CN114648267A - Optimization method and system for dispatching path of automatic stereoscopic warehouse - Google Patents
Optimization method and system for dispatching path of automatic stereoscopic warehouse Download PDFInfo
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Abstract
The invention discloses an optimization method and system for an automatic stereoscopic warehouse scheduling path, wherein the method comprises the following steps: performing priority analysis based on the first order sequence information to determine a material priority ex-warehouse sequencing result; obtaining material storage attribute information based on a space-time map model; inputting the material storage attribute information and the material prior ex-warehouse sorting result into a material allocation model to obtain a first material allocation result; determining coordinates of loaded material points and unloaded material points based on the first material blending result; fitting and generating an optimal dispatching route of the AGV robot according to the coordinates of the material loading points and the coordinates of the material unloading points, wherein the optimal dispatching route is the route with the shortest distance; and if the optimal scheduling route has conflict factors, acquiring an alternative scheduling path set of the AGV robot based on a time window algorithm. The technical problem that planning of a dispatching path of a stereoscopic warehouse is not accurate and reasonable enough and the material delivery efficiency is affected in the prior art is solved.
Description
Technical Field
The invention relates to the field of artificial intelligence, in particular to an optimization method and system for a dispatching path of an automatic stereoscopic warehouse.
Background
The stereoscopic warehouse is an advanced logistics management mode, fully utilizes the space of the warehouse, adopts multilayer stereoscopic stacking, can realize high-level rationalization, automatic access and simple and convenient operation of the warehouse, and is a form with higher technical level at present, so the scheduling path of the stereoscopic warehouse can be reasonably planned, and the delivery efficiency of materials can be improved.
However, the prior art has the technical problem that the scheduling path planning of the stereoscopic warehouse is not accurate and reasonable enough, which affects the delivery efficiency of the materials.
Disclosure of Invention
The application provides an optimization method and system for the dispatching path of the automatic stereoscopic warehouse, the technical problem that planning of the dispatching path of the stereoscopic warehouse is not accurate and reasonable enough and the material delivery efficiency is affected in the prior art is solved, the dispatching path of the stereoscopic warehouse is optimized by combining material storage information and a material priority delivery sequencing result, the accuracy and the reasonability of the path dispatching result are guaranteed, and the technical effect of the material delivery efficiency is improved.
In view of the above problems, the present invention provides a method and a system for optimizing a scheduling path of an automated stereoscopic warehouse.
In a first aspect, the present application provides a method for optimizing a scheduling path of an automated stereoscopic warehouse, where the method includes: obtaining first storage order information, and arranging the order information in the first storage order information according to a time priority order to obtain first order sequence information; performing priority analysis based on the first order sequence information to determine a material priority ex-warehouse sequencing result; constructing a space-time map model of an automatic stereoscopic warehouse, and acquiring material storage attribute information based on the space-time map model; inputting the material storage attribute information and the material prior ex-warehouse sorting result into a material allocation model to obtain a first material allocation result; determining coordinates of loaded material points and unloaded material points based on the first material blending result; fitting and generating an optimal scheduling route of the AGV robot according to the coordinates of the material loading points and the coordinates of the material unloading points, wherein the optimal scheduling route is the route with the shortest distance; and if the optimal scheduling route has conflict factors, acquiring an alternative scheduling path set of the AGV robot based on a time window algorithm.
In another aspect, the present application further provides an optimization system for an automatic stereoscopic warehouse scheduling path, where the system includes: the first obtaining unit is used for obtaining first warehouse order information, and arranging the order information in the first warehouse order information according to a time priority order to obtain first order sequence information; the first determining unit is used for performing priority analysis based on the first order sequence information and determining a material priority ex-warehouse sequencing result; the second obtaining unit is used for constructing a space-time map model of the automatic stereoscopic warehouse and obtaining material storage attribute information based on the space-time map model; a third obtaining unit, configured to input the material storage attribute information and the material priority ex-warehouse sorting result into a material allocation model, so as to obtain a first material allocation result; a second determining unit for determining coordinates of loaded material points and unloaded material points based on the first material blending result; the first generation unit is used for fitting and generating an optimal scheduling route of the AGV robot according to the coordinates of the loaded material points and the coordinates of the unloaded material points, wherein the optimal scheduling route is a route with the shortest distance; and the fourth obtaining unit is used for obtaining the alternative scheduling path set of the AGV robot based on a time window algorithm if the optimal scheduling route has conflict factors.
In a third aspect, the present application provides an electronic device comprising a bus, a transceiver, a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the transceiver, the memory, and the processor are connected via the bus, and the computer program implements the steps of any of the methods when executed by the processor.
In a fourth aspect, the present application also provides a computer-readable storage medium having a computer program stored thereon, which when executed by a processor, performs the steps of any of the methods described above.
One or more technical solutions provided in the present application have at least the following technical effects or advantages:
the technical scheme is that order information in the storage order information is arranged according to a time priority sequence, the arranged order sequence information is subjected to priority analysis, a material priority ex-warehouse sequencing result is determined, material storage attribute information is obtained based on a space-time map model of a stereoscopic warehouse, the material storage attribute information and the material priority ex-warehouse sequencing result are input into a material allocation model, a model output result, namely a material allocation result, is obtained, coordinates of material loading points and coordinates of material unloading points are determined according to the material storage attribute information and the material priority ex-warehouse sequencing result, an optimal scheduling route of the AGV robot is generated in a fitting mode, and if the optimal scheduling route has conflict factors, an alternative scheduling route set of the AGV robot is obtained based on a time window algorithm. And then the dispatching path of the stereoscopic warehouse is optimized by combining the material storage information and the material prior ex-warehouse sequencing result, the accuracy and the reasonability of the path dispatching result are ensured, and the technical effect of improving the ex-warehouse efficiency of the material is achieved.
The foregoing description is only an overview of the technical solutions of the present application, and the present application can be implemented according to the content of the description in order to make the technical means of the present application more clearly understood, and the following detailed description of the present application is given in order to make the above and other objects, features, and advantages of the present application more clearly understandable.
Drawings
Fig. 1 is a schematic flowchart of an optimization method for scheduling paths of an automated stereoscopic warehouse according to the present application;
fig. 2 is a schematic flow chart illustrating a spatiotemporal map model for constructing an automated stereoscopic warehouse in the optimization method for the scheduling path of the automated stereoscopic warehouse according to the present application;
fig. 3 is a schematic flowchart of a method for optimizing a scheduling path of an automated stereoscopic warehouse according to the present application to obtain a feature data set of the automated stereoscopic warehouse;
fig. 4 is a schematic flowchart illustrating a process of determining a result of a priority sorting of materials out of a warehouse in the optimization method for an automatic stereoscopic warehouse scheduling path according to the present application;
fig. 5 is a schematic structural diagram of an optimization system for scheduling paths of an automated stereoscopic warehouse according to the present application;
fig. 6 is a schematic structural diagram of an exemplary electronic device of the present application.
Description of reference numerals: a first obtaining unit 11, a first determining unit 12, a second obtaining unit 13, a third obtaining unit 14, a second determining unit 15, a first generating unit 16, a fourth obtaining unit 17, a bus 1110, a processor 1120, a transceiver 1130, a bus interface 1140, a memory 1150, an operating system 1151, an application 1152 and a user interface 1160.
Detailed Description
In the description of the present application, it will be appreciated by those skilled in the art that the present application may be embodied as methods, apparatuses, electronic devices, and computer-readable storage media. Thus, the present application may be embodied in the form of: entirely hardware, entirely software (including firmware, resident software, micro-code, etc.), a combination of hardware and software. Furthermore, in some embodiments, the present application may also be embodied in the form of a computer program product in one or more computer-readable storage media having computer program code embodied therein.
The computer-readable storage media described above may take any combination of one or more computer-readable storage media. The computer-readable storage medium includes: an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of the computer-readable storage medium include: a portable computer diskette, a hard disk, a random access memory, a read-only memory, an erasable programmable read-only memory, a flash memory, an optical fiber, a compact disc read-only memory, an optical storage device, a magnetic storage device, or any combination thereof. In the present application, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, device, or system.
According to the technical scheme, the data acquisition, storage, use, processing and the like meet relevant regulations of national laws.
The method, the device and the electronic equipment are described by the flow chart and/or the block diagram.
It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions. These computer-readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, 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/acts specified in the flowchart and/or block diagram block or blocks.
These computer-readable program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing apparatus to function in a particular manner. Thus, the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means which implement the function/act specified in the flowchart and/or block diagram block or blocks.
The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks.
The present application is described below with reference to the drawings attached hereto.
Example one
As shown in fig. 1, the present application provides a method for optimizing a dispatching path of an automated stereoscopic warehouse, the method including:
step S100: obtaining first storage order information, and arranging the order information in the first storage order information according to a time priority order to obtain first order sequence information;
particularly, the stereoscopic warehouse is an advanced logistics management mode, fully utilizes the space of the warehouse, adopts multilayer stereoscopic stacking, can realize high-level rationalization, automatic access and simple and convenient operation of the warehouse, and is a form with higher technical level at present, so that the dispatching path of the stereoscopic warehouse can be reasonably planned, and the material delivery efficiency can be improved. The first warehousing order information is goods order information placed by a customer and comprises order placing time, product names and specifications, required manufacturers, quantity, order placing amount and the like. The method comprises the steps of firstly sequencing all order information in first warehouse order information in a time priority order to obtain sequenced first order sequence information, wherein the first order sequence information is used for sequencing the order information of a client, the order information is sequenced in the front when the order is placed, and is sequenced in the back when the order is placed, so that the product delivery is prevented from being overtime.
Step S200: performing priority analysis based on the first order sequence information to determine a material priority ex-warehouse sequencing result;
as shown in fig. 4, further to determine the result of the sorting by preferentially discharging the materials out of the warehouse, step S200 of the present application further includes:
step S210: obtaining priority characteristic parameters, wherein the priority characteristic parameters comprise order value, cut-off time and total task volume;
step S220: constructing a material priority coordinate system, wherein the material priority coordinate system is a three-dimensional coordinate system;
step S230: inputting the attribute information of each order in the first order sequence information into the material priority coordinate system to obtain a material priority vector of each order;
step S240: obtaining the priority of each order based on the mode of the material priority vector of each order;
step S250: and sequencing according to the priority of each order, and determining the sequencing result of the materials which are preferentially discharged from the warehouse.
Specifically, the customer order information is subjected to secondary comprehensive arrangement, priority analysis is performed based on the first order sequence information, namely different priorities are given to the orders according to priority characteristics, and the orders are sorted according to the priorities. The priority characteristic parameters comprise order value, including the value of order goods and the value of order customers, and the higher the comprehensive value is, the higher the priority of the order is; the cut-off time is the cut-off time of the order goods, and the shorter the relative cut-off time is, the higher the priority of the order is correspondingly; the total task volume is the shipment task volume of the order goods, and the task volume directly reflects the length of the task completion time, so the smaller the total task volume is, the higher the priority of the order is. And constructing a material priority coordinate system, wherein the material priority coordinate system is a three-dimensional coordinate system, and the coordinate axis is the priority characteristic parameter.
And inputting the order attribute information in the first order sequence information into the material priority coordinate system to obtain corresponding order material priority vectors, wherein the order material priority vectors indicate the priority attributes of the orders. And obtaining the priority of each order based on the module of the priority vector of each order material, wherein the priority of each order is the module of the corresponding priority vector of the order material, and the larger the module is, the larger the priority of the order is. And sequencing according to the priority of each order, sequencing the orders with high priority, and sequencing the orders in a descending order, thereby determining the sequencing result of the materials which are preferentially discharged from the warehouse. The order material sequence is comprehensively sequenced through the priority characteristics, so that the material delivery sequence is more accurate and reasonable, the material delivery efficiency is improved, and the order material can arrive on time.
Step S300: constructing a space-time map model of an automatic stereoscopic warehouse, and acquiring material storage attribute information based on the space-time map model;
as shown in fig. 2, further to construct the spatio-temporal map model of the automated stereoscopic warehouse, step S300 of the present application further includes:
step S310: obtaining a characteristic data set of an automatic stereoscopic warehouse;
step S320: traversing, cleaning and integrating the characteristic data set to generate a standardized characteristic data set;
step S330: performing labeling classification on the standardized feature data set to obtain a feature label data set;
step S340: obtaining a corresponding visual image feature set based on the feature tag data set;
step S350: and constructing a space-time map model of the automatic stereoscopic warehouse according to the standard format feature data set and the visual image feature set.
Specifically, a space-time map model of the automatic stereoscopic warehouse is constructed for better scheduling path optimization, and the space-time map model is a geographic data model which effectively organizes and manages temporal geographic data and has more complete attributes, space and time semantics. The spatiotemporal map model expresses a time-varying dynamic structure for temporal variation analysis of the automated stereoscopic warehouse spatial data. The characteristic data set of the automatic stereoscopic warehouse is each attribute characteristic of the automatic stereoscopic warehouse, and comprises the characteristics of spatial characteristics, geographical positions, warehouse forms, operation modes, mechanical equipment parameters, warehouse capacity, material attributes, material coordinate numbers, cargo unit forms, specifications and the like of the stereoscopic warehouse.
Traversing, cleaning and integrating the characteristic data set, wherein the data cleaning refers to a processing mode of finding and correcting recognizable errors in a data file, and comprises the processes of checking data consistency, processing invalid values, missing values and the like, and reexamining and checking the data, and aims to delete repeated information, correct existing errors and provide data consistency; data integration is to map the data exchange formats of the data sources one by one, so that circulation and sharing of data are realized, the obtained data information is more complete, a standardized feature data set after data normalization processing is generated, data consistency and normalization are improved, and data integrity and utilization degree are enhanced. And performing labeling classification on the standardized feature data set, wherein different data features correspond to different label classification results, such as spatial feature labels, material attribute feature labels, material warehousing time feature labels and the like.
And acquiring a corresponding visual image feature set comprising a scene image, a visual warehouse map picture, a shelf combination picture, an application service picture and the like based on the feature tag data set, and ensuring that a subsequent model can be visualized through data. And constructing a space-time map model of the automatic stereoscopic warehouse according to the standard format feature data set and the visual image feature set, and acquiring material storage attribute information based on the space-time map model, wherein the material storage attribute information is detailed material attribute information such as types, quantity, production date, specifications, manufacturers, storage time, shelf numbers and the like of each material stored in the stereoscopic warehouse. The materials are allocated by constructing a space-time map model of the stereoscopic warehouse, the space application scene of the stereoscopic warehouse is restored visually and accurately, and the dynamic adjustment of the materials of the stereoscopic warehouse and the dynamic visual management of the materials in and out of the warehouse are realized.
Step S400: inputting the material storage attribute information and the material prior ex-warehouse sorting result into a material allocation model to obtain a first material allocation result;
further, step S400 of the present application further includes:
step S410: obtaining an initial hidden layer value of a recurrent neural network, and obtaining a first input weight matrix based on the initial hidden layer value;
step S420: taking historical material storage attribute information and a historical material priority ex-warehouse sorting result as input layer information, and training the recurrent neural network according to the input layer information and the first input weight matrix;
step S430: and taking the input layer information and the initial hidden layer value as a next hidden layer value, and taking a historical material allocation result as identification information to perform iterative training in sequence to construct the material allocation model.
Specifically, in order to enable the material allocation to be more reasonable and accurate, the material storage attribute information and the material prior ex-warehouse sorting result are input into a material allocation model, and the material allocation model is a recurrent neural network. The recurrent neural network is a recurrent neural network which takes sequence data as input, recurs in the evolution direction of the sequence and all nodes (recurrent units) are connected in a chain mode, and comprises an input layer, a hidden layer and an output layer. In the process of processing input information by the processing layer in the recurrent neural network, the processing layer not only processes the input information according to the current input information, but also stores output information of the previous time sequence, processes the output information as the input information of the current time sequence, and further obtains output, and the processing layer is continuously updated along with the advance of the time sequence. The recurrent neural network not only relates to the current input but also relates to the output at the last moment by using the neurons with self feedback, so that the recurrent neural network has short-term memory capability when processing time series data of any length.
The initial hidden layer value can be obtained in a self-defined mode, a first input weight matrix is obtained based on the initial hidden layer value, in the processing process, the output information of the current input information and the output information of the previous time sequence is predicted according to a certain weight ratio, namely the weight matrix is obtained, and in the updating process of the processing layer, the weight value in the weight matrix is stable and unchanged. In order to ensure the accuracy of model evaluation, a plurality of groups of historical material storage attribute information and historical material prior ex-warehouse sorting results are used as input layer information, the recurrent neural network is trained according to the input layer information and the first input weight matrix, each input layer and the last hidden layer are used as each hidden layer, the hidden layer at each time is the next hidden layer value, and the corresponding historical material allocation result is used as identification information and is an output result.
And (4) finishing supervision training and constructing the material allocation model by sequentially carrying out iterative training when the output result of the cyclic neural network reaches a certain accuracy rate or convergence. And based on the material allocation model, obtaining a training output result of the model, namely a first material allocation result, wherein the first material allocation result is to accurately match the stored material information with the order material information, and automatically matching and selecting material shelves with earlier material production dates and corresponding quantities by combining material attribute information in the order. The ex-warehouse materials are allocated and analyzed by constructing the material allocation model, so that the material allocation result is more accurate and reasonable, and the allocation efficiency and accuracy of the material allocation result are improved.
Step S500: determining coordinates of loaded material points and unloaded material points based on the first material blending result;
step S600: fitting and generating an optimal scheduling route of the AGV robot according to the coordinates of the material loading points and the coordinates of the material unloading points, wherein the optimal scheduling route is the route with the shortest distance;
specifically, based on the first material allocation result, determining coordinates of a material loading point and a material unloading point, and performing automatic path fitting through a path planning algorithm according to the coordinates of the material loading point and the coordinates of the material unloading point to generate an optimal scheduling route of the AGV robot, wherein the optimal scheduling route is the route with the shortest distance. The AGV robot is an automatic loading, unloading and carrying device, is widely researched and applied to a stereoscopic warehouse in society, can automatically deliver correct materials to correct stations in use time by effectively connecting production and operation links outside the warehouse, and greatly improves the production technical capacity. When the material is discharged from the warehouse, the AGV robot loads the distance from the material loading point to the material unloading point, so that the warehouse discharging time is shortened, and the warehouse discharging efficiency is improved.
Step S700: and if the optimal scheduling route has conflict factors, acquiring an alternative scheduling path set of the AGV robot based on a time window algorithm.
Specifically, if the optimal scheduling route has conflict factors, for example, obstacles such as construction exist in the scheduling route or conflicts with other material scheduling routes, the candidate scheduling path set of the AGV robot is obtained based on a time window algorithm. The time window algorithm comprises a static scheduling algorithm based on a time window, and can realize the following purposes: the opposite conflict is solved, the same road section can pass through a plurality of AGV in the same direction at the same time, and the distance between the AGV and the AGV is kept; the dynamic scheduling algorithm based on the time window can solve the opposite conflict caused by uncertain factors and the intersection conflict. A plurality of alternative paths are provided for the AGV robot through a time window algorithm, the scheduling path of the stereoscopic warehouse is optimized, the accuracy and the reasonability of the path scheduling result are guaranteed, and therefore the material warehouse-out efficiency is improved.
As shown in fig. 3, further to obtain the feature data set of the automated stereoscopic warehouse, step S310 of the present application further includes:
step S311: obtaining basic information of warehousing materials through a warehousing information management system;
step S312: performing characteristic classification on the basic information of the warehoused materials according to a warehousing characteristic decision tree to obtain characteristic information of the warehoused materials;
step S313: determining material space characteristic information, material attribute characteristic information and material time characteristic information according to the warehousing material characteristic information;
step S314: and performing feature fusion on the material space feature information, the material attribute feature information and the material time feature information to obtain a feature data set of the automatic stereoscopic warehouse.
Specifically, the warehousing information management system is a material management system of the automatic stereoscopic warehouse, and is used for registering and managing warehousing information of materials, and acquiring basic information of warehoused materials through the warehousing information management system, wherein the basic information of warehoused materials comprises information such as warehousing time, warehousing shelf numbers, warehousing material attributes and the like. The storage characteristic decision tree is a classifier for classifying according to the storage material characteristics, and the classifier can give correct classification to newly appeared objects and consists of a root node, an internal node and a leaf node.
The storage material characteristics can be used as internal nodes of the storage characteristic decision tree, and the characteristics with the minimum entropy value can be preferentially classified by calculating the information entropy of the storage material characteristics until the final characteristic leaf node can not be subdivided, so that the classification is finished, and the storage characteristic decision tree is formed. And performing characteristic classification on the basic information of the warehoused materials according to the warehousing characteristic decision tree to obtain corresponding characteristic information of each acquired warehoused material. And determining material space characteristic information, material attribute characteristic information and material time characteristic information according to the warehousing material characteristic information.
The material space characteristic information is a serial number position relation and a space relation corresponding to the warehoused materials, the material attribute characteristic information is attribute information such as specifications, production dates, manufacturers and material types corresponding to the warehoused materials, and the material time scale characteristic data set is information such as warehousing time points and warehousing time corresponding to the warehoused materials. And performing characteristic fusion on the material space characteristic information, the material attribute characteristic information and the material time characteristic information to obtain a characteristic data set of the automatic stereoscopic warehouse, so that the material storage information is more accurate and comprehensive, a data basis is provided for subsequently constructing a space-time map model of the stereoscopic warehouse, and dynamic adjustment of materials in the stereoscopic warehouse and dynamic visual management of materials in and out of the warehouse are further realized.
Further, the method further comprises the following steps:
step S810: evaluating the blending effect of the first material blending result to obtain the first material blending accuracy;
step S820: if the first material blending accuracy does not reach the preset accuracy, obtaining a material blending deviation degree based on the difference value between the first material blending accuracy and the preset accuracy;
step S830: and carrying out optimization training on the material allocation model based on a PSO algorithm and the material allocation deviation degree to obtain a material optimization allocation model.
Specifically, the first material allocation result is subjected to allocation effect evaluation, including analysis of allocation accuracy, whether material allocation is delayed, and the like, so that first material allocation accuracy is obtained, and the accuracy of material allocation performed by the material allocation model is indicated by the first material allocation accuracy. If the first material blending accuracy does not reach the preset accuracy, namely the training output accuracy of the material blending model does not reach the standard, based on the difference value between the first material blending accuracy and the preset accuracy, the material blending deviation degree is obtained, namely the accuracy needing to be optimized is obtained, and the larger the deviation degree is, the lower the material blending accuracy is.
And because the fitting degree of the material allocation model is low and cannot adapt to the allocation of the current material to be allocated, the material allocation model is subjected to optimization training based on a PSO algorithm and the material allocation deviation degree. The PSO algorithm (Particle swarm optimization) is a random optimization algorithm based on population, can simulate and continuously iterate until a balance or optimal state is finally reached, and stores the balance or optimal state to obtain a material optimization allocation model optimized by the PSO algorithm. The model is optimized through the PSO algorithm, so that the output deviation degree of the model is reduced, the accuracy and the efficiency of the output result of the model are improved, and the material allocation accuracy is further improved.
Further, the obtaining a material optimization blending model in step S830 further includes:
step S831: initializing particle swarm parameters based on a PSO algorithm, and iteratively calculating a particle swarm fitness function according to the material allocation deviation degree and the particle swarm parameters;
step S832: when a preset termination condition is reached, obtaining a first output result of the particle swarm fitness function, wherein the first output result comprises optimal result particles;
step S833: and mapping the optimal result particles to the material allocation model for optimization training to obtain the material optimization allocation model.
Specifically, a particle swarm optimization algorithm is initialized, and the optimized parameters are the set of all weights in the material blending model. And iteratively calculating a particle swarm fitness function according to the material allocation deviation degree and the particle swarm parameters, wherein the particle swarm fitness function can optimize the first material allocation result and reduce the deviation degree of the first material allocation result. And further updating the positions and the speeds of the particles in the particle swarm, inputting all the particles into a model for training, evaluating the quality of the particles by calculating a fitness function of the particle swarm, and adjusting the position and the speed of each particle by the fitness function. And when a preset termination condition is reached, obtaining a first output result of the particle swarm fitness function, wherein the first output result comprises optimal result particles.
In brief, the PSO algorithm stops including two possibilities, one is that the particles get a balanced or optimal state, the other is that the operation limit is exceeded, no specific analysis is performed on the condition that the operation limit is exceeded, and the optimal result particles are the optimal state of the particles; and mapping the optimal result particles to the material blending model for optimization training. The output accuracy of the optimally trained material optimal allocation model is improved, and the optimally trained material allocation model is optimally trained through a particle swarm optimization algorithm, so that the output deviation degree of the model is reduced, the accuracy and the efficiency of the output result of the model are improved, and the material allocation accuracy is further improved.
In summary, the method and system for optimizing the dispatching path of the automated stereoscopic warehouse provided by the present application have the following technical effects:
the technical scheme is that order information in the storage order information is arranged according to a time priority sequence, the arranged order sequence information is subjected to priority analysis, a material priority ex-warehouse sequencing result is determined, material storage attribute information is obtained based on a space-time map model of a stereoscopic warehouse, the material storage attribute information and the material priority ex-warehouse sequencing result are input into a material allocation model, a model output result, namely a material allocation result, is obtained, coordinates of material loading points and coordinates of material unloading points are determined according to the material storage attribute information and the material priority ex-warehouse sequencing result, an optimal scheduling route of the AGV robot is generated in a fitting mode, and if the optimal scheduling route has conflict factors, an alternative scheduling route set of the AGV robot is obtained based on a time window algorithm. And then the dispatching path of the stereoscopic warehouse is optimized by combining the material storage information and the material prior ex-warehouse sequencing result, so that the accuracy and the rationality of the path dispatching result are ensured, and the technical effect of improving the ex-warehouse efficiency of the materials is achieved.
Example two
Based on the same inventive concept as the method for optimizing the dispatching path of the automatic stereoscopic warehouse in the foregoing embodiment, the present invention further provides an optimizing system for the dispatching path of the automatic stereoscopic warehouse, as shown in fig. 5, the system includes:
the first obtaining unit 11 is configured to obtain first warehouse order information, and arrange order information in the first warehouse order information according to a time priority order to obtain first order sequence information;
the first determining unit 12 is configured to perform priority analysis based on the first order sequence information, and determine a material priority ex-warehouse sorting result;
the second obtaining unit 13 is configured to construct a space-time map model of the automated stereoscopic warehouse, and obtain material storage attribute information based on the space-time map model;
a third obtaining unit 14, where the third obtaining unit 14 is configured to input the material storage attribute information and the material priority ex-warehouse sorting result into a material allocation model, so as to obtain a first material allocation result;
a second determining unit 15, wherein the second determining unit 15 is used for determining coordinates of a point of loading material and coordinates of a point of unloading material based on the first material allocation result;
a first generating unit 16, wherein the first generating unit 16 is configured to fit and generate an optimal scheduling route of the AGV robot according to the coordinates of the material loading points and the coordinates of the material unloading points, and the optimal scheduling route is a route with a shortest distance;
a fourth obtaining unit 17, where the fourth obtaining unit 17 is configured to obtain a set of alternative scheduling paths of the AGV robot based on a time window algorithm if there is a conflict factor in the optimal scheduling route.
Further, the system further comprises:
a fifth obtaining unit, configured to obtain a feature data set of an automated stereoscopic warehouse;
the second generation unit is used for performing traversal cleaning and integration on the feature data set to generate a standardized feature data set;
a sixth obtaining unit, configured to perform labeling classification on the normalized feature data set to obtain a feature label data set;
a seventh obtaining unit, configured to obtain a corresponding visual image feature set based on the feature tag data set;
the first construction unit is used for constructing a space-time map model of the automatic stereoscopic warehouse according to the standard format feature data set and the visual image feature set.
Further, the system further comprises:
the eighth obtaining unit is used for obtaining basic information of warehousing materials through a warehousing information management system;
a ninth obtaining unit, configured to perform feature classification on the warehousing material basic information according to a warehousing feature decision tree to obtain warehousing material feature information;
the third determining unit is used for determining material space characteristic information, material attribute characteristic information and material time characteristic information according to the warehousing material characteristic information;
a tenth obtaining unit, configured to perform feature fusion on the material space feature information, the material attribute feature information, and the material time feature information, and obtain a feature data set of the automated stereoscopic warehouse.
Further, the system further comprises:
an eleventh obtaining unit, configured to obtain priority feature parameters, where the priority feature parameters include an order value, a deadline, and a total task volume;
the second construction unit is used for constructing a material priority coordinate system, and the material priority coordinate system is a three-dimensional coordinate system;
a twelfth obtaining unit, configured to input each order attribute information in the first order sequence information into the material priority coordinate system, and obtain a material priority vector of each order;
a thirteenth obtaining unit, configured to obtain a priority of each order based on a modulus of the material priority vector of each order;
and the fourth determining unit is used for sequencing according to the priority of each order and determining the sequencing result of the prior delivery of the materials.
Further, the system further comprises:
a fourteenth obtaining unit, configured to perform blending effect evaluation on the first material blending result to obtain first material blending accuracy;
a fifteenth obtaining unit, configured to obtain a material blending deviation degree based on a difference between the first material blending accuracy and a preset accuracy if the first material blending accuracy does not reach the preset accuracy;
and the sixteenth obtaining unit is used for carrying out optimization training on the material allocation model based on a PSO algorithm and the material allocation deviation degree to obtain a material optimization allocation model.
Further, the system further comprises:
the first calculating unit is used for initializing particle swarm parameters based on a PSO algorithm and iteratively calculating a particle swarm fitness function according to the material allocation deviation degree and the particle swarm parameters;
a seventeenth obtaining unit, configured to obtain a first output result of the particle swarm fitness function when a preset termination condition is reached, where the first output result includes optimal result particles;
and the first training unit is used for mapping the optimal result particles to the material allocation model for optimization training to obtain the material optimization allocation model.
Further, the system further comprises:
an eighteenth obtaining unit, configured to obtain an initial hidden layer value of a recurrent neural network, and obtain a first input weight matrix based on the initial hidden layer value;
the second training unit is used for taking historical material storage attribute information and a historical material priority ex-warehouse sorting result as input layer information and training the recurrent neural network according to the input layer information and the first input weight matrix;
and the third construction unit is used for taking the input layer information and the initial hidden layer value as a next hidden layer value, taking a historical material allocation result as identification information, and sequentially performing iterative training to construct the material allocation model.
Various changes and specific examples of the method for optimizing an automatic stereoscopic warehouse scheduling path in the first embodiment of fig. 1 are also applicable to the system for optimizing an automatic stereoscopic warehouse scheduling path in the present embodiment, and through the foregoing detailed description of the method for optimizing an automatic stereoscopic warehouse scheduling path, those skilled in the art can clearly know the method for implementing the system for optimizing an automatic stereoscopic warehouse scheduling path in the present embodiment, so for the brevity of the description, detailed descriptions are omitted here.
In addition, the present application further provides an electronic device, which includes a bus, a transceiver, a memory, a processor, and a computer program stored in the memory and capable of running on the processor, where the transceiver, the memory, and the processor are connected via the bus, respectively, and when the computer program is executed by the processor, the processes of the above-mentioned method for controlling output data are implemented, and the same technical effects can be achieved, and are not described herein again to avoid repetition.
Exemplary electronic device
In particular, referring to fig. 6, the present application further provides an electronic device comprising a bus 1110, a processor 1120, a transceiver 1130, a bus interface 1140, a memory 1150, and a user interface 1160.
In this application, the electronic device further includes: a computer program stored on the memory 1150 and executable on the processor 1120, the computer program, when executed by the processor 1120, implementing the various processes of the method embodiments of controlling output data described above.
A transceiver 1130 for receiving and transmitting data under the control of the processor 1120.
In this application, a bus architecture (represented by bus 1110), bus 1110 may include any number of interconnected buses and bridges, bus 1110 connecting various circuits including one or more processors, represented by processor 1120, and memory, represented by memory 1150.
Processor 1120 may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method embodiments may be performed by integrated logic circuits in hardware or instructions in software in a processor. The processor described above includes: general purpose processors, central processing units, network processors, digital signal processors, application specific integrated circuits, field programmable gate arrays, complex programmable logic devices, programmable logic arrays, micro-control units or other programmable logic devices, discrete gates, transistor logic devices, discrete hardware components. The various methods, steps and logic blocks disclosed in this application may be implemented or performed. For example, the processor may be a single core processor or a multi-core processor, which may be integrated on a single chip or located on multiple different chips.
Processor 1120 may be a microprocessor or any conventional processor. The method steps disclosed in connection with the present application may be performed directly by a hardware decoding processor or by a combination of hardware and software modules within the decoding processor. The software modules may reside in random access memory, flash memory, read only memory, programmable read only memory, erasable programmable read only memory, registers, and the like, as is known in the art. The readable storage medium is located in a memory, and a processor reads information in the memory and completes the steps of the method in combination with hardware of the processor.
The bus 1110 may also connect various other circuits such as peripherals, voltage regulators, or power management circuits to provide an interface between the bus 1110 and the transceiver 1130, as is well known in the art. Therefore, it will not be further described in this application.
The transceiver 1130 may be one element or a plurality of elements, such as a plurality of receivers and transmitters, providing a means for communicating with various other apparatus over a transmission medium. For example: the transceiver 1130 receives external data from other devices, and the transceiver 1130 transmits data processed by the processor 1120 to other devices. Depending on the nature of the computer device, a user interface 1160 may also be provided, such as: touch screen, physical keyboard, display, mouse, speaker, microphone, trackball, joystick, stylus.
It is to be appreciated that in the subject application, the memory 1150 can further include memory remotely located from the processor 1120, which can be coupled to a server via a network. One or more portions of the above-described network may be an ad hoc network, an intranet, an extranet, a virtual private network, a local area network, a wireless local area network, a wide area network, a wireless wide area network, a metropolitan area network, the internet, a public switched telephone network, a plain old telephone service network, a cellular telephone network, a wireless fidelity network, and a combination of two or more of the above. For example, the cellular telephone network and the wireless network may be global mobile communications devices, code division multiple access devices, global microwave interconnect access devices, general packet radio service devices, wideband code division multiple access devices, long term evolution devices, LTE frequency division duplex devices, LTE time division duplex devices, long term evolution advanced devices, universal mobile communications devices, enhanced mobile broadband devices, mass machine type communications devices, ultra-reliable low-latency communications devices, and the like.
It will be appreciated that the memory 1150 in the subject application can be either volatile memory or nonvolatile memory, or can include both volatile and nonvolatile memory. Wherein the nonvolatile memory includes: read-only memory, programmable read-only memory, erasable programmable read-only memory, electrically erasable programmable read-only memory, or flash memory.
The volatile memory includes: random access memory, which acts as an external cache. By way of example, and not limitation, many forms of RAM are available, such as: static random access memory, dynamic random access memory, synchronous dynamic random access memory, double data rate synchronous dynamic random access memory, enhanced synchronous dynamic random access memory, synchronous link dynamic random access memory, and direct memory bus random access memory. The memory 1150 of the electronic device described herein includes, but is not limited to, the above-described and any other suitable types of memory.
In the present application, memory 1150 stores the following elements of operating system 1151 and application programs 1152: an executable module, a data structure, or a subset thereof, or an expanded set thereof.
Specifically, the operating system 1151 includes various device programs, such as: a framework layer, a core library layer, a driver layer, etc. for implementing various basic services and processing hardware-based tasks. Applications 1152 include various applications such as: media player, browser, used to realize various application services. A program implementing the method of the present application may be included in the application 1152. The application programs 1152 include: applets, objects, components, logic, data structures, and other computer device-executable instructions that perform particular tasks or implement particular abstract data types.
In addition, the present application further provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements each process of the above method for controlling output data, and can achieve the same technical effect, and is not described herein again to avoid repetition.
The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present application, and shall be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (10)
1. A method for optimizing a scheduling path of an automated stereoscopic warehouse, the method comprising:
obtaining first storage order information, and arranging the order information in the first storage order information according to a time priority order to obtain first order sequence information;
performing priority analysis based on the first order sequence information to determine a material priority ex-warehouse sequencing result;
constructing a space-time map model of an automatic stereoscopic warehouse, and acquiring material storage attribute information based on the space-time map model;
inputting the material storage attribute information and the material prior ex-warehouse sorting result into a material allocation model to obtain a first material allocation result;
determining coordinates of loaded material points and unloaded material points based on the first material blending result;
fitting and generating an optimal scheduling route of the AGV robot according to the coordinates of the material loading points and the coordinates of the material unloading points, wherein the optimal scheduling route is the route with the shortest distance;
and if the optimal scheduling route has conflict factors, acquiring an alternative scheduling path set of the AGV robot based on a time window algorithm.
2. The method of claim 1, wherein constructing the spatiotemporal map model of the automated stereoscopic warehouse comprises:
obtaining a characteristic data set of an automatic stereoscopic warehouse;
traversing, cleaning and integrating the characteristic data set to generate a standardized characteristic data set;
performing labeling classification on the standardized feature data set to obtain a feature label data set;
obtaining a corresponding visual image feature set based on the feature tag data set;
and constructing a space-time map model of the automatic stereoscopic warehouse according to the standard format feature data set and the visual image feature set.
3. The method of claim 2, wherein the obtaining a characteristic dataset of an automated stereoscopic warehouse comprises:
obtaining basic information of warehousing materials through a warehousing information management system;
performing characteristic classification on the basic information of the warehoused materials according to a warehousing characteristic decision tree to obtain characteristic information of the warehoused materials;
determining material space characteristic information, material attribute characteristic information and material time characteristic information according to the warehousing material characteristic information;
and performing characteristic fusion on the material space characteristic information, the material attribute characteristic information and the material time characteristic information to obtain a characteristic data set of the automatic stereoscopic warehouse.
4. The method of claim 1, wherein determining the material-first-out-of-warehouse sequencing result comprises:
obtaining priority characteristic parameters, wherein the priority characteristic parameters comprise order value, deadline time and total task volume;
constructing a material priority coordinate system, wherein the material priority coordinate system is a three-dimensional coordinate system;
inputting the attribute information of each order in the first order sequence information into the material priority coordinate system to obtain a material priority vector of each order;
obtaining the priority of each order based on the mode of the material priority vector of each order;
and sequencing according to the priority of each order, and determining the sequencing result of the materials which are preferentially discharged from the warehouse.
5. The method of claim 1, wherein the method comprises:
evaluating the blending effect of the first material blending result to obtain the first material blending accuracy;
if the first material blending accuracy does not reach the preset accuracy, obtaining a material blending deviation degree based on the difference value between the first material blending accuracy and the preset accuracy;
and carrying out optimization training on the material allocation model based on a PSO algorithm and the material allocation deviation degree to obtain a material optimization allocation model.
6. The method of claim 5, wherein the obtaining a material optimization blending model comprises:
initializing particle swarm parameters based on a PSO algorithm, and iteratively calculating a particle swarm fitness function according to the material allocation deviation degree and the particle swarm parameters;
when a preset termination condition is reached, obtaining a first output result of the particle swarm fitness function, wherein the first output result comprises optimal result particles;
and mapping the optimal result particles to the material allocation model for optimization training to obtain the material optimization allocation model.
7. The method of claim 1, wherein the method comprises:
obtaining an initial hidden layer value of a recurrent neural network, and obtaining a first input weight matrix based on the initial hidden layer value;
taking historical material storage attribute information and a historical material priority ex-warehouse sorting result as input layer information, and training the recurrent neural network according to the input layer information and the first input weight matrix;
and taking the input layer information and the initial hidden layer value as a next hidden layer value, and taking a historical material allocation result as identification information to perform iterative training in sequence to construct the material allocation model.
8. An optimization system for an automated stereoscopic warehouse dispatch path, the system comprising:
the first obtaining unit is used for obtaining first warehouse order information, and arranging the order information in the first warehouse order information according to a time priority order to obtain first order sequence information;
the first determining unit is used for performing priority analysis based on the first order sequence information and determining a material priority ex-warehouse sequencing result;
the second obtaining unit is used for constructing a space-time map model of the automatic stereoscopic warehouse and obtaining material storage attribute information based on the space-time map model;
a third obtaining unit, configured to input the material storage attribute information and the material priority ex-warehouse sorting result into a material allocation model, so as to obtain a first material allocation result;
a second determining unit for determining coordinates of loaded material points and unloaded material points based on the first material blending result;
the first generating unit is used for fitting and generating an optimal scheduling route of the AGV robot according to the coordinates of the material loading points and the coordinates of the material unloading points, wherein the optimal scheduling route is the route with the shortest distance;
and the fourth obtaining unit is used for obtaining the alternative scheduling path set of the AGV robot based on a time window algorithm if the optimal scheduling route has conflict factors.
9. An electronic device for optimizing a dispatch path of an automated stereoscopic warehouse comprising a bus, a transceiver, a memory, a processor and a computer program stored in and executable on the memory, the transceiver, the memory and the processor being connected via the bus, characterized in that the computer program when executed by the processor implements the steps of the method as claimed in any one of claims 1 to 7.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method according to any one of claims 1-7.
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