A visualization method for weaving mill warehouse management

Abstract

The application belongs to the technical field of warehouse management, and particularly relates to a visual method for weaving workshop warehouse management, which comprises the following steps: constructing a dynamic time limit weight based on the creation time and urgency of a production material requisition; then calculating the time limit weighted individual activity of each material and the time limit weighted correlation strength between materials, and fusing into a dynamic comprehensive priority score; when allocating a storage location for a material to be stored, calculating the cross-zone picking attraction of the material to other physical storage partitions; when the attraction is strong enough, the materials with strong correlation are assigned to the boundary storage locations close to each other in the respective physical partitions, so that the picking path of the correlated materials is greatly shortened while complying with the physical constraints, and the global optimization of the warehouse layout is realized.

Description

Technical Field

[0001] This invention relates to the field of warehouse management technology. More specifically, this invention relates to a visualization method for warehouse management in a textile workshop. Background Technology

[0002] The warehousing system in the weaving workshop manages a large number and variety of materials, mainly including raw yarn, semi-finished fabric and finished fabric. The efficiency of the flow of these materials directly affects the production rhythm and delivery capacity of the entire workshop. In order to improve the efficiency of warehousing operations, the industry generally adopts a heuristic partitioning algorithm based on ABC analysis to manage storage locations. This method divides materials into high-frequency (Class A), medium-frequency (Class B) and low-frequency (Class C) materials according to their historical inbound and outbound frequencies, and stores them in preset areas in the warehouse with different levels of convenience.

[0003] However, in the actual production operations of the weaving workshop, the selection of materials is not carried out in isolation, but is closely related to the production work order. A single weaving work order often requires the simultaneous use of multiple different materials, such as warp and weft yarns of specific specifications.

[0004] Traditional ABC analysis methods, when making warehouse layout decisions, only consider the inbound and outbound frequencies of each material in isolation, analyzing and rating individual materials independently. This strategy fails to identify and utilize the common picking needs and strong correlations that objectively exist between materials in production work orders. For example, a high-frequency Class A warp yarn and a medium-frequency Class B weft yarn, although always used simultaneously in production, will be mechanically assigned to physically separate Area A and Area B according to traditional algorithms. This layout forces picking personnel to make long-distance back-and-forth movements between different areas to complete a work order, greatly increasing the total path length and operation time for a single material preparation, thereby reducing the overall material flow efficiency per order and failing to achieve global optimization of warehouse management.

[0005] Therefore, how to overcome the problems of unreasonable warehouse layout and long picking paths caused by neglecting the production correlation between materials in the existing technology is a technical problem that urgently needs to be solved in this field. Summary of the Invention

[0006] To address the technical problems of unreasonable warehouse layout and lengthy picking paths caused by neglecting the production correlation between materials in the prior art, this invention provides a visualization method for warehouse management in a weaving workshop, comprising: acquiring production material requisition data within a preset time period; constructing a dynamic timeliness weight for material requisitions based on their creation time and urgency level; calculating the timeliness-weighted individual activity of materials, which is the sum of the dynamic timeliness weights of all material requisitions containing that material; calculating the timeliness-weighted correlation strength between any two materials, which characterizes the weighted frequency of the two materials appearing together in the same material requisition; classifying materials into corresponding physical storage partitions according to their physical attributes; and calculating the timeliness of materials. The time-weighted correlation influence is the sum of the products of the time-weighted correlation strength of the material with other materials and the time-weighted individual activity of other materials. The time-weighted individual activity and time-weighted correlation influence of the material are combined to obtain a dynamic comprehensive priority score. For the material to be put into storage, its cross-zone picking attraction to other physical storage partitions is calculated. The cross-zone picking attraction is equal to the sum of the products of the time-weighted correlation strength of the material with all materials in the target partition and the dynamic comprehensive priority score. When the maximum cross-zone picking attraction is greater than or equal to a preset correlation threshold, the storage location closest to the boundary of the target partition corresponding to the maximum cross-zone picking attraction is assigned within the physical storage partition to which the material to be put into storage belongs.

[0007] Compared to existing ABC analysis methods that isolate and partition storage based solely on the material's own inbound and outbound frequency, this invention comprehensively considers the timeliness and urgency of material requisition forms, constructs a dynamic timeliness weight, and proposes the concepts of timeliness-weighted correlation strength and cross-zone picking attraction. This allows for the dynamic and accurate quantification of the strong correlation between materials due to their shared service to production work orders, and uses this as the basis for warehouse location assignment. In particular, for strongly correlated materials that must be placed in different physical areas due to different physical properties, this method can intelligently place them in their respective areas but close to each other's boundary warehouse locations by calculating cross-zone attraction. This fundamentally solves the problem of unreasonable warehouse layout and long-distance back-and-forth travel between different areas caused by existing technologies that ignore the production correlation between materials. As a result, it can significantly shorten the overall picking path per work order, greatly improve the overall efficiency of warehousing operations and the response speed to production cycle.

[0008] Preferably, the formula for calculating the dynamic timeliness weight of the material requisition form is: ;in: Material requisition form Dynamic timeliness weight; Material requisition form The urgency weight is used to obtain the material requisition form from the system. Corresponding work order The urgency level is determined and different weights are assigned to it; Material requisition form Time decay weight.

[0009] This invention multiplies the urgency weight of the material requisition form with the time decay weight, enabling warehouse layout decisions to simultaneously focus on recent and high-priority production tasks. This allows for the intelligent identification of hot materials and their associated combinations in current production, achieving a sensitive response of warehouse strategies to production dynamics.

[0010] Preferably, the formula for calculating the time decay weight is: ;in: Material requisition form Time decay weights; The current time; Material requisition form Creation time; This is the time decay coefficient.

[0011] This invention employs an exponential decay function to imbue historical material requisition data with a gradually decreasing influence over time. Compared to the coarse treatment of data within a certain time period in traditional methods, this refined time decay model can more accurately reflect the recent trends in material demand, ensuring that warehouse optimization is always based on the latest production model and improving the effectiveness of decision-making.

[0012] Preferably, the method for obtaining the urgency weight is as follows: for urgent work orders, the urgency weight is set to be equal to a first weight value; for priority work orders, the urgency weight is set to be equal to a second weight value; and for ordinary work orders, the urgency weight is set to be equal to a third weight value.

[0013] The first weight value is greater than the second weight value, and the second weight value is greater than the third weight value.

[0014] Preferably, the formula for calculating the time-weighted correlation strength between any two materials is: ;in: For materials and materials Time-weighted correlation strength; Material requisition form Dynamic timeliness weight; and Both are indicator functions, when the material Appears on the material requisition form China Times, =1, otherwise, =0, when material Appears on the material requisition form China Times, =1, otherwise, =0; This represents the quantity of all material requisition forms.

[0015] This invention not only statistically analyzes the co-occurrence of material pairs, but also incorporates the dynamic timeliness weight of the material requisition form at the time of each co-occurrence. This allows for a more accurate measurement of the closeness to which two materials need to be picked simultaneously under the current production environment, providing a reliable and dynamically changing mathematical basis for subsequent associated storage decisions.

[0016] Preferably, the formula for calculating the dynamic comprehensive priority score is: ;in, For materials Dynamic comprehensive priority score; For materials Time-weighted individual activity; For materials The time-weighted correlation influence; The maximum time-weighted individual activity level of all materials. It is the maximum value of the time-weighted correlation influence of all materials.

[0017] This invention integrates the individual activity level representing the importance of the material itself and the material association influence representing the importance of partners. This overcomes the one-sidedness of existing technologies that only focus on individual frequency. It can identify key supporting materials that are not active themselves but are always picked up together with core materials and improve their storage priority, thereby optimizing the picking efficiency of the entire material portfolio rather than individual materials.

[0018] Preferably, the formula for calculating the cross-zone picking attraction is: ;in, In addition to materials The first physical partition outside of its own physical partition One physical partition; For materials For other physical partitions Cross-regional picking attraction; For other physical partitions The serial number of the material; For materials and materials Time-weighted correlation strength; and Materials and materials The dynamic comprehensive priority score.

[0019] This invention combines the correlation strength between materials with their respective comprehensive priorities, quantifying the degree to which a material to be put into storage is attracted by all materials in another physical partition. This enables the system to overcome the limitations of physical partitions and perform global storage location optimization, providing a direct solution to the industry pain point of strongly correlated materials being forcibly separated by physical attributes.

[0020] Preferably, the method for setting the association threshold is as follows: sum the cross-zone picking attraction of the material to be put into storage to all other physical storage partitions to obtain the total attraction value; and use 30% of the total attraction value as the preset association threshold.

[0021] Preferably, the method for obtaining the target partition is as follows: targeting materials Calculate materials For cross-zone picking attraction in other physical zones; the system finds the largest cross-zone picking attraction and uses its corresponding physical zone as the material. The target partition.

[0022] Preferably, the method further includes: when the maximum cross-zone picking attraction is less than a preset association threshold, the system recommends, among all available storage locations in the physical partition to which the material belongs, the storage location closest to the entrance / exit of this partition, as the material storage location. The optimal storage location.

[0023] The beneficial effects of this invention are as follows:

[0024] This invention comprehensively considers the timeliness and urgency of material requisition forms, constructs a dynamic timeliness weight, and proposes the concepts of timeliness-weighted correlation strength and cross-zone picking attraction based on this. It can dynamically and accurately quantify the strong correlation between materials due to their shared service to production work orders, and use this as the basis for warehouse location assignment. In particular, for strongly correlated materials that must be placed in different physical areas due to different physical properties, this method can intelligently place them in boundary warehouse locations within their respective areas but close to each other by calculating cross-zone attraction. This fundamentally solves the problem of unreasonable warehouse layout and long-distance back-and-forth travel between different areas caused by neglecting the production correlation between materials in existing technologies. As a result, it can significantly shorten the overall picking path based on work orders, greatly improve the overall efficiency of warehousing operations and the response speed to production cycle. Attached Figure Description

[0025] Figure 1 This is a flowchart illustrating a visualization method for warehouse management in a weaving workshop according to the present invention;

[0026] Figure 2 It is a visualization of the results of a traditional heuristic partitioning strategy;

[0027] Figure 3This is a schematic visualization of the time-related partitioning strategy of the present invention. Detailed Implementation

[0028] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0029] The specific embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0030] This invention discloses a visualization method for warehouse management in a weaving workshop, referring to... Figure 1 This includes steps S1 to S5:

[0031] S1: Obtain production material requisition data within a preset time period, construct dynamic timeliness weights for material requisitions based on their creation time and urgency level, and then calculate the timeliness-weighted individual activity of materials.

[0032] Information is extracted from real-time production data, and a material individual activity level is constructed that can instantly reflect the production rhythm and has a time-sensitive weight.

[0033] First, the system retrieves all production material requisition data within a preset time period from the Manufacturing Execution System (MES) or Enterprise Resource Planning (ERP) system. The preset time period is in months; in this embodiment, the preset time period refers to the past three months.

[0034] Not all material requisition forms are of equal importance: In a weaving workshop setting, recent material requisition forms and those with high urgency, such as rush orders, should carry greater weight in warehousing decisions; therefore, a dynamic timeliness weight should be assigned to each material requisition form.

[0035]

[0036] in: Material requisition form Dynamic timeliness weight; Material requisition form The urgency weight is used to obtain the material requisition form from the system. Corresponding work order The urgency level is determined and assigned different weights: for expedited work orders, a specific weight is set. For priority work orders, set For regular work orders, set ; Material requisition form Time decay weight.

[0037] The time decay weight is calculated using an exponential decay function, specifically as follows:

[0038]

[0039] in: The current time; Material requisition form Creation time; It is a material requisition form The time difference between the creation time and the current time, in days; This is the time decay coefficient. Considering that the decay rate of the material requisition form is relatively slow, therefore... The value ranges from 0.01 to 0.2, corresponding to a half-life of 70 days to 3.5 days. In this embodiment, the time decay coefficient is... Set it to 0.05.

[0040] For example, the current time Urgent work order B1 was created on November 4, 2025. =November 3, 2025, which is 1 day ago, then , =0.951; Priority work order B2 was created in =November 2, 2025, which is 2 days ago, then , =0.905; Standard work order B3 was created in =October 5, 2025, which is 30 days ago, then , =0.223; therefore, the dynamic timeliness weight of expedited work order B1 is 0.223. Dynamic timeliness weighting of priority work order B2 Dynamic timeliness weighting for ordinary work orders B3 It is evident that the weight of recent expedited work orders (B1) is much higher than that of older, ordinary work orders (B3).

[0041] The activity level of an item should be defined by its recent, high-priority picking needs. Furthermore, based on dynamic timeliness weights, a timeliness-weighted individual activity level for each item is constructed, calculated as follows:

[0042]

[0043] in, For materials Time-weighted individual activity; Material requisition form Dynamic timeliness weight; It is an indicator function, when the material Appears on the material requisition form China Times, =1, otherwise, =0; This represents the quantity of all material requisition forms.

[0044] For example, materials For warp yarn A, material For weft yarn B; the material set required for expedited work order B1 is {warp yarn A, weft yarn B}, the material set required for priority work order B2 is {warp yarn A}, and the material set required for regular work order B3 is {warp yarn A, weft yarn B}; then the materials... Time-weighted individual activity =2.3775 + 1.3575 + 0.223 = 3.958, material Time-weighted individual activity =2.3775+0.223=2.6; It can be seen that the time-weighted individual activity of warp yarn A is higher than that of weft yarn B, because it participates in all recent and high-priority work orders.

[0045] In this way, by constructing dynamic timeliness weights and timeliness-weighted individual activity, the warehousing system can identify in real time which materials are truly high priority in the current production, ensuring that layout decisions can quickly respond to changes in production rhythm such as expedited work orders.

[0046] S2: Calculate the time-weighted correlation strength between any two materials.

[0047] Based on the weighted data from step S1, the inherent correlation between materials due to their shared service to the same production task is mined and quantified. This correlation should also be influenced by the timeliness and urgency of work orders. Therefore, based on the dynamic timeliness weight of material requisition forms, a timeliness-weighted correlation strength is constructed for any two materials, calculated as follows:

[0048]

[0049] in: For materials and materials Time-weighted correlation strength; Material requisition form Dynamic timeliness weight; and Both are indicator functions, when the material Appears on the material requisition form China Times, =1, otherwise, =0, when material Appears on the material requisition form China Times, =1, otherwise, =0; This represents the quantity of all material requisition forms.

[0050] Time-weighted correlation strength between two materials In the calculation formula, the numerator calculates the material... and The weighted total number of times items appearing on the same material requisition form is calculated in the denominator, which is the material's weighted total number of occurrences. or The weighted total number of material requisition forms that appear at least once; The range of values ​​is When the value approaches 1, it indicates that these two materials almost always appear together in high-weight work orders, and their production correlation is extremely strong.

[0051] It should be noted that when the denominator in the formula for calculating the time-weighted correlation strength between two materials is 0, the result of the calculation formula is 0.

[0052] For example, materials For warp yarn A, material It is weft yarn B, and =4.787, Calculate molecules, i.e., materials and The weighted total number of times that appear simultaneously in the same material requisition form, then the numerator =2.3775×1+1.3575×0+0.223×1=2.6; Calculate the denominator, which is the material... or Weighted total number of material requisition forms that appear at least once Then the material and materials Time-weighted correlation strength =0.657, indicating that they have a strong common picking relationship, especially in high-weight work orders.

[0053] Thus, by constructing a time-weighted correlation strength, this method can accurately and dynamically quantify the production correlation between materials, providing a scientific mathematical basis for subsequent correlation storage.

[0054] S3: Divide the materials into the corresponding physical storage partitions based on their physical properties.

[0055] This step aims to incorporate the physical properties of materials into the model to resolve storage conflicts for specific materials in the weaving workshop.

[0056] First, based on the physical properties of the materials, for all materials... Classify and define its physical constraint categories, the physical properties including but not limited to weight, volume and shape;

[0057] In one embodiment, materials can be classified into heavy, medium, and light types based on weight; large, standard, and small types based on volume; and long / cylindrical, rolled, pallet / boxed, and loose / boxed materials based on shape. In actual operation, the storage method of materials is determined by the combination of these physical attributes. The physical constraint category is the code for these combinations and must correspond one-to-one with the physical storage partition.

[0058] For each material, according to the above combination rules, a physical constraint category label is pre-labeled for it in the system; at the same time, in the warehouse map, a label for each shelf area is pre-labeled for its physical storage partition.

[0059] In the setting of a weaving workshop, this includes at least:

[0060] (1) This represents heavy materials, such as warp beams; for example, warp yarn A is typically wound on a warp beam and is therefore classified as... .

[0061] (2) This represents lightweight materials, such as weft yarn tubes and general accessories; for example, weft yarn B is a weft yarn tube and is therefore classified as... Excipient C is also classified as .

[0062] Then, based on the warehouse's shelving configuration, the system divides the physical storage locations into corresponding physical storage partitions:

[0063] (1) This represents the heavy-duty shelving area, used for storage. Type of material.

[0064] (2) This represents the light-duty shelving area, used for storage. Type of material.

[0065] Finally, based on the material's category and the correspondence between the category and the physical storage partition, the material's physical constraint category is determined, thereby establishing a mandatory constraint that the material must be stored in a specific physical partition.

[0066] For example, warp A is classified as Its mandatory constraint is that it belongs to Weft yarn B and accessory C are also classified as Its mandatory constraint is that it belongs to Warp yarn A and weft yarn B are clearly strongly related, yet they must be stored separately. This is a contradiction that traditional ABC analysis cannot resolve.

[0067] Thus, by defining physical constraints and partitions, this method ensures that the subsequent storage location optimization scheme is safe and executable in reality, and lays the foundation for resolving the core contradiction of "related materials, physical separation".

[0068] S4: Calculate the time-weighted association influence of the material based on the time-weighted correlation strength between the material and other materials and the time-weighted individual activity of other materials.

[0069] This step aims to incorporate the social relationship value of an item, i.e., the dynamic activity of its associated items, into its own priority assessment: the storage priority of an item should not be determined solely by its own activity, but should also be influenced by its partner items.

[0070] Therefore, based on the time-weighted correlation strength between a material and other materials, the time-weighted individual activity levels of other materials are weighted and merged to construct the time-weighted correlation influence of a single material. The specific calculation formula is as follows:

[0071]

[0072] in, For materials The time-weighted correlation influence; For materials and materials Time-weighted correlation strength; For materials Time-weighted individual activity; This represents the total number of material types.

[0073] In the formula for calculating the time-weighted correlation influence of materials: materials Its associated influence is its relationship with all other materials. Time-weighted correlation strength Other materials Time-weighted individual activity The sum of the products of .

[0074] For example, calculating materials That is, the time-weighted correlation influence of warp A. Besides warp yarn A, the system also contains two other materials: weft yarn B and auxiliary material C. The time-weighted individual activity of weft yarn B is 2.6005, while the activity of auxiliary material C is lower, with a time-weighted individual activity of 0.5. The time-weighted correlation strength between warp yarn A and weft yarn B is 0.657, while the correlation between warp yarn A and auxiliary material C is very weak, with a time-weighted correlation strength of 0.1. Therefore, the materials... That is, the time-weighted correlation influence of warp A. (0.657×2.6)+(0.1×0.5)=1.7582, the vast majority of which comes from its strongly correlated and highly active partner, weft yarn B.

[0075] In this way, by constructing a time-weighted correlation influence of materials, the priority of a material can be increased by the activity of its production partners, thus providing a basis for decision-making to bring them closer together physically, even when they are in different regions.

[0076] S5: Combine the time-weighted individual activity and time-weighted associated influence of materials to obtain the dynamic comprehensive priority score of materials; combine the dynamic comprehensive priority score of materials with physical storage partitions to assign storage locations.

[0077] This step aims to integrate the dynamic individual attributes of materials, namely, the time-weighted individual activity, the dynamic association attributes, namely, the time-weighted association influence, and physical constraints, to form the final warehouse location assignment scheme, in order to minimize the overall picking path.

[0078] Specifically, a dynamic overall priority score is calculated for each material, which determines its storage priority within its respective physical partition; the specific calculation formula is as follows:

[0079]

[0080] in, For materials Dynamic comprehensive priority score; For materials Time-weighted individual activity; For materials The time-weighted correlation influence; The maximum time-weighted individual activity level of all materials. It is the maximum value of the time-weighted correlation influence of all materials, used for normalization, so that... Value falls on Interval.

[0081] For example, for materials That is, warp A, time-weighted individual activity level. =3.958, time-weighted association influence =1.7582, and the maximum value of time-weighted individual activity. =4.5, the maximum value of time-weighted association influence. =2.5, then the material That is, the dynamic comprehensive priority score of warp A. Warp A has a very high overall priority score, and it should be stored in the prime location within its partition.

[0082] Furthermore, when performing the warehousing operation, the system will execute the following composite strategy based on the material's dynamic comprehensive priority score and physical constraints:

[0083] First, determine the physical zones of the materials based on their physical constraint categories.

[0084] Then, in order to solve the picking backhaul problem between different physical zones, the system calculates the materials The specific calculation formula for cross-zone picking attraction for other physical zones is as follows:

[0085]

[0086] in, In addition to materials The first physical partition outside of its own physical partition One physical partition; For materials For other physical partitions Cross-regional picking attraction; For other physical partitions The serial number of the material; For materials and materials Time-weighted correlation strength; and Materials and materials The dynamic comprehensive priority score.

[0087] For example, for materials That is, warp A, which is classified as heavy-duty shelving area. For materials Light-duty shelving area These are other physical partitions; therefore, calculating materials... That is, warp A to the light-duty shelving area Cross-regional picking gravity Among them, materials That is, the dynamic comprehensive priority score of warp A. =0.791, Light-duty shelving area The material contains weft yarn B and auxiliary material C, whose dynamic comprehensive priority scores are 0.715 and 0.15 respectively; the time-weighted correlation strength between warp yarn A and weft yarn B is 0.657, while the time-weighted correlation strength between warp yarn A and auxiliary material C is 0.1; therefore, the material... That is, warp A to the light-duty shelving area Cross-regional picking gravity 0.657×(0.791+0.715)+0.1×(0.791+0.15)=1.084, warp A to the light-duty shelving area Its gravitational pull is very strong, and the vast majority of it is contributed by its partner weft yarn B.

[0088] It should be noted that cross-regional picking gravity The value quantifies the total picking association of a material with all other materials in other physical zones: because , and All are time-weighted, so the gravity value will change dynamically, reflecting the demand for expedited orders in real time.

[0089] Finally, based on the cross-regional picking attraction, the optimal storage location is recommended, specifically as follows:

[0090] 1. For materials The system finds the largest cross-region picking attraction, denoted as . and its corresponding physical partitions are used as materials. The target partition.

[0091] 2. Set the association threshold ,and , equal to material The sum of cross-region picking gravitations across all other physical partitions, and the association threshold. Used to determine materials Is the cross-regional correlation strong enough to justify sacrificing its optimal position within the local region?

[0092] 3. The system is based on Has the association threshold been reached? Choose one of the following two recommended strategies:

[0093] if Explanation of materials The cross-regional gravitational pull is not strong enough, or the material... There is no clearly defined associated partner partition, therefore, materials The picking needs are primarily independent; in this case, the system does not need to pay attention to associated priority targets, but only focuses on executing individual priority targets; specifically, the system in the material Of all available storage locations within a given physical partition, the one closest to the partition's entrance / exit is recommended for material handling. The optimal storage location.

[0094] if Explanation of materials The cross-regional gravitational pull is very strong. In this case, the system prioritizes satisfying the correlation priority objective, and then tries to satisfy the individual priority objective as much as possible on this basis; specifically:

[0095] (1) The system in materials Calculate the distance from each available storage location to the material within the corresponding physical partition. The distance to the boundary of the target partition is calculated, and the partitions are sorted in ascending order of distance. The top 20% of available storage locations after sorting are then combined into a candidate pool.

[0096] (2) The system only compares within this candidate pool and selects the storage location closest to the entrance / exit of this partition as the material storage location. The optimal storage location.

[0097] For example, for materials That is, warp A, because warp A is used in the light-duty shelving area. The gravitational pull is very high, and the system will not simply place warp A in the heavy-duty shelf area. Instead of exporting them, they will be allocated to the heavy-duty shelving area. In the middle, the one closest to the light-duty shelving area In the boundary storage area; when weft yarn B enters the warehouse, it will also be assigned to the light-duty shelving area. The area closest to the heavy-duty shelving area In the storage location at the boundary.

[0098] Thus, by assigning warehouse locations across different zones, this method, without violating the physical constraint that warp beams must be stored in the heavy-duty area, brings the warp yarn A and weft yarn B, which are strongly associated with the urgent work order, to the closest possible physical location. When the picking personnel execute the urgent work order, they only need to locate the heavy-duty shelving area. Take warp yarn A from the boundary storage location, and you can immediately move to the light-duty shelving area. By taking the weft yarn B from the adjacent boundary storage location, the picking path is shortened to the extreme, achieving global optimization of picking efficiency.

[0099] For example, Figure 2 A visualization of the traditional heuristic partitioning strategy; Figure 3 The visualization results of the time-related partitioning strategy of this invention are analyzed as follows:

[0100] (1) In the figure, the blue dots represent the target materials that need to be picked in the current work order, the gray dots represent other unrelated materials in the warehouse, and the red dotted line represents the work movement path of the picking personnel, from material 1 at the entrance to material 2 and then to the entrance. The left area represents the heavy-duty shelving area, and the right area represents the light-duty shelving area.

[0101] (2) In Figure 2 In this system, materials are sorted in descending order based solely on their inbound and outbound frequency. High-frequency A-class materials, such as warp yarn A corresponding to M1, are preferentially assigned to the heavy-duty zone closest to the exit. The associated material, weft yarn B corresponding to M2, is classified as B due to its slightly lower historical total frequency and is assigned to a more distant location in the light-duty zone. Although M1 and M2 always appear in pairs in production orders, they are physically spaced further apart because they belong to different physical zones and have different individual frequencies. Figure 2 The red dotted line path in the diagram shows that after picking up M1, the picker must go deep into the alley to find M2 and then turn back to the entrance / exit. This long-distance back-and-forth movement greatly increases the total path length.

[0102] (3) In Figure 3 The diagram illustrates the optimized layout after introducing dynamic time-weighted picking and cross-regional picking attraction. There is a very strong time-weighted correlation between M1 and M2. Despite physical constraints—the warp yarn must be in the heavy-duty zone and the weft yarn in the light-duty zone—the system detects that the cross-regional attraction exceeds a threshold, triggering a correlation priority strategy. The system forcibly assigns M1 and M2 to adjacent boundary storage locations within their respective zones. Figure 3 The red dashed line path shows that after picking up M1 in the heavy-duty area, the picker can simply cross the aisle to pick up M2 directly at the corresponding location in the light-duty area, without needing to make a deep vertical movement; compared to Figure 2 , Figure 3 The path is more compact, and the total path length is significantly reduced.

Claims

1. A method for visualizing weaving mill warehouse management, characterized in that, The method comprises: acquiring production requisition data within a preset time period; constructing a dynamic timeliness weight of the requisition based on a creation time and an urgency level of the requisition; calculating a timeliness-weighted individual activity of a material, which is an accumulation sum of dynamic timeliness weights of all requisitions containing the material; calculating a timeliness-weighted correlation strength between any two materials, which represents a weighted frequency of co-occurrence of the two materials in the same requisition; dividing the materials into corresponding physical storage partitions according to physical attributes of the materials; calculating a timeliness-weighted correlation influence of the material, which is an accumulation sum of products of the timeliness-weighted correlation strength of the material and the timeliness-weighted individual activity of other materials; fusing the timeliness-weighted individual activity and the timeliness-weighted correlation influence of the material to obtain a dynamic comprehensive priority score; calculating a cross-zone picking attraction of the material to other physical storage partitions, which is equal to an accumulation sum of products of the timeliness-weighted correlation strength and the dynamic comprehensive priority score of all materials in a target partition; when a maximum cross-zone picking attraction is greater than or equal to a preset correlation threshold, assigning a storage location closest to a boundary of the target partition corresponding to the maximum cross-zone picking attraction in a physical storage partition to which the material to be stored belongs.

2. The method for visualizing warehouse management in a weaving mill according to claim 1, characterized in that, The calculation formula of the dynamic timeliness weight of the requisition is: ； Wherein: is a dynamic aging weight of the material requisition ; is a time decay weight of the material requisition ; corresponding to the urgency level of the work order , and different weights are given to it; is a time decay weight of the material requisition .

3. A method for visualizing warehouse management in a weaving mill according to claim 2, characterized in that, The calculation formula of the time decay weight is: ； wherein: is a requisition time decay weight; is a current time; is a requisition creation time; is a time decay coefficient.

4. The method for visualizing warehouse management in a weaving mill according to claim 2, characterized in that, The acquisition method of the urgency weight is: setting the urgency weight equal to a first weight value for urgent work orders; setting the urgency weight equal to a second weight value for priority work orders; setting the urgency weight equal to a third weight value for ordinary work orders; The first weight value is greater than the second weight value, and the second weight value is greater than the third weight value.

5. The method for visualizing warehouse management in a weaving mill according to claim 1, wherein, The calculation formula of the timeliness-weighted correlation strength between any two materials is: ； in: For materials and materials Time-weighted correlation strength; Material requisition form Dynamic timeliness weight; and Both are indicator functions, when the material Appears on the material requisition form China Times, =1, otherwise, =0, when material Appears on the material requisition form China Times, =1, otherwise, =0; This represents the quantity of all material requisition forms.

6. The method for visualizing warehouse management in a weaving mill according to claim 1, wherein, The calculation formula of the dynamic comprehensive priority score is: ； wherein, the dynamic overall priority score for the material ; the age-weighted individual activity for the material ; the age-weighted relational influence for the material ; the maximum of the age-weighted individual activity for all materials, is the maximum of the age-weighted relational influence for all materials.

7. The method for visualizing warehouse management in a weaving mill according to claim 1, characterized in that, The calculation formula of the cross-zone picking attraction is: ； in, In addition to materials The first physical partition outside of its own physical partition One physical partition; For materials For other physical partitions Cross-regional picking attraction; For other physical partitions The serial number of the material; For materials and materials Time-weighted correlation strength; and Materials and materials The dynamic comprehensive priority score.

8. The method for visualizing warehouse management in a weaving mill according to claim 1, characterized in that, The setting method of the correlation threshold is: accumulating and summing cross-zone picking attractions of the material to be stored to all other physical storage partitions to obtain a total attraction value; taking 30% of the total attraction value as the preset correlation threshold.

9. The method for visualizing warehouse management in a weaving mill according to claim 1, wherein, The acquisition method of the target partition is: For the material , calculate the material The cross-zone picking attraction of other physical partitions; the system finds the maximum cross-zone picking attraction, and takes the corresponding physical partition as the target partition of the material .

10. The method for visualizing warehouse management in a weaving mill according to claim 1, characterized in that, The method further comprises: When the maximum cross-zone picking attraction is less than the preset correlation threshold, the system recommends the storage location closest to the entrance and exit of the physical partition to which the material belongs as the optimal storage location of the material among all available storage locations in the physical partition. of the material.

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