A logistics warehousing management system based on digital twin

Abstract

The present invention relates to the field of logistics management technology, and specifically to a logistics warehousing management system based on digital twins, comprising: a warehousing equipment module, used to determine the location of warehousing equipment and the corresponding working status; an order acquisition module, used to obtain order information for inbound and outbound storage, and store the order information in a database; a goods allocation module, used to allocate order information, determine the differences obtained in the logistics area, and set the corresponding goods in the corresponding logistics area according to the order information; a goods tracking module, used to track the current status of the goods, determine the moving speed of each goods, and verify the location point of the corresponding storage equipment based on the moving trajectory of the location point; a tracking compensation module, used to determine the compensation coefficient of the goods allocation according to the output results of the goods allocation module and the goods tracking module, and adjust the goods allocation result based on the compensation coefficient; the logistics efficiency and the utilization rate of the storage space are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of logistics management, and in particular to a logistics warehousing management system based on digital twins. Background Art

[0002] With the improvement of science and technology, logistics and transportation use new equipment and new technologies to improve transportation efficiency and quality. Warehousing management is an important link in the logistics and transportation chain. In-depth research on logistics and warehousing management methods is of great significance to reducing transportation costs and improving transportation efficiency.

[0003] At present, the analysis of warehouse management is mainly aimed at the optimization of warehouses at a single node. By analyzing the warehouse needs of a single enterprise, a central warehouse and a distribution center are established to meet the enterprise's needs for warehouse management. However, with the continuous improvement of the supply chain, more and more enterprises are involved in the product production chain, and their businesses are intertwined. In the process of warehouse management, it is impossible to manage only one enterprise. It is necessary to centrally manage warehouse information to improve management efficiency. At the same time, due to the different types of goods and corresponding labels, the difficulty of management will increase accordingly. When a large number of different types of goods appear, it is impossible to accurately manage them dynamically according to different types of goods, which makes the warehouse system duplicate work when dynamically allocating goods, reducing the efficiency of warehouse management. Summary of the invention

[0004] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0005] As a preferred technical solution of the present invention, a logistics warehousing management system based on digital twins includes:

[0006] The storage equipment module is used to determine the location of storage equipment and the corresponding working status.

[0007] The order acquisition module is used to obtain the order information of incoming and outgoing goods and store the order information in the database.

[0008] The goods distribution module is used to distribute order information, determine the differences obtained in the logistics area, and set the corresponding goods in the corresponding logistics area according to the order information.

[0009] The cargo tracking module is used to track the current status of the cargo, determine the moving speed of each cargo, and verify the location points of the corresponding storage equipment based on the movement trajectory of the location points.

[0010] The tracking compensation module is used to determine the compensation coefficient of cargo allocation according to the output results of the cargo allocation module and the cargo tracking module, and adjust the cargo allocation result based on the compensation coefficient.

[0011] The beneficial effects of the present invention are as follows: 1. The present invention realizes the reasonable distribution of goods and improves the logistics efficiency and the utilization rate of storage space by calculating the regional difference point information of the logistics area.

[0012] Second, the present invention optimizes the task execution sequence and further improves logistics efficiency by determining the task urgency, resource availability and dependencies between tasks under different task types.

[0013] 3. The present invention realizes real-time monitoring of cargo status and timely compensation of abnormal situations through cargo tracking module and tracking compensation module, thereby improving the reliability and accuracy of logistics. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] The present invention is further described below in conjunction with the accompanying drawings and embodiments.

[0015] Figure 1 It is a system framework diagram of a logistics warehousing management system based on digital twins;

[0016] Figure 2 It is a flow chart of the cargo distribution module of the logistics warehousing management system based on digital twin;

[0017] Figure 3 The system framework diagram of another embodiment of a logistics warehousing management system based on digital twins. DETAILED DESCRIPTION

[0018] The embodiments of the present invention are described in detail below. The embodiments described below are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention. If no specific techniques or conditions are specified in the embodiments, the techniques or conditions described in the literature in the art or the product specifications are used.

[0019] See also Figure 1 , a logistics warehousing management system based on digital twin, including:

[0020] Storage equipment module, used to determine the location of storage equipment and the corresponding working status;

[0021] The order acquisition module is used to obtain the order information of incoming and outgoing goods and store the order information in the database;

[0022] The goods allocation module is used to allocate order information, determine the differences obtained in the logistics area, and place the corresponding goods in the corresponding logistics area according to the order information;

[0023] The cargo tracking module is used to track the current status of the cargo, determine the moving speed of each cargo, and verify the location points of the corresponding storage equipment based on the movement trajectory of the location points;

[0024] The tracking compensation module is used to determine the compensation coefficient of cargo allocation according to the output results of the cargo allocation module and the cargo tracking module, and adjust the cargo allocation result based on the compensation coefficient.

[0025] In the present invention, the corresponding cargo data is determined by obtaining the working status of each device in the corresponding warehouse and matching the working status of different devices with the corresponding order data. The cargo data is set according to different logistics area settings, and different cargoes are stored according to classification and entry and exit frequencies. The actual transportation location of each cargo during storage is determined to predict the distribution status of the corresponding area when the cargo arrives at the distribution area, so as to adjust the priority of cargo discharge. At the same time, due to the different entry and exit times and efficiencies of different cargoes, it is necessary to set additional compensation coefficients according to different cargoes, so as to facilitate the adjustment of the transportation status of each cargo on the operation line according to the different cargoes when a large number of types of cargoes enter, and timely adjust the distribution status of cargoes to improve the ability to cope with a large number of multi-type cargoes.

[0026] like Figure 2 As shown, the implementation methods for the different situations of the cargo distribution module include:

[0027] Step S11, select the current logistics area and determine the configuration information of the logistics area, the configuration information includes logistics attributes, cargo types, daily available shipment volume, and daily available incoming cargo volume.

[0028] The logistics attributes indicate the frequency of goods exchange and the average time spent on goods circulation in the current logistics area. The daily shipment volume of goods is determined based on the inventory and estimated storage volume of the corresponding goods to ensure that each type of goods can be continuously transported during the transportation process. The daily incoming goods volume is determined based on the frequency of goods updates. The greater the update frequency, the greater the daily incoming goods volume. The exchange frequency of goods indicates the number and frequency of goods shipment and incoming exchanges in a specific logistics area, reflecting the circulation activity of this logistics area. The update frequency indicates the update speed of the goods inventory, which is usually related to the demand and sales speed of the goods, indicating whether the goods need to be frequently replenished or replaced.

[0029] Step S12, based on the configuration information of multiple logistics areas, convert the configuration information into data features to obtain regional configuration features; take the current logistics area as the center point, take the logistics area closest to the current logistics area as the comparison area, determine the regional difference point information between the comparison area and the current logistics area, and adjust the regional difference point information within the logistics area, and allocate the goods that meet the regional difference point information to the corresponding logistics area.

[0030] Step S13, when the types of goods exceed the preset range, determine the regional configuration characteristics of the adjacent logistics area corresponding to the current logistics area, and select the adjacent logistics area with the largest change value of the regional configuration characteristics, integrate the data of the adjacent logistics area with the largest change value of the regional configuration characteristics and the current logistics area, determine the optimization information of the current logistics distribution, and adjust the cargo distribution according to the optimization information.

[0031] The regional configuration feature is obtained by extracting the corresponding configuration information in the logistics area and selecting the data that can express the current area as the regional configuration feature.

[0032] Specifically, the method of determining the regional difference point information between the logistics area and the comparison area includes:

[0033] According to the exchange frequency of goods and the average time spent on goods circulation, the transportation time cost of goods is determined; according to the inventory and expected storage volume of goods, the daily shipment volume of goods is determined; according to the update frequency of goods, the daily incoming volume of goods is determined; according to the calculated multiple configuration information, the regional difference point information of the logistics area is determined, and the formula is expressed as:

[0034] TTC=EF×T×C t ;

[0035] Among them, TTC is the transportation time cost, EF is the frequency of goods exchange, T is the average time spent on goods circulation, C t is the unit time cost coefficient.

[0036] DSO = min (I, FS) × U;

[0037] Among them, DSO is the daily shippable quantity, I is the inventory of goods, FS is the predicted storage quantity, and U is an adjustment coefficient used to adjust the range of daily shippable quantity, which is determined based on actual shipping capacity and other factors, such as working hours, staffing, etc.

[0038] DRI=UF. / P×C i ;

[0039] DRI is the daily available cargo volume, UF is the cargo update frequency, P is the cargo update cycle (for example, daily, weekly, etc.), C i It is the standard quantity for one update.

[0040] Through the calculation of the above formula, we can know the parameter indicators corresponding to the configuration information of different logistics areas. The comprehensive values ​​of this part of data can reflect the differences between different logistics areas.

[0041] Then the formula for regional difference point information is expressed as:

[0042] LDI=w1×CI1+w2×CI2+...+wn×CIn;

[0043] Among them, LDI is the regional difference point information of the logistics area, CIn is the configuration information corresponding to the logistics area, and wn is the weight of the configuration information of the logistics area.

[0044] The specific ways to obtain optimization information in logistics distribution include:

[0045] Based on the regional difference point information obtained, the logistics area is analyzed, and the average coefficient of cargo distribution is calculated based on the operating status and operation route of the warehousing equipment in the logistics area. Based on the obtained average coefficient, the data of multiple logistics areas are integrated to determine the corresponding optimization information for logistics distribution.

[0046] The average coefficient is used to represent the routes corresponding to the equipment and the transportation time of each cargo on the routes, as well as the distribution of the corresponding routes when all working equipment in a logistics area are transporting goods, so as to determine the variation range of the time, speed and work completion required for the corresponding work of each equipment under the optimal configuration route.

[0047] The specific ways to obtain optimization information in logistics distribution also include:

[0048] Determine the transportation mode and transportation route corresponding to the current logistics area, determine the operating route of the corresponding warehousing equipment on each transportation route based on the acquired transportation mode and transportation route, determine the task completion coefficient of the warehousing equipment based on the operating route, and determine the task completion coefficient based on the average value of the task volume completed by different warehousing equipment on the corresponding operating route. Based on the acquired task completion coefficient, determine multiple judgment indicators corresponding to the goods when they are processed.

[0049] The judgment indicators include equipment route layout indicators under the warehouse layout, accuracy indicators of equipment activities, and equipment utilization indicators. According to the comprehensive calculation results of these judgment indicators, the improvement direction in the optimization information is determined. The determined improvement direction includes dividing the logistics area into multiple target areas according to function, identifying the periodic change law of the judgment indicator value in each target area, connecting the target areas with the largest and smallest changes in judgment indicator values ​​according to the change law of the judgment indicator value, and identifying other target areas corresponding to the position of the target area connection line with the largest and smallest changes in judgment indicator values, and using the parameters with the highest similarity in other target areas as the optimization information of the corresponding logistics area.

[0050] The indicators in the selected judgment indicators are used to represent data related to the accuracy of the device's own operation during runtime and the device utilization rate, and are used to indicate whether the device can accurately complete the configured tasks under the preset scenario.

[0051] The equipment route layout index mainly evaluates the rationality of the layout of equipment in the warehouse or production area, which includes the placement of equipment, the distance between equipment, and the matching degree between equipment and work flow.

[0052] The accuracy index of equipment activity measures the precision and stability of the equipment when performing tasks, which includes the accuracy of equipment positioning, smoothness of operation, stability of repeated operations, etc.

[0053] The equipment utilization index reflects the ratio of the effective use time of the equipment to the total time within a certain period of time, and it measures the extent to which the equipment is fully utilized.

[0054] At the same time, in addition to the above-mentioned contents, for the currently selected judgment indicators, you can also obtain the weight based on the transmission position of the goods in the logistics area, and determine the position weight of the corresponding goods about the discrete trajectory based on the trajectory of each location point in the logistics area to determine the position-related mobile weight. According to the mobile weight of the storage equipment, the utilization rate of the path of the storage equipment at runtime and the optimal configuration path of each storage equipment are determined. Based on the utilization rate of the path of all storage equipment at runtime and the weight sum of the configuration path of each storage equipment, the trajectory of the minimum weight and the corresponding location point is used as the optimal configuration path of the corresponding storage equipment.

[0055] In one embodiment of the present invention, the current cargo is tracked and the movement of the cargo is predicted to determine the movement analysis of the cargo after the prediction. The specific implementation of the cargo tracking module includes:

[0056] Determine the movement trajectory of the storage equipment, determine the starting point and end point of the storage equipment task according to the execution task of the storage equipment, determine the movement trajectory of the storage equipment during the task, traverse the logistics area corresponding to each storage equipment during the movement process, determine the corresponding parameters of the storage equipment when each task is executed, and according to the preset matching items, convert multiple storage equipment into label groups according to the work tasks, identify each matching condition in the label group until all matching conditions are entered, and determine the location point of the storage equipment, fix the corresponding location point to determine whether the movement trajectory of the location point meets the first preset rule, and determine the second preset rule corresponding to the storage equipment and the logistics area.

[0057] The matching items and matching conditions are pre-set and are used to divide the storage equipment into multiple different tag groups according to the working parameters of the storage equipment, and to determine whether the data in the current tag group meets the processing specification requirements.

[0058] The first preset rule is that the moving trajectory of the position point satisfies the preset trajectory and the overlapping part of the moving trajectory of the position point is less than the preset length.

[0059] The first preset rule is used to identify whether repeated movement occurs and whether a trajectory change occurs.

[0060] The second preset rule is used to find the cargo space allocation plan with the lowest cost based on the Hungarian algorithm, determine whether the cargo space allocation plan with the lowest cost for the current storage equipment is less than the preset cost, and optimize the corresponding costs of cargo tracking and movement based on the cargo space allocation plan with the lowest cost to optimize the control and management of warehousing.

[0061] Specifically, the second preset rule is to determine whether the cargo space allocation plan corresponding to different location points in the logistics area is the one with the lowest cost. If it is the cargo space allocation plan with the lowest cost, the second preset rule is satisfied. Otherwise, the corresponding location points are adjusted until the cargo space allocation plan with the lowest cost is obtained.

[0062] The method of obtaining the minimum cost cargo space allocation plan includes:

[0063] Consider the cargo locations in the warehouse and the goods to be stored as two sets of bipartite graphs, and construct a cost matrix. The rows of the matrix represent the goods, and the columns of the matrix represent the cargo locations. Each element in the matrix represents the cost of storing specific goods in a specific cargo location. The cost is expressed as the average coefficient and task completion coefficient calculated in the above steps. According to the Gini impurity, the calculated average coefficient and task completion coefficient are selected.

[0064] Set the matching set to an empty set, start from the unmatched cargo vertex, and determine an augmenting path. The augmenting path is a path from the unmatched cargo vertex to the unmatched cargo location vertex, and the edges belonging to the matching set and the edges that do not belong to the matching set appear alternately on the path. If an augmenting path is found, then all the edges on the augmenting path are reversed, that is, the matched edges become unmatched, and the unmatched edges become matched, and the updated matching set is used as the new matching set. Repeat the operation until no augmenting path is found.

[0065] The matching set that has been matched is output, and the matching pairs in the matching set are the cargo space allocation plan with the lowest cost.

[0066] In the bipartite graph, the cargo vertex represents the cargo to be allocated. Each cargo is regarded as a vertex and represented by a node in the graph. The number of cargo vertices is equal to the number of cargo to be allocated. The cargo location vertex represents the available cargo locations in the bipartite graph. Each cargo location is regarded as a vertex and also represented by a node in the graph.

[0067] At the same time, for the average coefficient and task completion coefficient calculated by Gini impurity, the corresponding coefficient values ​​are selected by evaluating the values ​​of the average coefficient and the task completion coefficient, such as the degree of confusion of cargo distribution, the degree of confusion of equipment utilization, and the degree of confusion of task completion. By identifying these degree values, the average coefficient and the task completion coefficient are converted into corresponding values ​​to determine the cost of moving the cargo to the cargo location. The distribution of cargo may be uneven at the cargo location in the warehouse or logistics center. Gini impurity can measure the degree of confusion of cargo types between different cargo locations, which helps to identify which cargo locations have more mixed cargo distribution and need to be optimized. Logistics area The utilization rates of working equipment within a warehouse may vary. Gini impurity can help analyze the distribution of equipment utilization, thereby identifying which equipment has too high or too low utilization rates, so as to make adjustments and optimizations. Different storage equipment may have different tasks completed on corresponding operating routes. Gini impurity can evaluate the degree of confusion in task completion, revealing which equipment or routes have more concentrated or dispersed task completion, and provide managers with a basis for optimizing task allocation. According to the calculated average coefficient and task completion coefficient Gini impurity, the correlation coefficient between the two coefficients is determined, and the corresponding correlation coefficient is used as the cost of identifying goods at a specific cargo location.

[0068] According to the cargo space allocation plan with the lowest cost, the operation routes and work tasks of the storage equipment in the tracked logistics area are determined; when the work tasks on a single operation route need to be allocated to multiple storage equipment, the efficiency of each storage equipment in completing the task is determined, and the most efficient task allocation plan is determined based on the Hungarian algorithm.

[0069] The above-mentioned task allocation plan can be implemented in the form of a minimum cost cargo location plan, which will not be explained in detail here.

[0070] In this embodiment, the goods are tracked by calculating the goods allocation plan and the task allocation plan, and the corresponding allocation tasks of the goods when they are moved are determined, so as to facilitate real-time tracking of the task progress and the implementation method of the task.

[0071] In one embodiment of the present invention, after obtaining the output results of the cargo allocation module and the cargo tracking module, the tracking compensation module combines the output contents of the cargo and determines the compensation coefficient based on the allocation when the cargo is allocated.

[0072] The output results of the cargo allocation module and the cargo tracking module are in the form of problem decomposition. The problem of cargo allocation according to regions in the cargo allocation module and the problem of how to configure the cargo to the corresponding cargo positions in the cargo tracking module are adjusted. They are divided into cargo area adjustment and task execution order within the area to determine the storage location and efficiency of the corresponding cargo when the cargo is stored, so as to complete the compensation for cargo allocation.

[0073] Therefore, the compensation coefficient of the tracking compensation module includes a cargo area adjustment parameter based on the cargo area adjustment and an intra-regional task execution order parameter based on the intra-regional task execution order.

[0074] For cargo area adjustments, a collaborative optimization approach can be used to obtain cargo characteristics (such as size, weight, storage requirements, properties, etc.), regional capacity, cargo turnover rate and regional priority of the cargo, to find implementation plans for adjustments in different areas based on regional characteristics and cargo characteristics.

[0075] The cargo area adjustment parameter for cargo area adjustment can be expressed as:

[0076] The cargo characteristics are converted into risk coefficients, which are recorded as cargo characteristic risk coefficients. Ordinary cargo can be set to a base value of 1, while flammable, explosive cargo or cargo requiring temperature control can be assigned a higher coefficient, such as 1.5 or 2.0, based on their degree of danger or the complexity of their special needs. The value range is 1.0 (base value, representing ordinary cargo) to 3.0 (high-risk or high-special-need cargo).

[0077] The regional capacity is converted into a quantity coefficient of goods that can be stored, which is recorded as the regional capacity coefficient. The quantity value is based on the ratio of the number of remaining cargo spaces in the currently set logistics area to the preset standard quantity. It is used to determine the proportion of the number of remaining cargo spaces in the currently set logistics area. The size of this preset standard quantity can be the ratio of the current cargo demand to the remaining positions, or the ratio of the remaining quantity to the total capacity. The specific ratio used is adjusted according to the actual situation.

[0078] The cargo turnover rate is converted into a turnover rate coefficient, which is recorded as the cargo turnover rate coefficient. The cargo turnover rate can be calculated based on historical data and converted into a coefficient to reflect the turnover speed of the cargo. For example, the value range can be set to, assuming that the benchmark turnover rate is once a month, the corresponding turnover rate coefficient is 1.0. A turnover rate higher than the benchmark can be assigned a higher coefficient, such as 1.2, 1.5, etc.; a turnover rate lower than the benchmark can be assigned a lower coefficient, such as 0.8, 0.6, etc.

[0079] The coefficient of setting the regional priority is recorded as the regional priority coefficient; for example, the base priority can be set to 1.0 (representing a normal priority area), a high priority area can be assigned a higher coefficient, such as 1.5, 2.0, etc.; a low priority area can be assigned a lower coefficient, such as 0.7, 0.5, etc.

[0080] A weight is set for each coefficient, and then the weighted score of each type of cargo in each logistics area is calculated. The calculated weighted score is used as the cargo area adjustment parameter.

[0081] Therefore, the coefficients in the cargo area adjustment parameters are expressed as:

[0082] The formula for the cargo characteristic risk factor RC is:

[0083] RC = reference value + (goods characteristic addition coefficient - 1) × reference value;

[0084] The base value is usually set to 1, and the cargo characteristic bonus coefficient is set according to the cargo's flammability, explosiveness or temperature control requirements, ranging from 1.0 (ordinary cargo) to 3.0 (high-risk cargo).

[0085] The regional capacity coefficient CC is expressed as:

[0086] Formula a:

[0087] CC = remaining cargo space / preset standard quantity;

[0088] The number of remaining cargo spaces is the number of cargo spaces remaining in the currently set logistics area. The preset standard quantity can be set based on the historical average demand, total regional capacity or other business logic.

[0089] Formula b:

[0090] CC = remaining capacity / total capacity;

[0091] This formula directly uses the ratio of remaining capacity to total capacity to calculate the capacity factor.

[0092] The cargo turnover coefficient TRC is expressed as:

[0093] TRC = actual turnover rate / benchmark turnover rate;

[0094] The actual turnover rate is the cargo turnover rate calculated based on historical data. The benchmark turnover rate can be set to once a month, and the corresponding turnover rate coefficient is 1.0.

[0095] The regional priority coefficient PC is expressed as:

[0096] PC = priority bonus coefficient × base priority;

[0097] The base priority is usually set to 1.0 (representing a normal priority area), and the priority bonus coefficient is set according to the priority of the area. The bonus coefficient of a high priority area is greater than 1, and the bonus coefficient of a low priority area is less than 1.

[0098] For the cargo area adjustment considering the whole, the cargo area adjustment parameter is expressed as:

[0099]

[0100] Among them, RC is the cargo characteristic risk coefficient, CC is the regional capacity coefficient, TRC is the cargo turnover rate coefficient, PC is the regional priority coefficient, r1, r2, r3, and r4 are the weights corresponding to the cargo characteristic risk coefficient, regional capacity coefficient, cargo turnover rate coefficient, and regional priority coefficient, respectively.

[0101] For the execution order of tasks within the region, obtain the task type (such as warehousing, outbound, transfer, etc.), task urgency, resource availability and dependencies between tasks.

[0102] The intra-region task execution order parameter about the intra-region task execution order can be expressed as:

[0103] Determine task urgency, resource availability, and dependencies between tasks for different task types;

[0104] The tasks in the region are constructed into a directed graph based on task types and dependencies, and the weight of each node in the directed graph is determined according to task urgency and resource availability. The order of task execution is determined using a graph sorting algorithm, and the calculation results of the directed graph for different task types are used as the task execution order parameters in the current region.

[0105] When processing directed graphs, choose a suitable graph sorting algorithm, such as topological sorting, which can output a linear sequence of vertices of a directed acyclic graph (DAG), satisfying that for each directed edge (u, v), u appears before v in the sequence.

[0106] Take the constructed directed graph and node weights as input, apply topological sorting or other graph sorting algorithms, and obtain a task execution order that satisfies the dependencies and takes the weights into account.

[0107] During the actual execution process, the weight and execution order of tasks can be dynamically adjusted based on real-time feedback and resource changes.

[0108] Assume the following tasks and their related information:

[0109] Task A: warehousing task, high urgency, medium resource requirements.

[0110] Task B: Outbound task, depends on the completion of Task A, has medium urgency and low resource requirements.

[0111] Task C: warehouse transfer task, not dependent on other tasks, low urgency and high resource requirements.

[0112] step:

[0113] Construct a directed graph:

[0114] Nodes: A, B, C;

[0115] Edge: A>B (indicates that B depends on A);

[0116] Tasks without dependencies (such as C) are isolated nodes.

[0117] Determine the weights:

[0118] Assume that the urgency weight ranges from 1-10 and the resource availability weight ranges from 1-5.

[0119] A: Urgency 8, resource availability 3, the comprehensive weight is calculated as (8+3) / 2=5.5.

[0120] B: Urgency 5, resource availability 4, the comprehensive weight is calculated as (5+4) / 2=4.5 (taking into account the dependency A).

[0121] C: urgency 2, resource availability 2, the comprehensive weight is calculated as (2+2) / 2=2.

[0122] Topological sort:

[0123] Since B depends on A, A must execute before B.

[0124] C has no dependencies and can be arranged flexibly.

[0125] Depending on the combined weights, you may want to execute the tasks with higher weights first.

[0126] A possible ordering is: A, B, C or A, C, B (if C's resources can be released faster for use by other tasks, C may be executed first).

[0127] Output execution order:

[0128] According to the result of topological sorting, the task execution order is output, for example: A->B->C or A->C->B.

[0129] This process is simplified, and the calculation of weights and comprehensive scores can be customized more complexly based on actual conditions. In practical applications, more factors and constraints may need to be considered.

[0130] While optimizing these two problems, the correlation between the two problems is taken into account. That is, when the cargo area adjustment parameters and the task execution order parameters within the area are in the same scenario, the optimization results of the two problems are weighted calculated to ensure the synergy between the two, and continuously iterate until a stable optimization result is reached. After optimizing the cargo area, the corresponding task allocation can be adjusted in real time and reach the optimal setting.

[0131] In one embodiment of the present invention, Figure 3 As shown, the present invention also includes an adjustment evaluation module, which is used to determine the execution data of the corresponding equipment on the improved line for the task scenario corresponding to the parameters after determining the cargo area adjustment parameters and the task execution sequence parameters in the area, that is, to obtain the cargo transportation speed and the number of cargo transportation configurations on the corresponding line after improvement, and determine the effectiveness of the corresponding processing facilities in different scenarios according to the change in the corresponding cargo after the execution of the task.

[0132] The method for adjusting according to the cargo area adjustment parameter includes: obtaining data of the cargo to be entered, matching the cargo to be entered with the warehoused cargo in categories, determining the location of the cargo corresponding to the matching item in the logistics area when there are identical matching items, and determining the cargo area adjustment parameter of the logistics area, sorting the logistics areas with matching items according to the cargo area adjustment parameter, judging whether the capacity of the logistics area corresponding to the maximum value of the cargo area adjustment parameter is greater than the number of the cargo to be entered, when the capacity of the logistics area corresponding to the maximum value of the cargo area adjustment parameter is greater than the number of the cargo to be entered, allocating the cargo to be entered to the logistics area corresponding to the maximum value of the cargo area adjustment parameter, when the capacity of the logistics area corresponding to the maximum value of the cargo area adjustment parameter is less than the number of the cargo to be entered, storing the cargo to be entered in the corresponding logistics areas in order of the size of the cargo area adjustment parameter; if there are no identical matching items, judging the similarity between the cargo to be entered and the warehoused cargo, determining the logistics area with the greatest similarity between the cargo to be entered and the warehoused cargo categories, and storing the cargo to be entered in the logistics area with the greatest similarity.

[0133] When storing the incoming goods in the logistics area with the greatest similarity, the following conditions must also be met:

[0134] Match the goods to be stored in the warehouse, compare the risk coefficients of the goods characteristics of the goods to be stored in the corresponding logistics area, and store the parts of the goods characteristics risk coefficients of the goods to be stored in the corresponding logistics area if the similarity threshold is greater than the similarity threshold;

[0135] The logistics area for the transportation of goods to be stored should meet the requirement that the change rate of the goods turnover coefficient is small and the value is greater than the preset turnover coefficient;

[0136] When the regional priority coefficient corresponding to the goods to be entered exceeds the preset priority coefficient, the order of goods entering the warehouse will be set in the corresponding logistics area from large to small according to the size of the regional priority coefficient. When the regional priority coefficient corresponding to the goods to be entered is less than the preset priority coefficient, the goods to be entered will be set in the corresponding logistics area from large to small according to the size of the goods area adjustment parameter.

[0137] The method for adjusting according to the task execution sequence parameters in the area includes, after the logistics area for incoming storage is allocated, obtaining the task execution sequence parameters in the area for the goods to be incoming, and determining the working status of the equipment on the corresponding line during the current execution. The obtained working status formula includes: obtaining the corresponding equipment information in the current logistics area, such as determining the width, depth and effective load weight of the corresponding pallet of the current transported goods; the effective load amount indicates the weight that the equipment currently using the pallet to transport the goods can bear; through these values, it can be known that the main transportation route and type of transportation of the current pallet during sorting and transportation.

[0138] Determine the quantity and corresponding ratio range of each picking by the equipment; determine the equipment information passed by the goods during transportation, map the logistics area passed during transportation to the directed graph corresponding to the task execution order parameter in the area, determine the order of execution of the goods in the area, and execute the goods in the order of dependencies between the tasks. When there is no dependency between the tasks of cargo transportation, take a weighted average according to the urgency and completion speed of the tasks corresponding to the goods, and process the corresponding goods from large to small according to the weighted values ​​of the urgency and completion speed of the tasks corresponding to the goods.

[0139] After determining the cargo area adjustment, obtain the change value of the effective load weight of the cargo carried on each pallet, and judge whether the currently calculated change value of the effective load weight is in the normal operating state of the pallet. When the change value of the effective load weight is within the normal range, obtain the moving route of the pallet in the corresponding logistics area, and determine the direction of the change value of the effective load weight on the route. The direction of the change value of the effective load weight is compared based on the numerical difference before and after the adjustment of the logistics area. When the effective load weight is within the normal range and the quantity of a certain type of cargo increases, the direction of the current change value of the effective load weight is positive. On the contrary, if the quantity decreases, it is negative. When the direction of the change value of the effective load weight is positive, determine the growth rate of the task execution efficiency. When the growth rate of the task execution efficiency is greater than the preset growth rate, use the current cargo area adjustment parameters and the task execution sequence parameters in the area as the adjustment parameters of the corresponding area. When the direction of the change value of the effective load weight is negative, determine the largest cargo area adjustment parameters and the task execution sequence parameters in the adjacent area, and use the largest cargo area adjustment parameters and the task execution sequence parameters in the adjacent area as the adjustment parameters of the current area.

[0140] When the change value of the payload weight is not within the normal range, identify the transported cargo information, perform similarity matching on the current transported cargo information and other logistics areas, determine the logistics area corresponding to the median of the similarity matching value, and use the cargo area adjustment parameters and task execution sequence parameters within the area corresponding to the logistics area corresponding to the median of the similarity matching value as the adjustment parameters of the logistics area corresponding to the current cargo.

[0141] In this scenario, the judgment is based on whether the quantity of goods transported within a fixed time period meets certain requirements. If the payload weight is small, it means that the current transported goods are small. If the payload weight is large, it means that more goods are transported. At this time, the transportation amount must be controlled to prevent uneven distribution and imbalance caused by excessive transportation.

[0142] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations on the present invention. A person skilled in the art may make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention and they are still covered by the protection scope of the present invention.

Claims

1. A logistics warehousing management system based on digital twins, characterized in that: include: Storage equipment module, used to determine the location of storage equipment and the corresponding working status; The order acquisition module is used to obtain the order information of incoming and outgoing goods and store the order information in the database; The goods allocation module is used to allocate order information, determine the differences obtained in the logistics area, and place the corresponding goods in the corresponding logistics area according to the order information; A cargo tracking module, used to track the current status of cargo, determine the moving speed of each cargo, and verify the location point of the corresponding storage equipment, determine whether the first preset rule is met based on the moving trajectory of the location point, and determine the second preset rule corresponding to the storage equipment and the logistics area; A tracking compensation module, used to determine a compensation coefficient for cargo allocation according to output results of the cargo allocation module and the cargo tracking module, and adjust the cargo allocation result based on the compensation coefficient; The implementation methods for the differences in the cargo distribution module include: Select the current logistics area and determine the configuration information of the logistics area, which includes logistics attributes, cargo types, daily outgoing cargo volume, and daily incoming cargo volume. Logistics attributes indicate the frequency of cargo exchange and the average time it takes for cargo circulation in the current logistics area. According to the configuration information of multiple logistics areas, the configuration information is converted into data features to obtain regional configuration features; taking the current logistics area as the center point, the logistics area closest to the current logistics area is taken as the comparison area, the regional difference point information between the comparison area and the current logistics area is determined, and the regional difference point information is adjusted within the logistics area, and the goods that meet the regional difference point information are allocated to the corresponding logistics area; When the types of goods exceed the preset range, determine the regional configuration characteristics of the adjacent logistics areas corresponding to the current logistics area, and select the adjacent logistics area with the largest change value of the regional configuration characteristics. Integrate the data of the adjacent logistics area with the largest change value of the regional configuration characteristics and the current logistics area to determine the optimization information of the current logistics distribution, and adjust the distribution of goods according to the optimization information; Methods for determining regional difference point information between the logistics area and the comparison area include: Determine the transportation time cost of goods according to the exchange frequency of goods and the average time spent on goods circulation, determine the daily shipment volume of goods according to the inventory volume and estimated storage volume of goods, and determine the daily incoming volume of goods according to the update frequency of goods; determine the regional difference point information of the logistics area according to the calculated multiple configuration information; The regional difference point information of the logistics area is expressed as follows: TTC=EF×T×C t ; Among them, TTC is the transportation time cost, EF is the frequency of goods exchange, T is the average time spent on goods circulation, C t is the unit time cost coefficient; DSO = min(I, FS) × U; Among them, DSO is the daily shipment volume, I is the inventory volume of the goods, FS is the predicted storage volume, and U is an adjustment factor used to adjust the range of the daily shipment volume; DRI=UF / P×C i ; DRI is the daily available volume, UF is the frequency of goods update, P is the period of goods update, C i It is the standard incoming quantity for one update; The formula for regional difference point information is expressed as: LDI=w1×CI1+w2×CI2+...+wn×CIn; Among them, LDI is the regional difference point information of the logistics area, CIn is the configuration information corresponding to the logistics area, and wn is the weight of the configuration information of the logistics area; The specific ways to obtain optimization information in logistics distribution include: Based on the regional difference point information obtained, the logistics area is analyzed, and the average coefficient of cargo distribution is calculated based on the operating status and operation route of the storage equipment in the logistics area. Based on the obtained average coefficient, the data of multiple logistics areas are integrated to determine the corresponding optimization information for logistics distribution; The average coefficient is used to represent the routes corresponding to the equipment and the transportation time of each cargo on the routes, as well as the distribution of the corresponding routes when all working equipment in a logistics area transport goods; The specific ways to obtain optimization information in logistics distribution also include: Determine the transportation mode and transportation route corresponding to the current logistics area, determine the operation route of the corresponding storage equipment on each transportation route based on the obtained transportation mode and transportation route, determine the task completion coefficient of the storage equipment based on the operation route, the task completion coefficient is determined according to the average value of the task volume completed by different storage equipment on the corresponding operation route, and determine the corresponding multiple judgment indicators when handling the goods for the obtained task completion coefficient; The judgment index includes the equipment route layout index under the warehouse layout, the accuracy index of the equipment activity, and the equipment utilization index. According to the comprehensive calculation results of these judgment indexes, the improvement direction in the optimization information is determined. The determined improvement direction includes dividing the logistics area into multiple target areas according to function, identifying the periodic change law of the judgment index value in each target area, connecting the target areas with the largest and smallest changes in the judgment index value according to the change law of the judgment index value, and identifying other target areas corresponding to the position of the connecting line of the target areas with the largest and smallest changes in the judgment index value, and taking the parameters with the highest similarity in other target areas as the optimization information of the corresponding logistics area; The specific implementation of the cargo tracking module includes: Determine the moving trajectory of the storage equipment, determine the starting point and ending point of the storage equipment task according to the execution task of the storage equipment, determine the moving trajectory of the storage equipment during the task, traverse the logistics area corresponding to each storage equipment during the movement process, determine the corresponding parameters of the storage equipment when each task is executed, and according to the preset matching items, convert multiple storage equipment into a label group according to the work task, identify each matching condition in the label group, until all matching conditions are entered, and determine the location point of the storage equipment, fix the corresponding location point to determine whether the moving trajectory of the location point meets the first preset rule, and determine the second preset rule corresponding to the storage equipment and the logistics area; The first preset rule is that the moving trajectory of the position point satisfies the preset trajectory and the overlapping part of the moving trajectory of the position point is less than the preset length; The second preset rule is used to find the cargo allocation plan with the lowest cost according to the Hungarian algorithm, and determine whether the cargo allocation plan corresponding to different location points in the logistics area is the one with the lowest cost. If it is the cargo allocation plan with the lowest cost, the second preset rule is satisfied. Otherwise, the corresponding location points are adjusted until the cargo allocation plan with the lowest cost is obtained. The minimum cost cargo space allocation scheme in the second preset rule is expressed as: the cargo spaces in the warehouse and the goods to be stored are regarded as two sets of a bipartite graph; Construct a cost matrix, where the rows of the matrix represent goods, the columns of the matrix represent cargo locations, and each element in the matrix represents the cost of storing goods in the cargo location. The cost is expressed as the average coefficient and task completion coefficient calculated in the above steps, and the average coefficient and task completion coefficient calculated are selected according to the Gini impurity; Set the matching set to an empty set, start from the unmatched cargo vertex, and determine an augmenting path. The augmenting path is a path from the unmatched cargo vertex to the unmatched cargo location vertex, and the edges belonging to the matching set and the edges not belonging to the matching set appear alternately on the path. If an augmenting path is found, then all the edges on the augmenting path are reversed, the matched edges become unmatched, and the unmatched edges become matched, and the updated matching set is used as the new matching set. Repeat the operation until no augmenting path is found; Output the completed matching set. The matching pairs in the matching set are the cargo space allocation solutions with the lowest cost. The compensation coefficient of the tracking compensation module includes a cargo area adjustment parameter based on the cargo area adjustment and an intra-regional task execution order parameter based on the intra-regional task execution order; The cargo area adjustment parameter is expressed as: Among them, RC is the cargo characteristic risk coefficient, CC is the regional capacity coefficient, TRC is the cargo turnover coefficient, PC is the regional priority coefficient, r1, r2, r3, r4 are the weights corresponding to the cargo characteristic risk coefficient, regional capacity coefficient, cargo turnover coefficient, and regional priority coefficient, which are used to adjust the weights of the corresponding coefficients of the cargo area adjustment; The execution order parameters of tasks within a region are expressed as: Determine task urgency, resource availability, and dependencies between tasks for different task types; The tasks in the region are constructed into a directed graph based on the task type and dependency relationship, and the weight of each node in the directed graph is determined according to the task urgency and resource availability. The order of task execution is determined using a graph sorting algorithm, and the calculation results of the directed graph for different task types are used as the execution order parameters of tasks in the current region. The adjustment evaluation module is used to determine the execution data of the corresponding equipment on the improved line after determining the cargo area adjustment parameters and the task execution sequence parameters in the area, and determine the effectiveness of the corresponding processing facilities in different scenarios according to the change in the corresponding cargo after the execution of the task; The method for adjusting according to the cargo area adjustment parameter includes: obtaining data of the cargo to be entered, matching the cargo to be entered with the warehoused cargo in categories, determining the location of the cargo corresponding to the matching item in the logistics area when there are identical matching items, and determining the cargo area adjustment parameter of the logistics area, sorting the logistics areas with matching items according to the cargo area adjustment parameter, judging whether the capacity of the logistics area corresponding to the maximum value of the cargo area adjustment parameter is greater than the number of the cargo to be entered, when the capacity of the logistics area corresponding to the maximum value of the cargo area adjustment parameter is greater than the number of the cargo to be entered, allocating the cargo to be entered to the logistics area corresponding to the maximum value of the cargo area adjustment parameter, when the capacity of the logistics area corresponding to the maximum value of the cargo area adjustment parameter is less than the number of the cargo to be entered, storing the cargo to be entered in the corresponding logistics areas in order of the size of the cargo area adjustment parameter; if there are no identical matching items, judging the similarity between the cargo to be entered and the warehoused cargo, determining the logistics area with the greatest similarity between the cargo to be entered and the warehoused cargo categories, and storing the cargo to be entered in the logistics area with the greatest similarity; The method for adjusting according to the task execution sequence parameters in the area includes obtaining the task execution sequence parameters in the area of ​​the goods to be stored, and determining the working state of the equipment on the corresponding line during the current execution. The obtained working state formula includes: obtaining the corresponding equipment information in the current logistics area; Determine the quantity and corresponding ratio range of each picking by the equipment; determine the equipment information passed by the goods during transportation, map the logistics area passed during transportation to the directed graph corresponding to the task execution order parameter in the area, determine the order of execution of the goods in the area, and execute the goods in the order of dependencies between the tasks. When there is no dependency between the tasks of cargo transportation, take a weighted average according to the urgency and completion speed of the tasks corresponding to the goods, and process the corresponding goods from large to small according to the weighted values ​​of the urgency and completion speed of the tasks corresponding to the goods.

2. According to a digital twin-based logistics warehousing management system according to claim 1, it is characterized in that: When storing the incoming goods in the logistics area with the greatest similarity, the following conditions must also be met: Match the goods to be stored in the warehouse, compare the risk coefficients of the goods characteristics of the goods to be stored in the corresponding logistics area, and store the parts of the goods characteristics risk coefficients of the goods to be stored in the corresponding logistics area if the similarity threshold is greater than the similarity threshold; The logistics area for the transportation of goods to be stored should meet the requirement that the change rate of the goods turnover coefficient is small and the value is greater than the preset turnover coefficient; When the regional priority coefficient corresponding to the goods to be entered exceeds the preset priority coefficient, the order of goods entering the warehouse will be set in the corresponding logistics area from large to small according to the size of the regional priority coefficient. When the regional priority coefficient corresponding to the goods to be entered is less than the preset priority coefficient, the goods to be entered will be set in the corresponding logistics area from large to small according to the size of the goods area adjustment parameter.

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