Power material intelligent receiving strategy dynamic optimization method, system and device and storage medium

Abstract

The invention discloses an electric power material intelligent receiving strategy dynamic optimization method, system and device and a storage medium, and the method comprises the steps: obtaining time management data and task demand of electric power material receiving, and building a first quantitative model based on the time management data and the task demand; constructing a second quantitative model based on the receiving characteristics of the electric power materials; and based on calculation results of the second quantitative model and the first quantitative model, dynamically deciding an optimal ex-warehouse rule of the electric power materials. According to the method, a dynamic threshold calculation model, a mixed sorting execution mechanism and a multi-objective loss function are constructed, so that full-process self-adaptive optimization is realized; the method is especially suitable for material sorting strategy optimization in a single-bin centralized storage and multi-station dispersed distribution scene. The self-adaptive selection of a sorting mode and the dynamic planning of an execution path are realized by constructing a station emergency degree-category complexity joint decision model and quantitatively analyzing the interaction relationship between the station emergency degree and the category complexity.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent warehouse logistics management, and particularly relates to a power material intelligent taking strategy dynamic optimization method, system, device and storage medium. BACKGROUND

[0002] The existing warehouse taking system mainly has the following technical bottlenecks: (1) single strategy defect: the traditional system mainly adopts a fixed sorting mode (pure site priority or pure category priority), which cannot adapt to the changing order characteristics. For example, when there are high emergency sites and wide demand categories, the static strategy is easy to cause high equipment empty running rate or delivery delay. (2) Threshold rigid problem: the strategy switching in the existing technology depends on the fixed threshold set by experience, which is difficult to cope with dynamic scenes such as business volume fluctuation and equipment state change, resulting in frequent false triggering of the strategy. (3) Lack of self-evolution ability, the parameters depend on manual adjustment, and the historical data cannot be automatically optimized, resulting in long-term performance degradation due to parameter solidification. SUMMARY

[0003] In view of the above-mentioned existing problems, the present application is proposed. Therefore, the present application provides a power material intelligent taking strategy dynamic optimization method, system, device and storage medium to solve the problems mentioned in the background art.

[0004] To solve the above technical problems, the present application provides the following technical solutions:

[0005] In a first aspect, the embodiment of the present application provides a power material intelligent taking strategy dynamic optimization method, comprising: acquiring time management data and task demand quantity of power material taking, constructing a first quantitative model based on the time management data and the task demand quantity;

[0006] Constructing a second quantitative model based on the taking characteristics of the power material;

[0007] Based on the calculation results of the second quantitative model and the first quantitative model, the optimal outbound rule of the power material is dynamically decided.

[0008] As a preferred scheme of the power material intelligent taking strategy dynamic optimization method, the calculation results of the second quantitative model and the first quantitative model include: power material category complexity and power material site emergency degree.

[0009] As a preferred scheme of the power material intelligent taking strategy dynamic optimization method, the dynamic decision of the optimal outbound rule of the power material includes:

[0010] If the highest urgency value among all current power supply material stations is greater than the station urgency threshold, then the current station is determined to be a high-urgency station. When a station has high-urgency stations, the maximum distance between high-urgency stations is less than the geographical distance threshold. When the maximum urgency value of the current power supply material station is greater than the station urgency threshold, and the maximum distance between high-urgency stations is less than the geographical distance threshold, a station priority strategy is adopted for material requisition.

[0011] If the largest category complexity value among all current power material categories is greater than the category complexity threshold, then the sorting task of the current material category is determined to be complex; when the sorting task of the current material category is complex and the material demand exceeds the category demand threshold, a category priority strategy is adopted for material requisition.

[0012] When the highest urgency value among all current power material sites is not greater than the site urgency threshold, the maximum distance between high-urgency sites is not less than the geographical distance threshold, and the highest category complexity value among all current power material categories is not greater than the category complexity threshold and the material demand is not greater than the category demand threshold, a hybrid strategy is adopted for material requisition.

[0013] As a preferred embodiment of the dynamic optimization method for intelligent requisition strategy of power materials described in this invention, the dynamic decision-making optimal outbound rule for power materials further includes:

[0014] All sites using the aforementioned site priority strategy are sorted in descending order of urgency, generating a site queue {S1, S2, ..., S...}. N}; For each site S j Following the optimal path, it sequentially accesses the storage areas of the required categories, completes the retrieval of all power supplies, and places them uniformly in S. j A dedicated shipping area;

[0015] The category priorities of the categories using the aforementioned category-first strategy are sorted in descending order of complexity, generating a category queue {K1, K2, ..., K}. M}; For each category K k It will go to the storage area of ​​this category in one go, collect the total demand of this category from all sites, and then split the batch of power materials of this category according to the needs of each site and distribute them to the shipping area of ​​each site.

[0016] For power supply needs that do not meet the site priority strategy and category priority strategy, a hybrid strategy is adopted: the remaining sites are ranked in descending order of urgency, and within the same site, the categories are ranked in descending order of complexity to receive power supplies.

[0017] The beneficial effect of the preferred technical solution is that by constructing a site emergency degree-category complexity joint decision model, the interaction between site emergency degree and category complexity is quantitatively analyzed, and adaptive selection of sorting mode and dynamic planning of execution path are realized.

[0018] As a preferred scheme of the power material intelligent taking strategy dynamic optimization method, wherein: further comprising: when the power material taking task demand is high, the site priority strategy, the category priority strategy and the mixed strategy exist at the same time, the site emergency degree threshold value, the geographical distance threshold value, the category complexity threshold value and the category demand threshold value are optimized with the minimum total time cost as the target; the target function is represented as:

[0019] minT total (T E ,T D ,T C ,T Q )=T station +T category +T hybrid

[0020]

[0021] Wherein, T E is the site emergency degree threshold value, T D is the geographical distance threshold value, T C is the category complexity threshold value, T Q is the category demand threshold value, T station is the time required by the site priority strategy, T category is the time required by the category priority strategy, and T hybrid is the time required by the mixed strategy.

[0022] The beneficial effect of the preferred technical solution is that by constructing a dynamic threshold value calculation model, a mixed sorting execution mechanism and a multi-objective loss function, full-process adaptive optimization is realized.

[0023] As a preferred scheme of the power material intelligent taking strategy dynamic optimization method, wherein: further comprising: the selection of the power material taking strategy is controlled by the following inequality, represented as:

[0024]

[0025] Wherein, max(E j ) is the maximum emergency degree value in all current sites, max(C k ) is the maximum category complexity value in all current categories, ΔD is the maximum geographical distance between sites with high emergency degree, and ∑Q k is the total demand of category k.

[0026] As a preferred scheme of the power material intelligent taking strategy dynamic optimization method, the first quantitative model is represented as:

[0027]

[0028] Wherein, Q j is the total quantity of site orders, Q total is the total task quantity of the warehouse, and is a coefficient, T deadline is the order deadline, T current is the current time, T process is the estimated outbound processing time.

[0029] The second quantitative model is represented as:

[0030]

[0031] Wherein, W k is the weight of a single piece of the category, W avg is the average weight of the warehouse, is the number of sites of the category k, N s is the total number of sites, S k is the special requirement item, and is a weight.

[0032] In a second aspect, the present application provides a power material intelligent taking strategy dynamic optimization system, comprising:

[0033] A first quantitative model construction module is configured to acquire time management data and task demand quantity of power material taking, and to construct a first quantitative model based on the time management data and the task demand quantity.

[0034] A second quantitative model construction module is configured to construct a second quantitative model based on the taking characteristics of the power material.

[0035] A decision module is configured to dynamically decide the optimal outbound rule of the power material based on the calculation results of the second quantitative model and the first quantitative model.

[0036] In a third aspect, the present application provides an electronic device, comprising:

[0037] A memory and a processor.

[0038] The memory is configured to store computer executable instructions, and the processor is configured to execute the computer executable instructions, which realizes the steps of the power material intelligent taking strategy dynamic optimization method when executed by the processor.

[0039] In a fourth aspect, the present application provides a computer readable storage medium storing computer executable instructions, which, when executed by a processor, implement the steps of the power material intelligent taking strategy dynamic optimization method.

[0040] Compared with the prior art, the present application has the following beneficial effects: the present application realizes full-process adaptive optimization by constructing a dynamic threshold calculation model, a mixed sorting execution mechanism and a multi-objective loss function; it is particularly suitable for material sorting strategy optimization in the scenario of single warehouse centralized storage and multi-site distributed distribution; by constructing a site emergency degree-category complexity joint decision model, the interactive relationship between site emergency degree and category complexity is quantitatively analyzed, and adaptive selection of sorting mode and dynamic planning of execution path are realized. BRIEF DESCRIPTION OF DRAWINGS

[0041] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor. Among them:

[0042] Figure 1 The method flowchart of the power material intelligent taking strategy dynamic optimization method, system, device and storage medium of an embodiment of the present application. DETAILED DESCRIPTION

[0043] In order to make the above-mentioned purposes, features and advantages of the present application more apparent and easy to understand, the specific embodiments of the present application will be described in detail below with reference to the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor should be within the scope of protection of the present application.

[0044] Embodiment 1, refer to Figure 1 For an embodiment of the present application, the embodiment provides a power material intelligent taking strategy dynamic optimization method, comprising:

[0045] S100: acquiring time management data and task demand quantity of power material taking, and constructing a first quantitative model based on the time management data and the task demand quantity;

[0046] S200: constructing a second quantitative model based on the taking characteristics of the power material;

[0047] S300: dynamically deciding the optimal outbound rule of the power material based on the calculation results of the second quantitative model and the first quantitative model.

[0048] It should be pointed out that the traditional warehouse taking system adopts a fixed sorting mode (pure site priority or pure category priority), which cannot adapt to the changing order characteristics. For example, when there are both high urgency sites and widely demanded categories, static strategies are easy to cause high empty running rate of equipment or delay of distribution. In the prior art, strategy switching relies on fixed thresholds set by experience, which is difficult to cope with dynamic scenarios such as business volume fluctuation and equipment state change, resulting in frequent false triggering of strategies. Lack of self-evolution ability, parameters rely on manual adjustment, and cannot be automatically optimized according to historical data, resulting in long-term performance degradation due to parameter solidification. The present application realizes adaptive optimization of the whole process by constructing a dynamic threshold calculation model, a mixed sorting execution mechanism and a multi-objective loss function; it is especially suitable for optimization of material sorting strategies in the scenario of centralized storage in a single warehouse and distributed distribution in multiple sites; by constructing a site urgency-category complexity joint decision model, the interaction between site urgency and category complexity is quantitatively analyzed, and adaptive selection of sorting mode and dynamic planning of execution path are realized.

[0049] In the embodiment of the present application, the time management data in step S100 includes order deadline, current time and estimated warehouse processing time; the task demand quantity includes total site order quantity and total warehouse task quantity;

[0050] The first quantitative model is represented as:

[0051]

[0052] Wherein, Q j is the total site order quantity, Q total is the total warehouse task quantity, λ is the coefficient, T deadline is the order deadline, T current is the current time, and T process is the estimated warehouse processing time.

[0053] It should be pointed out that the first quantitative model is used to quantify the site urgency, and the larger the site urgency value is, the more the site needs to be processed in priority, triggering the system to prioritize the allocation of its materials.

[0054] Specifically, the site urgency is calculated by weighted superposition of time pressure and task size, and the steps include A1-A3:

[0055] A1: The time urgency term is calculated by the difference between the order deadline and the current time divided by the estimated warehouse processing time, which quantifies the availability of the remaining time;

[0056] A2: The task size term is calculated by the proportion of the total site order quantity to the total warehouse task quantity, which quantifies the resource occupation degree;

[0057] A3: The coefficient λ is used to balance the two types of indicators;

[0058] In the embodiment of the present application, the taking characteristics of the electric power materials in step S200 include demand breadth, operation difficulty, and special requirements.

[0059] The second quantification model is represented as:

[0060]

[0061] wherein W k is the single-piece weight of the category, W avg is the average weight in the warehouse, is the number of sites of the category k, N s is the total number of sites, S k is the special requirement item, and α, β, and γ are weights.

[0062] It should be noted that the second quantification model is used to quantify the category complexity, and the greater the category complexity, the higher the sorting complexity.

[0063] Specifically, the category complexity is calculated from three dimensions of demand breadth, operation difficulty, and special requirements, and the steps include B1-B4:

[0064] B1: The demand breadth item needs to count the number of sites of the category k divided by the total number of sites N s to reflect the demand distribution breadth of the category.

[0065] B2: The operation difficulty item compares the single-piece weight W k of the category with the average weight W avg of the warehouse to measure the load pressure of the sorting equipment.

[0066] B3: The special requirement item is set as S k = 1 to indicate that special treatment is needed, otherwise S k = 0.

[0067] B4: Weighted synthesis, through the weight distribution of α, β, and γ, the greater the category complexity, the higher the sorting complexity.

[0068] In the embodiment of the present application, the calculation results of the second quantification model and the first quantification model in step S300 include the category complexity of the electric power materials and the site emergency degree of the electric power materials.

[0069] In the embodiment of the present application, the dynamic decision of the optimal outbound rule of the electric power materials in step S300 includes:

[0070] If the maximum emergency value of all current power material sites is greater than the site emergency threshold value, it is determined that the current site is a high emergency site; when the site has a high emergency site, the maximum distance of the high emergency site is less than the geographical distance threshold value; when the maximum value of the current power material site emergency is greater than the site emergency threshold value, and the maximum distance of the high emergency site is less than the geographical distance threshold value, the site priority strategy is used for material taking;

[0071] If the maximum category complexity value of all current power material categories is greater than the category complexity threshold value, it is determined that the current material category sorting task is complex; when the current material category sorting task is complex, and the material demand is greater than the category demand threshold value, the category priority strategy is used for material taking;

[0072] When the maximum value of all current power material sites is not greater than the site emergency threshold value, the maximum distance of the high emergency site is not less than the geographical distance threshold value, and the maximum category complexity value of all current power material categories is not greater than the category complexity threshold value, and the material demand is not greater than the category demand threshold value, the mixed strategy is used for material taking.

[0073] For example, the site priority strategy: if max(E j )>T E , it indicates that there is a "high-risk site" that needs to be handled immediately, and the maximum distance of all high emergency sites is ΔD<T D , which ensures that multiple sites can be covered by a single path optimization. In this case, the site demand should be centered, and the geographically adjacent and urgent sites should be handled first.

[0074] For example, the category priority strategy: if max(C k )>T C , it indicates that the required material sorting task is large, which is the common demand of a large number of sites. And ∑Q k >T Q , it indicates that the demand of the material is large, and such materials should be concentrated to reduce the cost of grabbing and sorting materials.

[0075] In the embodiments of the present application, the dynamic decision of the optimal power material outbound rule in step S300 further includes:

[0076] All sites using the site priority strategy are arranged in descending order of emergency, and a site queue {S1, S2,..., S N} is generated; for each site S j , the storage area of the required category is accessed in turn according to the optimal path, all power material taking is completed, and is placed uniformly to the exclusive outbound area of S j ;

[0077] The category priority using the category priority strategy is ranked in descending order of complexity to generate a category queue {K1, K2,..., K M};For each category K k , go to the storage area of the category once, take all the total demand of the category for the site, split the batched category power supplies according to the site demand, and distribute them to the shipping area of each site.

[0078] For power supply demand that does not meet the site priority strategy and the category priority strategy, a hybrid strategy is used; for the remaining sites, the urgency is ranked in descending order, and for the same site, the category complexity is ranked in descending order to take the power supply.

[0079] For example, assume that the remaining sites are {S1 (E = 0.6), S2 (E = 0.5)}; the site category is: S1 needs category A (C = 0.4), B (C = 0.3); S2 needs category A (C = 0.4), C (C = 0.2), then generate a queue to process S1's category A→B first, and then S2's category A→C.

[0080] In the embodiments of the present application, step S300 further includes: when the power supply task demand is high, the site priority strategy, the category priority strategy and the hybrid strategy exist at the same time, optimizing the site urgency threshold, the geographical distance threshold, the category complexity threshold and the category demand threshold with the goal of minimizing the total time cost; the objective function is represented as:

[0081] minT total (T E ,T D ,T C ,T Q )=T station +T category +T hybrid

[0082]

[0083] Wherein, T E is the site urgency threshold, T D is the geographical distance threshold, T C is the category complexity threshold, T Q is the category demand threshold, T station is the time required using the site priority strategy, T category is the time required using the category priority strategy, and T hybrid is the time required using the hybrid strategy.

[0084] It should be noted that the threshold for category demand in this application embodiment directly determines the final total time cost. By optimizing the threshold for site urgency, geographical distance, category complexity, and category demand, the requisition time is minimized and the efficiency is maximized.

[0085] In an optional embodiment, each threshold parameter can be iteratively optimized using a dynamic optimization algorithm to minimize the total time overhead.

[0086] Furthermore, the site-priority strategy prioritizes sorting by site, using the site as an index to collect all required materials from a particular site at once and place them centrally in the corresponding shipping area. All sites using the site-priority strategy are sorted in descending order of urgency, {S1, S2, ..., S...}. N}; For each site S j The AGV sequentially accesses the storage areas of the required product categories according to the optimal path, completes the retrieval of all materials, and places them uniformly in S. j A dedicated shipping area. The site priority strategy's requisition time model is represented as:

[0087]

[0088] Among them, K j For site S j The set of product categories in demand. The time Q takes to move from the previous position to the category k storage area jk For site S j Demand for category k.

[0089] Furthermore, the category-priority strategy uses categories as indexes to batch retrieve the required materials of a certain category from all sites, and then allocates them to the shipping areas according to the sites. He first sorts the category priorities using this strategy according to a complexity of C. k Sort in descending order, {K1,K2,...,K M}; For each category K k The Automated Guided Vehicle (AGV) accesses its storage area in one go and retrieves the total demand ∑Q for this product category from all sites. jk Finally, the bulk-received goods are broken down according to site needs and allocated to the shipping areas of each site. The time consumption model for the category-priority strategy is represented as follows:

[0090]

[0091] in, Goods of category k are sorted by site S j The time cost of breaking down demand.

[0092] Furthermore, for the selected site S j The total sorting time T j The calculation is as follows:

[0093]

[0094] wherein, is the time for AGV to move from the current area to the storage area of category k, v pick is the single piece grabbing rate of the sorting equipment.

[0095] Further, for the material demand that does not meet the site priority strategy and the category priority strategy, a hybrid strategy is adopted. Assuming that the remaining site set S remain (not meeting the site priority condition), the remaining category set K remain (not meeting the category priority condition). First, the remaining sites are arranged in descending order of urgency, and within the same site, the categories are arranged in descending order of category complexity. The hybrid strategy consumes time model is represented as:

[0096]

[0097] wherein, δ detour ∈[0.5, 1] is a path piggybacking coefficient, if the AGV handles the same demand of other sites on the way to the site S j k, the coefficient is reduced.

[0098] In the embodiment of the application, the selection of the power material requisition strategy in step S300 is controlled by the following inequality, represented as:

[0099]

[0100] wherein, max(E j ) is the maximum urgency value in the current all sites, max(C k ) is the maximum category complexity value in the current all categories, ΔD is the maximum geographical distance between the high-urgency sites, and ∑Q k is the total demand of category k.

[0101] It should be noted that in the embodiment of the application, the values of the site urgency threshold, the geographical distance threshold, the category complexity threshold, and the threshold of the category demand quantity can be determined according to the actual running situation of the system and the empirical value.

[0102] Embodiment 2, referring to Figure 1 is an embodiment of the application, which is different from the first embodiment in that an intelligent power material requisition strategy dynamic optimization system is provided, comprising:

[0103] A first quantitative model construction module is configured to acquire time management data and task demand quantity of power material requisition, and construct a first quantitative model based on the time management data and the task demand quantity.

[0104] a second quantification model construction module configured to construct a second quantification model based on the use characteristics of the power materials;

[0105] a decision module configured to dynamically decide the optimal outbound rule of the power materials based on the second quantification model and the calculation result of the first quantification model.

[0106] Specifically, when each module of the power material intelligent use strategy dynamic optimization system of the embodiment is executed, the steps of the power material intelligent use strategy dynamic optimization method in Embodiment 1 are implemented, for example:

[0107] In an implementation manner, the power material intelligent use strategy dynamic optimization system can execute the following steps:

[0108] The calculation result of the second quantification model and the first quantification model includes the complexity of the power material category and the emergency degree of the power material site.

[0109] The first quantification model is expressed as:

[0110]

[0111] wherein, Q j is the total quantity of site orders, Q total is the total task quantity of the warehouse, λ is a coefficient, T deadline is the order deadline, T current is the current time, T process is the estimated outbound processing time.

[0112] The second quantification model is expressed as:

[0113]

[0114] wherein, W k is the weight of a single piece of the category, W avg is the average weight of the warehouse, is the site number of the category k, N s is the total site number, S k is the special requirement item, and α, β, γ are weights.

[0115] If the maximum emergency degree value of all the current power material sites is greater than the site emergency degree threshold value, it is determined that there is a high emergency site in the current site; when there is a high emergency site in the site, the maximum distance of the high emergency site is less than the geographical distance threshold value; when the maximum value of the current power material site emergency degree is greater than the site emergency degree threshold value, and the maximum distance of the high emergency site is less than the geographical distance threshold value, the site priority strategy is adopted for material use;

[0116] If the maximum complexity value of all power material categories is greater than the category complexity threshold value, it is determined that the sorting task of the current material category is complex; when the sorting task of the current material category is complex and the material demand is greater than the category demand threshold value, a category priority strategy is used for material requisition;

[0117] When the maximum urgency value of all power material sites is not greater than the site urgency threshold value, the maximum distance of the high-urgency site is not less than the geographical distance threshold value, and the maximum complexity value of all power material categories is not greater than the category complexity threshold value, and the material demand is not greater than the category demand threshold value, a mixed strategy is used for material requisition.

[0118] All sites using the site priority strategy are arranged in descending order of urgency to generate a site queue {S1, S2,..., S N} for each site S j , the storage area of the required category is accessed in turn according to the optimal path, all power material requisitions are completed, and are placed uniformly in the exclusive delivery area of S j ;

[0119] The category priority level using the category priority strategy is arranged in descending order of complexity to generate a category queue {K1, K2,..., K M} for each category K k , the storage area of the required category is accessed in turn according to the optimal path, all power material requisitions are completed, and are placed uniformly in the exclusive delivery area of S j ;

[0120] For power material demand that does not meet the site priority strategy and the category priority strategy, a mixed strategy is used; for the remaining sites, the urgency is arranged in descending order, and within the same site, the category complexity is arranged in descending order to requisition power materials.

[0121] When the power material requisition task demand is high, the site priority strategy, the category priority strategy and the mixed strategy exist at the same time, the site urgency threshold value, the geographical distance threshold value, the category complexity threshold value and the category demand threshold value are optimized to minimize the total time cost; the objective function is represented as:

[0122] minT total (T E ,T D ,T C ,T Q )=T station +T category +T hybrid

[0123]

[0124] Wherein, TE T is an emergency threshold for a site D T is a geographical distance threshold C T is a category complexity threshold Q T is a threshold for the demand of a category station T is the time required for the site priority strategy category T is the time required for the category priority strategy hybrid T is the time required for the mixed strategy.

[0125] The selection of the power material taking strategy is controlled by the following inequalities, denoted as:

[0126]

[0127] wherein max(E j ) is the maximum emergency value in all current sites, max(C k ) is the maximum category complexity value in all current categories, ΔD is the maximum geographical distance between sites with high emergency, and ∑Q k is the total demand of category k.

[0128] The embodiment also provides an electronic device suitable for the power material intelligent taking strategy dynamic optimization method, comprising:

[0129] a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute the computer executable instructions to realize the power material intelligent taking strategy dynamic optimization method proposed in the above embodiment.

[0130] The embodiment also provides a storage medium having a computer program stored thereon, the program being executed by a processor to realize the power material intelligent taking strategy dynamic optimization method proposed in the above embodiment.

[0131] The storage medium proposed in the embodiment and the power material intelligent taking strategy dynamic optimization method proposed in the above embodiment belong to the same inventive concept, and the technical details not described in the embodiment can be referred to the above embodiment, and the embodiment has the same beneficial effects as the above embodiment.

[0132] Those skilled in the art can clearly understand the present application by the description of the above embodiments, and the present application can be realized by software and necessary general hardware, and of course, can also be realized by hardware, but in many cases, the former is a better embodiment. Based on such understanding, the technical solutions of the present application or the part that contributes to the prior art can be embodied in the form of a software product, which can be stored in a computer readable storage medium, such as a floppy disk, a read-only memory (ROM), a random access memory (RAM), a FLASH, a hard disk, or an optical disc, etc., and includes a number of instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute the methods of various embodiments of the present application.

[0133] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present application but not limit the present application, and although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or replaced equivalently without departing from the spirit and scope of the present application, and all of them should be covered in the scope of the claims of the present application.​

Claims

1. A method for dynamically optimizing an intelligent power material taking strategy, characterized in that, The method comprises: acquiring time management data and task demand quantity of power material taking, and constructing a first quantitative model based on the time management data and the task demand quantity; constructing a second quantitative model based on the taking characteristics of the power material; dynamically deciding the optimal taking-out rule of the power material based on the calculation results of the second quantitative model and the first quantitative model.

2. The method of claim 1, wherein, The calculation results of the second quantitative model and the first quantitative model comprise: power material category complexity and power material site emergency degree.

3. The method of claim 2, wherein the method further comprises: The dynamic decision of the optimal taking-out rule of the power material comprises: if the maximum emergency degree value of all current power material sites is greater than the site emergency degree threshold value, it is determined that there is a high emergency site; when there is a high emergency site, the maximum distance of the high emergency site is less than the geographical distance threshold value; when the maximum value of the current power material site emergency degree is greater than the site emergency degree threshold value, and the maximum distance of the high emergency site is less than the geographical distance threshold value, the site priority strategy is adopted for material taking; if the maximum category complexity value of all current power material categories is greater than the category complexity threshold value, it is determined that the sorting task of the current material category is complex; when the sorting task of the current material category is complex, and the material demand quantity is greater than the category demand quantity threshold value, the category priority strategy is adopted for material taking; when the maximum emergency degree value of all current power material sites is not greater than the site emergency degree threshold value, the maximum distance of the high emergency site is not less than the geographical distance threshold value, and the maximum category complexity value of all current power material categories is not greater than the category complexity threshold value, and the material demand quantity is not greater than the category demand quantity threshold value, the mixed strategy is adopted for material taking.

4. The method of claim 3, wherein the method further comprises: The dynamic decision of the optimal taking-out rule of the power material further comprises: All the sites using the site priority strategy are ranked in descending order of urgency to generate a site queue {S1, S2,..., S N}; for each site S j , the storage area of its required categories is accessed in turn according to the optimal path, all power materials are picked up, and are placed uniformly in the dedicated delivery area of S j ; The category priorities using the category priority strategy are ranked in descending order of complexity to generate a category queue {K1, K2,..., K M}; for each category K k , a storage area for the category is visited once to take all the total demand of the category for all sites, the batched category power materials are split according to site demand and distributed to the shipping areas of the sites; for the power material demand that does not meet the site priority strategy and the category priority strategy, the mixed strategy is adopted; the emergency degrees of the remaining sites are arranged in descending order, and the category complexity is arranged in descending order in the same site to take the power material.

5. The method of claim 4, wherein, The method further comprises: when the power material taking task demand is high, and the site priority strategy, the category priority strategy and the mixed strategy exist at the same time, the site emergency degree threshold value, the geographical distance threshold value, the category complexity threshold value and the category demand quantity threshold value are optimized to minimize the total time cost; the objective function is represented as: minT total (T E ,T D ,T C ,T Q )=T station +T category +T hybrid where T E is a site urgency threshold, T D is a geographic distance threshold, T C is a category complexity threshold, T Q is a category demand threshold, T station is a time required for a site-first strategy, T category is a time required for a category-first strategy, and T hybrid is a time required for a hybrid strategy.

6. The method of claim 5, wherein, The method further comprises: The selection of the power material taking strategy is controlled by the following inequalities, represented as: where max(E j ) is the maximum urgency value among all current sites, max(C k ) is the maximum category complexity value among all current categories, ΔD is the maximum geographical distance between high-urgency sites, and ∑Q k is the total demand for category k.

7. The method of claim 2 or 6, wherein, The method comprises: The first quantitative model is represented as: Wherein, Q j is the total quantity of orders of the site, Q total is the total task quantity of the warehouse, λ is a coefficient, T deadline is the order deadline, T current is the current time, T process is the estimated outbound processing duration; The second quantitative model is represented as: where W k is the weight of the individual item of the category, avg is the average weight of the warehouse, is the number of sites for the category k, N s is the total number of sites, S k are the special requirements, and a, b, g are the weights.

8. An intelligent power material taking strategy dynamic optimization system applied to the method of any one of claims 1-7, characterized in that, The method comprises: The first quantitative model construction module is used to acquire the time management data and the task demand quantity of the power material taking, and construct the first quantitative model based on the time management data and the task demand quantity; The second quantitative model construction module is used to construct the second quantitative model based on the taking characteristics of the power material; The decision module is used to dynamically decide the optimal taking-out rule of the power material based on the calculation results of the second quantitative model and the first quantitative model. 9.An electronic device, comprising: a memory and a processor; The memory is configured to store computer executable instructions, and the processor is configured to execute the computer executable instructions, and the computer executable instructions, when executed by the processor, implement the steps of the power material intelligent taking strategy dynamic optimization method in any one of claims 1 to 7. 10.A computer readable storage medium storing computer executable instructions, and the computer executable instructions, when executed by a processor, implement the steps of the power material intelligent taking strategy dynamic optimization method in any one of claims 1 to 7.

Images