Cross-regional warehousing facility collaborative operation method and system

Abstract

The application discloses a cross-regional warehouse facility collaborative operation method and system, and particularly relates to the technical field of warehouse management. The method comprises the following steps: obtaining the operation evaluation coefficient of M warehouse devices in the nth warehouse, determining the operation state of the M warehouse devices based on the operation evaluation coefficient to obtain R idle devices; obtaining the inventory state of N warehouses and the total order demand, distributing the total order demand based on the inventory state of the N warehouses, and obtaining the sub-order quantity of the nth warehouse; and determining the best warehouse device combination based on the sub-order quantity of the nth warehouse, the R idle devices and the distribution destination. The application comprehensively considers the operation evaluation coefficient of the warehouse device, the inventory state, the order demand and the transportation cost, ensures the optimization of the resource allocation of the cross-regional warehouse facility, predicts the device state by using a fault analysis model, effectively avoids the resource waste caused by the device fault, and improves the device utilization rate and the task completion efficiency.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of warehouse management, and more particularly, to a cross-regional warehouse facility collaborative operation method and system. BACKGROUND

[0002] With the development of globalization and e-commerce, the warehouse network of enterprises gradually changes to cross-regional layout to adapt to the market demand in different regions. However, the existing warehouse management system is mostly centered on a single warehouse, lacking unified resource scheduling and collaborative mechanism, resulting in the formation of "island effect" in the operation of each warehouse facility. The traditional system is difficult to coordinate multiple warehouse resources in time, lacking rapid response capability. Overall, the complexity of cross-regional warehouse puts higher requirements on collaborative management technology.

[0003] In the existing method, for example, the patent application with publication number CN116228091A discloses an intelligent warehouse system and warehouse method based on BIM, which includes a data modeling module for updating modeling according to the real-time inventory information of each storage position in the warehouse; a transfer data entry module for entering the transfer data volume information of the warehouse; a warehouse query module for querying the transfer warehouse information in the data modeling module; a deployment data export module for allocating and exporting the storage position information corresponding to the queried transfer warehouse information to the route calling module; and a route calling module for allocating transportation route information according to the exported storage position information. Although the above method can improve the efficiency of warehouse transportation, through research and application of the above method and existing technology, it is found that the above method and existing technology at least have the following defects:

[0004] Due to the differences in geographical location and market demand, some resources are prone to surplus in different warehouses, while others are short of resources, which wastes warehouse capacity and is difficult to meet dynamic demand.

[0005] Therefore, the present application provides a cross-regional warehouse facility collaborative operation method and system. SUMMARY

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

[0007] To achieve the above purpose, the present application provides the following technical scheme:

[0008] In a first aspect, the present application provides a cross-regional warehouse facility collaborative operation method, comprising:

[0009] Step 1: Obtain the running evaluation coefficient of M warehouse devices in the nth warehouse, and determine the running state of the M warehouse devices based on the running evaluation coefficient to obtain R idle devices;

[0010] Step 2: Obtain the inventory status of N warehouses and the total order demand, distribute 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 devices, and the delivery destination, determine the optimal warehouse device combination, determine the optimal transportation path based on the optimal warehouse device combination, and complete the scheduling of the total order demand based on the optimal warehouse device combination and the optimal transportation path.

[0012] Further, the method for obtaining the operation evaluation coefficient of the M warehouse devices comprises:

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

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

[0015] Step a3: Formulate the operation progress of the mth warehouse device, the device failure coefficient in the future time period, the carrying speed, and the maximum load capacity, and calculate the operation evaluation coefficient;

[0016] Step a4: Let m = m + 1, repeat steps a1-a3 until m = M, and obtain the operation evaluation coefficient of the M warehouse devices;

[0017] Wherein, the method for obtaining the device failure coefficient of the mth warehouse device in the future time period comprises:

[0018] Step a21: Obtain the failure characteristic data of the mth warehouse device, wherein the failure characteristic data comprises amplitude difference, temperature difference, operation power difference, failure occurrence times, and total operation time;

[0019] Step a22: Input the failure characteristic data into the pre-constructed failure analysis model to predict the device failure coefficient in the future time period.

[0020] Further, the method for obtaining R idle devices based on the operation evaluation coefficient of the M warehouse devices comprises:

[0021] Step s1: Pre-set the evaluation coefficient threshold Px, and compare the operation evaluation coefficient of the mth warehouse device with the pre-set evaluation coefficient threshold Px;

[0022] If YX m ≥ Px, the mth warehouse device is marked as idle; YX m is the operation evaluation coefficient of the mth warehouse device;

[0023] If YX mIf P

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

[0025] Further, the method for obtaining the inventory state of N warehouses comprises:

[0026] The formula of each warehouse state is:

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

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

[0029] S n ={(P1,Q1),(P2,Q2),...,(P K ,Q K )};

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

[0031] Further, the method for obtaining the total demand quantity of the order comprises:

[0032] Reading the demand data of all orders in a predetermined time period from the order management system, and counting the total demand quantity according to the commodity type and quantity;

[0033] Aggregating all order demands to obtain the total demand quantity Dx, which is expressed by the formula:

[0034] Dx={(P1,D1),(P2,D2),...,(P K ,D K )};

[0035]

[0036] In the formula, Dx represents the total demand quantity of the order, D K represents the total demand quantity of the P K corresponding commodity, J is the total number of orders, and D i,k represents the demand quantity of the P K corresponding commodity in the jth order.

[0037] Further, the method for distributing the total order demand based on the inventory status of N warehouses comprises:

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

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

[0040] Step b3: performing formulaic calculation on the priority weight W n and the transportation cost C n of the nth warehouse to obtain the sub-order quantity D n,k allocated by the nth warehouse, and the calculation formula is:

[0041]

[0042] In the formula, D n,k represents the sub-order quantity allocated by the nth warehouse, W n represents the priority weight of the nth warehouse, C n represents the transportation cost of the nth warehouse to the target order destination; W i represents the priority weight of the ith warehouse, C i represents the transportation cost of the ith warehouse to the target order destination, Q i,k represents the inventory quantity of the product P K in the ith warehouse, and D k represents the inventory quantity of the product corresponding to the kth product type, k≤K.

[0043] Further, the method for determining the priority weight W n of the nth warehouse comprises:

[0044] Step b01: obtaining the transportation distance between the warehouse destination and the nth warehouse;

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

[0046] Step b03: performing formulaic calculation on the transportation distance, the average carrying speed, the historical response rate average, and the inventory update speed to obtain the priority weight W n of each warehouse;

[0047] W n =JL×μ1+Bp×μ2+XY×μ3+GS×μ4;

[0048] In the formula, JL represents the transportation distance, Bp represents the average transportation speed, XY represents the historical response rate average, GS represents the inventory update speed, μ1, μ2, μ3 and μ4 are corresponding weight factors, μ1+μ2+μ3+μ4=1;

[0049] The transportation cost obtaining method of the nth warehouse comprises:

[0050] Step b11: Obtain cost characteristic data, wherein the cost characteristic data comprises a basic cost, a distance cost, a time cost and a dynamic cost;

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

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

[0053] In the formula, C q represents 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 transportation time, and C f represents the dynamic data, including the fuel price or the vehicle empty load cost.

[0054] Further, the method for determining the optimal warehouse equipment combination comprises:

[0055] Step c1: Obtain priority characteristic data of the rth idle equipment based on the sub-order quantity of the nth warehouse, wherein the priority characteristic data comprises the loading capacity, the idle time proportion and the transportation distance weight of the rth idle equipment;

[0056] Step c2: Formulate the priority characteristic data to obtain a priority evaluation coefficient;

[0057]

[0058] In the formula, P r represents the priority evaluation coefficient of the rth idle equipment; L r represents the loading capacity of the rth idle equipment; T r represents the idle time proportion of the rth idle equipment, D r represents the transportation distance weight of the rth idle equipment, γ1, γ2 and γ3 are corresponding weight factors, γ1+γ2+γ3=1, and L max represents the maximum loading capacity of the rth idle equipment.

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

[0060] Distribute the task according to the priority of the idle device r: distribution quantity: 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, stop distribution;

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

[0064] Step c4: output the distribution quantity of each device in the nth warehouse, and let n = n + 1, repeat steps c1-c4 until n = N, end the loop, count the space devices in the N warehouses, and obtain the best warehouse device combination.

[0065] Further, the method for determining the best transportation path based on the best warehouse device combination comprises:

[0066] Step d1: obtain the geographical distribution map of the nth warehouse, and based on the geographical distribution map, take the position of the rth idle device in the nth warehouse as a transportation point, and take the destination of the total order demand as a transportation destination;

[0067] Step d2: based on the geographical distribution map of the warehouse, connect the transportation destination and the position of the rth idle device to obtain Y transportation paths, Y is an integer greater than zero;

[0068] Step d3: simulate each planned route using a pre-constructed digital twin model, obtain route training data for each planned route, and calculate a path evaluation coefficient Xp y for each planned route based on the route training data; the route training data includes route length and transportation speed of the rth idle device for each planned route; the path evaluation coefficient Xp yThe calculation formula is:

[0069] Xp y = Lc r * YS r * σ1+ σ2;

[0070] In the formula, Xp y is a path evaluation coefficient, Lc r is a route length, YS r is a transportation speed of the rth idle device, and σ1 and σ2 are correction factors greater than zero.

[0071] Step d4: The path evaluation coefficients of each planned route are sorted in ascending order of value, the transportation path corresponding to the minimum path evaluation coefficient is taken as the gth planned route, and r=r+1, n=n+1, g=1, 2,..., G.

[0072] Step d5: Steps d1-d4 are repeated until r=R and n=N, and the loop is ended, and the G planned routes of each warehouse are counted to obtain the best planned route.

[0073] In a second aspect, the present application provides a cross-regional warehouse facility collaborative operation system for realizing the cross-regional warehouse facility collaborative operation method, comprising:

[0074] A determination module is configured to obtain operation evaluation coefficients of M warehouse devices in an nth warehouse, determine the operation states of the M warehouse devices based on the operation evaluation coefficients, and obtain R idle devices.

[0075] An order allocation module is configured to obtain inventory states of N warehouses and a total order demand, allocate the total order demand based on the inventory states of the N warehouses, and obtain a sub-order quantity of the nth warehouse.

[0076] A scheduling module is configured to determine a best warehouse device combination based on the sub-order quantity of the nth warehouse, the R idle devices, and a delivery destination, determine a best transportation path based on the best warehouse device combination, and complete scheduling of the total order demand based on the best warehouse device combination and the best transportation path.

[0077] The present application has the following technical effects and advantages:

[0078] 1. The present application comprehensively considers the warehouse device operation evaluation coefficients, inventory states, order demand, and transportation costs to ensure optimal resource allocation of cross-regional warehouse facilities. The device state is predicted by using a fault analysis model to effectively avoid resource waste caused by device failure and improve device utilization and task completion efficiency.

[0079] 2.The method also dynamically optimizes the transportation path by using a digital twin model and a path evaluation coefficient, and quickly generates an optimal transportation path in combination with the real-time distribution of the device location and the transportation target location. The method shortens the delivery time, reduces the transportation cost, and improves the order response speed, thereby assisting efficient operation of cross-regional logistics collaboration. BRIEF DESCRIPTION OF DRAWINGS

[0080] Figure 1 A cross-regional warehouse facility collaborative operation method flowchart of Example 1;

[0081] Figure 2 A method flowchart for obtaining an operation evaluation coefficient of M warehouse devices of Example 1;

[0082] Figure 3 A method flowchart for determining an optimal transportation path based on an optimal warehouse device combination of Example 1;

[0083] Figure 4 A structural schematic diagram of a cross-regional warehouse facility collaborative operation system of Example 2. DETAILED DESCRIPTION

[0084] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of the present application.

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

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

[0087] Example 1

[0088] Referring to Figure 1 As shown in the drawings, the embodiment discloses a cross-territory warehouse facility collaborative operation method, which is applied to a cloud server, the cloud server is in remote communication connection with a management terminal installed in a cross-territory warehouse device, and the method comprises the following steps:

[0089] Step 1: obtaining an operation evaluation coefficient of M warehouse devices in the nth warehouse, determining the operation state of the M warehouse devices based on the operation evaluation coefficient, and obtaining R idle devices;

[0090] It should be noted that: there are N warehouses, the N warehouses are distributed in different regions, and form a cross-warehouse network, each warehouse has M warehouse devices inside, which are used to perform warehouse-related operations. Each warehouse device cooperates to complete tasks, such as jointly completing cargo loading and unloading, transferring or sorting, etc. The types of the warehouse devices include handling robots or loading vehicles.

[0091] Referring to Figure 2 In implementation, the method for obtaining the operation evaluation coefficient of the M warehouse devices comprises the following steps:

[0092] Step a1: obtaining the operation progress of the mth warehouse device; m = 1, 2, …, M;

[0093] It should be noted that: the operation progress represents the completion degree of the current task of the warehouse device, and the calculation method is as follows:

[0094] In the formula, JD m represents the operation progress of the mth warehouse device, WC m represents the completed task amount of the mth warehouse device, and ZR m represents the total task amount of the mth warehouse device.

[0095] Step a2: obtaining a device failure coefficient of the mth warehouse device in a future time period,

[0096] It should be noted that: the method for obtaining the device failure coefficient of the mth warehouse device in the future time period comprises the following steps:

[0097] Step a21: obtaining failure feature data of the mth warehouse device, the failure feature data comprising an amplitude difference value, a temperature difference value, an operation power difference value, a failure occurrence frequency and a total operation time length;

[0098] The amplitude difference value, the temperature difference value and the running power difference value are respectively measured by a vibration sensor, a temperature sensor and a power sensor, and the difference between the amplitude of the warehouse equipment and the normal amplitude reference value, the difference between the running temperature and the normal temperature reference value, and the difference between the running power and the normal power reference value are calculated. The number of failures and the total running time are obtained by the running log record of the warehouse equipment.

[0099] The total running time YZS is calculated according to the formula: In the formula, Y t2 represents the stop time, Y t1 represents the start time, T is the total number of warehouse equipment, and t represents the running cycle of the warehouse equipment.

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

[0101] The training method of the fault analysis model comprises:

[0102] Obtain historical fault training data, which comprises a plurality of sets of fault feature data in a time span and corresponding equipment failure coefficients;

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

[0104] The acquisition logic of the equipment failure coefficient in the historical fault training data is as follows:

[0105] Extract the fault feature data in the historical fault training data, and perform formula calculation on the fault feature data to obtain the equipment failure coefficient. The calculation formula is:

[0106]

[0107] In the formula, GZX m represents the equipment failure coefficient of the mth warehouse equipment, ZFC represents the current amplitude difference value, ΔZFC represents the normal amplitude reference value, WC represents the current temperature difference value, ΔWC represents the normal temperature reference value, GC represents the current power difference value, ΔGC represents the normal power reference value, CS represents the number of failures, YZS represents the total running time, φ1, φ2, φ3 and φ4 are weight factors,

[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 taken as the input of the regression network model, the equipment fault coefficient in the fault training set is taken as the output of the regression network model, the regression network model is trained, an initial regression network is obtained, the sum of the first prediction accuracies is taken as the training target, the initial regression network is evaluated by using the fault test set, and the initial regression network when the sum of the first prediction accuracies reaches convergence is taken as the 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: the operation progress of the mth warehouse equipment, the equipment fault coefficient in a future time period, the carrying speed and the maximum load capacity are formulaically processed, and an operation evaluation coefficient is calculated, and the calculation formula is:

[0110]

[0111] In the formula, YX m represents the operation evaluation coefficient of the mth warehouse equipment, BS m represents the carrying speed of the mth warehouse equipment, V max represents the maximum carrying speed, ZL represents the current load capacity, CZ represents the maximum load capacity, and β1, β2, β3 and β4 are weight factors, and β1+β2+β3+β4=1.

[0112] Step a4: let m=m+1, and repeat steps a1-a3 until m=M, and the operation evaluation coefficients of the M warehouse equipments are obtained.

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

[0114] In the implementation, the method for determining the operation state of the M warehouse equipments based on the operation evaluation coefficients to obtain R idle equipments comprises the following steps:

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

[0116] If YX m ≥Px, the mth warehouse equipment is marked as an idle state;

[0117] If YX m <Px, the mth warehouse equipment is marked as an operation state.

[0118] Step s2: let m = m + 1, repeat step s1 until m = M, obtain the running state of M warehouse equipment, count the number of idle state warehouse equipment, and mark as R idle equipment.

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

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

[0121] Obtain the basic information of the inventory items, including the number, type, volume and weight of the goods; use Internet of Things sensors or bar code / radio frequency identification (RFID) technology to monitor the inventory items in the warehouse in real time, and record the basic information of the inventory items. It should be noted that inventory is checked regularly, combined with historical data and real-time updating mechanism to ensure the accuracy of the inventory state.

[0122] The formula of each warehouse state is:

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

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

[0125] S n ={(P1,Q1),(P2,Q2),...,(P K ,Q K )};

[0126] In the formula, S n represents the inventory state of the nth warehouse, P K represents the Kth type of goods, Q K represents the inventory quantity of the Kth type of goods, and KC n represents each warehouse state.

[0127] In implementation, the method for obtaining the total order demand includes:

[0128] It should be noted that the demand data of all orders in a predetermined time period is read from the order management system, and the total demand is calculated 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={(P1,D1),(P2,D2),...,(P K ,D K )}.

[0131]

[0132] wherein Dx represents the total demand of orders, D K represents the total demand of the corresponding commodity, J is the total number of orders, D K represents the demand of the corresponding commodity in the jth order. i,k K

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

[0134] Step b1: determining the priority weight W n of the nth warehouse.

[0135] The method for determining the priority weight W n of the nth warehouse comprises:

[0136] Step b01: obtaining the transportation distance from the warehouse destination to the nth warehouse.

[0137] Step b02: obtaining the average handling speed, historical response rate average and inventory update speed of the nth warehouse.

[0138] Step b03: performing formulaic calculation according to the transportation distance, average handling speed, historical response rate average and inventory update speed to obtain the priority weight W n of each warehouse.

[0139] W n =JL×μ1+Bp×μ2+XY×μ3+GS×μ4.

[0140] wherein W n represents the priority weight in the nth warehouse, JL represents the transportation distance, Bp represents the average handling speed, XY represents the historical response rate average, GS represents the inventory update speed, μ1, μ2, μ3 and μ4 are corresponding weight factors, and μ1+μ2+μ3+μ4=1.

[0141] It should be noted that the transportation distance, average handling speed, historical response rate average and inventory update speed are standardized before formulaic calculation, so that data of different dimensions can be integrated.

[0142] Step b2: obtaining the transportation cost C n of the nth warehouse.

[0143] The method for obtaining the transportation cost of the nth warehouse comprises:

[0144] ​​Step b11: Obtain cost characteristic data, including base cost, distance cost, time cost and dynamic cost;

[0145] It should be noted that the base cost is the starting cost of the transportation activity, which is obtained through the supplier quotation or contract; the distance cost is the cost charged per unit distance according to the transportation distance, which is obtained based on GIS data and unit price table; the time cost is the cost charged per unit time according to the time required for transportation, combined with real-time traffic and time unit price; the dynamic cost is the additional cost caused by fuel price, temporary scheduling demand or other unpredictable factors, and the data such as fuel price is obtained in real time for dynamic adjustment.

[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 n represents the transportation cost of the nth warehouse, C q represents the base 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, and C f represents dynamic data such as fuel price or vehicle empty load cost.

[0149] Step b3: Formulate the priority weight W n of the nth warehouse and the transportation cost C n to obtain the sub-order quantity allocated to the nth warehouse, and the calculation formula is:

[0150]

[0151] In the formula, D n,k represents the sub-order quantity allocated to the nth warehouse, W n represents the priority weight of the nth warehouse, C n represents the transportation cost of the nth warehouse to the destination order destination; W i represents the priority weight of the i-th warehouse, C i represents the transportation cost of the i-th warehouse to the destination order destination, Q i,k represents the inventory of the commodity P K in the i-th warehouse, and D krepresents the inventory quantity of the corresponding commodity of the kth commodity type, k≤K.

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

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

[0154] In implementation, the method for determining the optimal warehouse device combination includes:

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

[0156] The loading capacity of the rth idle device represents the maximum weight or volume of goods that the rth idle device can carry at a time, and the actual carrying capacity of the device is detected by a sensor regularly. The idle time ratio represents 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 scheduling log of the device; the transportation distance weight represents the influence of the distance between the current location of the device and the delivery destination on the scheduling decision; the real-time position of the device is obtained by using the warehouse management system and the GPS positioning system.

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

[0158]

[0159] In the formula, 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 device, γ1, γ2, γ3 are corresponding weight factors, and γ1+γ2+γ3=1; L max represents the maximum loading capacity of the rth idle device.

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

[0161] Traverse the idle device r to assign tasks according to priority: assignment quantity: ΔQ r = min(Lr S n );

[0162] updating the remaining order quantity S' n : S' n = S n - AQ r ;

[0163] if S' n = 0, stop distribution;

[0164] It should be noted that S n represents the remaining sub-order quantity, AQ r represents the sub-order quantity allocated to the rth idle device; Q n represents the initial sub-order quantity of the nth warehouse; n represents the warehouse number, and the value range is 1 to N; min(L r , S n ) represents selecting the minimum value of the order quantity that the current device can handle and the remaining order quantity.

[0165] Step c4: output the allocation quantity of each device in the nth warehouse, and let n = n + 1, repeat steps c1-c4 until n = N, and the space devices in the N warehouses are counted to obtain the best warehouse device combination.

[0166] Exemplarily, it is assumed that the 1st warehouse has 3 idle devices, and the parameters are as follows:

[0167]

[0168] According to the above formula to calculate the priority, allocate the task quantity according to the sorting until the sub-order quantity is completely allocated.

[0169] This step allocates tasks one by one in priority order. Each time the task is allocated, the loading capacity of the device and the remaining order quantity are compared to ensure that the device will not be overloaded, and the warehouse order can be completed as efficiently as possible.

[0170] Please refer to Figure 3 , in the implementation, the method for determining the best transportation path based on the best warehouse device combination comprises:

[0171] Step d1: obtain the geographical distribution map of the nth warehouse, and take the position of the rth idle device in the nth warehouse as a transportation point and the destination of the total order demand as a transportation destination based on the geographical distribution map;

[0172] Step d2: based on the geographical distribution map of the warehouse, connect the transportation destination and the position of the rth idle device to obtain Y transportation paths, Y is an integer greater than zero;

[0173] It should be noted that the geographic distribution map of the warehouse is pre-stored in the cloud server, and the geographic distribution map of the warehouse at least includes length data of each line, number of warehouse equipment in each warehouse, and position of each warehouse equipment;

[0174] For example, assume that A is the location of the transportation destination, i.e., A is the transportation destination; B is the location of the rth idle device, i.e., B is the transportation point; assume that the transportation destination and the transportation point are connected by a line to obtain three planning routes Y1, Y2 and Y3, and thus Y = 3 at this time.

[0175] Step d3: simulate each planning route by using the pre-constructed digital twin model, obtain route training data of each planning route, and calculate a path evaluation coefficient Xp of each planning route based on the route training data y ; the route training data includes route length and transportation speed of the rth idle device of each planning route; and the calculation formula of the path evaluation coefficient Xp y is as follows:

[0176] Xp y = Lc r × σ1+ YS r × σ2;

[0177] In the formula, Xp y is the path evaluation coefficient, Lc r is the route length, YS r is the transportation speed of the rth idle device, and σ1 and σ2 are correction factors 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 planning route in ascending order of numerical value, take the transportation path corresponding to the smallest path evaluation coefficient as the gth planning route, and let r = r + 1, n = n + 1; g = 1, 2, …, G.

[0180] Step d5: repeat steps d1-d4 until r = R and n = N, end the loop, and count the G planning routes of each warehouse to obtain the best planning route.

[0181] This embodiment considers the warehouse equipment operation evaluation coefficient, inventory status, order demand and transportation cost to ensure that the resource allocation of cross-regional warehouse facilities is optimized. The fault analysis model is used to predict the device state, effectively avoid resource waste caused by device failure, and improve device utilization and task completion efficiency.

[0182] The embodiment also adopts a digital twin model and a path evaluation coefficient to dynamically optimize the transportation path, and quickly generates an optimal transportation path in combination with the real-time distribution of the device position and the transportation target location. This method significantly shortens the delivery time, reduces the transportation cost, and improves the order response speed, thereby assisting efficient operation of cross-regional logistics collaboration.

[0183] Embodiment 2

[0184] Referring to Figure 4 As shown in the drawings, the embodiment provides a cross-regional warehousing facility collaborative operation system, which is applied to a cloud server, the cloud server is in remote communication connection with a management terminal installed in a cross-regional warehousing device, and the system comprises a determination module, an order allocation module, and a scheduling module; each module is connected through wired and / or wireless mode to realize data transmission between the modules.

[0185] The determination module is configured to obtain an operation evaluation coefficient of M warehousing devices in the nth warehousing, determine the operation state of the M warehousing devices based on the operation evaluation coefficient, and obtain R idle devices.

[0186] The order allocation module is configured to obtain the inventory state of N warehouses and the total order demand, allocate the total order demand based on the inventory state of the N warehouses, and obtain the sub-order quantity of the nth warehousing.

[0187] The scheduling module is configured to determine the best combination of warehousing devices based on the sub-order quantity of the nth warehousing, the R idle devices, and the delivery destination, determine the best transportation path based on the best combination of warehousing devices, and complete the scheduling of the total order demand based on the best combination of warehousing devices and the best transportation path.

[0188] The formulas involved in the above embodiments are calculated by removing the dimension and taking the numerical value, and are obtained by software simulation of a large amount of data to obtain a formula closest to the real situation. The weight factor in the formula and the various preset thresholds in the analysis process are set by a person skilled in the art according to the actual situation or obtained by a large amount of data simulation. The size of the weight factor is a specific numerical value obtained by quantifying each parameter for subsequent comparison. The size of the weight factor depends on the number of sample data and the corresponding processing coefficient preliminarily set by the person skilled in the art for each group of sample data. As long as the proportional relationship between the parameters and the quantized numerical value is not affected, it is acceptable.

[0189] The above is only a specific embodiment of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or replacements within the technical scope disclosed in the present application, which should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

[0190] Finally: the above only for the preferred embodiments of the present application, and not for limiting the present application, any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application, should be included in the scope of protection of the present application.

Claims

1. A method for collaborative operation of cross-regional warehousing facilities, characterized in that, include: Step 1: Obtain the operation evaluation coefficients of M storage devices in the nth storage area, and determine the operation status of the M storage devices based on the operation evaluation coefficients to obtain R idle devices; 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 delivery destination, determine the optimal combination of warehouse equipment, determine the optimal transportation route based on the optimal combination of warehouse equipment, and complete the scheduling of the total order demand based on the optimal combination of warehouse equipment and the optimal transportation route. Methods for determining the optimal combination of storage equipment include: Step c1: Obtain the priority feature data of the r-th idle equipment based on the sub-order volume of the n-th warehouse. The priority feature data includes the loading capacity, idle time ratio, and transportation distance weight of the r-th idle equipment. Step c2: Perform formulaic calculations based on priority feature data to obtain priority evaluation coefficients. ; Step c3: Initialize the remaining sub-order quantity; Iterate through the idle devices r and assign tasks according to priority; Update remaining order quantity; If the remaining order quantity is zero, then the allocation will stop; Step c4: Output the allocation of each device in the nth warehouse, and let n=n+1. Repeat steps c1-c4 until n=N and the loop ends. Statistically analyze the space devices in the N warehouses to obtain the optimal combination of warehouse devices. Methods for determining the optimal transportation route based on the optimal combination of warehousing equipment include: Step d1: Obtain the geographical distribution map of the nth warehouse, and based on the geographical distribution map, take the location of the rth idle device in the nth warehouse as the transportation point, and take 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 location of the r-th idle equipment to obtain Y transportation routes, where Y is a positive integer; Step d3: Simulate each planned route using a pre-built digital twin model to obtain route training data for each planned route, and calculate the path evaluation coefficient for each planned route based on the route training data. The route training data includes the route length and the transport speed of the r-th idle device on each planned route. Step d4: Sort the path evaluation coefficients of each planned route in ascending order of value, and take the transportation route corresponding to the smallest path evaluation coefficient as the g-th planned route, and let r=r+1, n=n+1; g=1, 2, ..., G; Step d5: Repeat steps d1-d4 until r=R and n=N, then end the loop and statistically analyze the G planned routes for each warehouse to obtain the optimal planned route.

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

3. The method for collaborative operation of cross-regional warehousing facilities according to claim 2, characterized in that, Methods for determining the operating status of M storage devices and obtaining R idle devices based on operational evaluation coefficients include: Step s1: Preset evaluation coefficient threshold The operational evaluation coefficient of the m-th storage equipment is compared with the preset evaluation coefficient threshold. Compare; like If the m-th storage device is marked as idle, then the m-th storage device will be marked as idle. Let m be the operational evaluation coefficient for the m-th storage facility; like Then the m-th storage device will be marked 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 method for collaborative operation of cross-regional warehousing facilities according to claim 3, characterized in that, Methods for obtaining the inventory status of N warehouses include: The formula for each storage status is expressed as follows: ； The inventory status of the nth warehouse is: ； In the formula, This indicates the inventory status of the nth warehouse. This represents the Kth type of product. This represents the inventory quantity of the product corresponding to the Kth product type. Each warehouse status.

5. The method for collaborative operation of cross-regional warehousing facilities according to claim 4, characterized in that, Methods for obtaining the total order demand include: Retrieves demand data for all orders within a preset time period from the order management system, and calculates the total demand by product type and quantity; Aggregate all order demands to obtain the total order demand. .

6. The method for collaborative operation of cross-regional warehousing facilities according to claim 5, characterized in that, Methods for allocating total order demand based on the inventory status of N warehouses include: Step b1: Determine the priority weight of the nth warehouse. ; Step b2: Obtain the transportation cost of the nth warehouse. ; Step b3: Priority weight for the nth warehouse and transportation costs Perform formulaic calculations to obtain the sub-order quantity for the nth warehouse allocation. .

7. The method for collaborative operation of cross-regional warehousing facilities according to claim 6, characterized in that, Determine the priority weight of the nth warehouse. The methods include: Step b01: Obtain the transportation distance between the destination of the warehouse and the nth warehouse; Step b02: Obtain the average handling speed, historical average response rate, and inventory update speed for the nth warehouse; Step b03: Based on transportation distance, average handling speed, historical average response rate, and inventory update speed, calculate the priority weight of each warehouse using a formulaic approach. ; Methods for obtaining the transportation cost of the nth warehouse include: Step b11: Obtain cost characteristic data, which includes basic costs, distance costs, time costs, and dynamic costs; Step b12: Formulate the cost characteristic data to calculate the transportation cost of the nth warehouse. .

8. A cross-regional warehousing facility collaborative operation system, used to implement the cross-regional warehousing facility collaborative operation method according to any one of claims 1-7, characterized in that, include: The determination module is used to obtain the operation evaluation coefficients of M storage devices in the nth storage, determine the operation status of the M storage devices based on the operation evaluation coefficients, and obtain R idle devices; The order allocation module is used to 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. The scheduling module determines the optimal combination of warehouse equipment based on the sub-order quantity of the nth warehouse, R idle equipment, and delivery destination. Based on the optimal combination of warehouse equipment, it determines the optimal transportation route and completes the scheduling of the total order demand based on the optimal combination of warehouse equipment and the optimal transportation route.

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