An order distribution method based on fresh flower preservation remaining shelf life and flower shop real-time production capacity

Abstract

The application relates to the technical field of fresh flower order distribution, in particular to an order distribution method based on fresh flower preservation residual shelf life and flower shop real-time production capacity. The method comprises the following steps: acquiring a fresh flower preservation information set and a flower shop production capacity information set, analyzing coupling constraint features of flower material opening degree dynamic changes and flower packaging process complexity under different environmental conditions based on the fresh flower preservation information set and in combination with the flower shop production capacity information set, and obtaining a preservation-production capacity coupling information set; acquiring an order information set, analyzing flower packaging time consumption fluctuation caused by flower art category differences and contract delay risk caused by uneven multi-store parallel processing in combination with the preservation-production capacity coupling information set according to the order information set, and obtaining an order distribution constraint information set; and according to the order distribution constraint information set, evaluating time-sequenced order receiving carrying capacity of each store and fresh flower shelf life adaptation degree, formulating and executing a multi-store predictive order distribution strategy. The timely rate of order contract performance and the utilization efficiency of store production capacity are improved.

Description

Technical Field

[0001] This application relates to the field of flower order distribution technology, and in particular to an order distribution method based on the remaining shelf life of flowers and the real-time capacity of florists. Background Technology

[0002] With the improvement of living standards and the upgrading of consumption patterns, flowers, as a special commodity with high time sensitivity and perishability, have seen their online ordering and instant delivery services widely applied in daily life. In existing flower order distribution systems, task scheduling typically employs models such as geographical proximity allocation, matching with store static inventory, or merchant order bidding. The entire distribution process focuses on spatial resource matching and static data verification, aiming to shorten delivery distances and improve order response speed.

[0003] However, current technologies often lack consideration of the dynamic relationship between the natural properties of flowers and the complexity of the production process. This often results in situations where orders are successfully distributed, but the flowers become over-opened while waiting for processing, failing to meet the specific requirements of the floral arrangement category. Alternatively, underestimating the time required for complex processes can prevent completion within the flowers' optimal freshness period. This situation leads to high flower spoilage rates, increased risk of order fulfillment delays, and insufficient utilization of actual store capacity. Summary of the Invention

[0004] This application provides an order distribution method based on the remaining shelf life of fresh flowers and the real-time capacity of florists to solve the above-mentioned problems. The method includes: acquiring a fresh flower preservation information set and a florist capacity information set; based on the fresh flower preservation information set and the florist capacity information set, analyzing the coupling constraint characteristics of the dynamic changes in the openness of flower materials and the complexity of flower wrapping processes under different environmental conditions to obtain a preservation-capacity coupling information set; acquiring an order information set; based on the order information set and the preservation-capacity coupling information set, analyzing the fluctuations in flower wrapping time caused by differences in floral categories and the risk of fulfillment delays caused by uneven parallel processing by multiple stores to obtain an order distribution constraint information set; and evaluating the time-sequential order acceptance capacity and the adaptability of fresh flower shelf life of each store according to the order distribution constraint information set, and formulating and executing a multi-store predictive order distribution strategy.

[0005] Optionally, based on the flower preservation information set and combined with the florist's production capacity information set, the coupling constraint characteristics of the dynamic changes in the openness of flowers under different environmental conditions and the complexity of the flower wrapping process are analyzed to obtain a preservation-production capacity coupled information set, including: the flower preservation information set includes the openness of flowers and the time spent on flower pretreatment; the florist's production capacity information set includes store weather information and the flower types suitable for flower arrangement; based on the openness of flowers, the time spent on flower pretreatment, and combined with the store weather information, the expected openness of flowers at each time point is analyzed to obtain... The sequence of flower material openness changes is analyzed; based on the flower material's compatibility with the floral arrangement category, the complexity of the wrapping process for each floral arrangement category is determined; based on the sequence of flower material openness changes and the complexity of the wrapping process, it is determined whether the flower material openness meets the process execution requirements of the corresponding floral arrangement category under the current store weather information conditions; if it does, a "makeable" identifier for the corresponding floral arrangement category in the current store is generated; if it does not, a "production capacity restricted" identifier is generated; the "makeable" identifier and the "production capacity restricted" identifier are integrated to construct the freshness-production capacity coupling information set.

[0006] Optionally, the process of constructing the flower material openness change sequence includes: determining the initial openness of the flower material after pre-processing based on the flower material pre-processing time; determining the baseline openness rhythm of the flower material in the current season based on the current season in the store weather information, and adjusting the baseline openness rhythm by accelerating or delaying it according to the specific weather conditions corresponding to the current date to obtain adjusted openness progress information; calculating the change in openness of the flower material at each time node according to the adjusted openness progress information, and arranging each node and its corresponding openness in sequence to obtain the flower material openness change sequence.

[0007] Optionally, determining the complexity of the wrapping process for each floral arrangement category based on the matching floral materials includes: identifying the number of types of floral materials required in different floral arrangement categories and the types of cutting and shaping operations corresponding to each type of floral material; calculating the proportion of fine operations involved in the cutting and shaping operations, wherein the fine operations include at least one of ribbon wrapping, leaf trimming, and flower head angle adjustment; classifying the matching floral arrangement categories into different levels of wrapping process complexity based on the number of types and the fine operations; the higher the number of types or the higher the proportion of fine operations in the wrapping process complexity, the higher the corresponding wrapping process complexity.

[0008] Optionally, based on the order information set and combined with the freshness-capacity coupling information set, the analysis of fluctuations in flower wrapping time due to differences in floral categories and the risk of fulfillment delays caused by uneven parallel processing by multiple stores yields an order distribution constraint information set. This set includes: the order information set includes information on the required floral materials, floral specifications, and the number of orders to be processed; based on the required floral material information and combined with the freshness-capacity coupling information set, stores with the corresponding floral materials having the capacity restriction flag are removed to obtain a preliminary set of stores; based on the floral specifications, the corresponding flower wrapping process complexity is matched, and combined with the current number of orders to be processed in the preliminary set of stores, the time required for each store to complete the current order is estimated; the required time is compared with the remaining time that the corresponding floral materials can be processed in the floral material openness change sequence; if the required time exceeds the remaining time that can be processed, a fulfillment delay warning is marked for the corresponding store; the fulfillment delay warnings in the preliminary set of stores are summarized to obtain the order distribution constraint information set.

[0009] Optionally, the process of constructing the initial screening store set includes: based on the flower material information required by the order, retrieving the workable identifier or the capacity-restricted identifier for the corresponding flowers of each store from the freshness-capacity coupling information set; directly excluding stores with the capacity-restricted identifier; further extracting the openness of the store at the current time point in the flower material openness change sequence, and according to the minimum openness threshold corresponding to the complexity of the flower wrapping process in the floral specification information; if the openness at the current time point is lower than the minimum openness threshold, determining that the store cannot meet the process execution requirements at the current time point, marking it as a temporarily unavailable store, and removing it from the candidate stores; and using the remaining stores after exclusion and removal as the initial screening store set.

[0010] Optionally, the process of constructing the required duration includes: determining order backlog information based on the current number of pending orders in the initial screening store set; inserting the current order into the sorting queue of pending orders in each store according to the order backlog information and the complexity of the wrapping process; accumulating the wrapping time of all orders before the current order in the sorting queue as the waiting time; and adding the waiting time to the wrapping time of the current order itself to obtain the required duration.

[0011] Optionally, the step of evaluating the time-series order-taking capacity and flower timeliness adaptability of each store based on the order distribution constraint information set, and formulating and executing a multi-store predictive order distribution strategy, includes: extracting the required duration of each store from the order distribution constraint information set, determining the critical time node when flowers reach an overripe state by combining the flower openness change sequence, and obtaining time-series capacity information; evaluating the order-taking capacity of each store at subsequent consecutive time nodes based on the time-series capacity information, sorting the order-taking capacity from high to low, and obtaining a store acceptance priority sequence; issuing orders to each store sequentially according to the store acceptance priority sequence, and synchronously updating the number of pending orders and the flower consumption status in the flower openness change sequence of the corresponding store after each distribution, thereby completing the execution of the multi-store predictive order distribution strategy.

[0012] Optionally, the process of constructing the time-series information includes: using the required duration of each store as the starting offset, shifting backward in the sequence of changes in the openness of the flowers, and locating the critical time node when the openness of the flowers progresses to the overripe state; determining the time difference between the critical time node and the end time of the required duration as the production margin corresponding to each store; and sorting each store in descending order of the production margin to obtain the time-series information.

[0013] Optionally, the process of constructing the store acceptance priority sequence includes: evaluating the maximum number of new orders that each store can accommodate at subsequent consecutive time nodes based on the production capacity of each store in the time-seriesd carrying information, and obtaining the order acceptance capacity corresponding to each store; sorting the order acceptance capacity of each store from high to low according to the numerical value, and arranging the sorted stores in order to obtain the store acceptance priority sequence.

[0014] Through the above solution, this application achieves the following beneficial effects: This study delves into the coupling constraints between the dynamic changes in the openness of floral materials and the complexity of the flower wrapping process, constructing a preservation-capacity coupling information set. This breaks away from the existing distribution logic that relies solely on static inventory. Combined with order information sets, the study further analyzes the fluctuations in wrapping time due to differences in floral categories and the risk of fulfillment delays caused by uneven parallel processing across multiple stores, generating an order distribution constraint information set that enables early warning of potential fulfillment failures. Finally, based on the order distribution constraint information set, the study evaluates the time-series order-taking capacity and flower timeliness adaptability of each store, formulating a multi-store predictive order distribution strategy. This ensures that order distribution decisions match the optimal processing window for floral materials, effectively avoiding flower waste due to overripe materials or excessive processing time, while simultaneously improving order fulfillment on-time rate and store capacity utilization efficiency.

[0015] This system achieves deep integration of the remaining freshness of fresh flowers with the real-time production capacity of florists. By incorporating store weather information into the deduction of flower bloom changes, it can dynamically perceive the accelerating or delaying effects of the environment on flower lifespan. Simultaneously, by quantifying the complexity of the flower wrapping process and using it as a judgment threshold, it can identify the sensitivity of different floral categories to the state of flowers. The generation mechanism of production capacity-limited labels essentially transforms vague experience-based judgments into explicit digital constraints. The two work together to ensure that orders are only distributed to stores with actual production capabilities and to proactively avoid the risk of fulfillment failure due to rapid flower decay, thereby reducing the flower spoilage rate and improving the on-time fulfillment rate.

[0016] This technology enables a shift from static estimation to dynamic extrapolation in predicting the openness of flowers. By combining the initial state, which is determined by the pre-processing time of the flowers, with the changing rhythm influenced by both seasons and real-time weather, the constructed sequence of flower openness changes can simulate the natural life process of flowers in complex and variable environments. This allows for the early identification of risks such as time window compression due to accelerated opening caused by high temperatures, or opportunities for extended production windows due to delayed opening caused by low temperatures. This provides highly timely and reliable data support for order distribution strategies, reduces the loss rate caused by flowers that are too ripe to be processed, and improves the overall operational efficiency of the fresh flower supply chain.

[0017] A quantitative assessment system for the complexity of flower wrapping processes was constructed. The number of types of flowers reflects the breadth of material organization, while the proportion of detailed operations reflects the depth of skill execution. The two work together to determine the specific real-time production capacity requirements of stores for orders. Through this grading mechanism, orders with a small number of flowers but extremely complicated processes (such as structural floral arrangements requiring a large number of angle adjustments) or orders with a wide variety of flowers that result in extremely long matching times can be identified. This allows for matching orders with stores that have the corresponding processing capabilities and whose flowers are in good condition during the order distribution stage. This not only avoids misallocating high-difficulty orders to stores where the flowers are about to ripen or where novice florists work, but also provides time parameter inputs for time-series-based production capacity forecasting, effectively improving the on-time fulfillment rate and the utilization rate of fresh flower resources.

[0018] This system deeply integrates the physical demand for orders, the real-time load of stores, and the lifecycle characteristics of flowers. By first eliminating physically unavailable stores, then estimating the actual production time including queuing time, and finally comparing it with the flower's shelf life, a rigorous fulfillment risk screening mechanism has been formed. This mechanism can effectively identify and avoid the hidden risk of having flowers but being unable to complete production due to overstocking. It upgrades the existing static inventory matching to dynamic, time-series capacity matching. As a result, it can proactively intercept erroneous decisions that may lead to flower spoilage or delivery delays during the order distribution stage, improving the on-time fulfillment rate of the flower e-commerce platform, reducing unnecessary losses caused by overripe flowers, and optimizing the order load distribution of each store, avoiding the imbalance where some stores are overloaded while others are idle.

[0019] By combining macro-level capacity identification with micro-level real-time openness threshold verification, a rigorous initial screening mechanism is formed. First, it quickly categorizes orders based on whether they are "ready to produce" or "capacity restricted," eliminating absolutely unusable store resources. Then, by comparing real-time data from the flower openness variation sequence with the minimum openness threshold corresponding to the complexity of the wrapping process, it identifies and eliminates stores that, while generally usable, have not yet reached the required processing time. This progressive screening logic not only avoids allocating orders to stores with overripe flowers or poor environments but also prevents quality degradation caused by forcing production with insufficient flower openness.

[0020] By determining the backlog of orders based on the number of orders to be processed and inserting the current order into the sorting queue in combination with the complexity of the wrapping process, a digital mapping of the actual production order of the store is realized. On this basis, the waiting time is obtained by accumulating the time consumed by the preceding orders and adding it to the production time of the order itself. This not only quantifies the absolute time cost of order execution, but also reveals the relative time delay under the parallel processing of multiple orders.

[0021] This system enables a shift from a passive response to a proactive prediction-based order distribution model. By extracting the required time duration and combining it with the sequence of changes in flower openness to determine critical time nodes, it can identify the effective working window for each store before the flowers expire. Based on this, the time window is converted into the remaining capacity for accepting orders and a priority sequence for store acceptance is constructed, so that orders can be prioritized to stores with surplus capacity and the highest value of the flowers' timeliness, thereby maximizing the utilization of the freshness period of flowers and balancing the load of each store.

[0022] The natural decay pattern of flowers was deeply coupled with the production rhythm of the store. By using the required duration as the starting offset to locate the critical time node, the end point of the effective production window was defined. Then, by calculating the time difference, the remaining production capacity was obtained, and the vague shelf life was transformed into an order-acceptable time asset. Finally, the information was sorted according to the size of the asset to construct a time-series information.

[0023] It realizes the transformation from time-series information to specific execution strategies. By converting the production capacity of each store into the order-accepting capacity, and constructing a store order priority sequence accordingly, the order distribution decision is no longer limited to static matching at a single point in time, but extends to dynamic prediction of future continuous time nodes. This improves the accuracy and timeliness of order distribution, and maximizes the fulfillment efficiency of the overall supply chain while ensuring the quality of flowers. Attached Figure Description

[0024] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1 This is a schematic diagram illustrating an application scenario provided in one embodiment of this application; Figure 2 This is a flowchart illustrating an order distribution method based on the remaining shelf life of fresh flowers and the real-time capacity of a florist, as provided in one embodiment of this application. Detailed Implementation

[0026] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application 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 this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0027] Furthermore, the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article, unless otherwise specified, generally indicates that the preceding and following related objects have an "or" relationship.

[0028] The embodiments of this application will now be described in further detail with reference to the accompanying drawings.

[0029] Current flower order distribution technologies typically allocate orders based solely on static inventory data and geographical distance. However, flowers, as perishable and highly time-sensitive fresh produce, experience dynamic changes in bloomability over time and with environmental variations. Existing technologies neglect the coupling between the remaining shelf life of flowers and a store's real-time production capacity. They cannot predict whether the extended production time due to complex floral arrangements will exceed the flowers' optimal shelf life. This deficiency often results in orders being distributed to stores with stock, but whose flowers are already overripe or nearly overripe. This leads to fulfillment delays, such as having stock but being unable to produce the flowers, or experiencing quality degradation after production. Consequently, this results in higher flower wastage rates and decreased customer satisfaction.

[0030] Based on this, this application provides an order distribution method based on the remaining shelf life of fresh flowers and the real-time capacity of florists. It deeply analyzes the coupling constraints between the openness of flower materials and the flower wrapping process, constructs a freshness capacity information set, and overcomes the limitations of static inventory distribution. By combining order data to analyze the differences in consumption time by category and the fulfillment delays caused by the imbalance of multiple stores operating concurrently, it constructs a constraint information set to predict fulfillment risks. Based on this set, it calculates the store's time-series order-taking capacity and the adaptability to the fresh flower's shelf life, outputs a predictive distribution strategy, matches the optimal processing time for flower materials, reduces flower waste, and improves on-time fulfillment rate and store capacity utilization.

[0031] Figure 1 This application provides an illustration of an application scenario. In the process of order distribution based on the remaining shelf life of fresh flowers and the real-time capacity of florists, the method provided in this application addresses the issues of relying solely on inventory, frequent losses, and delays in flower distribution. It links the openness of flower materials with the packaging process to build a data set, predicts the delivery risks caused by the imbalance of parallel processing in stores, dynamically calculates the upper limit of store capacity, and launches a predictive order dispatch scheme that fits the suitable production cycle of fresh flowers, reduces losses, and improves on-time delivery and store capacity.

[0032] Specifically, the method provided in this application can be applied to any server. The server interacts with the store work order database, store basic files, and flower e-commerce platform to obtain the flower preservation information set provided by the store work order database, the florist capacity information set provided by the store basic files, and the order information set provided by the flower e-commerce platform. It breaks away from the single inventory allocation logic, captures the delay problems caused by different floral arrangements and stores processing in clusters, avoids delivery accidents in advance, formulates and executes multi-store predictive order distribution strategies, intelligently allocates orders, adapts to the best processing time for flowers, reduces flower waste, and balances delivery speed and store capacity.

[0033] The specific implementation method can be referred to in the following embodiments, wherein the data mentioned in the embodiments are only for reference and examples, so that relevant personnel can better understand them.

[0034] Figure 2This document presents a flowchart illustrating an order distribution method based on the remaining shelf life of fresh flowers and the real-time capacity of a florist, as provided in an embodiment of this application. The method described in this embodiment can be applied to servers in the aforementioned scenarios. Figure 2 As shown, the method includes: Example 1: S201. Obtain the fresh flower preservation information set and the florist's production capacity information set. Based on the fresh flower preservation information set and the florist's production capacity information set, analyze the coupling constraint characteristics of the dynamic changes in the openness of flower materials and the complexity of the flower wrapping process under different environmental conditions, and obtain the preservation-production capacity coupling information set.

[0035] The flower preservation information set refers to a data collection recording the current physiological state and processing history of flowers, using the store's work order database as the data source. It specifically includes key indicators such as flower openness and pre-processing time. Flower openness characterizes the percentage or stage of flower blooming and is the core basis for determining whether flowers are in their optimal processing window. Pre-processing time refers to the time consumed from flower arrival to completion of basic processing such as blooming and leaf removal; this data determines the starting point when flowers enter a processing-ready state. The florist's production capacity information set refers to a set of parameters reflecting the store's adaptability to the external environment and internal processes, using the store's basic records as the data source. This includes store weather information and compatible floral arrangement categories. Store weather information covers meteorological data such as temperature, humidity, and light intensity, which directly affect the metabolic rate of flowers. Compatible floral arrangement categories define the specific bouquets, flower boxes, or flower baskets that the store's current inventory of flowers can support.

[0036] Analyzing the coupling constraints between the dynamic changes in the openness of floral materials and the complexity of the wrapping process under different environmental conditions can be understood as establishing a mapping relationship between the natural decay patterns of floral materials and the requirements of artificial production processes. Specifically, based on temperature and humidity information from the store's weather forecast, the rate of change in the openness of floral materials can be accelerated or slowed down. For example, in the high temperatures of summer, the rate of rose opening may be 1.5 times faster than under standard conditions, while in the low temperatures of winter, it may slow down to 0.8 times. The complexity of the wrapping process varies greatly depending on the type of floral arrangement. Simple single-stem wrapping may only take 5 minutes and has a high tolerance for the openness of the flowers, while complex Korean spiral bouquets may require 30 minutes and strictly require the openness of the flowers to be within a specific range of 30%-60%.

[0037] Based on the above analysis, the expected openness sequence of floral materials at various future time points is matched and compared with the processing requirements of specific floral art categories. If, at a certain time point, the expected openness of the floral materials falls within the optimal range required for that floral art category, the coupling constraint is deemed satisfied; conversely, if the floral materials are expected to be overripe or have not yet reached the openness requirement, the constraint is deemed not satisfied. Finally, these matching results are integrated to construct a freshness-production capacity coupling information set. This information set contains the status indicators of each store's ability to produce or limited production capacity for specific floral art categories within a specific time period, thereby quantifying the store's production capacity over time.

[0038] This step aims to address the problem that existing methods only focus on static inventory and ignore the dynamic life cycle of flowers. By coupling environmental variables, physiological characteristics of flowers and processing difficulty in multiple dimensions, it provides a timeliness constraint basis for subsequent order distribution and effectively avoids assigning orders to stores where the condition of the flowers does not match.

[0039] S202. Obtain the order information set. Based on the order information set and the freshness-capacity coupling information set, analyze the fluctuations in flower wrapping time caused by differences in flower categories and the risk of fulfillment delays caused by uneven parallel processing in multiple stores, and obtain the order distribution constraint information set.

[0040] The order information set refers to the collection of user order data awaiting processing, specifically including information on the required flowers, floral arrangement specifications, and the number of orders to be processed, with the online flower platform as the data source. The required flower information clarifies the types and quantities of flowers needed to create the order; the floral arrangement specifications define the size, shape, and corresponding craftsmanship level of the finished product, directly impacting the difficulty of creation; and the number of orders awaiting processing reflects the current backlog of tasks at the store.

[0041] Analyzing the fluctuations in flower wrapping time due to differences in floral arrangement categories can involve dynamically calculating the estimated production time for each order at a specific store based on the complexity of the craftsmanship corresponding to different floral arrangement specifications. Due to subtle differences in the skill levels of different florists and the condition of the flowers, even bouquets of the same specifications can have varying actual production times, requiring estimations that combine historical data with the current level of craftsmanship complexity. Analyzing the risk of fulfillment delays caused by uneven parallel processing across multiple stores can involve comprehensively considering the current queue length at each store and the production time of new orders to predict the absolute completion time of each order.

[0042] Specifically, the process begins by using a freshness-capacity coupling information set to eliminate stores whose flower conditions no longer meet order requirements. For the remaining candidate stores, the waiting time required to complete the current backlog of orders is calculated, and this is added to the estimated production time for the new order itself to obtain the total required time. This total required time is then compared to the remaining time window for maintaining the flowers in optimal condition (i.e., the time difference from the current moment to the point where the flowers are overripe). If the total required time exceeds the remaining production time of the flowers, it means that even if the store accepts the order, it cannot complete high-quality delivery within the freshness period of the flowers; this situation is then marked as a fulfillment delay risk. This risk information from all candidate stores is then aggregated to form an order distribution constraint information set.

[0043] For example, an order for a gift box of 33 roses is highly complex and is expected to take 45 minutes to complete. Store A currently has a backlog of orders that need 30 minutes to fulfill, for a total of 75 minutes. However, the roses at this store are affected by the high temperature and are expected to overripe in 60 minutes. In this case, it is determined that Store A faces a risk of delivery delay. This step, by introducing a time-based risk warning mechanism, can identify and avoid situations where orders can be accepted but not completed, or where completion results in spoilage, thus improving the reliability of order fulfillment.

[0044] S203. Based on the order distribution constraint information set, assess the time-series order acceptance capacity and flower delivery time adaptability of each store, and formulate and implement a multi-store predictive order distribution strategy.

[0045] Assessing the time-series order-taking capacity of each store involves calculating how many new orders each store can accommodate over a continuous future timeline, based on the required time and critical time points for flower distribution constraints. This considers not only the current idle status but also predicts the pace of capacity release over a future period. Flower timeliness suitability refers to the degree of matching between the store's current flower availability and order demand within the time window. A higher suitability means a lower risk of loss when processing the order at that store.

[0046] Developing and implementing a multi-store predictive order distribution strategy involves generating a dynamic store priority sequence based on evaluation results and issuing orders accordingly. Specifically, this involves calculating the production capacity of each store (i.e., the critical time for flowers to ripen minus the estimated order completion time) and converting this capacity into a quantity indicator of available orders. Stores are then prioritized according to their available order capacity, from highest to lowest. Orders are preferentially distributed to stores that can guarantee completion within the optimal timeframe for flower production and have sufficient subsequent order fulfillment capacity.

[0047] During execution, a closed-loop feedback mechanism is employed. Each time an order is distributed to a store, the number of pending orders for that store is immediately updated synchronously, and the remaining time window in the flower consumption status and bloom change sequence is recalculated. This real-time updating ensures that the next distribution decision is based on the latest production capacity and timeliness data, thereby achieving predictive intervention and dynamic load balancing of the overall order flow.

[0048] Through the above strategy, this application has achieved a shift from passive response to proactive prediction, which not only solves the problem of instant matching of individual orders, but also optimizes the utilization rate of flower resources from a global perspective, ensuring that each bouquet is made and delivered during its period of highest life value, thereby minimizing losses and improving user experience.

[0049] Example 2: In some embodiments, based on a fresh flower preservation information set and a florist's production capacity information set, the coupling constraint characteristics between the dynamic changes in the openness of flower materials and the complexity of the flower wrapping process under different environmental conditions are analyzed to obtain a preservation-production capacity coupling information set. The method further includes the following steps: Step 1: The fresh flower preservation information set includes the openness of the flowers and the time required for flower pre-processing; the florist's production capacity information set includes store weather information and the types of floral arrangements that the flowers are compatible with. The fresh flower preservation information set refers to a dataset characterizing the current physical state and processing history of flowers. Its core dimensions include flower openness and pre-processing time. Flower openness refers to the degree to which flower buds are open, usually quantified as a percentage or grade; for example, 30% openness for roses indicates slightly open buds, while 60% indicates half-open buds. Pre-processing time refers to the time taken from when flowers arrive at the warehouse to when basic processing such as thawing, thorn removal, and stem trimming is completed. The florist production capacity information set reflects the store's external environmental constraints and internal technical capabilities, specifically including store weather information and compatible floral arrangement categories. Store weather information includes meteorological parameters such as current temperature, humidity, and light intensity, used to correct natural decay models of flowers. Compatible floral arrangement categories refer to the specific types of floral products that the store's current inventory of flowers can support, such as Korean-style bouquets and opening flower baskets. The openness of the flowers is used in conjunction with store weather information to dynamically predict the life cycle of the flowers; the compatibility of the flowers with different floral categories and the complexity of the wrapping process are used to define the technical boundaries that the store can handle. By integrating the above multi-dimensional data, a basic data foundation reflecting the three-dimensional relationship between material status, environmental factors, and process requirements can be constructed.

[0050] Step 2: Based on the openness of the flowers, the time spent on flower pretreatment, and the weather information of the store, analyze the expected openness of the flowers at each time point to obtain the sequence of changes in the openness of the flowers; The flower blooming degree change sequence refers to a list of predicted future blooming degrees of flowers arranged along a timeline. Its generation logic is based on a coupled calculation of the flowers' biological characteristics and environmental factors. Specifically, the initial blooming degree of the flowers after pre-treatment is determined first based on the pre-treatment time. For example, if a batch of roses is awakened at 4 AM, the initial blooming degree is marked as 15%. Then, based on the current season from the store's weather information, a baseline blooming rhythm for the flowers in the current season is determined. This baseline rhythm is then adjusted to accelerate or delay based on the specific weather conditions corresponding to the current date, resulting in adjusted blooming progress information. For example, in a high-temperature (35℃) summer environment, the blooming rate of roses may be 1.5 times higher than at a standard temperature (25℃), thus accelerating the baseline rhythm; while in a low-temperature or high-humidity winter environment, a delay adjustment is implemented. Based on the adjusted blooming progress information, the blooming degree changes of the flowers at various future time points (e.g., every 30 minutes as a node) are calculated. Arranging each node and its corresponding blooming degree in sequence yields the flower blooming degree change sequence. This sequence not only includes the current openness, but also extends to the predicted values ​​for the next few hours or even days, thus reserving sufficient time window data for subsequent judgment.

[0051] Step 3: Determine the complexity of the flower wrapping process for each type of floral arrangement based on the flowers used; The complexity of flower wrapping is an indicator that quantifies the difficulty of creating a specific floral arrangement. Its determination relies on statistical analysis of the number of flower types and the proportion of intricate operations. Specifically, based on the floral arrangement categories to which the flowers are suited, the number of flower types required for each category and the types of cutting and shaping operations corresponding to each type of flower are identified. For example, a simple single-stem wrap may involve only one type of flower and simple stem trimming, while a complex multi-layered flower box may involve more than five types of flowers and various foliage. Next, the proportion of intricate operations within the cutting and shaping operations is statistically analyzed. Intricate operations include, but are not limited to, ribbon wrapping, individual leaf trimming, and adjusting the angle and orientation of the flower heads—all time-consuming actions. Based on the number of types and the intricate operations, the floral arrangement categories to which the flowers are suited are categorized into different levels of flower wrapping complexity. Higher levels indicate longer production time and more stringent requirements for the condition of the flowers. For example, if a floral arrangement requires extensive individual leaf trimming, its high proportion of intricate operations would classify it as a high-complexity level. This quantification method transforms the abstract production difficulty into calculable numerical parameters, making it easier to compare with the timeliness data of floral materials.

[0052] Step 4: Based on the sequence of changes in the openness of the flowers and the complexity of the wrapping process, determine whether the openness of the flowers meets the process execution requirements of the corresponding floral category under the current weather conditions of the store. The process execution requirements refer to the threshold range of openness that flowers must be within under a specific complexity of the floral arrangement process. Different levels of floral complexity impose strict limitations on the state of the flowers. For example, highly complex spiral bouquets typically require flowers to be 30%-50% open; overly unripe flowers are difficult to shape, while overripe flowers are prone to breakage. In contrast, low-complexity loose flower arrangements may allow an openness range of 20%-70%. The predicted openness values ​​for the current time point and the expected future production period in the generated sequence of flower openness changes are compared with the minimum and maximum openness thresholds corresponding to the determined complexity of the floral arrangement process. If the openness of the flowers remains within the process execution requirements within the time window of the flower openness change sequence, it is considered satisfied; conversely, if the prediction indicates that the openness will exceed the allowable range before completion (e.g., blooming too quickly and becoming unfixable), it is considered unsatisfactory. This judgment process fully considers the real-time impact of store weather information on the opening speed of the flowers, ensuring the dynamic accuracy of the judgment results.

[0053] Step 5: If the conditions are met, generate the corresponding floral arrangement category's makerable identifier for the current store; The "ready to produce" tag is a status-based label indicating that the current store has the capability to fulfill orders for a specific floral arrangement category within a specific time window. When the above criteria are met, a "ready to produce" tag is immediately generated for that floral arrangement category-store combination. For example, for the combination of store A and Korean-style rose gift boxes, if it is determined that the roses' bloom rate will remain within the ideal range of 40%-60% for the next hour, and the store's weather is stable, then a "ready to produce" tag will be applied. This tag serves as an access credential for subsequent order distribution, meaning that the store not only has inventory but also the effective production capacity to complete high-quality production within the shelf life.

[0054] Step Six: If the requirements are not met, a capacity restriction flag will be generated; The "Limited Production Capacity" flag is another status label used to warn stores that while they may have flower stock, environmental or time-related factors may prevent them from meeting specific process requirements. This flag is generated when the above criteria are not met. This situation may arise from two scenarios: first, the flowers are not fully open and have not yet reached the minimum threshold for the process, forcing them to be made would result in a stiff shape; second, due to weather information (such as a sudden high temperature), the flower openness sequence shows that it will surpass the maximum threshold and enter an overripe state within a very short time, leaving insufficient time to complete the highly complex wrapping process. After generating this flag, the store's eligibility to accept orders for this type of floral arrangement will be automatically blocked in subsequent distribution logic, thus avoiding the risk of invalid fulfillment due to order acceptance and subsequent production losses.

[0055] Step 7: Integrate the labels that can be made and the labels that are restricted in production capacity to build a freshness-production capacity coupled information set.

[0056] The preservation-capacity coupling information set serves as the final carrier of all the aforementioned analysis results. It dynamically maps the real-time availability status of different floral categories at various stores at different times. This information set integrates the generated "makeable" and "capacity-restricted" identifiers to form a real-time capacity map. In this map, each data unit contains structured information including store ID, floral category, timestamp, and status identifier. For example, the information set might record: Store M01, rose bouquet, 14:00-15:00, makeable; Store M01, large flower basket, 14:00-15:00, capacity restricted. The construction of this information set completes the transformation from raw environmental data and floral status data to decision support information, deeply coupling the perishable nature of fresh flowers with the technical attributes of florist production processes, providing core constraints for predictive order distribution in subsequent steps.

[0057] Example 3: In some embodiments, the process of constructing a sequence of changes in the openness of floral materials further includes the following steps: Step 1: Determine the initial openness of the flowers after pretreatment based on the time taken for pretreatment; The pre-treatment time for flowers refers to the length of time from when the flowers enter the store until the basic processing steps such as leaf removal, stem trimming, and water absorption are completed. Initial openness is the biological openness state of the flowers at the moment of completion of the pre-treatment, usually expressed as a percentage or grade. It is derived from the state detection of the flowers at the moment of completion of pre-treatment or from calculations based on the storage time and pre-treatment time. This initial openness serves as the starting point for predicting the natural opening process of the flowers over time. Specifically, the initial openness of the flowers upon entering the storage is recorded, combined with the duration of the flowers' stay in the cold storage or processing room (i.e., the pre-treatment time), and the state at the end of pre-treatment is calculated using a pre-set low-temperature / normal-temperature decay model. For example, if a batch of roses has an openness of 10% upon entering the storage, after 4 hours of pre-treatment at 20℃, according to an empirical model, its openness may naturally increase to 25%, and this 25% is the determined initial openness. By locking the initial openness, prediction bias caused by time differences in the preprocessing stage can be eliminated, providing accurate starting data for constructing a high-precision openness change sequence.

[0058] Step 2: Based on the current season in the store's weather information, determine the baseline opening rhythm of the flowers in the current season, and adjust the baseline opening rhythm by speeding up or delaying it according to the specific weather conditions corresponding to the current date, so as to obtain the adjusted opening progress information; The baseline opening rhythm refers to the average opening rate of flowers per unit time under typical climatic conditions in a specific season. It is derived from a statistical correlation analysis of historical meteorological data and the biological characteristics of the flowers. The current season provides a macro-level reference for environmental temperature and light trends, establishing a long-term baseline for the opening process. Specific weather conditions include real-time monitored micro-environmental parameters such as temperature, humidity, and light intensity, which serve to dynamically adjust the baseline rhythm in the short term. The adjusted opening progress information is a real-time opening rate vector calculated using environmental factors. When specific weather conditions show that the temperature is higher than the seasonal average, the baseline opening rhythm is accelerated, increasing the increment of opening rate per unit time; conversely, when encountering cloudy, rainy, and cold weather, a delayed adjustment is implemented, reducing the opening rate. For example, under the summer baseline rhythm, roses open 2% more per hour. If a sudden high-temperature warning is issued, and the temperature is 5°C higher than the same period in previous years, the opening rhythm can be accelerated to 3.5% per hour; while during a winter cold snap, the rhythm may be delayed to 0.8% per hour. This dual mechanism of seasonal benchmark and real-time correction ensures that the open-ended information can accurately reflect the actual development speed of the flowers in the current microenvironment, avoiding the prediction distortion caused by a single static model.

[0059] Step 3: Based on the adjusted opening progress information, calculate the changes in the degree of opening of the flowers at each time point, arrange each point and the corresponding degree of opening in sequence to obtain the sequence of changes in the degree of opening of the flowers.

[0060] Here, a time node can refer to a series of discrete time points from the current moment to a preset future time period, such as setting a node every 30 minutes or 1 hour. The change in openness is the result of cumulative iterative calculation based on the adjusted openness advancement information obtained above, using the determined initial openness. The flower material openness change sequence is an ordered set composed of multiple time-openness key-value pairs, which is used to visually display the life cycle trajectory of the flower material over a future period of time. Specifically, starting from the initial openness, according to the rate defined by the adjusted openness advancement information, the openness values ​​for the next 1 hour, 2 hours, ... until the flower material reaches the overripe critical point are calculated sequentially, and these values ​​are bound and stored with their corresponding time nodes. For example, if the initial openness is 25%, and the adjusted openness advancement information shows a rate of 3% / h for the first 2 hours, and then decreases to 2% / h, then the sequence will contain data points such as (T+1h, 28%), (T+2h, 31%), and (T+3h, 33%). By generating this sequence, we can not only know the current availability of the flowers, but also predict their maturity at any future time. This provides a crucial time window for determining whether the flowers will become overripe before the order is completed, effectively supporting subsequent performance risk assessment and order distribution decisions.

[0061] Example 4: In some embodiments, the complexity of the flower wrapping process for each floral arrangement category is determined based on the matching of the floral materials. The method further includes the following steps: Step 1: Based on the flower materials and matching them with the floral arrangement category, identify the types and quantities of flowers required in different floral arrangement categories, as well as the corresponding cutting and shaping operations for each type of flower material; The flower material matching category can refer to the specific fresh flower product form specified in the order, such as a Korean spiral bouquet, a European table centerpiece, or a gift flower box. The identification process aims to extract the physical and motion elements that constitute this category. The variety quantity can refer to the total number of different varieties of fresh flowers required to complete a standard unit of floral arrangement, which is derived from the Bill of Materials (BOM) in the order information set. The trimming and shaping operation type can refer to the set of pre-processing actions that must be performed on each type of flower material before assembly, including basic operations such as leaf removal, thorn trimming, stem slanting, and petal arrangement, as well as special processing actions for specific styling requirements. Specifically, through a pre-built floral knowledge base, floral categories are mapped to a structured set of operation instructions. For example, for a simple single-stem rose arrangement, the number of flower types identified is 1 (rose only), and the corresponding trimming and shaping operations are mainly removing lower leaves and cutting the stem flat. However, for a multi-layered mixed flower basket, the number of flower types identified might be 8 (including roses, lilies, eucalyptus leaves, and other main and secondary flowers), and the corresponding operations would encompass a variety of complex actions such as spiral leaf removal, thinning flower heads, and bending and shaping branches. This identification mechanism transforms abstract floral styles into quantifiable data features, providing a foundation for subsequent computational complexity calculations.

[0062] Step 2: Calculate the percentage of fine operations involved in the trimming and shaping operations. Fine operations include at least one of the following: ribbon wrapping, leaf trimming, and flower head angle adjustment. Among these, "detailed operations" refer to special processes beyond routine pre-treatment that require florists to invest extra time, possess high levels of manual skill, and have a decisive impact on the final product's aesthetics. The statistical percentage is used to quantify the density of manual skills in the production process. Ribbon wrapping refers to the process of using wrapping paper or ribbon to fold, knot, and fix the shape of the bouquet handle or joint in multiple layers; leaf trimming refers to the need to independently adjust the position and trim the edges of each leaf to create a specific sense of layering or avoid obstruction, rather than removing them in batches; flower head angle adjustment refers to strictly controlling the orientation, tilt angle, and open surface of each flower head to construct a specific visual focal point or geometric shape. Specifically, all the identified operation types are reviewed, items belonging to the category of detailed operations are marked, and their proportion of the total number of operation types is calculated. For example, when making high-end celebratory corsages, the total number of operation types includes 5 items, among which flower head angle adjustment and thin wire wrapping and fixing both belong to detailed operations. If these two items dominate, the proportion of detailed operations is high, indicating that this category relies heavily on manual skills and has a low tolerance for error. This statistic reflects the intensity of effective working hours consumed per unit of time for this floral category.

[0063] Step 3: Based on the type and quantity of flowers and the level of detail required, classify the flowers and their suitability for different floral art categories into different levels of complexity in the wrapping process; The complexity of flower wrapping is a multi-dimensional comprehensive evaluation index used to characterize the overall resource consumption level required to complete a specific floral arrangement, including time cost, skill threshold, and sensitivity to the condition of the flowers. The grading system discretizes continuous numerical characteristics into labels for easier decision-making, such as low complexity, medium complexity, and high complexity. The grading logic is based on a weighted combination of the number of flower types and the proportion of detailed operations. A larger number of flower types means a non-linear increase in the difficulty of material preparation, matching, and color coordination; a higher proportion of detailed operations means a longer production time per unit and more stringent requirements for flower freshness (such as stem hardness and petal toughness). Specifically, threshold ranges can be set: when the number of flower types is less than 3 and the proportion of detailed operations is less than 20%, it is classified as low complexity, suitable for standardized orders with rapid turnover; when the number of flower types is between 3 and 6 or the proportion of detailed operations is between 20% and 50%, it is classified as medium complexity; when the number of flower types exceeds 6 or the proportion of detailed operations exceeds 50%, it is classified as high complexity. For example, a large opening flower basket containing 10 kinds of flowers and requiring each leaf to be artistically trimmed will be clearly identified as high complexity, thus triggering stricter production capacity matching rules.

[0064] Step 4: The more types and quantities of wrapping flowers there are, or the higher the proportion of intricate operations, the higher the complexity of the wrapping flower process.

[0065] This step establishes a positive correlation between the complexity of the flower wrapping process and various influencing factors, serving as the core criterion for grading. Increasing the number of flower types not only linearly increases the number of physical operations but also exponentially increases the difficulty of matching the shapes of different flowers. The increased proportion of meticulous operations directly extends the production cycle of a single piece and increases the probability of failure due to wilted flowers. For example, comparing two bouquets, A requires two types of flowers and no meticulous operations, while B requires five types of flowers and includes extensive ribbon wrapping. According to this rule, B's complexity score is higher than A's. This clear correlation ensures that in subsequent steps, high-complexity orders can be allocated to stores with suitable flower availability and sufficient production capacity, avoiding delivery delays or quality degradation caused by underestimating the production difficulty.

[0066] Example 5: In some embodiments, based on the order information set and combined with the freshness-capacity coupling information set, the method analyzes the fluctuations in flower wrapping time caused by differences in floral categories and the risk of fulfillment delays caused by uneven parallel processing across multiple stores, thereby obtaining an order distribution constraint information set. The method further includes the following steps: Step 1: The order information set includes information on the flowers required for the order, the specifications of the floral arrangement, and the number of orders to be processed; The order information set refers to the core data collection about the order and its processing environment gathered when a user places an order or when an order is scheduled. Specific flower material information includes the flower varieties, quantities, grades, and specific color requirements specified in the order, such as 33 Grade A red roses or 5 lily buds. This information comes from the user's selection on the front-end interface. Floral specifications define the final product's form, size, and packaging style, such as an 11-stem minimalist style, a multi-layered Korean-style package, or a large opening flower basket. This information directly relates to the complexity of the subsequent production process. The number of pending orders refers to the total number of orders queued at the target store that have not yet been completed, reflecting the store's real-time load pressure. For example, when a Mother's Day carnation bouquet order is received, the order information set includes the specific specifications of the carnations, the standard packaging style corresponding to the bouquet, and the number of unprocessed orders currently backlogged at each candidate store (e.g., Store A has 5 backlogged orders, Store B has 12 backlogged orders). By integrating these three types of information, the physical attributes and time urgency of orders can be fully quantified, providing a data input basis for subsequent store selection and risk assessment.

[0067] Step 2: Based on the flower material information required by the order, and combined with the freshness-production capacity coupling information set, stores with limited production capacity for the corresponding flowers are removed, resulting in a preliminary set of stores. The initial screening of stores refers to a subset of stores that, after the first layer of logical filtering, are physically capable of fulfilling the current order. This step relies on the preservation-capacity coupling information set generated in the previous step, which pre-marks the status of each store's flowers. Specifically, the required flower information for the order is compared with the inventory status of each store. If a store is marked with a capacity-restricted flag for a specific flower, it means that the store's flowers have exceeded the allowable opening range of the process (e.g., over-opening leading to inability to shape) or that pre-processing has failed due to environmental factors, making it unsuitable for production. Such stores will be directly excluded from the candidate list. Conversely, only stores with a production-ready flag are retained. For example, if an order requires tulips in a half-open state, and store C's tulips are fully open due to high temperatures and marked as having limited capacity, store C will be eliminated; store D's tulips are in good condition and marked as production-ready, so store D will enter the initial screening of stores. This process effectively avoids distributing orders to stores with stock but unavailable flowers, eliminating the risk of fulfillment failure due to substandard flower quality at the source.

[0068] Step 3: Match the complexity of the flower wrapping process according to the floral specifications, and estimate the time required for each store to complete the current order based on the number of pending orders in the initial screening store set; The required time refers to the total time expected to take for a store to complete the production of a new order and reach a deliverable state from the current moment. This time is constructed using two core dimensions: first, the complexity of the flower arrangement process based on matching floral specifications; and second, the queuing time calculated based on the number of pending orders. Specifically, the floral specifications are first parsed and mapped to specific complexity levels (e.g., simple, medium, complex), with different levels corresponding to different standard production times (e.g., 5 minutes for a simple bouquet, 30 minutes for a complex gift box). Then, the number of pending orders for each store in the initial screening store set is read, and combined with the complexity of each backlog of orders, the waiting time before the new order begins production is calculated. Finally, the waiting time is added to the estimated production time of the current order itself to obtain the required time. For example, if store E currently has 3 simple orders (requiring a total of 15 minutes) and 1 complex order (requiring 30 minutes), and the new order is a medium-complexity bouquet (requiring 20 minutes), then the required time for the store to complete the new order is 15 + 30 + 20 = 65 minutes. This dynamic estimation not only considers the difficulty of a single production run, but also incorporates the delayed impact of the store's real-time load on delivery time, making the time prediction more in line with the actual production scenario.

[0069] Step 4: Compare the required time with the remaining time available for production of the corresponding flowers in the flower openness change sequence. If the required time exceeds the remaining time available for production, mark the corresponding store with a delivery delay warning. The remaining processing time refers to the time window calculated based on the changing sequence of flower bloom rates, from the current moment until the flowers reach the overripe critical point (i.e., no longer meeting the processing requirements). The core logic of this step lies in performing a dual check of time and state. The required time calculated above is compared with this time window. If the required time is less than or equal to the remaining processing time, it means that the store has the ability to complete the processing before the flowers spoil; if the required time exceeds the remaining processing time, it means that even if the store starts queuing for processing now, by the time the order is placed, the flowers may have already overripe or wilted and cannot meet the floral arrangement specifications. In this case, a delivery delay warning will be marked for the store. For example, if store A's remaining processing time for flowers is 40 minutes (i.e., the flowers will be overripe in 40 minutes), while the estimated required time based on the backlog is 50 minutes, since 50 > 40, it is determined that there is an extremely high risk of delivery delay, and a warning sign is generated. This mechanism enables proactive identification of potential service failures, preventing flower waste and customer complaints due to time mismatch.

[0070] Step 5: Summarize the fulfillment delay warnings from the initial screening of stores to obtain the order distribution constraint information set.

[0071] The order distribution constraint information set serves as the final decision-making dataset, integrating store availability screening results and time risk assessment results. This set not only includes a list of stores that passed the initial screening but also details the fulfillment delay warning status, required duration, and remaining production time difference for each store. By aggregating this warning information, three categories of stores can be clearly identified: completely unavailable (already removed above), available but high-risk (marked with a warning), and available and low-risk (no warning). For example, after aggregation, it might be concluded that store B, although on the initial screening list, is marked with a warning due to severe backlog, while store C is on the initial screening list but has no warning. This information set serves as direct input for subsequent development of multi-store predictive order distribution strategies, ensuring that order distribution decisions are not only based on static inventory matching but also deeply integrated with dynamic time constraints and capacity load factors, thereby achieving high efficiency in resource scheduling.

[0072] Example 6: In some embodiments, the process of constructing the initial screening set of stores further includes the following steps: Step 1: Based on the flower material information required by the order, search the freshness-production capacity coupling information set for each store to find the corresponding flower material with the available production label or the production capacity restriction label. The required flower information for an order can refer to the specific flower types, grades, and quantities specified in a newly received customer order, such as 11 cappuccino roses or 5 multi-headed lilies. The preservation-production capacity coupling information set is a global state database generated after the aforementioned steps based on the dynamic changes in flower openness and the complexity of the flower wrapping process. It stores real-time capability tags for each store for specific flowers. A "producible" tag indicates that the store possesses the basic conditions to perform the corresponding floral arrangement process under the current environment and flower conditions; a "production capacity limited" tag indicates that the process cannot be performed due to overripe flowers, incomplete pre-treatment, or harsh environmental conditions. The retrieval process uses flower keywords in the order as an index, traversing all candidate store records in the coupling information set and extracting the corresponding tag bits. For example, when an order requires lisianthus, a quick scan of all stores in the city reveals that store A is marked as "producible," while store B is marked as "production capacity limited" because the lisianthus open too quickly due to high temperatures. This tag-based rapid retrieval instantly identifies a macroscopic range of stores with production potential, providing a data foundation for subsequent screening.

[0073] Step 2: Exclude stores with signs indicating restricted production capacity. Direct exclusion can mean permanently removing stores marked with limited production capacity from the current candidate list, preventing them from participating in any subsequent calculations for this order distribution. This operation is based on deterministic negation logic: once the condition of the flowers or environmental conditions is determined to be irreversibly unsuitable for processing (e.g., flowers have wilted or the temperature exceeds the preservation limit), regardless of the store's geographical proximity or the low number of backlogged orders, fulfillment is impossible. For example, if a store's roses have reached over 90% openness due to a cold chain disruption, exceeding the 30%-70% range required for bouquet making, they will be immediately marked and removed. This step significantly reduces the scale of calculations, avoiding order distribution to paths destined for failure, thereby reducing flower spoilage and customer complaint risks at the source.

[0074] Step 3: For stores with available labels, further extract the store's openness at the current time point in the sequence of changes in flower openness, and determine the minimum openness threshold corresponding to the complexity of the flower wrapping process based on the floral specifications. The flower openness variation sequence is a discrete dataset describing the change in the openness of flowers throughout their entire life cycle, from their initial state to their overripe state. Its construction considers multiple variables such as season, weather, and pre-processing time. The openness at the current time point refers to the openness value of the node in the sequence that strictly corresponds to the current moment. Floral specifications include the specific style requirements of the order. Different styles correspond to different levels of complexity in the flower wrapping process, and each level of complexity has a preset minimum openness threshold—the minimum openness the flowers must reach before specific cutting, shaping, or assembly operations can begin. For example, making a Korean-style spiral bouquet may require flowers with at least 40% openness to allow petals to unfold and the shape to hold. If the flowers are too closed (e.g., only 20% openness), forcing the creation will result in a stiff finished product and easily damage the flower heads. The extraction process involves reading real-time data from the sequence and calling the threshold parameters bound to the order specifications from the process library, preparing for the subsequent comparison.

[0075] Step 4: If the openness at the current time is lower than the minimum openness threshold, it is determined that the store cannot meet the process execution requirements at the current time and is marked as a temporarily unavailable store, and removed from the candidate stores; The judgment logic is based on real-time decision-making using numerical comparison: when the measured openness at the current time point is less than the minimum openness threshold required by the process, it means that although the store's flowers are generally usable (marked as available for production), they have not yet reached the starting point of the optimal production window. Marking a store as temporarily unavailable means assigning it a time-sensitive negative label, distinct from a permanent capacity limitation. This indicates that the store may become available again after a period of natural opening, but it cannot accept tasks within the current order distribution time slice. Removing a store from the candidate store means removing the temporarily unavailable store from the immediate queue of this distribution. For example, if a store's tulips currently have an openness of 15%, while the minimum threshold for the cup-shaped flower pattern required by the order is 25%, even if the store's flowers are fresh and there are no other restrictions, it will still be judged as temporarily unavailable and removed to prevent increased difficulty in wrapping flowers or incorrect finished product shapes due to flowers not being fully open. This step achieves timing control, ensuring that distributed orders can not only be made, but can also begin production immediately in the optimal state.

[0076] Step 5: Select the remaining stores after exclusion and elimination as the initial screening store set.

[0077] The initial screening of stores is a high-quality candidate pool formed through a two-layer filtering mechanism: the first layer eliminates stores that lack production capacity (limited capacity) on a macro level, and the second layer eliminates stores that do not meet the current process requirements (temporarily unavailable) on a micro level. The remaining stores possess the required flower materials and are within the appropriate production window, meeting the necessary and sufficient conditions to accept the current orders. This pool serves as the input for subsequent estimations of fulfillment time and assessments of capacity, ensuring that complex time-series calculations are performed only within truly effective stores, thus improving the accuracy and efficiency of the order distribution strategy. By constructing such a high-confidence initial screening of stores, rework or waste caused by mismatched flower material conditions can be effectively avoided, achieving a match between flower resources and order demand.

[0078] Example 7: In some embodiments, the required construction time, the method further includes: Step 1: Determine the order backlog information based on the current number of pending orders in the initial screening of stores; The order backlog information refers to the aggregated status of all orders that have not yet been completed by each store in the initial screening store set at the current time. Specifically, it includes the total number of orders currently waiting to be processed, the current production stage of each order, and the estimated release time. The purpose of the order backlog information is to quantify the current load level of a store, serving as the basic input for calculating the waiting time for new orders. For example, if a store in the initial screening set currently displays 5 orders awaiting processing, and these 5 orders are at different stages such as waiting to be cut or packaged, these statuses are aggregated to form the store's order backlog information. By obtaining the number of orders awaiting processing in real time, the actual busy level of the store can be dynamically reflected, avoiding deviations caused by static estimations based solely on theoretical capacity.

[0079] Step 2: Based on the order backlog information and the complexity of the wrapping process, insert the current order into the sorting queue of pending orders for each store according to the complexity of the wrapping process; The sorting queue is a logical sequence of all pending orders within the store, arranged according to specific rules. Inserting the current order into the queue involves comparing and sorting the complexity of the flower wrapping process corresponding to the current order's floral specifications against the complexity of existing orders in the order backlog. Specifically, the type of detailed operation and the quantity of flowers required for the current order are first identified to determine its flower wrapping complexity level. Then, based on a preset scheduling strategy (such as shortest-job priority, urgency priority, or complexity balancing strategy), a suitable insertion position is found in the sorting queue. For example, if the current order is a high-complexity multi-layered Korean-style flower box, while the queue mainly contains low-complexity single-rose wrappings, it can be inserted at the end of the queue to avoid interrupting the continuous low-complexity operation process, or it can be inserted into a specific skill group queue based on the florist's skill specialties. This insertion mechanism ensures that order scheduling considers both time priority and the efficiency of process conversion and the rationality of resource allocation.

[0080] Step 3: In the sorting queue, accumulate the total time taken for all orders before the current order as the waiting time; The waiting time refers to the length of time an order takes to queue from the moment it enters the queue until production begins. This time is calculated by iterating through all historical orders preceding the current order in the queue, extracting the estimated wrapping time for each historical order, and then summing them up. The wrapping time for each historical order is a pre-defined or dynamically calculated value based on its floral arrangement type and process complexity. For example, if the current order is third in the queue, with two orders ahead of it, the first estimated to take 10 minutes and the second estimated to take 15 minutes, the total waiting time is 25 minutes. This process realistically simulates queuing on a production line, capturing the time delays caused by the backlog of preceding tasks, and providing a time benchmark for subsequent performance risk assessment.

[0081] Step 4: Add the waiting time to the flower wrapping time of the current order to get the required duration.

[0082] The required time is the predicted total time span needed for the store to complete the current order, covering the entire period from the current moment until the order is finished. The flower wrapping time for the current order itself is the pure production time calculated based on the floral specifications, the types and quantities of flowers required, and the proportion of detailed operations. The final required time is obtained by arithmetically adding the waiting time calculated in the previous steps to the flower wrapping time of the current order. For example, if the calculated waiting time is 25 minutes and the production time of the current order itself is 20 minutes, then the required time for the store to complete this order is 45 minutes. This required time, as a dynamic variable, not only reflects the production difficulty of the order itself but also fully reflects the store's current real-time congestion status. This allows for comparison of this time with the remaining production time of the flowers to determine whether there is a risk of delivery delay due to over-ripening of flowers caused by production time exceeding the allotted time.

[0083] Example 8: In some embodiments, based on the order distribution constraint information set, the time-series order-taking capacity of each store and the timeliness of flower delivery are evaluated, and a multi-store predictive order distribution strategy is formulated and executed. The method further includes the following steps: Step 1: Extract the required duration for each store from the order distribution constraint information set, and determine the critical time node when the flowers reach the overripe state by combining the flower opening degree change sequence, thus obtaining the time-series carrying information; The required time refers to the total time needed for each store to complete the current backlog of orders and new orders, as estimated above. This timeframe is derived from performance risk data compiled from order distribution constraint information. The flower blooming degree change sequence is a dynamic data set describing the degree of blooming of flowers at different time points, providing a basis for predicting the evolution of the flower life cycle. The critical time point refers to the specific moment when the flower blooming degree progresses to an overripe state, no longer meeting the specific requirements for the execution of floral art techniques. Specifically, using the required time for each store as the starting offset, the flower blooming degree change sequence is shifted backward to locate the critical time point when the flower blooming degree progresses to an overripe state. The time difference between the critical time point and the end of the required time is determined as the production margin for each store, and the stores are sorted in descending order of production margin to obtain the time-series information. For example, if a store needs 40 minutes to complete all current orders, and based on the weather and initial opening conditions, its roses will reach the overripe point in 90 minutes, then the store has a processing margin of 50 minutes. If another store requires less time but its flowers are already close to overripe, its processing margin might only be 10 minutes. This calculation method transforms the abstract concept of flower preservation time into a quantifiable time window indicator. This time-series information not only reflects the store's current busyness but also reveals the length of time the store can effectively operate before the flowers expire, providing data support for subsequent prioritization.

[0084] Step 2: Evaluate the order availability of each store at subsequent consecutive time nodes based on the time-series carrying capacity information, and sort the stores from high to low according to the order availability to obtain the store order priority sequence. The available order capacity refers to the maximum number of new orders each store can accommodate in subsequent consecutive time periods. It is defined based on the ratio of the available production capacity in the time-series capacity information to the standard order production time. The store priority sequence is a queue formed by sorting stores according to their available order capacity values ​​from highest to lowest. Specifically, based on the available production capacity of each store in the time-series capacity information, combined with the preset average production time for standard floral categories, the maximum order capacity of each store in the future is assessed. For example, if store A has a available production capacity of 120 minutes, and it takes an average of 10 minutes to make a standard bouquet, then its available order capacity is 12 bouquets; if store B has a available production capacity of 60 minutes, and it takes 15 minutes to make the same type of bouquet, then its available order capacity is 4 bouquets. Store A is ranked before store B, forming a priority distribution sequence. In this process, the available production capacity and the standard production time are used together to quantify the future production capacity of the stores; through their collaborative calculation, a profile of the stores' potential capacity is achieved. This step aims to convert the remaining validity period in the time dimension into the remaining order quantity in the quantity dimension, so that stores with different efficiencies and different flower material conditions can be fairly compared and sorted on the same dimension, thereby generating a scientific distribution priority.

[0085] Step 3: Issue orders to each store in sequence according to the store's priority order sequence, and after each distribution, update the number of pending orders and the flower consumption status in the flower availability sequence for the corresponding store to complete the execution of the multi-store predictive order distribution strategy.

[0086] In this context, "order placement" refers to the control platform sending newly received flower order instructions to the store terminals at the top of the priority list based on a generated priority sequence. The number of pending orders is a dynamic variable reflecting the current workload of each store, while the flower consumption status records information about the time segments or quantity changes of flowers occupied due to pre-orders. Specifically, orders are placed to each store sequentially according to their acceptance priority sequence. Once a store confirms acceptance, a feedback mechanism is immediately triggered, increasing the number of pending orders for that store. Based on the estimated production time for that order, the corresponding time segment is marked as occupied in the flower availability change sequence, or the estimated availability curve of the remaining flowers is adjusted. For example, after an order is distributed to store A, store A's number of pending orders increases from 2 to 3, and the next 15 minutes in its flower availability change sequence is marked as unavailable, resulting in a corresponding reduction in its available production capacity for the next calculation. This update mechanism ensures that the data model remains synchronized with the actual physical world in real time. By updating the number of pending orders and the status of flower consumption in real time, subsequent distribution decisions can be dynamically corrected, avoiding overloaded distribution or flower waste caused by information lag. This forms a closed-loop control logic of evaluation-distribution-update-re-evaluation, effectively ensuring the overall fulfillment efficiency and flower quality under the parallel operation of multiple stores.

[0087] Example 9: In some embodiments, the process of constructing time-series carrying information further includes the following steps: Step 1: Using the required duration for each store as the starting offset, shift backward in the sequence of changes in flower openness to locate the critical time node when the flower openness progresses to the overripe state. The required time can refer to the estimated total time needed for the store to complete the current backlog of orders and new orders, serving as the starting reference point on the timeline. The flower blooming degree change sequence is a dynamic data set recording the expected blooming degree of flowers at various future time points starting from the current moment. The critical time point can be the point in the sequence where the blooming degree of the flowers first reaches or exceeds a preset overripe state threshold. Once this point is exceeded, the flowers will no longer meet the process requirements of any floral arrangement category and will lose their value for creation. Specifically, taking the current moment as zero point and adding the calculated required time, a starting offset moment is determined. Starting from this starting offset moment, the flower blooming degree change sequence is traversed in the future direction to retrieve the trend of the blooming degree value until the moment when the blooming degree value jumps to the overripe threshold is captured. The time corresponding to this moment is marked as the critical time point. For example, if a store needs 40 minutes to complete the current order, and based on weather and flower characteristics, it is predicted that the store's roses will reach overripe state (openness > 85%) 90 minutes after the current moment, then the calculation will start from the 40-minute mark and work backwards until the critical time point of the 90-minute mark is reached. This backward-shifting positioning mechanism based on the initial offset can pinpoint the end boundary of the remaining effective lifespan of the flowers after the current task is completed, providing a time anchor for subsequent resource quantification.

[0088] Step 2: Determine the time difference between the critical time node and the end time of the required duration as the corresponding production margin for each store; The available production margin measures the time resources a store can use to continue taking on new orders after completing its current task, utilizing the remaining flowers. This value is obtained through a simple linear subtraction operation: subtracting the expected end time of the current order (i.e., the end of the required time) from the critical time node identified above. The time difference directly reflects the extra time window available for the flowers while they are not fully ripe. If the difference is positive, it means the store still has surplus time to process other orders before the flowers become unusable; if the difference is zero or negative, it means the flowers will expire immediately or have already expired after the current order is completed, leaving no additional capacity. For example, continuing the previous example, if the critical time node is 90 minutes and the required time ends at 40 minutes, the difference gives a production margin of 50 minutes. This 50 minutes is the maximum time budget that the store can theoretically continue floral arrangements without wasting flowers. By transforming the abstract shelf life of floral materials into a concrete, workable surplus, the perishable natural attributes are quantified into dispatchable production resources, making the carrying capacity potential of different stores comparable.

[0089] Step 3: Sort each store in descending order of available production capacity to obtain time-series carrying information.

[0090] The time-series information is an optimized and sorted list of stores, ranked according to the calculated remaining production capacity of each store. The sorting follows a priority principle, placing the store with the largest remaining production capacity at the top of the list and the store with the smallest at the bottom. This sorting process essentially provides a comprehensive inventory and classification of the time-sensitive resources of all candidate stores in the city or region. Stores with larger remaining production capacity have a longer window before the flowers become overripe after completing their current task, indicating greater potential and leeway to handle subsequent orders. Conversely, stores with smaller remaining production capacity are in a resource-scarce state and should prioritize using their remaining capacity or avoid being assigned long-running orders. For example, if store A has a production capacity of 120 minutes, store B has 60 minutes, and store C has 30 minutes, the generated time-series information sequence would be: Store A → Store B → Store C. This sequence not only demonstrates the static carrying capacity of each store, but also implies a dynamic scheduling strategy in the time dimension. It provides a direct basis for subsequent decision-making for the development of multi-store predictive order distribution strategies, ensuring that orders are prioritized to stores that can fulfill orders immediately and maximize the use of the remaining lifespan of flowers.

[0091] Example 10: In some embodiments, the store undertakes the process of constructing the priority sequence, and the method further includes the following steps: Step 1: Based on the production capacity of each store in the time-series carrying information, assess the maximum number of new orders that each store can accommodate in subsequent consecutive time nodes, and obtain the corresponding order-accepting capacity of each store. The "feasible capacity" refers to the remaining time window, calculated above, during which each store's flowers will remain in optimal condition (not yet overripe) after completing current backlog and pending orders. The "order-taking capacity" is the maximum number of new orders a store can accept at future consecutive time points, calculated based on this time window and the preset standard order production time or historical average time. Specifically, the "feasible capacity" for each store is first obtained, then divided by the average production time per order for that store's corresponding floral category, thus quantifying the order-taking capacity. For example, if a store's "feasible capacity" is 120 minutes, and the average time for producing a standard bouquet of the same type is 10 minutes, then the store's order-taking capacity is calculated to be 12 bouquets. If another store's "feasible capacity" is only 30 minutes, under the same conditions, its order-taking capacity is 3 bouquets. This approach, which transforms the remaining shelf life of fresh flowers into order capacity in the business dimension, makes abstract time constraints measurable and comparable, providing an intuitive data foundation for subsequent prioritization. By assessing the remaining order capacity, it directly reflects the true potential production capacity of each store while ensuring flower quality, avoiding the one-sidedness of judging solely based on current idle status.

[0092] Step 2: Sort the remaining order capacity of each store from highest to lowest value, and arrange the sorted stores in order to obtain the store order priority sequence.

[0093] The store priority sequence serves as the core basis for guiding the actual order distribution order. The execution logic of this step involves using the remaining order capacity of each store calculated in the previous step as the sorting key, employing a descending sorting algorithm to place the store with the largest remaining order capacity at the beginning of the sequence and the store with the smallest at the end. Specifically, it iterates through all candidate stores participating in the distribution, extracts their corresponding remaining order capacity values, and constructs an ordered list. For example, if there are three stores, A, B, and C, with calculated remaining order capacity of 12, 4, and 8 respectively, they will be arranged in the order 12>8>4, generating a store priority sequence of: Store A, Store C, Store B. This sequence indicates that the order distribution strategy prioritizes stores that can not only currently fulfill orders but also continue to efficiently accept orders in the future without causing flower waste. The resulting store priority sequence determines the execution path of the multi-store predictive order distribution strategy, ensuring that orders flow to nodes with high capacity surplus and optimal flower timeliness utilization, thereby achieving global resource balance and minimizing losses.

[0094] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. An order distribution method based on the remaining shelf life of fresh flowers and the real-time capacity of florists, characterized in that, include: Obtain a fresh flower preservation information set and a florist production capacity information set. Based on the fresh flower preservation information set and the florist production capacity information set, analyze the coupling constraint characteristics of the dynamic changes in the openness of flower materials and the complexity of the flower wrapping process under different environmental conditions to obtain a preservation-production capacity coupling information set. Obtain the order information set, and based on the order information set and the freshness-capacity coupling information set, analyze the fluctuation of flower wrapping time caused by differences in flower categories and the risk of fulfillment delay caused by uneven parallel processing of multiple stores, and obtain the order distribution constraint information set. Based on the order distribution constraint information set, assess the time-series order acceptance capacity and flower delivery time adaptability of each store, and formulate and implement a multi-store predictive order distribution strategy.

2. The method according to claim 1, characterized in that, Based on the fresh flower preservation information set and the florist capacity information set, the coupling constraint characteristics of the dynamic changes in the openness of flowers under different environmental conditions and the complexity of the flower wrapping process are analyzed to obtain a preservation-capacity coupling information set, including: The flower preservation information set includes the openness of the flowers and the time spent on flower pretreatment. The flower shop capacity information set includes store weather information and flower materials and matching floral art categories; Based on the flower material openness, the flower material pretreatment time, and the store weather information, the expected openness of the flowers at each time point is analyzed to obtain the flower material openness change sequence. Based on the flower materials and their compatibility with different floral art categories, determine the complexity of the flower wrapping process for each category. Based on the sequence of changes in the openness of the floral materials, and in combination with the complexity of the flower wrapping process, it is determined whether the openness of the floral materials meets the process execution requirements of the corresponding floral art category under the current weather information of the store. If the conditions are met, a corresponding makerable identifier for the floral arrangement category will be generated for the current store. If the requirements are not met, a capacity restriction flag will be generated. By integrating the makerable identifier and the production capacity restricted identifier, the preservation-production capacity coupled information set is constructed.

3. The method according to claim 2, characterized in that, The process of constructing the sequence of changes in the openness of the floral materials includes: The initial openness of the flowers after the pretreatment is completed is determined based on the time taken for pretreatment. Based on the current season in the store's weather information, determine the baseline opening rhythm of the flowers in the current season, and adjust the baseline opening rhythm by accelerating or delaying it according to the specific weather conditions corresponding to the current date, to obtain the adjusted opening progress information; Based on the adjusted opening progress information, the changes in the degree of opening of the flowers at each time point are calculated, and the nodes and their corresponding degrees of opening are arranged in order to obtain the sequence of the changes in the degree of opening of the flowers.

4. The method according to claim 2, characterized in that, The step of determining the complexity of the flower wrapping process for each floral arrangement category based on the matching of the floral materials includes: Based on the flower materials and their matching floral art categories, identify the types and quantities of flowers required in different floral art categories, as well as the types of cutting and shaping operations corresponding to each type of flower material; The percentage of fine operations involved in the cutting and shaping operations is statistically analyzed. The fine operations include at least one of ribbon wrapping, leaf trimming, and flower head angle directional adjustment. Based on the variety and quantity of the flowers and the fine operation, the floral materials are classified into different levels of complexity of the flower wrapping process according to the floral art categories they are compatible with. The greater the number of types or the higher the proportion of fine operations in the wrapping process, the higher the complexity of the wrapping process.

5. The method according to claim 2, characterized in that, Based on the order information set and the freshness-capacity coupling information set, the system analyzes the fluctuations in flower wrapping time caused by differences in floral product categories and the risk of fulfillment delays caused by uneven parallel processing across multiple stores, thus obtaining an order distribution constraint information set, including: The order information set includes information on the flowers required for the order, floral arrangement specifications, and the number of orders to be processed. Based on the flower material information required by the order, and combined with the freshness-capacity coupling information set, stores with the corresponding flower material having the capacity restriction mark are removed to obtain the initial set of stores; Based on the floral design specifications, the complexity of the flower wrapping process is matched accordingly, and combined with the current number of pending orders in the initial screening store set, the time required for each store to complete the current order is estimated. The required time is compared with the remaining time that the corresponding flowers in the flower opening degree change sequence. If the required time exceeds the remaining time, a delivery delay warning is marked for the corresponding store. The order distribution constraint information set is obtained by summarizing the fulfillment delay warnings from the initial screening store set.

6. The method according to claim 5, characterized in that, The process of constructing the initial screening set of stores includes: Based on the flower material information required by the order, the production availability identifier or the production capacity restriction identifier of the corresponding flower materials in each store is retrieved from the freshness-production capacity coupling information set. Stores displaying the aforementioned production capacity restriction label will be excluded. For stores with the aforementioned makerable identifier, further extract the store's openness at the current time point in the sequence of changes in the openness of the floral materials, and determine the minimum openness threshold corresponding to the complexity of the flower wrapping process in the floral specification information. If the current openness is lower than the minimum openness threshold, it is determined that the store cannot meet the process execution requirements at the current time and is marked as a temporarily unavailable store, and removed from the candidate stores; The remaining stores after exclusion and elimination are referred to as the initial screening store set.

7. The method according to claim 5, characterized in that, The required construction process includes: Based on the current number of pending orders in the initial screening store set, determine the order backlog information; Based on the order backlog information and the complexity of the wrapping process, the current order is inserted into the sorting queue of pending orders in each store according to the complexity of the wrapping process. In the sorting queue, the total time spent on all orders preceding the current order is accumulated as the waiting time; The required duration is obtained by adding the waiting time to the packaging time of the current order itself.

8. The method according to claim 5, characterized in that, The step of evaluating the time-series order-taking capacity and flower delivery timeliness of each store based on the order distribution constraint information set, and formulating and executing a multi-store predictive order distribution strategy includes: Extract the required duration of each store from the order distribution constraint information set, and determine the critical time node when the flowers reach the overripe state by combining the flower opening degree change sequence, thus obtaining the time-series carrying information; Based on the time-series carrying information, the order capacity of each store at subsequent consecutive time nodes is evaluated, and the stores are sorted from high to low according to the order capacity to obtain the store order priority sequence. Orders are distributed to each store in sequence according to the store acceptance priority sequence, and the number of pending orders and the consumption status of flowers in the flower material openness change sequence are updated synchronously after each distribution, thus completing the execution of the multi-store predictive order distribution strategy.

9. The method according to claim 8, characterized in that, The process of constructing the time-series information includes: Using the required duration for each store as the starting offset, the sequence of changes in the openness of the flowers is shifted backward to locate the critical time node when the openness of the flowers progresses to the overripe state. The time difference between the critical time node and the end time of the required duration is determined as the production margin for each store. The stores are sorted in descending order of the available production capacity to obtain the time-series carrying information.

10. The method according to claim 9, characterized in that, The process of constructing the store acceptance priority sequence includes: Based on the production capacity of each store in the time-series carrying information, the maximum number of new orders that each store can accommodate in subsequent consecutive time nodes is evaluated, and the order-accepting capacity of each store is obtained. The order availability of each store is sorted from highest to lowest numerical value, and the sorted stores are arranged sequentially to obtain the store order priority sequence.

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