A thermal analysis-based shelf warehousing method, comprising:
According to the turnover rate of the goods, the time interval of delivery and the delivery quantity of the goods in the historical order, the heat of each goods is respectively determined;
according to the number of goods positions of the goods shelves and the heat of the goods stored in each goods position, determining the heat of each goods shelf respectively;
Determining heat distribution of storage positions according to the heat of all the shelves;
after the target goods shelf is taken out of the warehouse, determining the update heat of the target goods shelf;
Determining the cost of each storage position according to the path cost from each storage position to a picking workstation and the cost of moving other storage racks, wherein the cost is the cost of moving the target storage rack from the picking workstation to the storage position;
And carrying out storage allocation on the target goods shelf according to the cost of each storage, the updated heat of the target goods shelf and the heat distribution of the storage.
2. The method of claim 1, wherein the determining the heat of each of the goods according to the turnover rate of the goods, the time interval of the delivery, the number of the delivery of the goods in the historical order, respectively, comprises:
determining the turnover rate of the goods according to the occurrence frequency of the goods in the historical orders, and taking the turnover rate as the first heat of the goods;
Determining the time interval of the shipment of the goods according to the target deadline and the last shipment time of the goods;
multiplying the time attenuation coefficient by the time interval of the warehouse-out, and adding the sum of preset values to obtain the inverse of the sum of the preset values to obtain the second heat of the goods;
Determining the picking number of the cargoes under the measurement according to the quotient of the total ex-warehouse number of the cargoes and the measurement in the historical order, wherein the measurement is the maximum picking ex-warehouse number of the cargoes;
determining a third heat of the good according to a quotient of the good and a sum of the picked numbers of all the good under measurement;
and carrying out weighted calculation on the first heat, the second heat and the third heat to obtain the heat of the goods.
3. The method of claim 1, wherein said determining a storage location heat distribution based on the heat of all of the shelves comprises:
Sampling according to the heat of each goods shelf to obtain a plurality of sampling distributions;
And performing nuclear density estimation on the plurality of sampling distributions to obtain the heat distribution of the storage bit.
4. A method according to claim 3, wherein said sampling based on the heat of each of said shelves to obtain a plurality of sample distributions comprises:
step S1, resampling is carried out on all the shelves to obtain a sample set;
step S2, determining the heat of each goods shelf in the sample set;
And repeatedly executing the step S1 and the step S2 to obtain a plurality of sampling distributions.
5. The method of claim 3, wherein said performing a nuclear density estimation on said plurality of sample distributions to obtain a heat storage location distribution comprises:
Determining a bandwidth parameter according to the distance between the data point in the sampling distribution and other data points with the nearest distance to the preset number;
according to the bandwidth parameter, determining a kernel density estimation function of each sampling distribution;
And obtaining the heat distribution of the storage bit according to the calculation result of the nuclear density estimation function.
6. The method of claim 1, wherein the allocating the storage locations to the target shelf based on the cost per storage location, the updated heat of the target shelf, and the storage location heat distribution comprises:
Ordering the cost of each storage bit to obtain a storage bit cost sequence;
Dividing the storage cost sequence according to the storage heat distribution to obtain storage IDs corresponding to each storage heat level;
Determining a corresponding target storage heat level according to the updated heat of the target shelf;
and selecting the spare storage ID with the minimum substitution value from all the storage IDs corresponding to the target storage heat level so as to finish the storage allocation of the target goods shelf.
7. The method as recited in claim 1, further comprising:
and storing and reading the heat of the goods by using a Redis database.
8. A thermal analysis-based shelf return device, comprising:
a memory configured to store instructions;
A processor configured to invoke the instructions from the memory and when executing the instructions is capable of implementing a thermal analysis based shelf-banking method according to any one of claims 1 to 7.
9. A computing device, comprising:
The thermal analysis-based shelf restocking device of claim 8.
10. A machine-readable storage medium having instructions stored thereon for causing a machine to perform the thermal analysis-based shelf-return method of any one of claims 1 to 7.
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