Cross-regional storage facility collaborative operation method and system

Abstract

The invention discloses a cross-regional storage facility collaborative operation method and system, and particularly relates to the technical field of storage management, and the method comprises the steps: obtaining the operation evaluation coefficients of M storage devices in an nth storage, and determining the operation states of the M storage devices based on the operation evaluation coefficients, and obtaining R idle devices; obtaining the inventory states of the N warehouses and the total demand quantity of orders, distributing the total demand quantity of the orders based on the inventory states of the N warehouses, and obtaining the sub-order quantity of the nth warehouse; and determining an optimal storage equipment combination based on the sub-order quantity of the nth storage, the R idle equipment and the delivery destination, and ensuring resource allocation optimization of cross-regional storage facilities by comprehensively considering the storage equipment operation evaluation coefficient, the inventory state, the order demand quantity and the transportation cost; the fault analysis model is used for predicting the equipment state, resource waste caused by equipment faults is effectively avoided, and the equipment utilization rate and the task completion efficiency are improved.

Description

Technical Field

[0001] The present invention relates to the field of warehouse management technology, and more specifically, to a method and system for collaborative operation of cross-regional warehouse facilities. Background Art

[0002] With the development of globalization and e-commerce, the warehouse network of enterprises has gradually shifted to a cross-regional layout to adapt to the market needs of different regions. However, the existing warehouse management system is mostly centered on a single warehouse and lacks a unified resource scheduling and coordination mechanism, resulting in the formation of an "island effect" in the operation of various warehouse facilities. Traditional systems are difficult to coordinate multiple warehouse resources in a timely manner and lack rapid response capabilities. Overall, the complexity of cross-regional warehousing has put forward higher requirements for collaborative management technology.

[0003] In the existing methods, for example, the patent application with publication number CN116228091A discloses a BIM-based intelligent warehousing system and warehousing method, including a data modeling module, which is used to update the modeling according to the real-time inventory information of each warehouse in the warehouse; a transfer data entry module, which is used to enter the transfer data volume information of the warehouse; a warehousing query module, which is used to query the transfer warehousing information in the data modeling module: a dispatching data export module, which is used to allocate the warehouse information corresponding to the exported transfer warehousing information to the route retrieval module; a route retrieval module, which is used to allocate the transportation route information according to the exported warehouse information. Although the above method can improve the efficiency of warehousing and transportation, it is found through research and application of the above method and the existing technology that the above method and the existing technology have at least the following defects:

[0004] Due to differences in geographical location and market demand, different warehouses are prone to having excess resources in some warehouses and shortages in others, which wastes storage capacity and makes it difficult to meet dynamic demand.

[0005] To this end, the present invention provides a method and system for collaborative operation of cross-regional storage facilities. Summary of the invention

[0006] In order to overcome the above-mentioned defects of the prior art, the present invention provides a method and system for collaborative operation of cross-regional storage facilities to solve the problems raised in the above-mentioned background technology.

[0007] To achieve the above purpose, the present invention provides the following technical solutions:

[0008] In a first aspect, the present invention provides a method for collaborative operation of cross-regional storage facilities, comprising:

[0009] Step 1: Obtain the operation evaluation coefficients of M storage equipment in the nth storage, determine the operation status of the M storage equipment based on the operation evaluation coefficients, and obtain R idle equipment;

[0010] Step 2: Obtain the inventory status and total order demand of N warehouses, allocate the total order demand based on the inventory status of N warehouses, and obtain the sub-order quantity of the nth warehouse;

[0011] Step 3: Based on the sub-order quantity of the nth warehouse, R idle equipment and the delivery destination, determine the optimal warehouse equipment combination, determine the optimal transportation route based on the optimal warehouse equipment combination, and complete the scheduling of the total order demand based on the optimal warehouse equipment combination and the optimal transportation route.

[0012] Furthermore, the method for obtaining the operation evaluation coefficients of the M storage equipment includes:

[0013] Step a1: Obtain the operation progress of the mth storage device; m=1, 2, ..., M;

[0014] Step a2: Obtain the equipment failure coefficient of the mth storage equipment in the future time period;

[0015] Step a3: Formulate the operation progress of the mth storage equipment, the equipment failure coefficient in the future time period, the handling speed and the maximum load capacity to calculate the operation evaluation coefficient;

[0016] Step a4: let m=m+1, repeat steps a1 to a3 until m=M, and obtain the operation evaluation coefficients of M storage equipment;

[0017] The method for obtaining the equipment failure coefficient of the mth storage equipment in the future time period includes:

[0018] Step a21: Acquire fault characteristic data of the mth storage device, wherein the fault characteristic data includes amplitude difference, temperature difference, operating power difference, number of faults, and total operating time;

[0019] Step a22: Input the fault feature data into the pre-built fault analysis model to predict the equipment failure coefficient in the future time period.

[0020] Further, the method for obtaining R idle devices by determining the operating status of M storage devices based on the operating evaluation coefficient includes:

[0021] Step s1: preset an evaluation coefficient threshold value Px, and compare the operation evaluation coefficient of the mth storage equipment with the preset evaluation coefficient threshold value Px;

[0022] If YX m ≥Px, then mark the mth storage device as idle; YX m is the operation evaluation coefficient of the mth storage equipment;

[0023] If YX m<Px, then mark the mth storage equipment as running;

[0024] Step s2: Let m=m+1, repeat step s1 until m=M, obtain the operating status of M storage devices, count the number of storage devices in the idle state, and mark them as R idle devices.

[0025] Furthermore, the method for obtaining the inventory status of N warehouses includes:

[0026] The formula for each storage status is expressed as:

[0027] KC n ={S n |n=1,2,...,N};

[0028] The inventory status of the nth warehouse is:

[0029] S n ={(P 1 ,Q 1 ),(P 2 ,Q 2 ),...,(P K ,Q K )};

[0030] In the formula, S n represents the inventory status of the nth warehouse, P K represents the Kth commodity type, Q K Indicates the inventory quantity of the Kth commodity type, KC n Represents each repository status.

[0031] Furthermore, the method of obtaining the total order demand includes:

[0032] Read the demand data of all orders within a preset time period from the order management system, and calculate the total demand according to the type and quantity of goods;

[0033] Aggregate all order demands to obtain the total order demand Dx, which is expressed as:

[0034] Dx={(P 1 ,D 1 ),(P 2 ,D 2 ),...,(P K ,D K )};

[0035]

[0036] In the formula, Dx represents the total order demand, D K Indicates P KThe total demand for the corresponding product, J is the total number of orders, D i,k Indicates that P in the jth order K The demand for the corresponding product.

[0037] Furthermore, the method for allocating the total order demand based on the inventory status of N warehouses includes:

[0038] Step b1: Determine the priority weight W of the nth warehouse n ;

[0039] Step b2: Get the transportation cost C of the nth warehouse n ;

[0040] Step b3: Priority weight W for the nth warehouse n and transportation cost C n Perform formula calculation to obtain the sub-order quantity D of the nth warehouse allocation n,k , and its calculation formula is:

[0041]

[0042] Where D n,k represents the sub-order quantity assigned to the nth warehouse, W n represents the priority weight of the nth warehouse, C n W represents the transportation cost from the nth warehouse to the target order destination; i represents the priority weight of the i-th warehouse, C i represents the transportation cost from the i-th warehouse to the target order destination, Q i,k Represents the product P in the i-th warehouse K The inventory volume, D k Indicates the inventory quantity of the k-th product type, k≤K.

[0043] Further, determine the priority weight W of the nth warehouse n The methods include:

[0044] Step b01: Obtain the transportation distance between the storage destination and the nth storage;

[0045] Step b02: Obtain the average transportation speed, historical response rate mean, and inventory update speed of the nth warehouse;

[0046] Step b03: According to the transportation distance, average handling speed, historical response rate average and inventory update speed, the priority weight W of each warehouse is obtained by formula calculation. n ;

[0047] W n =JL×μ 1 +Bp×μ2 +XY×μ 3 +GS×μ 4 ;

[0048] In the formula, JL represents the transportation distance, Bp represents the average handling speed, XY represents the mean historical response rate, GS represents the inventory update speed, μ 1 , μ 2 , μ 3 and μ 4 is the corresponding weight factor, μ 1 +μ 2 +μ 3 +μ 4 =1;

[0049] The methods for obtaining the transportation cost of the nth warehouse include:

[0050] Step b11: Acquire cost characteristic data, wherein the cost characteristic data includes basic cost, distance cost, time cost and dynamic cost;

[0051] Step b12: Formulate the cost characteristic data to obtain the transportation cost C of the nth warehouse n ; The calculation formula is:

[0052] C n =C q +(C d ×DX)+(C s ×Sc)+C f ;

[0053] In the formula, C q Indicates the basic cost, C d represents the distance cost, DX represents the shortest route distance from the warehouse to the destination, C s represents the time cost, Sc represents the time used for transportation, C f Represents dynamic data, including fuel prices or vehicle idle charges.

[0054] Further, the method for determining the best storage equipment combination includes:

[0055] Step c1: obtaining priority feature data of the rth idle equipment based on the sub-order quantity of the nth warehouse, wherein the priority feature data includes the loading capacity, idle time ratio and transportation distance weight of the rth idle equipment;

[0056] Step c2: Perform formula calculation based on the priority feature data to obtain the priority evaluation coefficient;

[0057]

[0058] Where P rrepresents the priority evaluation coefficient of the rth idle device; L r represents the loading capacity of the rth idle device; T r represents the idle time ratio of the rth idle device, D r represents the transportation distance weight of the rth idle equipment, γ 1 , γ 2 , γ 3 is the corresponding weight factor, γ 1 +γ 2 +γ 3 =1,L max Indicates the maximum loading capacity of the rth idle device;

[0059] Step c3: Initialize the remaining sub-order volume: S n =Q n ;

[0060] Traverse the idle devices r and assign tasks according to priority: Allocation amount: ΔQ r =min(L r ,S n );

[0061] Update the remaining order quantity S' n :S' n =S n -ΔQ r ;

[0062] If S' n =0, then stop allocating;

[0063] In the formula, S n represents the remaining sub-order quantity, ΔQ r represents the number of sub-orders assigned to the rth idle device; Q n Indicates the initial sub-order quantity of the nth warehouse; min(L r ,S n ) indicates selecting the minimum value of the order quantity that the current device can process and the remaining order quantity;

[0064] Step c4: Output the allocation of each device in the nth warehouse, and set n=n+1, repeat steps c1-c4 until n=N, then end the loop, count the space equipment in the N warehouses, and obtain the best warehouse equipment combination.

[0065] Furthermore, the method for determining the optimal transportation path based on the optimal storage equipment combination includes:

[0066] Step d1: Obtain a geographical distribution map of the nth warehouse, and based on the geographical distribution map, use the location of the rth idle equipment in the nth warehouse as the transportation point, and use the destination of the total order demand as the transportation destination;

[0067] Step d2: Based on the geographical distribution map of the warehouse, connect the transportation destination with the rth idle equipment location to obtain Y transportation routes, where Y is an integer greater than zero;

[0068] Step d3: Use the pre-built digital twin model to simulate each planned route, obtain the route training data of each planned route, and calculate the path evaluation coefficient Xp of each planned route based on the route training data. y ; The route training data includes the route length and the transport speed of the rth idle device of each planned route; the path evaluation coefficient Xp y The calculation formula is:

[0069] Xp y =Lc r ×σ 1 +YS r ×σ 2 ;

[0070] In the formula, Xp y is the path evaluation coefficient, Lc r is the route length, YS r is the transport speed of the rth idle device, σ 1 and σ 2 is a correction factor greater than zero.

[0071] Step d4: sort the path evaluation coefficients of each planned route from small to large, take the transportation path corresponding to the minimum path evaluation coefficient as the gth planned route, and set r = r + 1, n = n + 1; g = 1, 2, ..., G;

[0072] Step d5: Repeat steps d1 to d4 until r=R and n=N, then end the loop, count the G planned routes for each warehouse, and obtain the best planned route.

[0073] In a second aspect, the present invention provides a cross-regional storage facility collaborative operation system, which is used to implement the above-mentioned cross-regional storage facility collaborative operation method, including:

[0074] A determination module is used to obtain the operation evaluation coefficients of M storage devices in the nth storage, and determine the operation status of the M storage devices based on the operation evaluation coefficients to obtain R idle devices;

[0075] The order allocation module is used to obtain the inventory status of N warehouses and the total order demand, allocate the total order demand based on the inventory status of N warehouses, and obtain the sub-order quantity of the nth warehouse;

[0076] The scheduling module determines the optimal storage equipment combination based on the sub-order quantity of the nth warehouse, R idle equipment and the delivery destination, determines the optimal transportation path based on the optimal storage equipment combination, and completes the scheduling of the total order demand based on the optimal storage equipment combination and the optimal transportation path.

[0077] Technical effects and advantages of the present invention:

[0078] 1. The present invention ensures the optimal resource allocation of cross-regional storage facilities by comprehensively considering the storage equipment operation evaluation coefficient, inventory status, order demand and transportation cost. The fault analysis model is used to predict the equipment status, effectively avoid resource waste caused by equipment failure, and improve equipment utilization and task completion efficiency.

[0079] 2. The present invention also uses a digital twin model and a path evaluation coefficient to dynamically optimize the transportation path, combining the real-time distribution of the equipment location and the transportation target location to quickly generate the optimal transportation path. The present invention shortens delivery time, reduces transportation costs, and improves order response speed, helping cross-regional logistics collaboration to operate efficiently. BRIEF DESCRIPTION OF THE DRAWINGS

[0080] Figure 1 This is a flow chart of the cross-regional storage facility collaborative operation method of Example 1;

[0081] Figure 2 This is a flow chart of a method for obtaining operation evaluation coefficients of M storage equipment in Example 1;

[0082] Figure 3 This is a flow chart of a method for determining an optimal transportation path based on an optimal storage equipment combination in Example 1;

[0083] Figure 4 This is a schematic diagram of the structure of the cross-regional warehousing facility collaborative operation system of Example 2. DETAILED DESCRIPTION

[0084] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0085] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0086] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, a first unit may be referred to as a second unit, and a similar second unit may be referred to as a first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0087] Example 1

[0088] See also Figure 1 As shown, this embodiment discloses a method for collaborative operation of cross-regional storage facilities, which is applied to a cloud server, and the cloud server is remotely connected to a management terminal installed in the cross-regional storage equipment, and the method includes:

[0089] Step 1: Obtain the operation evaluation coefficients of M storage equipment in the nth storage, determine the operation status of the M storage equipment based on the operation evaluation coefficients, and obtain R idle equipment;

[0090] It should be noted that: there are N warehouses, which are distributed in different regions, forming a cross-warehouse network. Each warehouse has M storage equipment inside, which is used to perform storage-related operations. Each storage equipment collaborates to complete tasks, such as loading and unloading, transferring or sorting goods. The types of storage equipment include handling robots or loading trucks.

[0091] See also Figure 2 As shown, in implementation, the method for obtaining the operation evaluation coefficients of M storage equipment includes:

[0092] Step a1: Obtain the operation progress of the mth storage device; m=1, 2, ..., M;

[0093] It should be noted that the operation progress indicates the degree of completion of the current task of the storage equipment, and its calculation method is:

[0094] In the formula, JD mIndicates the operation progress of the mth storage equipment, WC m represents the completed task volume of the mth storage equipment, ZR m Represents the total task volume of the mth storage device.

[0095] Step a2: Obtain the equipment failure coefficient of the mth storage equipment in the future time period.

[0096] It should be noted that the method for obtaining the equipment failure coefficient of the mth storage equipment in the future time period includes:

[0097] Step a21: Acquire fault characteristic data of the mth storage device, wherein the fault characteristic data includes amplitude difference, temperature difference, operating power difference, number of faults, and total operating time;

[0098] The amplitude difference, temperature difference and operating power difference are obtained by measuring and calculating the difference between the amplitude of the storage equipment and the normal amplitude reference value, the difference between the operating temperature and the normal temperature reference value, and the difference between the operating power and the normal power reference value through the vibration sensor, temperature sensor and power sensor respectively. The number of failures and the total operating time are obtained through the operation log records of the storage equipment.

[0099] Among them, the calculation formula for the total running time YZS is: Where Y t2 Indicates the stop time, Y t1 represents the start time, T represents the total number of storage equipment, and t represents the operation cycle of the storage equipment.

[0100] Step a22: Input the fault feature data into the pre-built fault analysis model to predict the equipment failure coefficient in the future time period.

[0101] Wherein, the training method of the fault analysis model includes:

[0102] Acquire historical fault training data, wherein the historical fault training data includes multiple groups of fault feature data within a time span and corresponding equipment failure coefficients;

[0103] It should be noted that the specific duration of each group of time spans is determined according to the time spans preset by those skilled in the art.

[0104] The logic for obtaining the equipment failure coefficient in the historical failure training data is as follows:

[0105] Extract the fault feature data from the historical fault training data, perform formula calculation on the fault feature data, and obtain the equipment failure coefficient; the calculation formula is:

[0106]

[0107] In the formula, GZX m represents the equipment failure coefficient of the mth storage equipment, ZFC represents the current amplitude difference, ΔZFC represents the normal amplitude reference value, WC represents the current temperature difference, ΔWC represents the normal temperature reference value, GC represents the current power difference, ΔGC represents the normal power reference value, CS represents the number of failures, YZS represents the total operating time, φ 1 ,φ 2 ,φ 3 and φ 4 is the weight factor,

[0108] The historical fault training data is divided into a fault training set and a fault test set, a regression network model is constructed, the fault feature data in the fault training set is used as the input of the regression network model, the equipment failure coefficient in the fault training set is used as the output of the regression network model, the regression network model is trained to obtain an initial regression network, the training goal is to minimize the sum of the first prediction accuracies, the initial regression network is evaluated using the fault test set, and the initial regression network when the sum of the first prediction accuracies reaches convergence is used as a pre-constructed fault analysis model; the regression network model is an RNN model, a support vector machine regression network model, a linear regression network model or a random forest regression network model.

[0109] Step a3: Formulate the operation progress of the mth storage equipment, the equipment failure coefficient in the future time period, the handling speed and the maximum load capacity to calculate the operation evaluation coefficient. The calculation formula is:

[0110]

[0111] In the formula, YX m represents the operation evaluation coefficient of the mth storage equipment, BS m represents the handling speed of the mth storage device, V max Indicates the maximum handling speed, ZL indicates the current load, CZ indicates the maximum load, β 1 , β 2 , β 3 and β 4 is the weight factor, β 1 +β 2 +β 3 +β 4 =1.

[0112] Step a4: Let m=m+1, repeat steps a1 to a3 until m=M, and obtain the operation evaluation coefficients of M storage equipment.

[0113] It should be noted that the higher the value of the operation evaluation coefficient, the better.

[0114] In implementation, the method for determining the operating status of M storage devices based on the operating evaluation coefficient to obtain R idle devices includes:

[0115] Step s1: preset an evaluation coefficient threshold value Px, and compare the operation evaluation coefficient of the mth storage equipment with the preset evaluation coefficient threshold value Px;

[0116] If YX m ≥Px, then mark the mth storage device as idle;

[0117] If YX m <Px, the mth storage equipment is marked as running.

[0118] Step s2: Let m=m+1, repeat step s1 until m=M, obtain the operating status of M storage devices, count the number of storage devices in the idle state, and mark them as R idle devices.

[0119] Step 2: Obtain the inventory status and total order demand of N warehouses, allocate the total order demand based on the inventory status of N warehouses, and obtain the sub-order quantity of the nth warehouse;

[0120] In implementation, the method for obtaining the inventory status of N warehouses includes:

[0121] Obtain basic information about inventory items, including quantity, type, volume, and weight of goods; use IoT sensors or barcode / RFID technology to monitor inventory items in real time and record basic information about inventory items. It should be noted that regular inventory counts are conducted, combining historical data with a real-time update mechanism to ensure the accuracy of inventory status.

[0122] The formula for each storage status is expressed as:

[0123] KC n ={S n |n=1,2,...,N};

[0124] The inventory status of the nth warehouse is:

[0125] S n ={(P 1 ,Q 1 ),(P 2 ,Q 2 ),...,(P K ,Q K )};

[0126] In the formula, Sn Indicates the inventory status of the nth warehouse, P K represents the Kth commodity type, Q K Indicates the inventory quantity of the Kth product type, KC n Represents each repository status.

[0127] In implementation, the methods for obtaining the total order demand include:

[0128] It should be noted that: the demand data of all orders within a preset time period is read from the order management system, and the total demand is counted according to the type and quantity of goods.

[0129] Aggregate all order demands to obtain the total order demand, which is expressed as:

[0130] Dx={(P 1 ,D 1 ),(P 2 ,D 2 ),...,(P K ,D K )};

[0131]

[0132] In the formula, Dx represents the total order demand, D K Indicates P K The total demand for the corresponding product, J is the total number of orders, D i,k Indicates that P in the jth order K The demand for the corresponding product.

[0133] In implementation, the method for allocating the total order demand based on the inventory status of N warehouses includes:

[0134] Step b1: Determine the priority weight W of the nth warehouse n ;

[0135] Among them, determine the priority weight W of the nth warehouse n The methods include:

[0136] Step b01: Obtain the transportation distance between the storage destination and the nth storage;

[0137] Step b02: Obtain the average transportation speed, historical response rate mean, and inventory update speed of the nth warehouse;

[0138] Step b03: According to the transportation distance, average handling speed, historical response rate average and inventory update speed, the priority weight W of each warehouse is obtained by formula calculation. n ;

[0139] Wn =JL×μ 1 +Bp×μ 2 +XY×μ 3 +GS×μ 4 ;

[0140] Where W n represents the priority weight within the nth warehouse, JL represents the transportation distance, Bp represents the average handling speed, XY represents the historical response rate mean, GS represents the inventory update speed, μ 1 , μ 2 , μ 3 and μ 4 is the corresponding weight factor, μ 1 +μ 2 +μ 3 +μ 4 =1.

[0141] It should be noted that before formulating the calculation of transportation distance, average handling speed, historical response rate mean and inventory update speed, the indicator values ​​of each warehouse are standardized to ensure that data of different dimensions can be integrated.

[0142] Step b2: Get the transportation cost C of the nth warehouse n ;

[0143] Among them, the method for obtaining the transportation cost of the nth warehouse includes:

[0144] Step b11: Acquire cost characteristic data, wherein the cost characteristic data includes basic cost, distance cost, time cost and dynamic cost;

[0145] It should be noted that the basic fee is the starting fee for transportation activities, which is obtained through supplier quotations or contracts; the distance fee is the fee charged per unit distance based on the transportation distance, which is obtained based on GIS data and unit price tables; the time fee is the fee charged per unit time based on the time required for transportation, which is a combination of real-time traffic and time unit prices; the dynamic fee is the additional fee caused by fuel prices, temporary scheduling needs or other unpredictable factors, and is dynamically adjusted by obtaining real-time fuel price data and other data.

[0146] Step b12: Formulate the cost characteristic data to obtain the transportation cost of the nth warehouse; the calculation formula is:

[0147] C n =C q +(C d ×DX)+(C s ×Sc)+C f ;

[0148] In the formula, C nrepresents the transportation cost of the nth warehouse, C q Indicates the basic cost, C d represents the distance cost, DX represents the shortest route distance from the warehouse to the destination, C s represents the time cost, Sc represents the time used for transportation, C f Represents dynamic data, such as fuel prices or vehicle idle charges.

[0149] Step b3: Priority weight W for the nth warehouse n and transportation cost C n Perform a formula calculation to obtain the sub-order quantity of the nth storage allocation. The calculation formula is:

[0150]

[0151] Where D n,k represents the sub-order quantity assigned to the nth warehouse, W n represents the priority weight of the nth warehouse, C n W represents the transportation cost from the nth warehouse to the target order destination; i represents the priority weight of the i-th warehouse, C i represents the transportation cost from the i-th warehouse to the target order destination, Q i,k Represents the product P in the i-th warehouse K The inventory volume, D k Indicates the inventory quantity of the k-th product type, k≤K.

[0152] It should be noted that the priority weight of the nth warehouse is determined by geographical location, operational efficiency, etc.

[0153] Step 3: Based on the sub-order quantity of the nth warehouse, R idle equipment and the delivery destination, determine the best warehouse equipment combination, determine the best transportation path based on the best warehouse equipment combination, and complete the scheduling of the total order demand according to the best transportation path;

[0154] In practice, methods for determining the best storage equipment combination include:

[0155] Step c1: obtaining priority feature data of the rth idle equipment based on the sub-order quantity of the nth warehouse, wherein the priority feature data includes the loading capacity, idle time ratio and transportation distance weight of the rth idle equipment;

[0156] The loading capacity of the rth idle device indicates the maximum weight or volume of goods that the rth idle device can carry at one time, and the actual carrying capacity of the device is detected regularly by sensors. The idle time ratio refers to the proportion of the rth idle device in an idle state within a period of time, reflecting the availability of the device, and the task completion time and idle time are counted from the device's scheduling log; the transportation distance weight refers to the impact of the distance between the current location of the device and the delivery destination on the scheduling decision; the real-time location of the device is obtained using the warehouse management system and GPS positioning system.

[0157] Step c2: Perform formula calculation based on the priority feature data to obtain the priority evaluation coefficient;

[0158]

[0159] Where P r represents the priority evaluation coefficient of the rth idle device; L r represents the loading capacity of the rth idle device; T r represents the idle time ratio of the rth idle device, D r represents the transportation distance weight of the rth idle equipment, γ 1 , γ 2 , γ 3 is the corresponding weight factor, γ 1 +γ 2 +γ 3 =1;L max Indicates the maximum loading capacity of the rth idle device.

[0160] Step c3: Initialize the remaining sub-order volume: S n =Q n ;

[0161] Traverse the idle devices r and assign tasks according to priority: Allocation amount: ΔQ r =min(L r ,S n );

[0162] Update the remaining order quantity S' n :S' n =S n -ΔQ r ;

[0163] If S' n =0, then stop allocating;

[0164] It should be noted that: S n represents the remaining sub-order quantity, ΔQ r represents the number of sub-orders assigned to the rth idle device; Q nIndicates the initial sub-order quantity of the nth warehouse; n represents the warehouse number, ranging from 1 to N; min(L r ,S n ) means selecting the minimum value of the order quantity that the current device can process and the remaining order quantity.

[0165] Step c4: Output the allocation of each device in the nth warehouse, and set n=n+1, repeat steps c1-c4 until n=N, then end the loop, count the space equipment in the N warehouses, and obtain the best warehouse equipment combination.

[0166] For example, assume that there are 3 idle devices in the first warehouse, and their parameters are as follows:

[0167]

[0168] Calculate the priority according to the above formula and allocate the tasks according to the sorting until the sub-order quantity is fully allocated.

[0169] This step assigns tasks one by one in order of priority. At each assignment, the loading capacity of the equipment is compared with the remaining order volume to ensure that the equipment is not overloaded and the warehouse orders can be completed as efficiently as possible.

[0170] See also Figure 3 As shown, in implementation, the method for determining the optimal transportation path based on the optimal storage equipment combination includes:

[0171] Step d1: Obtain a geographical distribution map of the nth warehouse, and based on the geographical distribution map, use the location of the rth idle equipment in the nth warehouse as the transportation point, and use the destination of the total order demand as the transportation destination;

[0172] Step d2: Based on the geographical distribution map of the warehouse, connect the transportation destination with the rth idle equipment location to obtain Y transportation routes, where Y is an integer greater than zero;

[0173] It should be noted that: the geographical distribution map of the storage is pre-stored in the cloud server, and the geographical distribution map of the storage at least includes the length data of each line segment, the number of storage equipment in each storage, and the location of each storage equipment;

[0174] For example: Assume that A is the location of the transport destination, that is, A is the transport destination; B is the location of the rth idle device, that is, B is the transport point; Assume that the transport destination is connected to the transport point, and three planned routes Y1, Y2 and Y3 are obtained, so at this time Y=3;

[0175] Step d3: Use the pre-built digital twin model to simulate each planned route, obtain the route training data of each planned route, and calculate the path evaluation coefficient Xp of each planned route based on the route training data. y ; The route training data includes the route length and the transport speed of the rth idle device of each planned route; the path evaluation coefficient Xp y The calculation formula is:

[0176] Xp y =Lc r ×σ 1 +YS r ×σ 2 ;

[0177] In the formula, Xp y is the path evaluation coefficient, Lc r is the route length, YS r is the transport speed of the rth idle device, σ 1 and σ 2 is a correction factor greater than zero.

[0178] It should be understood that the smaller the path evaluation coefficient, the better the transportation path.

[0179] Step d4: sort the path evaluation coefficients of each planned route from small to large, take the transportation path corresponding to the minimum path evaluation coefficient as the gth planned route, and set r = r + 1, n = n + 1; g = 1, 2, ..., G;

[0180] Step d5: Repeat steps d1 to d4 until r=R and n=N, then end the loop, count the G planned routes for each warehouse, and obtain the best planned route.

[0181] This embodiment ensures the optimal resource allocation of cross-regional storage facilities by comprehensively considering the storage equipment operation evaluation coefficient, inventory status, order demand and transportation cost. The fault analysis model is used to predict the equipment status, effectively avoid resource waste caused by equipment failure, and improve equipment utilization and task completion efficiency.

[0182] This embodiment also uses a digital twin model and path evaluation coefficient to dynamically optimize the transportation path, combining the real-time distribution of equipment locations and transportation target locations to quickly generate the optimal transportation path. This method significantly shortens delivery time, reduces transportation costs, and improves order response speed, helping cross-regional logistics collaboration to operate efficiently.

[0183] Example 2

[0184] See also Figure 4As shown, this embodiment provides a cross-regional storage facility collaborative operation system, the system is applied to a cloud server, the cloud server is remotely connected to a management terminal installed in a cross-regional storage facility, the system includes: a determination module, an order allocation module and a scheduling module; each module is connected by wire and / or wireless means to achieve data transmission between modules;

[0185] A determination module is used to obtain the operation evaluation coefficients of M storage equipment in the nth storage, determine the operation status of the M storage equipment based on the operation evaluation coefficients, and obtain R idle equipment;

[0186] The order allocation module is used to obtain the inventory status of N warehouses and the total order demand, allocate the total order demand based on the inventory status of N warehouses, and obtain the sub-order quantity of the nth warehouse;

[0187] The scheduling module determines the optimal storage equipment combination based on the sub-order quantity of the nth warehouse, R idle equipment and the delivery destination, determines the optimal transportation path based on the optimal storage equipment combination, and completes the scheduling of the total order demand based on the optimal storage equipment combination and the optimal transportation path.

[0188] The formulas involved in the above are all calculated by removing dimensions and taking their numerical values. They are a formula that is closest to the actual situation obtained by collecting a large amount of data and performing software simulation. The weight factors in the formula and the preset thresholds in the analysis process are set by technicians in this field according to actual conditions or obtained by simulating a large amount of data; the size of the weight factor is to quantify each parameter to obtain a specific value for subsequent comparison. The size of the weight factor depends on the amount of sample data and the corresponding processing coefficient initially set by technicians in this field for each group of sample data; as long as it does not affect the proportional relationship between the parameter and the quantized value.

[0189] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0190] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A cross-regional storage facility collaborative operation method, characterized in that: include: Step 1: Obtain the operation evaluation coefficients of M storage equipment in the nth storage, determine the operation status of the M storage equipment based on the operation evaluation coefficients, and obtain R idle equipment; Step 2: Obtain the inventory status and total order demand of N warehouses, allocate the total order demand based on the inventory status of N warehouses, and obtain the sub-order quantity of the nth warehouse; Step 3: Based on the sub-order quantity of the nth warehouse, R idle equipment and the delivery destination, determine the optimal warehouse equipment combination, determine the optimal transportation route based on the optimal warehouse equipment combination, and complete the scheduling of the total order demand based on the optimal warehouse equipment combination and the optimal transportation route.

2. The cross-regional storage facility collaborative operation method according to claim 1 is characterized in that: The method for obtaining the operation evaluation coefficients of M storage equipment includes: Step a1: Obtain the operation progress of the mth storage device; m=1, 2, ..., M; Step a2: Obtain the equipment failure coefficient of the mth storage equipment in the future time period; Step a3: Formulate the operation progress of the mth storage equipment, the equipment failure coefficient in the future time period, the handling speed and the maximum load capacity to calculate the operation evaluation coefficient; Step a4: let m=m+1, repeat steps a1 to a3 until m=M, and obtain the operation evaluation coefficients of M storage equipment; The method for obtaining the equipment failure coefficient of the mth storage equipment in the future time period includes: Step a21: Acquire fault characteristic data of the mth storage device, wherein the fault characteristic data includes amplitude difference, temperature difference, operating power difference, number of faults, and total operating time; Step a22: Input the fault feature data into the pre-built fault analysis model to predict the equipment failure coefficient in the future time period.

3. The cross-regional storage facility collaborative operation method according to claim 2 is characterized in that: The method for obtaining R idle devices by determining the operating status of M storage devices based on the operating evaluation coefficient includes: Step s1: preset an evaluation coefficient threshold value Px, and compare the operation evaluation coefficient of the mth storage equipment with the preset evaluation coefficient threshold value Px; If YX m ≥Px, then mark the mth storage device as idle; YX m is the operation evaluation coefficient of the mth storage equipment; If YX m <Px, then mark the mth storage equipment as running; Step s2: Let m=m+1, repeat step s1 until m=M, obtain the operating status of M storage devices, count the number of storage devices in the idle state, and mark them as R idle devices.

4. The cross-regional storage facility collaborative operation method according to claim 3 is characterized in that: Methods for obtaining the inventory status of N warehouses include: The formula for each storage status is expressed as: KC n ={S n |n=1,2,...,N}; The inventory status of the nth warehouse is: S n ={(P1,Q1),(P2,Q2),...,(P K ,Q K )}; In the formula, S n Indicates the inventory status of the nth warehouse, P K represents the Kth commodity type, Q K Indicates the inventory quantity of the Kth product type, KC n Represents each repository status.

5. The cross-regional storage facility collaborative operation method according to claim 4 is characterized in that: Methods for obtaining the total order demand include: Read the demand data of all orders within a preset time period from the order management system, and calculate the total demand according to the type and quantity of goods; Aggregate all order demands to obtain the total order demand Dx.

6. The cross-regional storage facility collaborative operation method according to claim 5 is characterized in that: Methods for allocating total order demand based on the inventory status of N warehouses include: Step b1: Determine the priority weight W of the nth warehouse n ; Step b2: Get the transportation cost C of the nth warehouse n ; Step b3: Priority weight W for the nth warehouse n and transportation cost C n Perform formula calculation to obtain the sub-order quantity D of the nth warehouse allocation n,k .

7. The cross-regional storage facility collaborative operation method according to claim 6 is characterized in that: Determine the priority weight W of the nth warehouse n The methods include: Step b01: Obtain the transportation distance between the storage destination and the nth storage; Step b02: Obtain the average transportation speed, historical response rate mean, and inventory update speed of the nth warehouse; Step b03: According to the transportation distance, average handling speed, historical response rate average and inventory update speed, the priority weight W of each warehouse is obtained by formula calculation. n ; The methods for obtaining the transportation cost of the nth warehouse include: Step b11: Acquire cost characteristic data, wherein the cost characteristic data includes basic cost, distance cost, time cost and dynamic cost; Step b12: Formulate the cost characteristic data to obtain the transportation cost C of the nth warehouse n .

8. The cross-regional storage facility collaborative operation method according to claim 7 is characterized in that: Methods for determining the best storage equipment combination include: Step c1: obtaining priority feature data of the rth idle equipment based on the sub-order quantity of the nth warehouse, wherein the priority feature data includes the loading capacity, idle time ratio and transportation distance weight of the rth idle equipment; Step c2: Perform formula calculation based on the priority feature data to obtain the priority evaluation coefficient P r ; Step c3: Initialize the remaining sub-order quantity; Traverse the idle devices r and assign tasks according to priority; Update remaining order quantity; If the remaining order volume is zero, the allocation will stop; Step c4: Output the allocation of each device in the nth warehouse, and set n=n+1, repeat steps c1-c4 until n=N, then end the loop, count the space equipment in the N warehouses, and obtain the best warehouse equipment combination.

9. The cross-regional storage facility collaborative operation method according to claim 8 is characterized in that: Methods for determining the best transportation path based on the best storage equipment combination include: Step d1: Obtain a geographical distribution map of the nth warehouse, and based on the geographical distribution map, use the location of the rth idle equipment in the nth warehouse as the transportation point, and use the destination of the total order demand as the transportation destination; Step d2: Based on the geographical distribution map of the warehouse, connect the transportation destination with the rth idle equipment location to obtain Y transportation routes, where Y is an integer greater than zero; Step d3: Use the pre-built digital twin model to simulate each planned route, obtain the route training data of each planned route, and calculate the path evaluation coefficient Xp of each planned route based on the route training data. y ; The route training data includes the route length and the transportation speed of the rth idle device of each planned route. Step d4: sort the path evaluation coefficients of each planned route from small to large, take the transportation path corresponding to the minimum path evaluation coefficient as the gth planned route, and set r = r + 1, n = n + 1; g = 1, 2, ..., G; Step d5: Repeat steps d1 to d4 until r=R and n=N, then end the loop, count the G planned routes for each warehouse, and obtain the best planned route.

10. A cross-regional storage facility collaborative operation system, used to implement the cross-regional storage facility collaborative operation method according to any one of claims 1 to 9, characterized in that: include: A determination module is used to obtain the operation evaluation coefficients of M storage equipment in the nth storage, determine the operation status of the M storage equipment based on the operation evaluation coefficients, and obtain R idle equipment; The order allocation module is used to obtain the inventory status of N warehouses and the total order demand, allocate the total order demand based on the inventory status of N warehouses, and obtain the sub-order quantity of the nth warehouse; The scheduling module determines the optimal storage equipment combination based on the sub-order quantity of the nth warehouse, R idle equipment and the delivery destination, determines the optimal transportation path based on the optimal storage equipment combination, and completes the scheduling of the total order demand based on the optimal storage equipment combination and the optimal transportation path.

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