Goods allocation distribution method and system for intelligent three-dimensional warehouse of four-way shuttle vehicle

Abstract

The invention provides a goods allocation distribution method and system for a four-way shuttle vehicle intelligent three-dimensional warehouse, and relates to the technical field of intelligent warehousing, and the method comprises the steps: obtaining goods allocation information, goods information and equipment information; constructing a shelf stability function and a demand prediction adaptation function according to the goods allocation information and the goods information; constructing a warehouse-in and warehouse-out efficiency function and an equipment electric energy consumption function according to the goods allocation information, the goods information and the equipment information; constructing a dynamic scheduling equalization function according to the equipment information of the shuttle vehicle; and acquiring a plurality of constraint conditions, and calculating optimal allocation schemes corresponding to the shelf stability function, the demand prediction adaptation function, the warehousing efficiency function, the equipment power consumption function and the dynamic scheduling equilibrium function under the plurality of constraint conditions by adopting the calculation model. According to the method, the multiple functions are established according to the data of the multiple dimensions, then the multiple constraint conditions are set, the corresponding optimal allocation scheme when the multiple functions meet the multiple constraint conditions is solved, and the accuracy of goods allocation is ensured.

Description

Technical Field

[0001] This application relates to the field of intelligent warehousing technology, and in particular to a method and system for allocating storage locations in a four-way shuttle intelligent automated warehouse. Background Technology

[0002] With the rapid development of the logistics industry, the automation and intelligence levels of warehousing systems are constantly improving. As an advanced warehousing system, the four-way shuttle intelligent automated storage and retrieval system (AS / RS) allows four-way shuttles to move freely horizontally and vertically within the AS / RS and quickly transport goods, enhancing the AS / RS's storage and retrieval capacity and location management capabilities. In a four-way shuttle intelligent AS / RS, the location allocation algorithm is one of the key factors determining warehousing efficiency. Reasonable location allocation not only improves the stability of the racking structure but also optimizes the efficiency of goods entering and leaving the warehouse, reduces the power consumption of equipment such as shuttles, and thus lowers operating costs.

[0003] However, existing location allocation methods often only consider a single optimization objective. Although some warehousing systems have attempted to improve location allocation strategies through multi-objective optimization algorithms, significant limitations remain in practical applications. For example, existing location allocation methods do not fully integrate real-time dynamic factors, especially during shuttle operation, which can easily lead to path conflicts or congestion, resulting in increased equipment idle rates and task execution delays. Simultaneously, existing location allocation methods lack the ability to predict changes in cargo turnover rates and warehousing demand, failing to dynamically adjust location layouts in high-frequency inbound and outbound scenarios, causing uneven load distribution or resource waste in localized areas. Furthermore, the scheduling strategy of four-way shuttles is closely related to location allocation; without coordinated optimization, efficiency bottlenecks may arise due to path planning conflicts during multi-shuttle collaborative operations, further exacerbating the problem of improper location allocation in warehousing systems.

[0004] Therefore, there is an urgent need for a new method of allocating cargo space that can fully consider multiple factors. Summary of the Invention

[0005] To address the aforementioned technical problems, this application provides a method and system for allocating storage locations in a four-way shuttle intelligent automated warehouse. By constructing a multi-objective function, setting multiple constraints, and solving for the optimal allocation scheme under all constraints, the accuracy of storage location allocation is improved. The technical solution is as follows:

[0006] Firstly, a method for allocating storage locations in a four-way shuttle-based intelligent automated warehouse is provided, including:

[0007] Acquire location information, cargo information, and equipment information, wherein the equipment includes shuttle cars and hoists;

[0008] Based on the location information and the goods information, a shelf stability function and a demand forecasting adaptation function are constructed respectively; wherein, the shelf stability function is used to evaluate the stability of the shelf, and the demand forecasting adaptation function is used to evaluate the gap between the demand for storage locations and the reserved storage locations.

[0009] Based on the location information, cargo information, and equipment information, an inbound / outbound efficiency function and an equipment power consumption function are constructed respectively; wherein, the inbound / outbound efficiency function is used to evaluate the efficiency of cargo transportation, and the equipment power consumption function is used to evaluate the total power consumption of the shuttle and the hoist;

[0010] A dynamic scheduling and balancing function is constructed based on the equipment information of the shuttle vehicle. The dynamic scheduling and balancing function is used to evaluate the efficiency of the shuttle vehicle in transporting goods.

[0011] Obtain multiple preset constraints;

[0012] Using a preset calculation model, the optimal allocation schemes for the shelf stability function, the demand forecasting and adaptation function, the warehousing efficiency function, the equipment power consumption function, and the dynamic scheduling and balancing function are calculated under multiple constraints.

[0013] In one possible implementation, the location information includes location coordinates and location congestion coefficient, the cargo information includes cargo weight and cargo turnover rate, and the shelf stability function is:

[0014]

[0015] Where F1 represents the shelf stability function, x, y, z represent the location coordinates (x, y, z), and M represents the shelf location coordinates. xyz This represents the weight of goods located in the storage cell with coordinates (x, y, z), ∑x∑y∑z M xyz The total weight of all goods located on the shelf is represented by , and z represents the shelf level of the shelf where the storage cell with coordinates (x, y, z) is located. The average number of shelves is represented by , and h represents the height of the shelf with coordinates (x, y, z).

[0016] In one possible implementation, the storage location information further includes storage location demand and storage location reservation, and the demand forecasting and adaptation function is:

[0017]

[0018] Where F2 represents the demand forecasting adaptation function, K represents the total number of storage locations, and q j Indicates the amount of space reserved for storage. Indicates the demand for storage space, ω jω represents the weight of demand forecasting. j It is obtained by predicting the turnover rate and storage space demand of various goods in the next storage cycle based on historical warehousing data.

[0019] In one possible implementation, the location information includes a location congestion coefficient, the cargo information includes cargo turnover rate, the shuttle vehicle equipment information includes shuttle vehicle operating time, the elevator equipment information includes elevator operating time, and the inbound / outbound efficiency function is:

[0020]

[0021] Where F3 represents the inbound / outbound efficiency function, N represents the total number of devices, and t bi The time t represents the shuttle's travel time. ci p represents the hoist's running time. i λ represents the goods turnover rate. xy This indicates the congestion coefficient of the cargo space.

[0022] In one possible implementation, the cargo location information further includes cargo location coordinates; the cargo information further includes cargo weight; the shuttle vehicle's equipment information further includes the shuttle vehicle's power consumption and acceleration / deceleration influence coefficient; and the hoist's equipment information further includes the hoist's power consumption. The equipment power consumption function is:

[0023] F4=∑x∑y∑z(E bi +E ci )×M xyz ×(1+δ v ),

[0024] Where F4 represents the equipment power consumption function, x, y, z represent the cargo location coordinates (x, y, z), and E bi E represents the electrical energy consumed by the shuttle to transport goods to the storage cell with coordinates (x, y, z). ci M represents the electrical energy consumption used by the hoist-assisted shuttle to transport goods to the storage cell with coordinates (x, y, z). xyz δ represents the weight of the goods transported by the shuttle. v This represents the coefficient of influence of acceleration and deceleration.

[0025] In one possible implementation, the shuttle's equipment information includes the actual distance and maximum allowable distance of the transport path when the shuttle is transporting goods, and the dynamic scheduling and balancing function is:

[0026]

[0027] Where F5 represents the dynamic scheduling equilibrium function, B represents the total number of shuttles, T represents the total time for shuttles to transport goods, and d b,t D represents the actual distance the b-th shuttle transports goods at time t. max This represents the maximum permissible distance that the b-th shuttle can transport goods at time t.

[0028] In one possible implementation, the plurality of said constraints include:

[0029] Location coordinate constraint: 0 <x<X max ,0 <y<Y max ,0 <z<Z max Where x, y, and z represent the coordinates of the cargo location (x, y, z), X max Y max Z max This represents the maximum position coordinate of the cell with position coordinates (x, y, z); and

[0030] Shelf load-bearing weight constraint: ∑x∑y∑z M xyz ≤W max Among them, W max This indicates the weight threshold that the shelf can withstand; and

[0031] Dynamic reservation constraint for empty storage space: ∑x∑y∑z(1-θ) xyz )≥γ×X max Y max Z max , where θ xyz This indicates the occupancy status of the storage cell with coordinates (x, y, z), θ. xyz =1 or θ xyz =0, γ is the dynamic reserve ratio, 0<γ<1; and

[0032] Shuttle load balancing constraints: Among them, L b L represents the load capacity of the b-th shuttle. avg The average load of all shuttles is ε, and the allowable deviation rate is ε, which is preset in advance.

[0033] In one possible implementation, an improved non-dominated sorting genetic algorithm is used to calculate the optimal allocation schemes corresponding to the shelf stability function, the demand prediction and adaptation function, the warehousing efficiency function, the equipment power consumption function, and the dynamic scheduling equilibrium function under multiple constraints.

[0034] In one possible implementation, the method further includes:

[0035] Extract the congestion coefficient of the storage location from the storage location information;

[0036] When the congestion coefficient of the storage location reaches the congestion coefficient threshold, the shelf stability function, demand forecasting adaptation function, inbound efficiency function, equipment power consumption function, and dynamic scheduling balance function are reconstructed.

[0037] An improved non-dominated sorting genetic algorithm is used to calculate the optimal allocation schemes for the newly constructed shelf stability function, demand prediction and adaptation function, warehousing efficiency function, equipment power consumption function, and dynamic scheduling equilibrium function under multiple constraints.

[0038] Secondly, a four-way shuttle intelligent warehouse location allocation system is provided, including:

[0039] The first acquisition module is used to acquire cargo location information, cargo information, and equipment information, wherein the equipment includes a shuttle car and a hoist.

[0040] The first construction module is used to construct a shelf stability function and a demand forecasting adaptation function based on the storage location information and the goods information, respectively; wherein, the shelf stability function is used to evaluate the stability of the shelf, and the demand forecasting adaptation function is used to evaluate the gap between the storage location demand and the storage location reservation.

[0041] The second construction module is used to construct an inbound / outbound efficiency function and an equipment power consumption function based on the storage location information, the cargo information, and the equipment information, respectively; wherein, the inbound / outbound efficiency function is used to evaluate the efficiency of cargo transportation, and the equipment power consumption function is used to evaluate the total power consumption of the shuttle and the hoist;

[0042] The third construction module is used to construct a dynamic scheduling and balancing function based on the equipment information of the shuttle car. The dynamic scheduling and balancing function is used to evaluate the efficiency of the shuttle car in transporting goods.

[0043] The second acquisition module is used to acquire multiple preset constraints.

[0044] The data calculation module is used to calculate the optimal allocation schemes of the shelf stability function, the demand forecasting and adaptation function, the warehousing efficiency function, the equipment power consumption function, and the dynamic scheduling and balancing function under multiple constraints using a preset calculation model.

[0045] The technical solutions provided in this application can achieve the following technical effects:

[0046] (1) First, data from various dimensions are obtained from the warehousing system, including location information, cargo information, shuttle equipment information and elevator equipment information. Then, multiple objective functions are constructed based on the data from multiple dimensions. Each objective function is used to evaluate a performance aspect of the warehousing system, such as the stability of the racks, the gap between the demand for and the reserved space for the location, so as to facilitate the improvement of multiple key performance aspects of the warehousing system when performing location allocation in the future, and improve the rationality of location allocation.

[0047] (2) When performing storage location allocation, multiple constraints are set in advance to jointly constrain multiple objective functions, and the optimal set of storage location allocation schemes is selected from the solution set that satisfies multiple constraints, so that the output optimal allocation scheme is the best allocation scheme that best fits the current warehousing system, thereby further improving the rationality of storage location allocation.

[0048] (3) In addition, this application also monitors the congestion coefficient of the storage location in real time, and when the congestion coefficient of the storage location reaches the congestion coefficient threshold, it automatically drives a new round of storage location allocation to achieve the purpose of dynamic management of storage locations in the warehousing system and improve the intelligent management level of the warehousing system. Attached Figure Description

[0049] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the description of the embodiments of this application will be briefly introduced below. In the drawings:

[0050] Figure 1 This is a schematic diagram of an exemplary operating environment according to an embodiment of this application;

[0051] Figure 2 This is a flowchart of a four-way shuttle intelligent warehouse storage location allocation method according to an embodiment of this application;

[0052] Figure 3 This is a block diagram of a four-way shuttle intelligent warehouse storage location allocation system according to an embodiment of this application;

[0053] Figure 4 This is a structural diagram of an electronic device provided in an embodiment of this application. Detailed Implementation

[0054] Exemplary embodiments of the present application will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present application are shown in the drawings, it should be understood that the present application may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this application will be thorough and complete, and will fully convey the scope of the present application to those skilled in the art.

[0055] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such use can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the term "comprising" and its variations should be interpreted as open-ended terms meaning "including but not limited to."

[0056] Figure 1 This is a schematic diagram of an exemplary operating environment for an embodiment of this application, which is a four-way shuttle intelligent warehouse. Figure 1 As shown, a four-way shuttle intelligent automated warehouse includes at least shelves, goods, and equipment. There are multiple shelves arranged in an array, each shelf containing multiple storage compartments. Each compartment stores one or more goods and has a location coordinate to distinguish its position. The goods are items requiring logistics transportation; there are various types of goods, which may have different weights and shapes. This embodiment does not limit the types of goods. The equipment includes elevators and shuttles. Elevators transport goods vertically or provide transport channels for shuttles, while shuttles can move freely horizontally and vertically to achieve rapid goods transportation. This embodiment also does not limit the elevators and shuttles, as long as they can perform the corresponding transportation tasks within the automated warehouse.

[0057] Figure 1 In this diagram, shelves are represented by N, for example, N1 and N2 represent different shelves; goods are represented by M, such as M1-M13 representing different goods; shuttles are represented by b, such as b1-b10 representing different shuttles; and elevators are represented by c, such as... Figure 1 In this context, c1 represents the hoist on shelf N1. It should be noted that... Figure 1 This is merely an example. In real-world scenarios, a four-way shuttle intelligent automated warehouse may include more shelves, goods, shuttles, and elevators, and even the arrangement of the shelves, goods, shuttles, and elevators may differ. This embodiment does not impose any limitations.

[0058] The aforementioned shelves are equipped with a variety of sensors. Figure 1(Not shown) Various sensors, such as infrared sensors, weight sensors, RFID readers, laser profile scanners, and position sensors, are used to collect information on the storage status of each storage compartment, the stacking of goods, and the flow of shuttles around the compartments. Based on the statistical results of this information, a storage compartment congestion coefficient can be calculated, indicating the degree of congestion. Simultaneously, leveraging the monitoring capabilities of these sensors, the real-time status of various items on the shelves, including storage compartments, goods, shuttles, and elevators, can also be obtained.

[0059] In addition, the aforementioned shuttle vehicle also integrates a variety of sensors. Figure 1 Not shown, but including, for example, navigation and positioning sensor groups, timing sensors, speed sensors, and position sensors, various sensors are installed on them to monitor the specific situation of the shuttle car during the transportation of goods.

[0060] To enable intelligent control of storage location allocation in automated warehouses, the aforementioned equipment may also include servers or other types of execution terminals with data processing capabilities. Figure 1 Also not shown. The execution terminal interacts with various sensors in the automated warehouse via wired or wireless communication to obtain data from various dimensions, thereby allocating storage locations based on this data. In this embodiment, the execution terminal can automatically allocate storage locations when the warehouse system is put into use, or it can obtain the storage location congestion coefficient of each area in the shelving in real time. When the storage location congestion coefficient of any area reaches the congestion coefficient threshold, the storage location is re-allocated. In practical applications, the execution terminal can also be set to periodically self-drive storage location allocation, for example, reallocating storage locations when the goods entrance / exit reaches 100 times, to ensure that the storage location allocation of the warehouse system meets the current demand.

[0061] Figure 2 This is a flowchart illustrating a method for allocating storage locations in a four-way shuttle intelligent automated warehouse according to an embodiment of this application. Figure 2 As shown, the method may include the following steps S101 to S106:

[0062] Step S101: Obtain location information, cargo information, and equipment information. The equipment includes shuttle cars and elevators.

[0063] Location information refers to information related to storage spaces, mainly including location coordinates, congestion coefficient, space height, space demand, and space reserve. Location coordinates are obtained by sensors integrated into the shelving to pinpoint the location of the storage space. These sensors also measure the space height. The congestion coefficient is calculated by analyzing data on the storage status of each storage space, the degree of goods accumulation, and the flow of shuttles around the space. The closer a storage space is to its maximum capacity, the more goods are piled up, and the higher the flow of shuttles around the space, the greater the congestion coefficient. In other words, the congestion coefficient primarily indicates the urgency or scarcity of storage spaces. Storage space demand corresponds to storage space reservation. Storage space demand refers to the number of storage spaces required to store goods, while storage space reservation refers to the number of storage spaces reserved to meet the storage demand. Storage space demand can be obtained by predicting the number of storage spaces needed in the next storage cycle using historical warehousing data, by reserving storage spaces in advance, or by combining the predicted number of storage spaces with the actual number of reserved storage spaces. Ultimately, based on storage space demand, a number of storage spaces exceeding the demand are reserved to obtain the storage space reservation.

[0064] In this embodiment, the coordinates of the storage location, the congestion coefficient of the storage location, the height of the storage compartment, the demand for storage locations, and the reserved storage locations are represented by (x, y, z) and λ, respectively. xy h q j express.

[0065] Cargo information refers to information related to the cargo, mainly including cargo weight and cargo turnover rate. Cargo weight is obtained by weight sensors installed in the storage compartments or shuttle cars. Cargo turnover rate refers to the number of times cargo is turned over per unit of time, such as a day, a week, or a month; this embodiment does not impose a limitation. In this embodiment, cargo weight and cargo turnover rate are represented by M. xyz p i express.

[0066] The equipment information is divided into equipment information for shuttle cars and equipment information for elevators.

[0067] The shuttle's equipment information refers to information related to the shuttle, mainly including its running time, power consumption, acceleration / deceleration influence coefficient, actual transport distance, and maximum permissible distance. The running time refers to the time consumed during cargo transport. Power consumption refers to the power consumed during cargo transport. The acceleration / deceleration influence coefficient indicates the degree of impact on the final power consumption when attempting to reduce power consumption by decreasing or increasing the transport speed. For example, if the shuttle's transport speed exceeds a speed threshold, power consumption increases exponentially, necessitating a speed reduction. Conversely, if the shuttle's transport speed is significantly lower than the speed threshold, increasing the speed, while ensuring the increased speed remains below the threshold, can reduce the running time and thus lower power consumption to some extent. The actual transport distance refers to the path length chosen by the shuttle to avoid congested areas; therefore, the path length may not be the shortest distance from the starting point to the destination. The maximum allowable distance for transporting goods refers to the maximum path length that the shuttle is allowed to take each time it transports goods. This maximum allowable distance is also the longest distance from the starting point to the destination. If the shuttle exceeds this maximum allowable distance, there may be abnormal situations such as spinning in place or going back and forth continuously.

[0068] In this embodiment, the shuttle's running time, power consumption, acceleration / deceleration influence coefficient, actual distance of transported goods, and maximum permissible distance are respectively represented by t. bi E bi δ v d b,t 、D max express.

[0069] The equipment information of the hoist refers to information related to the hoist, mainly including the hoist's operating time and power consumption. The hoist's operating time refers to the time consumed during transporting goods or providing a transport path for shuttles, while its power consumption refers to the power consumed during this period. In this embodiment, the hoist's operating time and power consumption are represented by t. ci E ci express.

[0070] Step S102: Construct a shelf stability function and a demand forecasting adaptation function based on the storage location information and the goods information, respectively; wherein, the shelf stability function is used to evaluate the stability of the shelf, and the demand forecasting adaptation function is used to evaluate the gap between the storage location demand and the storage location reservation.

[0071] 1) The shelf stability function is:

[0072]

[0073] Where F1 represents the shelf stability function, x, y, z represent the location coordinates (x, y, z), and M represents the shelf location coordinates. xyz This represents the weight of goods located in the storage cell with coordinates (x, y, z), ∑x∑y∑z M xyz The total weight of all goods located on the shelf is represented by , and z represents the shelf level of the shelf where the storage cell with coordinates (x, y, z) is located. The average number of shelves is represented by , and h represents the height of the shelf with coordinates (x, y, z).

[0074] 2) The demand forecasting adaptation function is:

[0075]

[0076] Where F2 represents the demand forecasting adaptation function, K represents the total number of storage locations, and q j Indicates the amount of space reserved for storage. Indicates the demand for storage space, ω j ω represents the weight of demand forecasting. j It is derived by predicting the turnover rate and storage space demand of various goods in the next warehousing cycle based on historical warehousing data. For example, if the turnover rate of goods is predicted to increase in the next warehousing cycle, then the storage demand will remain basically unchanged, so ω j It can be set to 1, but when it is predicted that the demand for storage space will increase in the next storage cycle, ω can be increased from 1. j The value is used to dynamically adjust the demand for storage space. The purpose is to ensure the reserved space q j Capable of meeting storage space demand greater than the real-time demand. The requirement is to ensure that all goods can be stored in their corresponding storage compartments.

[0077] In this embodiment, a Long Short-Term Memory (LSTM) network is used to predict the turnover rate and storage space demand in the next warehousing cycle. In practical applications, other networks with the same prediction function can also be used for prediction, and this embodiment does not impose any restrictions.

[0078] Step S103: Construct an inbound / outbound efficiency function and an equipment power consumption function based on the location information, cargo information, and equipment information, respectively; wherein, the inbound / outbound efficiency function is used to evaluate the efficiency of cargo transportation, and the equipment power consumption function is used to evaluate the total power consumption of the shuttle and the hoist.

[0079] 1) The efficiency function for inbound and outbound operations is:

[0080]

[0081] Where F3 represents the inbound / outbound efficiency function, N represents the total number of devices, and t bi The time t represents the shuttle's travel time. ci p represents the hoist's running time. i λ represents the goods turnover rate. xy This indicates the congestion coefficient of the cargo space.

[0082] 2) The equipment's power consumption function is:

[0083] F4=∑x∑y∑z(E bi +E ci )×M xyz ×(1+δ v (2.2)

[0084] Where F4 represents the equipment power consumption function, x, y, z represent the cargo location coordinates (x, y, z), and E bi E represents the electrical energy consumed by the shuttle to transport goods to the storage cell with coordinates (x, y, z). ci M represents the electrical energy consumption used by the hoist-assisted shuttle to transport goods to the storage cell with coordinates (x, y, z). xyz δ represents the weight of the goods transported by the shuttle. v This represents the coefficient of influence of acceleration and deceleration.

[0085] Step S104: Construct a dynamic scheduling equilibrium function based on the shuttle's equipment information. The dynamic scheduling equilibrium function is used to evaluate the efficiency of the shuttle in transporting goods.

[0086] Specifically, the dynamic scheduling load balancing function is:

[0087]

[0088] Where F5 represents the dynamic scheduling equilibrium function, B represents the total number of shuttles, T represents the total time for shuttles to transport goods, and d b,t D represents the actual distance the b-th shuttle transports goods at time t. max This represents the maximum permissible distance that the b-th shuttle can transport goods at time t.

[0089] Step S105: Obtain multiple preset constraints.

[0090] Based on the above functions, several constraints are pre-defined, including:

[0091] 1) Cargo location coordinate constraint: 0 <x<X max ,0 <y<Y max ,0 <z<Z max, where X max Y max Z max This represents the maximum storage location coordinates of the storage cell with coordinates (x, y, z). This constraint is to prevent goods from exceeding the storage space when transported by shuttle vehicles, i.e., it is used for standardized storage.

[0092] 2) Shelf load-bearing capacity constraint: ∑x∑y∑z M xyz ≤W max Among them, W max This indicates the weight capacity threshold of the shelving unit. This constraint is designed to prevent the total weight of all goods in the currently assigned storage compartments from exceeding the shelving unit's weight capacity threshold, thus avoiding shelving overload and ensuring shelving stability.

[0093] 3) Dynamic reservation constraint for empty storage space: ∑x∑y∑z(1-θ) xyz )≥γ×X max Y max Z max , where θ xyz This indicates the occupancy status of the storage cell with coordinates (x, y, z). In this embodiment, when the storage cell is occupied, θ xyz =1, and when the storage slot is not occupied, θ xyz =0; γ is the dynamic reservation ratio, and 0<γ<1, which means that the reservation ratio of each area of ​​the shelf is dynamically adjusted according to the demand for storage space to ensure that all goods can be stored in the corresponding storage space.

[0094] 4) Shuttle load balancing constraints: Among them, L b L represents the load capacity of the b-th shuttle. avg The average load of all shuttles is ε, and the allowable deviation rate is preset. This constraint can prevent shuttles from being overloaded or idle.

[0095] Step S106: Using a preset calculation model, calculate the optimal allocation schemes for the shelf stability function, demand forecasting adaptation function, warehousing efficiency function, equipment power consumption function, and dynamic scheduling equilibrium function under multiple constraints.

[0096] The computational model can be an improved Non-Dominated Ranking Genetic Algorithm (NSGA-II) or other network models with similar computational functions. This embodiment uses NSGA-II as an example. NSGA-II is an efficient multi-objective optimization algorithm that solves the computational complexity and diversity preservation problems of traditional NSGA by introducing non-dominated ranking, crowding comparison, and elite retention strategies.

[0097] Initialization population: Randomly generate initial solutions that satisfy multiple constraints in step S105. For example, allocation schemes that satisfy the following constraints are all used as initial solutions: the goods in the storage cell do not exceed the storage location coordinates, the total weight of all goods in the currently allocated storage cell does not exceed the shelf's weight capacity threshold, the storage location reserve is greater than the storage location demand, and the shuttle car is not overloaded.

[0098] Fast non-dominated sorting: For each selected initial solution, calculate the values ​​of the five function outputs obtained in steps S102 to S104, and stratify them according to the multi-objective dominance relationship. For example, the priority of shelf stability is higher than the priority of inbound efficiency.

[0099] Crowding calculation: For multiple initial solutions in the same level, sort them according to the distribution density in the target space, and prioritize retaining sparse solutions to avoid overly similar shelf configuration schemes.

[0100] Elite selection and cross mutation: When selecting, prioritize solutions with low frontier level and high congestion; when crossing, exchange parameters in each initial solution group, such as replacing the location coordinates or replacing equipment with different power consumption; when mutating, perform polynomial mutation on the storage racks or shuttle transport paths located on high shelves to ensure that constraints are not violated.

[0101] Optimal allocation scheme output: Select an optimal allocation scheme from the initial solution or the initial solution after mutation. The optimal allocation scheme can balance the values ​​of the output of multiple functions constructed in steps S102 to S104, thereby ensuring that the obtained optimal allocation scheme can simultaneously satisfy the requirements of enhancing shelf stability, improving inbound and outbound efficiency and reducing equipment energy consumption, making the location allocation method of this embodiment more suitable for complex warehousing scenarios.

[0102] In summary, the storage location allocation method of this application takes rack stability, inbound / outbound efficiency, and equipment power consumption as its core objectives. Combining the dynamic scheduling balance of shuttle vehicles and the adaptability of warehouse demand forecasting, it constructs a multi-objective function including a rack stability function (F1), a demand forecasting adaptation function (F2), an inbound / outbound efficiency function (F3), an equipment power consumption function (F4), and a dynamic scheduling balance function (F5). Based on the actual warehousing system, it defines various parameters such as cargo box weight, storage location coordinates, congestion coefficient, and forecast weight to establish and solve initial solutions that simultaneously satisfy multiple constraints such as storage location coordinates, load capacity, empty storage location reservation, and equipment load balance. The optimal allocation scheme is obtained from multiple initial solutions using NSGA-II. This achieves the goals of dynamically adjusting storage location allocation, optimizing shuttle vehicle transport routes and storage location layout, improving cargo turnover and retrieval efficiency, reducing equipment power consumption, enhancing the intelligence level of the warehousing system, and realizing intelligent management.

[0103] In a specific example, the location allocation algorithm provided in this embodiment significantly improves the adaptability of the warehousing system in complex scenarios by integrating dynamic scheduling, intelligent prediction and multi-objective optimization. According to actual tests, the stability of the shelves is improved by 20%, the efficiency of inbound and outbound operations is increased by 18%, and the power consumption of equipment is reduced by 15%, effectively solving the problems of single-objective limitations and insufficient dynamic adaptability of traditional algorithms.

[0104] It should be noted that the sequence numbers of the steps in the above embodiments do not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application. In practical applications, all the above possible implementation methods can be arbitrarily combined in a combined manner to form possible embodiments of this application, which will not be described in detail here.

[0105] Based on the above embodiments of the method for allocating storage locations in a four-way shuttle intelligent automated warehouse, this application also provides a storage location allocation system for a four-way shuttle intelligent automated warehouse based on the same inventive concept.

[0106] Figure 3 This is a structural diagram of a four-way shuttle intelligent automated warehouse location allocation system provided in an embodiment of this application. Figure 3 As shown, the system may specifically include a first acquisition module, a first construction module, a second construction module, a third construction module, a second acquisition module, and a data calculation module.

[0107] The first acquisition module is used to acquire location information, cargo information, and equipment information, including shuttle cars and hoists.

[0108] The first construction module is used to construct a shelf stability function and a demand forecasting adaptation function based on the storage location information and the goods information, respectively. The shelf stability function is used to evaluate the stability of the shelf, and the demand forecasting adaptation function is used to evaluate the gap between the storage location demand and the storage location reservation.

[0109] The second construction module is used to construct an inbound / outbound efficiency function and an equipment power consumption function based on the location information, cargo information, and equipment information, respectively. The inbound / outbound efficiency function is used to evaluate the allocation efficiency of the location, and the equipment power consumption function is used to evaluate the total power consumption of the shuttle and the hoist.

[0110] The third module is used to construct a dynamic scheduling and balancing function based on the shuttle's equipment information. The dynamic scheduling and balancing function is used to evaluate the efficiency of the shuttle in transporting goods.

[0111] The second acquisition module is used to acquire multiple preset constraints.

[0112] The data calculation module is used to calculate the optimal allocation schemes for the shelf stability function, demand forecasting adaptation function, warehousing efficiency function, and equipment power consumption function under multiple constraints using a preset calculation model.

[0113] Based on the same inventive concept, this application also provides an electronic device, including a processor and a memory, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute a four-way shuttle intelligent warehouse location allocation method of any of the above embodiments.

[0114] In an exemplary embodiment, an electronic device is provided, such as Figure 4 As shown, Figure 4 The illustrated electronic device 400 includes a processor 401 and a memory 403. The processor 401 and the memory 403 are connected, for example, via a bus 402. Optionally, the electronic device 400 may also include a transceiver 404. It should be noted that in practical applications, the transceiver 404 is not limited to one type, and the structure of this electronic device 400 does not constitute a limitation on the embodiments of this application.

[0115] Processor 401 may be a CPU (Central Processing Unit), DSP (Digital Signal Processor), ASIC (Application Specific Integrated Circuit), FPGA (Field Programmable Gate Array), or other programmable logic device, transistor logic device, hardware component, or any combination thereof. It may implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. Processor 401 may also be a combination that implements computational functions, such as including one or more microprocessor combinations, a combination of a DSP and a microprocessor, etc.

[0116] Bus 402 may include a pathway for transmitting information between the aforementioned components. Bus 402 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. Bus 402 can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 4 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0117] The memory 403 may be a ROM (Read Only Memory) or other type of static storage device capable of storing static information and instructions, RAM (Random Access Memory) or other type of dynamic storage device capable of storing information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), CD-ROM (Compact Disc Read Only Memory) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto.

[0118] The memory 403 stores computer program code that executes the scheme of this application, and its execution is controlled by the processor 401. The processor 401 executes the computer program code stored in the memory 403 to implement the content shown in the foregoing method embodiments.

[0119] Among them, electronic devices include, but are not limited to: mobile terminals such as mobile phones, laptops, digital radio receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), and in-vehicle terminals (such as in-vehicle navigation terminals), as well as fixed terminals such as digital TVs and desktop computers. Figure 4 The electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0120] Based on the same inventive concept, this application also provides a storage medium storing a computer program, wherein the computer program is configured to execute a four-way shuttle intelligent warehouse location allocation method of any of the above embodiments when running.

[0121] Those skilled in the art will clearly understand that the specific working process of the systems, devices, and modules described above can be referred to the corresponding process in the foregoing method embodiments. For the sake of brevity, it will not be repeated here.

[0122] Those skilled in the art will understand that the technical solution of this application, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several program instructions to cause an electronic device (e.g., a personal computer, server, or network device) to execute all or part of the steps of the methods described in the embodiments of this application when running the program instructions. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, portable hard drive, read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk.

[0123] Alternatively, all or part of the steps of the foregoing method embodiments can be implemented by hardware (such as electronic devices like personal computers, servers, or network devices) associated with program instructions. The program instructions can be stored in a computer-readable storage medium. When the program instructions are executed by the processor of the electronic device, the electronic device executes all or part of the steps of the methods described in the embodiments of this application.

[0124] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit it. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that within the spirit and principles of this application, modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein; and these modifications or substitutions do not cause the corresponding technical solutions to leave the protection scope of this application.

Claims

1. A method for allocating storage locations in a four-way shuttle intelligent automated warehouse, characterized in that, include: Acquire location information, cargo information, and equipment information, wherein the equipment includes shuttle cars and hoists; Based on the location information and the goods information, a shelf stability function and a demand forecasting adaptation function are constructed respectively; wherein, the shelf stability function is used to evaluate the stability of the shelf, and the demand forecasting adaptation function is used to evaluate the gap between the demand for storage locations and the reserved storage locations. Based on the location information, cargo information, and equipment information, an inbound / outbound efficiency function and an equipment power consumption function are constructed respectively; wherein, the inbound / outbound efficiency function is used to evaluate the efficiency of cargo transportation, and the equipment power consumption function is used to evaluate the total power consumption of the shuttle and the hoist; A dynamic scheduling and balancing function is constructed based on the equipment information of the shuttle vehicle. The dynamic scheduling and balancing function is used to evaluate the efficiency of the shuttle vehicle in transporting goods. Obtain multiple preset constraints; Using a preset calculation model, the optimal allocation schemes for the shelf stability function, the demand forecasting and adaptation function, the warehousing efficiency function, the equipment power consumption function, and the dynamic scheduling and balancing function are calculated under multiple constraints.

2. The method according to claim 1, characterized in that, The location information includes location coordinates and location congestion coefficient; the cargo information includes cargo weight and cargo turnover rate; and the shelf stability function is: Where F1 represents the shelf stability function, x, y, z represent the location coordinates (x, y, z), and M represents the shelf location coordinates. xyz This represents the weight of goods located in the storage cell with coordinates (x, y, z), ∑x∑y∑zM xyz The total weight of all goods located on the shelf is represented by , and z represents the shelf level of the shelf where the storage cell with coordinates (x, y, z) is located. The average number of shelves is represented by , and h represents the height of the shelf with coordinates (x, y, z).

3. The method according to claim 2, characterized in that, The storage location information also includes the storage location demand and the storage location reserve. The demand forecasting and adaptation function is: Where F2 represents the demand forecasting adaptation function, K represents the total number of storage locations, and q j Indicates the amount of space reserved for storage. Indicates the demand for storage space, ω j ω represents the weight of demand forecasting. j It is obtained by predicting the turnover rate and storage space demand of various goods in the next storage cycle based on historical warehousing data.

4. The method according to claim 1, characterized in that, The location information includes a location congestion coefficient; the cargo information includes cargo turnover rate; the shuttle vehicle equipment information includes shuttle vehicle operating time; the elevator equipment information includes elevator operating time; and the inbound / outbound efficiency function is: Where F3 represents the inbound / outbound efficiency function, N represents the total number of devices, and t bi The time t represents the shuttle's travel time. ci p represents the hoist's running time. i λ represents the goods turnover rate. xy This indicates the congestion coefficient of the cargo space.

5. The method according to claim 4, characterized in that, The cargo location information also includes cargo location coordinates; the cargo information also includes cargo weight; the shuttle car equipment information also includes shuttle car power consumption and acceleration / deceleration influence coefficients; the hoist equipment information also includes hoist power consumption; and the equipment power consumption function is: F4=∑x∑y∑z(E bi +E ci )×M xyz ×(1+δ v ), Where F4 represents the equipment power consumption function, x, y, z represent the cargo location coordinates (x, y, z), and E bi E represents the electrical energy consumed by the shuttle to transport goods to the storage cell with coordinates (x, y, z). ci M represents the electrical energy consumption used by the hoist-assisted shuttle to transport goods to the storage cell with coordinates (x, y, z). xyz δ represents the weight of the goods transported by the shuttle. v This represents the coefficient of influence of acceleration and deceleration.

6. The method according to claim 1, characterized in that, The shuttle's equipment information includes the actual distance and maximum allowable distance of the transport path when the shuttle is transporting goods, and the dynamic scheduling and balancing function is: Where F5 represents the dynamic scheduling equilibrium function, B represents the total number of shuttles, T represents the total time for shuttles to transport goods, and d b,t D represents the actual distance the b-th shuttle transports goods at time t. max This represents the maximum permissible distance that the b-th shuttle can transport goods at time t.

7. The method according to claim 1, characterized in that, The constraints include: Location coordinate constraint: 0 <x<X max ,0 <y<Y max ,0 <z<Z max Where x, y, and z represent the coordinates of the cargo location (x, y, z), X max Y max Z max This represents the maximum position coordinate of the cell with position coordinates (x, y, z); and Shelf load-bearing weight constraint: ∑x∑y∑zM xyz ≤W max Among them, W max This indicates the weight threshold that the shelf can withstand; and Dynamic reservation constraint for empty storage space: ∑x∑y∑z(1-θ) xyz )≥γ×X max Y max Z max , where θ xyz This indicates the occupancy status of the storage cell with coordinates (x, y, z), θ. xyz =1 or θ xyz =0, γ is the dynamic reserve ratio, 0<γ<1; and Shuttle load balancing constraints: Among them, L b L represents the load capacity of the b-th shuttle. avg The average load of all shuttles is ε, and the allowable deviation rate is ε, which is preset in advance.

8. The method according to claim 1, characterized in that, An improved non-dominated sorting genetic algorithm is used to calculate the optimal allocation schemes for the shelf stability function, the demand prediction and adaptation function, the warehousing efficiency function, and the equipment power consumption function under multiple constraints.

9. The method according to claim 8, characterized in that, The method further comprises: Extract the congestion coefficient of the storage location from the storage location information; When the congestion coefficient of the storage location reaches the congestion coefficient threshold, the shelf stability function, demand forecasting adaptation function, inbound efficiency function, equipment power consumption function, and dynamic scheduling balance function are reconstructed. An improved non-dominated sorting genetic algorithm is used to calculate the optimal allocation schemes for the newly constructed shelf stability function, demand prediction and adaptation function, warehousing efficiency function, equipment power consumption function, and dynamic scheduling equilibrium function under multiple constraints.

10. A storage location allocation system for a four-way shuttle intelligent automated warehouse, characterized in that, include: The first acquisition module is used to acquire cargo location information, cargo information, and equipment information, wherein the equipment includes a shuttle car and a hoist. The first construction module is used to construct a shelf stability function and a demand forecasting adaptation function based on the storage location information and the goods information, respectively; wherein, the shelf stability function is used to evaluate the stability of the shelf, and the demand forecasting adaptation function is used to evaluate the gap between the storage location demand and the storage location reservation. The second construction module is used to construct an inbound / outbound efficiency function and an equipment power consumption function based on the storage location information, the cargo information, and the equipment information, respectively; wherein, the inbound / outbound efficiency function is used to evaluate the efficiency of cargo transportation, and the equipment power consumption function is used to evaluate the total power consumption of the shuttle and the hoist; The third construction module is used to construct a dynamic scheduling and balancing function based on the equipment information of the shuttle car. The dynamic scheduling and balancing function is used to evaluate the efficiency of the shuttle car in transporting goods. The second acquisition module is used to acquire multiple preset constraints. The data calculation module is used to calculate the optimal allocation schemes of the shelf stability function, the demand forecasting and adaptation function, the warehousing efficiency function, the equipment power consumption function, and the dynamic scheduling and balancing function under multiple constraints using a preset calculation model.

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