CN118552128B

CN118552128B - Logistics storage management system based on digital twinning

Info

Publication number: CN118552128B
Application number: CN202410726776.5A
Authority: CN
Inventors: 舒坤; 徐铮; 周凯翔
Original assignee: Wuxi Xinjuli Technology Co ltd
Current assignee: Wuxi Xinjuli Technology Co ltd
Priority date: 2024-06-06
Filing date: 2024-06-06
Publication date: 2024-12-20
Anticipated expiration: 2044-06-06
Also published as: CN118552128A

Abstract

The invention relates to the technical field of logistics management, in particular to a logistics warehouse management system based on digital twinning, which comprises a warehouse equipment module, an order acquisition module, a goods distribution module, a goods tracking module, a tracking compensation module and a compensation module, wherein the warehouse equipment module is used for determining the position and the corresponding working state of the warehouse equipment, the order acquisition module is used for acquiring order information of warehouse entry and warehouse exit, storing the order information in a database, the goods distribution module is used for distributing the order information, determining the acquired difference condition in a logistics area, setting corresponding goods in the corresponding logistics area according to the order information, the goods tracking module is used for tracking the condition of the current goods, determining the moving speed of each goods, verifying the position point of the corresponding warehouse equipment and based on the moving track of the position point, and the tracking compensation module is used for determining the compensation coefficient of goods distribution according to the output results of the goods distribution module and the goods distribution result is adjusted based on the compensation coefficient, so that the logistics efficiency and the utilization rate of warehouse space are improved.

Description

Logistics storage management system based on digital twinning

Technical Field

The invention relates to the technical field of logistics management, in particular to a logistics warehouse management system based on digital twinning.

Background

With the improvement of the scientific and technical level, the logistics transportation uses new equipment and new technology to improve the transportation efficiency and the transportation quality, and the warehouse management is taken as an important link in a logistics transportation chain, so that the deep research on the logistics warehouse management method has very important significance for reducing the transportation cost and improving the transportation efficiency.

At present, the analysis of warehouse management mainly aims at the warehouse optimization of a single node, and the warehouse requirement of a single enterprise is analyzed, and then a central warehouse and a distribution center are established, so that the requirement of the enterprise on warehouse management is met. However, with the continuous perfection of the supply chain, more and more enterprises involved in the product production chain carry out business blending with each other, the management can not be carried out only for one enterprise in the process of warehouse management, the warehouse information is required to be managed in a centralized way to improve the management efficiency, meanwhile, the difficulty of management is correspondingly increased due to different commodity types and different corresponding labels in the process of management, when a large number of different types of cargoes appear, the dynamic management can not be accurately carried out according to the different cargo types, so that the warehouse system can repeatedly work when carrying out dynamic distribution on the cargoes, and the efficiency of warehouse management is reduced.

Disclosure of Invention

In order to solve the technical problems, the invention adopts the following technical scheme:

As a preferred technical scheme of the present invention, a logistics warehouse management system based on digital twinning includes:

And the warehousing equipment module is used for determining the position and the corresponding working state of the warehousing equipment.

The order acquisition module is used for acquiring the order information of warehouse entry and warehouse exit and storing the order information into the database.

The goods distribution module is used for distributing order information, determining the difference condition acquired in the logistics area and setting corresponding goods in the corresponding logistics area according to the order information.

And the goods tracking module is used for tracking the current goods, determining the moving speed of each goods, verifying the position point of the corresponding storage equipment and based on the moving track of the position point.

And the tracking compensation module is used for determining a compensation coefficient of cargo distribution according to the output results of the cargo distribution module and the cargo tracking module, and adjusting the cargo distribution result based on the compensation coefficient.

The method has the beneficial effects that firstly, the reasonable distribution of goods is realized by calculating the area difference point information of the logistics area, and the logistics efficiency and the utilization rate of the storage space are improved.

2. According to the invention, the task execution sequence is optimized by determining the task urgency, the resource availability and the dependency relationship among the tasks under different task types, and the logistics efficiency is further improved.

3. According to the invention, through the cargo tracking module and the tracking compensation module, real-time monitoring of cargo states and timely compensation of abnormal conditions are realized, and the reliability and accuracy of logistics are improved.

Drawings

The invention will be further described with reference to the drawings and examples.

FIG. 1 is a system frame diagram of a digital twinning-based logistics warehouse management system;

FIG. 2 is a schematic flow diagram of a cargo distribution module of a digital twinning-based logistics warehouse management system;

fig. 3 is a system framework diagram of another embodiment of a digital twinning-based logistics warehouse management system.

Detailed Description

Embodiments of the present invention are described in detail below. The following examples are illustrative only and are not to be construed as limiting the invention. The examples are not to be construed as limiting the specific techniques or conditions described in the literature in this field or as per the specifications of the product.

Referring to fig. 1, a logistics warehouse management system based on digital twinning includes:

The storage equipment module is used for determining the position and the corresponding working state of storage equipment;

The order acquisition module is used for acquiring order information of warehouse entry and warehouse exit and storing the order information into a database;

The goods distribution module is used for distributing order information, determining the difference condition acquired in the logistics area and setting corresponding goods in the corresponding logistics area according to the order information;

the goods tracking module is used for tracking the current goods, determining the moving speed of each goods, verifying the position point of the corresponding storage equipment and based on the moving track of the position point;

According to the invention, the working states of all the equipment in the corresponding warehouse are obtained, the corresponding goods data are determined by matching the working states of different equipment with the corresponding order data, different goods are stored according to the different setting of the logistics area and the priority of the goods according to the sorting and the frequency of entering and exiting, the distribution condition of the corresponding area when the goods arrive at the distribution area is predicted by determining the actual transportation position of each goods during storage, the priority of the goods emission is adjusted, and meanwhile, due to the different time and efficiency of entering and exiting of different goods, additional compensation coefficients are required to be set according to different goods, so that when a large number of types of goods enter, the transportation condition of each goods on the operation line can be adjusted according to the different types of goods, the distribution condition of the goods can be adjusted in time, and the capability of coping with a large number of types of goods is improved.

As shown in fig. 2, the implementation of the differential case for the cargo allocation module includes:

Step S11, selecting a current logistics area, and determining configuration information of the logistics area, wherein the configuration information comprises logistics attributes, goods types, daily goods output and daily goods input.

The logistics attribute indicates the exchange frequency and the average time spent for the goods circulation in the current logistics area, the daily shipment volume of the goods is determined according to the stock volume and the expected storage volume of the corresponding goods, the condition that each type of goods can be continuously transported in the transportation process is guaranteed, the daily shipment volume of the goods is determined according to the update frequency of the goods, the larger the update frequency is, the larger the daily shipment volume of the goods is, the exchange frequency of the goods indicates the frequency and the frequency of the goods shipment exchange in the specific logistics area, the circulation activity of the logistics area is reflected, the update frequency is the update speed of the goods inventory and is usually related to the demand and the sales speed of the goods, and whether the goods need frequent supplement or replacement is indicated.

And S12, converting the configuration information into data characteristics according to the configuration information of a plurality of logistics areas to obtain area configuration characteristics, taking the current logistics area as a center point, taking the logistics area closest to the current logistics area as a comparison area, determining area difference point information between the comparison area and the current logistics area, adjusting the area difference point information in the logistics area, and distributing cargoes meeting the area difference point information to the corresponding logistics area.

And S13, when the cargo types exceed the preset range, determining the regional configuration characteristics of the adjacent logistics regions corresponding to the current logistics region, selecting the adjacent logistics region with the largest regional configuration characteristic change value, integrating the data of the adjacent logistics region with the largest regional configuration characteristic change value and the current logistics region, determining the optimization information of the current logistics distribution, and adjusting the cargo distribution according to the optimization information.

The regional configuration features are that the data capable of expressing the current region is selected as the regional configuration features by extracting the corresponding configuration information in the logistics region.

Specifically, the method for judging the area difference point information between the logistics area and the comparison area comprises the following steps:

Determining the cost of the transportation time of the goods according to the exchange frequency of the goods and the average spending time of the goods circulation, determining the daily available goods quantity of the goods according to the stock quantity of the goods and the estimated storage quantity of the goods, and determining the daily available goods quantity of the goods according to the update frequency of the goods:

TTC=EF×T×C_t;

Where TTC is the transit time cost, EF is the exchange frequency of the goods, T is the average time spent in the circulation of the goods, and C _t is the cost per unit time coefficient.

DSO=min(I,FS)×U;

Wherein DSO is daily-available-shipment, I is inventory of goods, FS is a predicted storage amount, U is an adjustment coefficient for adjusting a range of daily-available-shipment, and is determined based on actual shipment capability and other factors such as working time length, staffing, etc.

DRI=UF./P×C_i;

DRI is daily inventory, UF is the update frequency of the inventory, P is the period of inventory updates (e.g., daily, weekly, etc.), C _i is the standard inventory of one update

Through the calculation of the formula, the parameter indexes corresponding to the configuration information of different logistics areas can be known, and the comprehensive numerical value among the data can reflect the difference among the different logistics areas.

Then the formula for the region difference point information is:

LDI=w1×CI1+w2×CI2+...+wn×CIn;

where LDI is area difference point information of a logistics area, cif is configuration information corresponding to the logistics area, and wn is weight of the configuration information of the logistics area.

The specific acquisition mode of the optimization information in the logistics distribution comprises the following steps:

According to the obtained regional difference point information, the logistics regions are analyzed, according to the running state and the running route of the storage equipment in the logistics regions, the average coefficient for distributing the goods is calculated, and according to the obtained average coefficient, the data of a plurality of logistics regions are fused, and the corresponding optimization information during logistics distribution is determined.

The average coefficient is used for representing the transportation time of each cargo on the corresponding route of the equipment and the distribution condition of the corresponding route when all the working equipment in a logistics area transport the cargo, so as to determine the time, speed and change interval of the work completion amount required by the corresponding work when each equipment is in the optimal configuration route.

The specific obtaining mode of the optimizing information in the logistics distribution further comprises the following steps:

Determining a transportation mode and a transportation path corresponding to a current logistics area, determining a running route of corresponding storage equipment on each transportation path based on the obtained transportation mode and transportation path, determining a task completion coefficient of the storage equipment based on the running route, determining the task completion coefficient according to an average value of task amount completed by different storage equipment on the corresponding running route, determining a plurality of judgment indexes corresponding to the obtained task completion coefficient when goods are processed.

The judging indexes comprise equipment route layout indexes, equipment activity accuracy indexes and equipment utilization rate indexes under the warehouse layout, the improvement direction in the optimizing information is determined according to the comprehensive calculation results of the judging indexes, the determined improvement direction comprises dividing a logistics area into a plurality of target areas according to functions, for the periodic variation rule of judging index values in the identification target areas in each target area, connecting the target areas with the maximum and minimum judging index value variation according to the variation rule of the judging index values, identifying other corresponding target areas on the connecting line positions of the target areas with the maximum and minimum judging index value variation, and taking the parameter with the highest similarity in the other target areas as the optimizing information of the corresponding logistics area.

The index in the selected judging index is data related to the accuracy and the equipment utilization rate of the equipment during the operation of the equipment during the running process, and is used for indicating whether the equipment can accurately complete the configuration task in a preset scene.

The equipment route layout index mainly evaluates the layout rationality of equipment in a warehouse or a production area, and the layout rationality comprises the placement position of the equipment, the distance between the equipment, the matching degree of the equipment and a workflow and the like.

Accuracy indicators of device activity measure the accuracy and stability of a device in performing tasks, including accuracy of device positioning, smoothness of operation, stability of repeated operation, and the like.

The device utilization index reflects the ratio of the effective use time of the device to the total time within a certain period of time, and measures the degree to which the device is fully utilized.

Meanwhile, besides the indicated content, the judgment index selected at present can also acquire the weight based on the transmission position of the goods in the logistics area, determine the position weight of the corresponding goods on the discrete track based on the track in the logistics area of each position point, so as to determine the movement weight related to the position, determine the utilization rate of the path of the storage equipment in running and the optimal configuration path of each storage equipment according to the acquired movement weight of the storage equipment, and take the track of the minimum weight and the corresponding position point as the optimal configuration path of the corresponding storage equipment based on the sum of the utilization rate of the path of all the storage equipment in running and the weight of the configuration path of each storage equipment.

In one embodiment of the present invention, tracking a current cargo, predicting a movement of the cargo to determine a movement analysis of the predicted cargo, and the cargo tracking module specifically includes:

Determining a moving track of storage equipment, determining a starting point and an ending point of a task of the storage equipment according to an execution task of the storage equipment, determining a moving track of the storage equipment in a task process, traversing a logistics area corresponding to each storage equipment in a moving process, determining parameters corresponding to the storage equipment when each task is executed, converting a plurality of storage equipment into a tag group according to a working task according to a preset matching item, identifying each matching condition in the tag group until all matching conditions are keyed in, determining a position point of the storage equipment, fixedly identifying the corresponding position point, determining whether the moving track of the position point meets a first preset rule, and determining a second preset rule corresponding to the logistics area.

The matching items and the matching conditions are preset and are used for dividing the storage equipment into a plurality of different tag groups according to the working parameters of the storage equipment and determining that the data in the current tag groups meet the processing standard requirements.

The first preset rule is that the moving track of the position point meets the preset track and the overlapping part of the moving track of the position point is smaller than the preset length.

The first preset rule is used for identifying whether repeated movement occurs and whether track change occurs.

The second preset rule is used for finding a cargo space distribution scheme with the minimum cost according to the Hungary algorithm, determining whether the cargo space distribution scheme with the minimum cost of the current storage equipment is smaller than the preset cost, and optimizing the corresponding cost during cargo tracking and moving according to the cargo space distribution scheme with the minimum cost so as to optimize control and management of storage.

Specifically, the second preset rule is to determine whether the cargo space allocation schemes corresponding to different position points in the logistics area are the least costly, if so, the second preset rule is satisfied, otherwise, the corresponding position points are adjusted until the cargo space allocation scheme with the least cost is obtained.

The acquisition mode of the minimum-cost cargo space allocation scheme comprises the following steps:

Taking the goods in the warehouse and the goods to be stored as two sets of bipartite graphs, constructing a cost matrix, wherein rows of the matrix represent the goods, columns of the matrix represent the goods, each element in the matrix represents the cost for storing the specific goods in the specific goods, the cost is represented as an average coefficient and a task completion coefficient calculated in the steps, and the calculated average coefficient and task completion coefficient are selected according to the Arney purity;

And setting the matched set as an empty set, starting from the non-matched cargo vertex, determining an augmented path, wherein the augmented path is a path from the non-matched cargo vertex to the non-matched cargo position vertex, the edges belonging to the matched set and the edges not belonging to the matched set alternately appear on the path, if one augmented path is found, performing inverse operation on all the edges on the augmented path, namely changing the matched edges into non-matched edges, changing the non-matched edges into matched edges, and repeatedly executing the updated matched set as a new matched set until the augmented path cannot be found.

And outputting the matched matching set, wherein the matching pairs in the matching set are the cargo space allocation schemes with the minimum cost.

In the bipartite graph, cargo vertices represent cargoes to be distributed, each cargo is regarded as a vertex represented by a node in the graph, the number of cargo vertices is equal to the number of cargoes to be distributed, cargo space vertices represent available cargo space in the bipartite graph, and each cargo space is regarded as a vertex, which is also represented by a node in the graph.

The average coefficient and the task completion coefficient calculated by the base-Ni-based non-purity are evaluated to select corresponding coefficient values, such as the degree of mixing of cargo distribution, the degree of mixing of equipment utilization and the degree of mixing of task completion, by identifying the degree values, the well average coefficient and the task completion coefficient are converted into corresponding values to determine the cost spent on moving the cargo to the cargo location, the cargo distribution may be uneven on the cargo location of a warehouse or a logistics center, the base-Ni-based non-purity can measure the degree of mixing of cargo types between different cargo locations, the cargo distribution of which cargo locations is more mixed and needs to be optimized, the utilization rate of working equipment in a logistics area may be different, the base-Ni-based non-purity can help to analyze the distribution situation of the equipment utilization rate, and accordingly the utilization rate of which equipment is too high or too low so as to be adjusted and optimized, the situation of the task completion amount of different storage equipment on a corresponding operation route may have difference, the degree of mixing of cargo on the cargo location or the logistics center may not be uneven, the distribution of cargo between different cargo locations may be measured, the relative coefficient is determined according to the average cost of the cargo distribution between the two cargo locations, and the cost is calculated.

And when the work tasks on the single operation route need to be distributed to a plurality of storage devices, determining the efficiency of completing the tasks of each storage device, and determining the task distribution scheme with highest efficiency based on the form of Hungary algorithm.

The task allocation scheme described above may take the form of a minimum cost inventory scheme implementation, and is not described in detail herein.

In this embodiment, the goods are tracked by calculating the goods allocation scheme and the task allocation scheme, so as to determine the allocation task corresponding to the goods when the goods are moved in the goods space, thereby facilitating real-time tracking of task progress and realization mode of the task.

In one embodiment of the invention, the tracking compensation module combines the output content of the goods after obtaining the output results of the goods distribution module and the goods tracking module, and determines the compensation coefficient of the goods when being distributed based on the distribution.

The output results of the goods distribution module and the goods tracking module are in a problem decomposition mode, the problem of goods distribution according to the region in the goods distribution module and the problem of how to arrange the goods on the corresponding goods positions in the goods tracking module are divided into goods region adjustment and task execution sequences in the region, and therefore the positions and the efficiency of the corresponding goods required to be stored are determined when the goods are stored, and the goods distribution compensation is completed.

The compensation coefficients of the tracking compensation module include cargo area adjustment parameters based on cargo area adjustment and in-area task execution order parameters based on in-area task execution order.

For cargo area adjustment, a collaborative optimization mode can be adopted to acquire cargo characteristics (such as size, weight, preservation requirement, property and the like) of cargoes, area capacity, cargo turnover rate and priority of areas, so as to find out an implementation mode of adjusting the different areas according to the characteristics of the areas and the characteristics of the cargoes.

The cargo area adjustment parameters for cargo area adjustment can be expressed as:

The method comprises the steps of converting the characteristics of the goods into risk coefficients, recording the risk coefficients as the characteristic risk coefficients of the goods, setting a standard value of 1 for common goods, and giving higher coefficients, such as 1.5 and 2.0, to the flammable, explosive or temperature-controlled goods according to the dangerous degree or the complexity of special requirements, wherein the value ranges from 1.0 (standard value, representing common goods) to 3.0 (high risk or high special requirements goods).

The regional capacity is converted into a quantity coefficient capable of storing cargoes and recorded as a regional capacity coefficient, the quantity value is a ratio of the quantity of the cargoes remained in the current logistics region to the preset standard quantity according to the current setting, the ratio of the quantity of the cargoes remained in the current logistics region is determined, the preset standard quantity can be a ratio of the current cargo demand to the residual position or a ratio of the residual quantity to the total capacity, and the specific adopted ratio is adjusted according to the actual situation.

The turnover rate of the goods is converted into a turnover rate coefficient and recorded as the turnover rate coefficient of the goods, the turnover rate of the goods can be calculated according to historical data and converted into a coefficient to reflect the circulation speed of the goods, for example, a value range can be set to be 1 time per month on the assumption that the reference turnover rate corresponds to 1.0, the turnover rate can be higher than the reference and can be given with higher coefficients, such as 1.2 and 1.5, and lower coefficients, such as 0.8 and 0.6, can be given below the reference.

For example, a reference priority of 1.0 (representing a normal priority region), a high priority region may be given a higher coefficient such as 1.5, 2.0, etc., and a low priority region may be given a lower coefficient such as 0.7, 0.5, etc.

A weight is set for each coefficient, then a weighted score for each good in each logistics area is calculated, and the weighted score is used as a good area adjustment parameter according to the calculated weighted score.

The individual coefficients in the cargo area adjustment parameter are expressed as:

the cargo characteristic risk coefficient RC formula is:

rc=reference value+ (cargo characteristic addition coefficient-1) ×reference value;

The reference value is usually set to 1, and the characteristic addition coefficient of the cargo is set according to the characteristics of the cargo such as inflammability, explosiveness, or temperature control requirement, etc., ranging from 1.0 (normal cargo) to 3.0 (high risk cargo).

The regional capacity coefficient CC is expressed as:

equation a:

Cc=remaining cargo space/preset standard quantity;

The number of remaining cargo space is the number of cargo space remaining in the currently set logistics area, and the preset standard number can be set based on historical average demand, total area capacity or other business logic.

Equation b:

Cc=residual/total capacity;

This formula directly uses the ratio of the remaining amount to the total capacity to calculate the capacity coefficient.

The cargo turnover coefficient TRC is expressed as:

TRC = actual turnover rate/reference turnover rate;

The actual turnover rate is the cargo turnover rate calculated according to the historical data, the reference turnover rate can be set to be 1 time per month, and the corresponding turnover rate coefficient is 1.0.

The region priority coefficient PC is expressed as:

Pc=priority addition coefficient x reference priority;

The reference priority is usually set to 1.0 (representing a normal priority region), the priority addition coefficient is set according to the priority of the region, the high priority region addition coefficient is greater than 1, and the low priority region addition coefficient is less than 1.

For cargo area adjustment taking into account the whole, the cargo area adjustment parameters are expressed as:

Wherein RC is a cargo characteristic risk coefficient, CC is a region capacity coefficient, TRC is a cargo turnover rate coefficient, PC is a region priority coefficient, and r ₁、r₂、r₃、r₄ is a weight corresponding to the cargo characteristic risk coefficient, the region capacity coefficient, the cargo turnover rate coefficient and the region priority coefficient, respectively.

And acquiring the task type (such as warehouse entry, warehouse exit, warehouse shift and the like), the task urgency, the resource availability and the dependency relationship among the tasks for the task execution sequence in the region.

The in-region task execution order parameter regarding the in-region task execution order may be expressed as:

Determining task urgency, resource availability and dependency relationship among tasks under different task types;

And determining the task execution sequence by using a graph ordering algorithm, and taking the calculation results of the directed graph in different task types as the task execution sequence parameters in the current region.

In processing directed graphs, a suitable graph ordering algorithm is selected, such as a topological ordering, which outputs 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.

And taking the constructed directed graph and the weights of the nodes as input, and applying a topological sorting algorithm or other graph sorting algorithms to obtain a task execution sequence which satisfies the dependency relationship and considers the weights.

In the actual execution process, the weight and the execution sequence of the tasks can be dynamically adjusted according to the real-time feedback and the resource change.

Assume the following tasks and their related information:

And the task A is a warehouse-in task, the emergency degree is high, and the resource requirement is moderate.

And the task B is a warehouse-out task, depends on the completion of the task A, and has low resource requirement in emergency degree.

And the task C is a library moving task, is not dependent on other tasks, and has low urgency and high resource requirement.

The steps are as follows:

Constructing a directed graph:

Nodes A, B and C;

edge A > B (meaning B depends on A);

the task without dependency (e.g., C) is an orphaned node.

Determining weights:

Assume that the emergency weight range is 1-10 and the resource availability weight range is 1-5.

The emergency degree is 8, the resource availability is 3, and the comprehensive weight is calculated as (8+3)/2=5.5.

B degree of urgency 5, resource availability 4, comprehensive weight calculation is (5+4)/2=4.5 (considering dependency a).

And C, emergency degree 2, resource availability 2, and comprehensive weight calculation as (2+2)/2=2.

Topological ordering:

since B depends on a, a must be performed before B.

C has no dependency relationship and can be flexibly arranged.

Depending on the aggregate weights, it may be desirable to perform higher weight tasks first.

One possible ordering is A, B, C or A, C, B (C may be preferred if C's resources can be released faster for use by other tasks).

Output execution sequence:

According to the topological sequencing result, outputting a task execution sequence, such as A- > B- > C or A- > C- > B.

This procedure is simplified and the calculation of weights and composite scores can be tailored more complex to the actual situation, possibly taking into account more factors and constraints in practical applications.

And when the two problems are optimized, the correlation between the two problems is considered, namely, when the cargo area adjusting parameter and the task execution sequence parameter in the area are in the same scene, the optimization results of the two problems are weighted and calculated to ensure the synergy between the two problems, and the two problems are iterated continuously until a stable optimization result is achieved, the corresponding task allocation can be adjusted in real time after the cargo area is optimized, and the optimal setting is achieved.

In one embodiment of the present invention, as shown in fig. 3, the present invention further includes an adjustment evaluation module, configured to determine, for a task scenario corresponding to a cargo area adjustment parameter and an in-area task execution sequence parameter, execution data of a corresponding device on a modified line, that is, obtain a transport speed of cargo and a number of cargo transport configurations on the corresponding line after modification, and determine, according to a change amount of the corresponding cargo after execution of the task, validity of a corresponding processing facility in different scenarios.

The method for adjusting the goods according to the goods area adjusting parameters comprises the steps of obtaining data of the goods to be put in storage, carrying out category matching on the goods to be put in storage and the goods to be put in storage, determining the positions of the goods corresponding to the matching items in the logistics areas when the same matching items exist, determining the goods area adjusting parameters of the logistics areas, sorting the logistics areas with the matching items according to the goods area adjusting parameters, judging whether the capacity of the logistics areas corresponding to the maximum value of the goods area adjusting parameters is larger than the number of the goods to be put in storage, distributing the goods to be put in storage to the logistics areas corresponding to the maximum value of the goods area adjusting parameters when the capacity of the logistics areas corresponding to the maximum value of the goods area adjusting parameters is larger than the number of the goods to be put in storage, sequentially storing the goods to be put in storage to the corresponding logistics areas according to the size sequence of the goods area adjusting parameters, judging the similarity between the goods to be put in storage and the logistics areas with the maximum similarity of the goods to be put in storage, and storing the goods to be put in storage to the logistics areas with the maximum similarity.

In the logistics area where the goods to be put in storage are stored to the maximum similarity, the following conditions are also required to be satisfied:

Corresponding to-be-put goods, comparing the to-be-put goods with the goods characteristic risk coefficients of the corresponding logistics areas according to the goods characteristic risk coefficients of the to-be-put goods and the corresponding logistics areas, and storing the parts, of which the goods characteristic risk coefficients are larger than the similarity threshold, of the to-be-put goods in the corresponding logistics areas;

The logistics area for conveying the warehoused goods is required to meet the conditions that the change rate of the goods turnover rate coefficient is small and the value is larger than the preset turnover coefficient;

When the regional priority coefficient corresponding to the goods to be put in storage exceeds the preset priority coefficient, the storage sequence of the goods is set into the corresponding logistics region from large to small according to the size of the regional priority coefficient, and when the regional priority coefficient corresponding to the goods to be put in storage is smaller than the preset priority coefficient, the goods to be put in storage are set into the corresponding logistics region from large to small according to the size of the goods region adjustment parameter.

The method for adjusting the task execution sequence parameters in the areas comprises the steps of acquiring task execution sequence parameters in the areas of the goods to be put in storage after distributing the logistics areas of the goods to be put in storage, and determining the working state of equipment on a corresponding line when the goods to be put in storage are currently executed, wherein the acquired working state comprises the steps of acquiring corresponding equipment information in the current logistics areas, such as determining the width, depth and effective load weight of a corresponding tray for currently transmitting the goods, wherein the effective load represents the weight which can be borne by equipment for currently using the tray and the like for transporting the goods, and the main transportation route and the main transportation type can be known through the values when the current tray is used for sorting and transporting.

Determining the quantity of the goods selected each time and the range of the corresponding proportion, determining the information of the equipment passing during the transportation of the goods, mapping the logistics area passing during the transportation into a directed graph corresponding to the task execution sequence parameters in the area, determining the execution sequence of the goods in the area, executing the goods according to the dependency relationship sequence among the execution tasks, and when the dependency relationship does not exist among the tasks of the goods transportation, carrying out weighted averaging according to the task urgency and the completion speed corresponding to the goods, and processing the goods corresponding to the tasks urgency and the completion speed weighted according to the value from large to small.

After the cargo area adjustment is determined, the change value of the effective load weight of the cargo carried on each tray is obtained, whether the change value of the effective load weight is in the normal running state of the tray or not is judged, the moving route of the tray in the corresponding logistics area is obtained when the change value of the effective load weight is in the normal range, the direction of the change value of the effective load weight on the route is determined, the directions of the change value of the effective load weight are compared according to the numerical difference adjusted before and after the logistics area, when the effective load weight is in the normal range, the direction of the change value of the current effective load weight is positive, otherwise, if the number is reduced, the direction of the change value of the current effective load weight is negative, the increase rate of the task execution efficiency is determined, when the increase rate of the task execution efficiency is larger than the preset increase rate, the current cargo area adjustment parameter and the task execution sequence parameter in the area are used as adjustment parameters of the corresponding area, when the direction of the change value of the effective load weight is negative, the largest cargo area adjustment parameter in the adjacent area and the maximum cargo area adjustment parameter and the task execution sequence parameter in the largest in the area are determined as the cargo area adjustment parameter in the sequence of the current area adjustment parameter.

And identifying the transported cargo information when the change value of the payload weight is not in the normal range, performing similarity matching on the currently transported cargo information and other logistics areas, determining a logistics area corresponding to the median of the similarity matching value, and taking cargo area adjusting parameters corresponding to the logistics area corresponding to the median of the similarity matching value and task execution sequence parameters in the area as adjusting parameters of the logistics area corresponding to the current cargo.

In the scene, whether the number of cargos transported in a fixed time period meets a certain requirement is judged, if the weight of the payload is small, the current transported cargos are less, and if the weight of the payload is large, the transported amount is controlled, so that uneven distribution and unbalanced distribution caused by excessive transportation are prevented.

While embodiments of the present invention have been shown and described above, it should be understood that the above embodiments are illustrative and not to be construed as limiting the invention, and that variations, modifications, alternatives, and variations may be made to the above embodiments by one of ordinary skill in the art within the scope of the invention, which is also intended to be covered by the present invention.

Claims

1. The logistics warehouse management system based on digital twinning is characterized by comprising a warehouse equipment module, a storage module and a storage module, wherein the warehouse equipment module is used for determining the position and the corresponding working state of the warehouse equipment;

The goods tracking module is used for tracking the current goods, determining the moving speed of each goods, verifying the position point of the corresponding storage equipment, determining whether the first preset rule is met or not based on the moving track of the position point, and determining the second preset rule corresponding to the storage equipment and the logistics area;

the tracking compensation module is used for determining a compensation coefficient of cargo distribution according to output results of the cargo distribution module and the cargo tracking module and adjusting a cargo distribution result based on the compensation coefficient;

the implementation manner of the difference condition of the goods distribution module comprises the following steps:

Selecting a current logistics area, and determining configuration information of the logistics area, wherein the configuration information comprises logistics attributes, cargo types, daily cargo output and daily cargo input;

The method comprises the steps of obtaining configuration information of a plurality of logistics areas, converting the configuration information into data characteristics to obtain area configuration characteristics according to the configuration information of the plurality of logistics areas, taking a current logistics area as a center point, taking a logistics area closest to the current logistics area as a comparison area, determining area difference point information between the comparison area and the current logistics area, adjusting the area difference point information in the logistics area, and distributing cargoes meeting the area difference point information to the corresponding logistics area;

When the goods types exceed the preset range, determining the area configuration characteristics of the adjacent logistics areas corresponding to the current logistics areas, selecting the adjacent logistics areas with the largest area configuration characteristic change values, integrating the adjacent logistics areas with the largest area configuration characteristic change values with the data of the current logistics areas, determining the optimization information of the current logistics distribution, and adjusting the goods distribution according to the optimization information;

the method for judging the area difference point information between the logistics area and the comparison area comprises the following steps:

Determining the cost of the transportation time of the goods according to the exchange frequency of the goods and the average spending time of the goods circulation, determining the daily available goods quantity of the goods according to the stock quantity of the goods and the estimated storage quantity of the goods, and determining the daily available goods quantity of the goods according to the update frequency of the goods;

regional difference point information of the logistics region is expressed as:

TTC=EF×T×C_t;

wherein TTC is the transport time cost, EF is the exchange frequency of the goods, T is the average time spent in the circulation of the goods, and C _t is the cost coefficient per unit time;

DSO=min(I,FS)×U;

wherein DSO is daily available quantity, I is stock quantity of goods, FS is predictive storage quantity, U is an adjustment coefficient for adjusting daily available quantity range;

DRI=UF/P×C_i;

DRI is daily commodity quantity, UF is commodity updating frequency, P is commodity updating period, and C _i is standard commodity quantity updated once;

the formula of the region difference point information is expressed as:

LDI=w1×CI1+w2×CI2+...+wn×CIn;

Wherein LDI is regional difference point information of a logistics region, CIn is configuration information corresponding to the logistics region, and wn is the weight of the configuration information of the logistics region;

analyzing the logistics areas according to the obtained area difference point information, calculating an average coefficient for distributing the goods according to the running state and the running route of the storage equipment in the logistics areas, and fusing the data of a plurality of logistics areas according to the obtained average coefficient to determine the corresponding optimization information during logistics distribution;

The average coefficient is used for representing the transportation time of each cargo on the corresponding route of the equipment and the corresponding route distribution condition when all the working equipment in a logistics area transport the cargo;

Determining a transportation mode and a transportation path corresponding to a current logistics area, determining a running route of corresponding storage equipment on each transportation path based on the obtained transportation mode and the transportation path, determining a task completion coefficient of the storage equipment based on the running route, determining the task completion coefficient according to an average value of task amount completed by different storage equipment on the corresponding running route, and determining a plurality of judgment indexes corresponding to the obtained task completion coefficient when goods are processed;

The judging indexes comprise equipment route layout indexes, equipment activity accuracy indexes and equipment utilization rate indexes under storage layout, and according to comprehensive calculation results of the judging indexes, an improvement direction in optimizing information is determined, wherein the determined improvement direction comprises dividing a logistics area into a plurality of target areas according to functions, for the periodic variation rule of judging index values in the identification target areas in each target area, connecting the target areas with the maximum and minimum judging index value variation according to the variation rule of the judging index values, identifying other corresponding target areas on the connecting line positions of the target areas with the maximum and minimum judging index value variation, and taking the parameter with the highest similarity in the other target areas as optimizing information of the corresponding logistics area;

The cargo tracking module comprises the following specific implementation modes:

Determining a moving track of storage equipment, determining a starting point and an ending point of a task of the storage equipment according to an execution task of the storage equipment, determining a moving track of the storage equipment in a task process, traversing a logistics area corresponding to each storage equipment in a moving process, determining parameters corresponding to the storage equipment when each task is executed, converting a plurality of storage equipment into a tag group according to a working task according to a preset matching item, identifying each matching condition in the tag group until all matching conditions are keyed in, determining a position point of the storage equipment, fixedly identifying the corresponding position point, determining whether the moving track of the position point meets a first preset rule, and determining a second preset rule corresponding to the logistics area;

the first preset rule is that the moving track of the position point meets the preset track and the overlapping part of the moving track of the position point is smaller than the preset length;

The second preset rule is used for finding a cargo space distribution scheme with the minimum cost according to the Hungary algorithm, determining whether the cargo space distribution scheme corresponding to different position points in the logistics area is the cargo space distribution scheme with the minimum cost, if so, meeting the second preset rule, otherwise, adjusting the corresponding position points until the cargo space distribution scheme with the minimum cost is obtained;

the cargo space allocation scheme of the minimum cost in the second preset rule is expressed as two sets of the cargo space in the warehouse and the cargo to be stored as bipartite graphs;

Constructing a cost matrix, wherein rows of the matrix represent cargoes, columns of the matrix represent cargo positions, each element in the matrix represents cost for storing the cargoes in the cargo positions, the cost is represented as an average coefficient and a task completion coefficient calculated in the steps, and the calculated average coefficient and task completion coefficient are selected according to the Arrhenius purity;

Setting a matching set as an empty set, starting from an unmatched cargo vertex, determining an augmented path, wherein the augmented path is a path from the unmatched cargo vertex to the unmatched cargo position vertex, the edges belonging to the matching set and the edges not belonging to the matching set alternately appear on the path, if one augmented path is found, performing inverse operation on all the edges on the augmented path, changing the matched edges into unmatched edges, changing the unmatched edges into matched edges, and repeatedly executing the updated matching set as a new matching set until the augmented path cannot be found;

outputting a matched set which is matched, wherein matched pairs in the matched set are the goods allocation schemes with the minimum cost;

the compensation coefficient of the tracking compensation module comprises cargo area adjustment parameters adjusted based on the cargo area and in-area task execution sequence parameters based on in-area task execution sequence;

The cargo area adjustment parameter is expressed as:

Wherein RC is a cargo characteristic risk coefficient, CC is a region capacity coefficient, TRC is a cargo turnover rate coefficient, PC is a region priority coefficient, r ₁、r₂、r₃、r₄ is weights corresponding to the cargo characteristic risk coefficient, the region capacity coefficient, the cargo turnover rate coefficient and the region priority coefficient respectively, and the weights are used for adjusting cargo region adjustment corresponding coefficients;

the task execution sequence parameters in the region are expressed as:

The method comprises the steps of constructing a directed graph of tasks in an area according to task types and dependency relationships, and determining the weight of each node in the directed graph according to the task urgency and resource availability;

The adjusting and evaluating module is used for determining the execution data of the corresponding equipment on the improved line after determining the cargo area adjusting parameters and the task execution sequence parameters in the area, and determining the effectiveness of the corresponding processing facilities in different scenes according to the variation of the corresponding cargoes after executing the tasks;

The method for adjusting the goods according to the goods area adjusting parameters comprises the steps of obtaining data of the goods to be put in storage, carrying out category matching on the goods to be put in storage and the goods to be put in storage, determining the positions of the goods corresponding to the matching items in the logistics areas when the same matching items exist, determining the goods area adjusting parameters of the logistics areas, sorting the logistics areas with the matching items according to the goods area adjusting parameters, judging whether the capacity of the logistics areas corresponding to the maximum value of the goods area adjusting parameters is larger than the number of the goods to be put in storage, distributing the goods to be put in storage to the logistics areas corresponding to the maximum value of the goods area adjusting parameters when the capacity of the logistics areas corresponding to the maximum value of the goods area adjusting parameters is larger than the number of the goods to be put in storage, and sequentially storing the goods to be put in storage to the corresponding logistics areas according to the size sequence of the goods area adjusting parameters when the same matching items do not exist;

the method for adjusting the task execution sequence parameters in the region comprises the steps of acquiring the task execution sequence parameters in the region of the goods to be put in storage, determining the working state of equipment on a corresponding line during current execution, wherein the acquired working state comprises the steps of acquiring the corresponding equipment information in the current logistics region;

2. The logistics warehouse management system based on digital twinning as claimed in claim 1, wherein in the logistics area where the similarity of the goods to be warehoused is the greatest, the following conditions are also required to be satisfied: