Intelligent warehouse material allocation system

Abstract

The invention discloses an intelligent warehouse material allocation system, relates to the technical field of warehouse material allocation, and solves the technical problems that the existing material allocation scheme cannot predict the inventory demand of a warehouse in advance, so that material supplementation is lagged, the delivery efficiency is low, and the shopping experience of consumers is influenced. According to the method, inventory pre-demand information of warehouses in each market period in the future is analyzed, the redundant inventory of pre-demand materials of each warehouse is counted, and when the redundant inventory exceeds the pre-demand, an optimal allocation scheme is obtained through a transportation and distribution time objective function; if the direct purchase duration of the to-be-supplemented warehouses is smaller than the market cycle, extracting the pre-demanded quantity from the sufficient warehouses to the to-be-supplemented warehouses in sequence according to the size sequence of the shortest transportation duration from each warehouse to the to-be-supplemented warehouses; otherwise, the to-be-supplemented warehouse directly purchases the pre-demanded quantity; through the efficient warehouse operation and quick response mechanism, the shopping experience of consumers is improved.

Description

Technical Field

[0001] The invention belongs to the field of warehouse material allocation, and in particular to an intelligent warehouse material allocation system. Background Art

[0002] With the booming development of e-commerce, more and more manufacturers or brands choose to directly connect with consumers and sell their products through e-commerce platforms. This model not only broadens sales channels, but also greatly promotes market activity and consumer shopping convenience. A notable feature of e-commerce is its high transaction frequency and fragmented order volume.

[0003] In order to cope with this high-frequency, fragmented order model and enable consumers to get goods more quickly, merchants are constantly exploring and optimizing delivery routes and order processing procedures. In terms of delivery route optimization, merchants use advanced logistics management systems and algorithms to intelligently plan the most efficient delivery routes based on multi-dimensional information such as order distribution, traffic conditions, and delivery personnel locations, which reduces delivery time and improves consumers' shopping experience. However, none of them solves the problem at the source, because when the warehouse inventory is insufficient, it will need to be retrieved from other warehouses. This process is not only cumbersome, but also time-consuming, delaying the delivery process of orders, resulting in a longer product delivery cycle, affecting consumers' shopping experience. Therefore, the present invention provides an intelligent warehouse material allocation system. Summary of the invention

[0004] The present invention aims to solve at least one of the technical problems existing in the prior art; to this end, the present invention proposes an intelligent warehouse material allocation system to solve the technical problem that the existing material allocation scheme fails to predict the warehouse's inventory demand in advance, resulting in delayed material replenishment and low shipping efficiency, which in turn affects consumers' shopping experience.

[0005] To achieve the above-mentioned object, the first aspect of the present invention provides an intelligent warehouse material allocation system, including a data analysis module, and a data acquisition module and a material allocation module connected thereto;

[0006] Data collection module: used to collect basic information of the warehouse; the basic information includes the location of the warehouse, the service area and the current inventory information of the warehouse; the inventory information includes the types of materials stored in the warehouse and the corresponding inventory quantity;

[0007] Data analysis module: used to analyze the inventory pre-demand information of warehouses in each future market cycle, and compare it with the inventory information of the current warehouse to obtain the inventory information of the warehouse to be replenished; among them, the inventory pre-demand information includes the pre-demand material category and the corresponding pre-demand quantity;

[0008] Goods allocation module: used to formulate material allocation plans for warehouses to be replenished based on the inventory information and inventory demand information of each warehouse; analyze the transportation time required for each allocation plan; among them, the allocation plan with the shortest transportation time is the optimal allocation plan.

[0009] Preferably, the analyzing of the inventory forecast demand information of warehouses in each future market cycle includes:

[0010] Extracting market demand information of each warehouse service area in several different market cycles from historical data; wherein the market demand information is the demand for each material by consumers in each service area;

[0011] The demand for each material in each warehouse in the same market cycle is averaged to obtain the inventory demand information of each warehouse in different market cycles.

[0012] The present invention analyzes historical data, averages the demand for each material in the same market cycle, comprehensively evaluates the market demand dynamics of each warehouse service area in different market cycles, provides strong data support for inventory management, discovers the laws and trends of material demand, and thus arranges the procurement, storage and allocation of materials more reasonably, avoids inventory backlogs and out-of-stock situations, and improves inventory turnover and utilization.

[0013] Preferably, each year is divided into several time periods, each time period being a market cycle; wherein the number of time periods divided each year is the same.

[0014] Preferably, the optimal deployment scheme comprises the following steps:

[0015] Step S1: Analyze whether the direct purchase time is less than the market cycle; if yes, purchase directly; if not, proceed to step S2; wherein the direct purchase time is the transportation time that the warehouse to be replenished purchases from the production factory;

[0016] Step S2: extract the basic information of warehouses with excess pre-demand materials during the market cycle, count the excess inventory of pre-demand materials, and determine whether it exceeds the pre-demand quantity; if not, proceed to step S3; if yes, solve the shortest transportation time through the transportation and delivery time objective function and the corresponding constraints, and determine whether the transportation time is less than the direct purchase time; if yes, it is the optimal allocation plan; if not, direct purchase is the optimal allocation plan;

[0017] It should be noted that excess pre-demand materials refer to the inventory in the warehouse that is in excess of the demand in the area it serves.

[0018] Step S3: When Σxi < B and Tgi - TS ≥ TC, the materials with a pre-demand of B - xi are sequentially extracted from the warehouse to the warehouse to be replenished in ascending order of ti; otherwise, the warehouse to be replenished directly purchases the pre-demand; where TC is the difference threshold.

[0019] When the materials in the sufficient warehouse meet the requirements of the warehouse to be replenished in the present invention, through the allocation of materials between warehouses and transported to the warehouse to be replenished in the shortest time, it ensures the rapid replenishment of materials, while reducing the purchase demand for new materials and lowering costs; when the allocation between warehouses cannot meet the demand, the method with the shortest transportation time is selected between directly purchasing and further retrieving materials from the sufficient warehouse to ensure the rapid replenishment of materials; the efficient operation and timely response of the warehouse can ensure that consumers can obtain the required goods when shopping, reduce out-of-stock and waiting times, and contribute to improving consumer satisfaction and loyalty, thereby enhancing the overall shopping experience.

[0020] Preferably, the transportation and distribution time objective function and the corresponding constraints include:

[0021] min(TS) = Σti;

[0022] The constraints are: Σxi ≥ B and min(TS) < Tgi; where ti is the shortest transportation duration for the sufficient warehouse to transport materials to the warehouse to be replenished, xi is the excess inventory of the pre-demand materials in each warehouse, B is the pre-demand, Tgi is the direct purchase duration, i is the number of the sufficient warehouse, i = 0, 1,..., N, and N is a positive integer.

[0023] Preferably, the shortest transportation duration for the sufficient warehouse to transport materials to the warehouse to be replenished includes:

[0024] Each transportation route is divided into several sections according to the speed limit intervals;

[0025] Extract several driving information of each section in different transportation routes from historical data; where the driving information includes vehicle speed, traffic flow, congestion duration, and the traffic light duration of the transportation route extracted from the traffic management bureau;

[0026] The transportation duration ti of each transportation route is calculated by ti = min[Σ(Lkj / Vkj + βkj × Ykj) + tdj]; compare the transportation durations of different transportation routes to obtain the shortest transportation duration, that is, the optimal transportation route;

[0027] where j is the transportation route number, j = 0, 1,..., M, M is a positive integer, k is the speed limit interval, and Σ sums over k;

[0028] Lkj is the total distance of the speed limit section k in the j-th transport path; βkj is the congestion risk coefficient of the speed limit section k in the j-th transport path; Vkj is the average vehicle speed of the speed limit section k in the j-th transport path; Ykj is the congestion duration of the speed limit section k in the j-th transport path; tdj is the traffic light duration of the j-th transport path.

[0029] The present invention extracts a number of driving information from historical data, including vehicle speed, traffic volume and congestion duration, and comprehensively considers the impact of various factors on transportation time, which helps to select the optimal transportation route. In addition, the congestion risk coefficient is introduced, so that the calculation model can consider the impact of road congestion on transportation time, further improving the accuracy of transportation time prediction; by comparing the transportation time of different transportation routes, the shortest transportation time, that is, the optimal transportation route, can be determined, which improves transportation efficiency and reduces transportation costs.

[0030] Preferably, the congestion risk coefficient of the speed limit section k in the j-th transport path includes:

[0031] Based on the average traffic volume and average speed of the speed limit section k in the historical data, the traffic density Mkj of the speed limit section k in the j-th transport path is calculated by Mkj=α×Qkj×Vkj; wherein Qkj is the average traffic volume of the speed limit section k in the j-th transport path;

[0032] Determine whether Mkj exceeds a preset density threshold; if yes, the congestion risk coefficient βkj=1; if no, the congestion risk coefficient βkj=0.

[0033] The present invention also fully considers the impact of average traffic flow and average vehicle speed on traffic density, pointing out that when the average traffic flow increases and the vehicle speed is faster, the traffic density increases accordingly, and the risk of congestion also increases accordingly, which provides an important basis for predicting the congestion condition of the transportation route, helps to analyze the transportation time more comprehensively, and makes the evaluation result of the optimal route more accurate and reliable.

[0034] Preferably, the average vehicle speed and the average vehicle flow are both averages of corresponding driving information.

[0035] Preferably, a deployment method of an intelligent warehouse material deployment system comprises the following steps:

[0036] Step P1: Analyze the inventory demand information of warehouses in each future market cycle and compare it with the inventory information of the current warehouse to obtain the inventory information of the warehouse to be replenished;

[0037] Step P2: Determine whether the direct purchase time of the warehouse to be replenished is less than the market cycle; if yes, purchase directly; if no, proceed to step P2;

[0038] Step P3: Count the excess inventory of pre-demand materials in each warehouse to determine whether it exceeds the pre-demand quantity; if so, solve the shortest transportation time through the transportation and delivery time objective function and the corresponding constraints; if not, extract the pre-demand quantity from sufficient warehouses to the warehouse to be replenished in order of the shortest transportation time from each warehouse to the warehouse to be replenished; among them, when the shortest transportation time is greater than the purchase time, purchase it directly.

[0039] Compared with the prior art, the present invention has the following beneficial effects:

[0040] 1. The present invention estimates the expected demand of each warehouse in advance, selects the warehouses to be replenished, and replenishes the inventory of the warehouses to be replenished in time. The replenishment method is to allocate materials between warehouses or directly purchase them, and select the method with the shortest transportation time so that the materials in the warehouses to be replenished can be replenished as soon as possible. Through this efficient warehouse operation and rapid response mechanism, the probability of unsalable goods is reduced, and consumers can be ensured to obtain the required goods smoothly when shopping, effectively reducing out-of-stock and waiting time, thereby improving consumer satisfaction and loyalty, and ultimately optimizing the overall shopping experience;

[0041] 2. The present invention extracts some driving information from historical data, and provides strong support for selecting the optimal transportation route by comprehensively considering the impact of multiple factors on transportation time. At the same time, the present invention introduces a congestion risk coefficient, incorporates the road congestion situation into the calculation model, and further improves the accuracy of transportation time prediction. By comparing the duration of different transportation routes, the present invention can determine the shortest transportation duration, that is, the optimal transportation route, thereby improving transportation efficiency and reducing transportation costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0043] Figure 1 It is a schematic block diagram of the system structure of the present invention;

[0044] Figure 2 It is a schematic diagram of the congestion risk analysis process of the present invention;

[0045] Figure 3 It is a schematic diagram of the method flow of the present invention. DETAILED DESCRIPTION

[0046] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0047] See also Figure 1 , the first aspect of the present invention provides an intelligent warehouse material allocation system, including a data analysis module, and a data acquisition module and a material allocation module connected thereto;

[0048] The data collection module collects basic information of the warehouse;

[0049] Among them, basic information includes the location of the warehouse, service area and current warehouse inventory information; inventory information includes the types of materials stored in the warehouse and the corresponding inventory quantities;

[0050] It should be noted that warehouses will have a specified distribution range, which is the area served by the warehouse.

[0051] The data analysis module analyzes the inventory pre-demand information of warehouses in each future market cycle, and compares it with the inventory information of the current warehouse to obtain the inventory information of the warehouse to be replenished; wherein the inventory pre-demand information includes the pre-demand material category and the corresponding pre-demand quantity;

[0052] Specifically, the market demand information of each warehouse service area in several different market cycles is extracted from the historical data; wherein the market demand information is the demand for each material by consumers in each service area;

[0053] The market demand for each material in the same market cycle of each warehouse is averaged respectively to obtain the inventory forecast demand information of each warehouse in different market cycles.

[0054] It should be noted that each year is divided into several time periods, each of which is a market cycle; the number of time periods divided each year is the same.

[0055] For example: divide each year into four time periods, namely January-March, April-June, July-September, and October-December, which are four market cycles; suppose that the demand information for material Z1 of consumers in the service area of ​​warehouse A in several market cycles is extracted from historical data, and the demand for material Z1 from January to March is averaged to obtain the demand for material Z1 of warehouse A in the market cycle from January to March. In this way, the demand for material Z1 of warehouse A in April-June, July-September, and October-December is analyzed. The demand in different market cycles is the estimated inventory demand of warehouse A in each future market cycle.

[0056] The goods allocation module formulates a material allocation plan for the warehouses to be replenished according to the inventory information and inventory demand information of each warehouse; analyzes the transportation duration required for each allocation plan; among them, the allocation plan with the shortest transportation duration is the optimal allocation plan.

[0057] Specifically, the analysis process of the optimal allocation plan is as follows:

[0058] Step S1: Determine whether the direct purchase duration of the warehouse to be replenished is less than the market cycle; if so, directly purchase; if not, go to step S2;

[0059] It should be noted that the direct purchase duration is the transportation duration for the warehouse to be replenished to purchase from the production factory, and it is the shortest transportation duration;

[0060] Step S2: Extract the basic information of the warehouses with surplus pre-demand materials within the market cycle, count the surplus inventory of the pre-demand materials, and determine whether it exceeds the pre-demand quantity; if not, go to step S3; if so, solve the shortest transportation duration through the transportation and distribution time objective function and the corresponding constraint conditions, and determine whether the transportation duration is less than the direct purchase duration; if so, it is the optimal allocation plan; if not, the direct purchase is the optimal allocation plan;

[0061] It should be noted that the surplus pre-demand materials are the inventory in the warehouse that exceeds the demand of its own service area.

[0062] Specifically, the transportation and distribution time objective function and the corresponding constraint conditions are as follows:

[0063] min(TS) = Σti;

[0064] The constraint conditions are: Σxi ≥ B and min(TS) ≤ Tgi; where, ti is the shortest transportation duration for the sufficient warehouse to transport materials to the warehouse to be replenished; xi is the surplus inventory of the pre-demand materials in each warehouse, B is the pre-demand quantity, Tgi is the direct purchase duration, i is the number of the sufficient warehouse, i = 0, 1,..., N, and N is a positive integer;

[0065] Step S3: When Σxi < B and Tgi - TS ≥ TC, extract materials with a pre-demand quantity of B - xi from the warehouse to the warehouse to be replenished in ascending order of ti; otherwise, the warehouse to be replenished directly purchases the pre-demand quantity; where, TC is the difference threshold.

[0066] It should be noted that materials with a pre-demand quantity of B-xi are extracted from warehouses in sequence to the warehouse to be replenished. Specifically, assuming that warehouse A4 has insufficient material Z1, and warehouses A1, A2 and A3 all have excess material Z1, and the total amount of material Z1 in the three warehouses is less than the quantity required by warehouse A4, and the difference between the direct purchase time and the transportation time allocated between warehouses is greater than the difference threshold, the warehouse with the shortest delivery time to warehouse A4 is selected from warehouses A1, A2 and A3 to extract the remaining required material Z1 to warehouse A4 again. If the amount of material Z1 in the warehouse with the shortest delivery time is insufficient, it will be extracted from the warehouse next to the warehouse with the shortest delivery time until the amount of material Z1 extracted meets the pre-demand quantity of warehouse A4.

[0067] The optimal distribution plan is to minimize the time required for distribution from the warehouse to the warehouse to be replenished; on the premise of allocating materials between warehouses and directly purchasing materials to meet the pre-demand of each warehouse, the transportation time of material allocation is reduced as much as possible to ensure that the warehouse can respond to market demand in a timely manner and improve consumers' shopping experience.

[0068] See also Figure 2 , where the shortest transportation time for transporting materials from a sufficient warehouse to a warehouse to be replenished is analyzed as follows:

[0069] Divide each transport route into several sections according to speed limit intervals;

[0070] Extracting some driving information of each section in different transport routes from historical data; the driving information includes vehicle speed, traffic volume and congestion duration, and the duration of traffic lights on the transport routes extracted from the traffic management bureau;

[0071] The transportation time ti of each transportation path is calculated by ti=min[Σ(Lkj / Vkj+βkj×Ykj)+tdj];

[0072] Wherein, j is the transport route number, j = 0, 1, ... M, M is a positive integer, k is the speed limit interval, and Σ sums over k;

[0073] Lkj is the total distance of speed limit zone k in the jth transport route;

[0074] βkj is the congestion risk coefficient of the speed limit section k in the j-th transportation route;

[0075] Vkj is the average vehicle speed in the speed limit section k of the j-th transport route;

[0076] Ykj is the congestion duration of the speed limit section k in the j-th transport route;

[0077] tdj is the duration of the traffic light on the jth transport route;

[0078] Compare the transportation time of different transportation routes and obtain the shortest transportation time, that is, the optimal transportation route.

[0079] It should be noted that the direct purchase duration is also obtained using this analysis method.

[0080] The present invention analyzes the transportation time of each transportation route to the warehouse to be replenished, and selects the transportation route with the shortest transportation time to transport materials; the transportation time of each transportation route is mainly closely related to the vehicle speed, congestion time and traffic light time. When the vehicle speed is faster, the congestion time and the traffic light time are shorter, the vehicle takes a shorter time to travel on this transportation route, and can reach the warehouse to be replenished as quickly as possible, so as to replenish the inventory in time.

[0081] Among them, the congestion risk coefficient analysis process of the speed limit section k in the jth transportation route is as follows:

[0082] Based on the average traffic volume and average speed of the speed limit section k in the historical data, the traffic density Mkj of the speed limit section k in the j-th transport path is calculated by Mkj=α×Qkj×Vkj; wherein Qkj is the average traffic volume of the speed limit section k in the j-th transport path;

[0083] Determine whether Mkj exceeds a preset density threshold; if yes, the congestion risk coefficient βkj=1; if no, the congestion risk coefficient βkj=0.

[0084] It should be noted that the average vehicle speed and the average traffic volume are both averages of corresponding driving information.

[0085] The present invention evaluates the congestion risk probability of each speed limit section through the traffic density of the speed limit section; the probability of congestion is mainly closely related to the average vehicle flow and the average vehicle speed. When the average vehicle flow and the average vehicle speed are both large, it indicates that there are more vehicles per unit time in the speed limit section and the vehicle speed is faster, that is, the traffic density of the section is large, and the probability of congestion is high. Therefore, when the traffic density exceeds the preset density threshold, the possibility of congestion is large, so the congestion risk coefficient is set to 1. When the traffic density does not exceed the preset density threshold, the possibility of congestion is small or there is no congestion, so the congestion risk coefficient is set to 0.

[0086] See also Figure 3 The second aspect of the present invention proposes a deployment method of an intelligent warehouse material deployment system, comprising the following steps:

[0087] Step P1: Analyze the inventory demand information of warehouses in each future market cycle and compare it with the inventory information of the current warehouse to obtain the inventory information of the warehouse to be replenished;

[0088] Step P2: Determine whether the direct purchase time of the warehouse to be replenished is less than the market cycle; if yes, purchase directly; if no, proceed to step P2;

[0089] Step P3: Count the excess inventory of pre-demand materials in each warehouse to determine whether it exceeds the pre-demand quantity; if so, solve the shortest transportation time through the transportation and delivery time objective function and the corresponding constraints; if not, extract the pre-demand quantity from the sufficient warehouses to the warehouse to be replenished in order of the shortest transportation time from each warehouse to the warehouse to be replenished;

[0090] Among them, when the shortest transportation time is longer than the purchase time, it will be purchased directly.

[0091] Part of the data in the above formula is calculated by removing the dimension and taking its numerical value. The formula is a formula closest to the actual situation obtained by software simulation of a large amount of collected data; the preset parameters and preset thresholds in the formula are set by technical personnel in this field according to actual conditions or obtained through simulation of a large amount of data.

[0092] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. An intelligent warehouse material allocation system, characterized in that: It includes a data analysis module, as well as a data acquisition module and a material allocation module connected thereto; Data acquisition module: used to collect the basic information of the warehouse; among them, the basic information includes the location of the warehouse, the service area, and the inventory information of the current warehouse; the inventory information includes the material categories stored in the warehouse and the corresponding inventory quantities; Data analysis module: used to analyze the inventory pre-demand information of the warehouse in each future market cycle, and compare it with the current inventory information of the warehouse to obtain the inventory information of the warehouse to be replenished; among them, the inventory pre-demand information all includes the pre-demand material categories and the corresponding pre-demand quantities; Goods allocation module: used to formulate a material allocation plan for the warehouse to be replenished according to the inventory information and inventory demand information of each warehouse; analyze the transportation duration required for each allocation plan; among them, the allocation plan with the shortest transportation duration is the optimal allocation plan.

2. The intelligent warehouse material allocation system according to claim 1 is characterized in that: The analysis of the inventory pre-demand information of the warehouse in each future market cycle includes: Extracting the market demand information of the service area of each warehouse in several different market cycles from historical data; among them, the market demand information is the demand quantity of consumers in each service area for each material; Performing mean processing on the demand quantities of each material in the same market cycle of each warehouse respectively to obtain the inventory pre-demand information of each warehouse in different market cycles.

3. The intelligent warehouse material allocation system according to claim 2 is characterized in that: Dividing each year into several time periods, and each time period is a market cycle; among them, the number of time periods divided each year is the same.

4. The intelligent warehouse material allocation system according to claim 1 is characterized in that: The optimal allocation plan includes the following steps: Step S1: Analyze whether the direct purchase duration is less than the market cycle; if so, directly purchase; if not, enter step S2; among them, the direct purchase duration is the transportation duration for the warehouse to be replenished to purchase from the production factory; Step S2: Extract the basic information of the warehouse with surplus pre-demand materials during the market cycle, count the surplus inventory quantity of the pre-demand materials, and judge whether it exceeds the pre-demand quantity; if not, enter step S3; if so, solve the shortest transportation duration through the transportation and distribution time objective function and the corresponding constraint conditions, and judge whether the transportation duration is less than the direct purchase duration; if so, it is the optimal allocation plan; if not, the direct purchase is the optimal allocation plan; Step S3: When Σxi < B and Tgi - TS ≥ TC, in ascending order of ti, sequentially extract materials with a pre-demand quantity of B - xi from the warehouse to the warehouse to be replenished; otherwise, the warehouse to be replenished directly purchases the pre-demand quantity; where TC is the difference threshold.

5. The intelligent warehouse material allocation system according to claim 4 is characterized in that: The transportation and distribution time objective function and the corresponding constraint conditions include: min(TS) = Σti; The constraint conditions are: Σxi ≥ B and min(TS) < Tgi; where ti is the shortest transportation duration for the sufficient warehouse to transport materials to the warehouse to be replenished, xi is the surplus inventory quantity of the pre-demand materials in each warehouse, B is the pre-demand quantity, Tgi is the direct purchase duration, i is the number of the sufficient warehouse, i = 0, 1,..., N, and N is a positive integer.

6. The intelligent warehouse material allocation system according to claim 5 is characterized in that: The shortest transportation duration for the sufficient warehouse to transport materials to the warehouse to be replenished includes: Dividing each transportation path into several sections according to the speed limit intervals; Extracting some driving information of each section in different transport routes from historical data; the driving information includes vehicle speed, traffic volume and congestion duration, and the duration of traffic lights on the transport routes extracted from the traffic management bureau; The transportation time ti of each transportation path is calculated by ti=min[Σ(Lkj / Vkj+βkj×Ykj)+tdj]; the transportation time of different transportation paths is compared to obtain the shortest transportation time, that is, the optimal transportation path; Wherein, j is the transport route number, j = 0, 1, ... M, M is a positive integer, k is the speed limit interval, and Σ sums over k; Lkj is the total distance of the speed limit section k in the j-th transport path; βkj is the congestion risk coefficient of the speed limit section k in the j-th transport path; Vkj is the average vehicle speed of the speed limit section k in the j-th transport path; Ykj is the congestion duration of the speed limit section k in the j-th transport path; tdj is the traffic light duration of the j-th transport path.

7. The intelligent warehouse material allocation system according to claim 6 is characterized in that: The congestion risk coefficient of the speed limit section k in the j-th transport route includes: Based on the average traffic volume and average speed of the speed limit section k in the historical data, the traffic density Mkj of the speed limit section k in the j-th transport path is calculated by Mkj=α×Qkj×Vkj; wherein Qkj is the average traffic volume of the speed limit section k in the j-th transport path; Determine whether Mkj exceeds the preset density threshold; if yes, the congestion risk coefficient βkj=1; if no, the congestion risk coefficient βkj=0.

8. The intelligent warehouse material allocation system according to claim 6 is characterized in that: The average vehicle speed and the average vehicle flow are both averages of corresponding driving information.

9. An intelligent warehouse material allocation method, based on the intelligent warehouse material allocation system according to any one of claims 1 to 8, characterized in that: The following steps are involved: Step P1: Analyze the inventory demand information of warehouses in each future market cycle and compare it with the inventory information of the current warehouse to obtain the inventory information of the warehouse to be replenished; Step P2: Determine whether the direct purchase time of the warehouse to be replenished is less than the market cycle; if yes, purchase directly; if no, proceed to step P2; Step P3: Count the excess inventory of pre-demand materials in each warehouse and determine whether it exceeds the pre-demand quantity; If yes, then the shortest transportation time is solved through the transportation and delivery time objective function and the corresponding constraints; if no, then the pre-demand quantity is extracted from sufficient warehouses to the warehouse to be replenished in order of the shortest transportation time from each warehouse to the warehouse to be replenished; among them, when the shortest transportation time is greater than the purchase time, it is purchased directly.

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