A dynamic order scheduling method and system across stores and franchised flower delivery cooperation

Abstract

The application relates to the technical field of intelligent order scheduling, in particular to a dynamic order scheduling method and system cooperating with cross-store and franchised flower delivery. The method comprises the following steps: acquiring real-time order data and store information set, analyzing order flower art demand and matching information of different store flower art specialists based on the real-time order data and in combination with the store information set, and obtaining skill matching information set; analyzing the influence of the time evolution effect of the multiple physical fields on the flower materials under the predicted distribution path based on the skill matching information set, and obtaining path evaluation information set; analyzing the path risk factors leading to the decline of order completion quality and the increase of distribution loss based on the path evaluation information set, and obtaining order performance risk information; dynamically adjusting the order assignment and path planning in real time according to the order performance risk information, minimizing the skill mismatch and transportation loss, and outputting a dynamic order scheduling log. Resource allocation is optimized, and the order completion quality and user satisfaction are improved.

Description

Technical Field

[0001] This application relates to the field of intelligent order scheduling technology, and in particular to a dynamic order scheduling method and system for cross-store and franchised delivery services. Background Technology

[0002] Currently, the flower retail industry generally adopts order scheduling methods based on fixed zones or simple proximity rules. In the mixed network of direct-operated and franchised stores, most of them only undertake the functions of order recording and forwarding. The resources and status data of each store are isolated from each other, forming "data silos". The delivery process usually relies on external transportation capacity for extensive order dispatch. Overall, the existing technology lacks the ability to coordinate and optimize the scheduling of real-time resources and dynamic demands of the entire network.

[0003] Uneven workloads and flower material waste are common problems. The inability to make comprehensive decisions based on factors such as florist skills, flower characteristics, real-time road conditions, and weather affects service quality and customer experience. At the same time, static and rigid scheduling rules make it difficult to fairly and flexibly take into account the interests of franchisees, which restricts the improvement of the brand's overall network collaborative operation level. Therefore, there is an urgent need for a new method that can achieve dynamic, collaborative, and intelligent scheduling. Summary of the Invention

[0004] This application provides a dynamic order scheduling method and system for cross-store and franchised delivery services to solve the above problems.

[0005] Firstly, this application provides a dynamic order scheduling method for cross-store and franchised flower delivery collaboration. The method includes: acquiring real-time order data and a store information set; based on the real-time order data and the store information set, analyzing the matching information between the order's floral needs and the expertise of florists in different stores to obtain a skill matching information set; based on the skill matching information set, analyzing the impact of the evolution of multi-physics field effects on floral materials under the expected delivery route to obtain a path evaluation information set; based on the path evaluation information set, analyzing path risk factors that lead to a decline in order completion quality and increased delivery losses to obtain order fulfillment risk information; and dynamically adjusting order assignment and path planning in real time according to the order fulfillment risk information to minimize skill mismatch and transportation losses, and outputting a dynamic order scheduling log.

[0006] The above technical solutions can accurately match floral design needs with florists' expertise, ensuring the quality of the finished products and meeting user expectations; optimize delivery routes by combining multi-physics field analysis to reduce flower material transportation losses and save costs; dynamically adjust mechanisms to adapt to various unexpected changes and improve order fulfillment rates; optimize resource allocation across stores and franchise systems to improve operational efficiency; and provide data support for subsequent optimization through scheduling logs to enhance the company's core competitiveness.

[0007] Optionally, the step of analyzing the matching information between the order's floral arrangement requirements and the florists' expertise in different stores, based on the real-time order data and the store information set, to obtain a skill matching information set, includes: the real-time order data including the order delivery address and the order's floral arrangement requirements; the store information set including store location information and store florist information set; based on the order delivery address and the store location information, analyzing the geographical accessibility of each store's service orders to obtain geographical matching information including distance factors and estimated delivery time; based on the order's floral arrangement requirements and the store florist information set, analyzing the compatibility information between the florist's expertise and the corresponding production techniques for the floral arrangement requirements of the order to obtain skill matching information; and based on the geographical matching information and the skill matching information, performing a geographical-skill trade-off for each store to prioritize matching florists with the highest skill compatibility within an acceptable service range, thus obtaining the skill matching information set.

[0008] Optionally, the process of constructing the skill matching information includes: based on the order's floral arrangement requirements, analyzing the types and styles of floral works required for the order to obtain the required techniques information; based on the store's florist information set, analyzing the florist's historical work data and skill certification data to obtain the florist's expertise information; based on the order's required techniques information, combined with the florist's expertise information, analyzing the correspondence between the two in terms of technique category, technique complexity, and historical application effect of the techniques to obtain the technique fit for each florist; based on the technique fit, sorting and filtering all candidate florists from high to low to obtain the skill matching information.

[0009] Optionally, the specific implementation of the geo-skill trade-off for each store based on the geographic matching information and the skill matching information includes: analyzing the mutually exclusive relationship between the increase or decrease of physical distance and the rise or fall of florist skill compatibility based on the geographic matching information and the skill matching information; analyzing the differentiated emphasis of the current order on delivery time and craftsmanship based on the order's floral requirements; constructing a dynamic priority fusion rule based on the mutually exclusive relationship and the differentiated emphasis: for orders emphasizing craftsmanship, prioritizing the nearest store with a skill compatibility not lower than a set threshold; for orders emphasizing timeliness, prioritizing the store with the highest skill compatibility within the fastest delivery range; and, according to the dynamic priority fusion rule, synchronously traversing and logically adjudicating candidate stores and corresponding florist resources that meet the basic conditions to obtain a target assignment scheme that simultaneously carries the optimal geographic attributes and the optimal skill attributes, thereby completing the geo-skill trade-off.

[0010] Optionally, the step of analyzing the impact of multi-physics field time-dependent evolution on floral materials under the expected delivery route based on the skill matching information set to obtain a route evaluation information set includes: determining the starting delivery store location and the order delivery address based on the skill matching information set to obtain the expected delivery route; analyzing the vibration field intensity distribution of traffic flow data acting on the transport vehicle during the delivery period based on the expected delivery route to obtain a multi-physics field interaction spectrum; the traffic flow data includes road surface smoothness and traffic density, used to quantify the level of physical disturbance that causes mechanical damage to floral materials during delivery; analyzing the tolerance threshold of different floral material categories to physical vibration based on the order's floral requirements to obtain floral material category vulnerability information; and analyzing the impact of the multi-physics field interaction spectrum on the quality degradation of floral materials within the expected delivery route time, based on the floral material category vulnerability information, to obtain the route evaluation information set.

[0011] Optionally, the process of constructing the multiphysics field interaction spectrum includes: based on the expected delivery route, combined with the road surface smoothness and the traffic flow density, analyzing the vehicle foundation vibration information caused by the combined effects of smoothness differences and traffic flow density fluctuations to obtain the foundation vibration spectrum for each road segment; based on the foundation vibration spectrum, according to the inherent vibration isolation properties of the transport vehicle used for delivery, analyzing the dynamic response and vibration transmission process of the vehicle to foundation vibration to obtain the vibration field intensity distribution; based on the vibration field intensity distribution, combined with the order's floral requirements, analyzing the sensitivity characteristics and damage threshold of the floral materials to vibration frequencies; based on the sensitivity characteristics, the damage threshold, and combined with the expected delivery route, analyzing the cumulative and changing information of floral material damage during the delivery process to generate the multiphysics field interaction spectrum used to quantify the risk of mechanical damage to floral materials during the delivery process.

[0012] Optionally, the step of analyzing the tolerance threshold of different flower categories to physical vibration based on the order's floral arrangement requirements to obtain flower category vulnerability information includes: analyzing the category and morphological characteristics of the flowers included in the order based on the order's floral arrangement requirements to obtain a set of flower material physical structure information; analyzing the differences in petal thickness, stem lignification degree, and flower structure compactness of the transported flower categories based on the flower material physical structure information set to obtain inherent mechanical strength information; and analyzing and quantifying the risk information of wilting, breakage, and shedding of the transported flower categories under the influence of vibration intensity and duration during transportation based on the flower material physical structure information set and the inherent mechanical strength information to obtain the flower category vulnerability information.

[0013] Optionally, the step of analyzing path risk factors that lead to decreased order completion quality and increased delivery losses based on the path assessment information set to obtain order fulfillment risk information includes: based on the path assessment information set and combined with the vulnerability information of the flower material category, comparing the vibration field intensity of each segment in the transportation path with the tolerance threshold of the currently transported flowers material, screening out risky segments with vibration intensity exceeding the limit, and obtaining risky segment identification information; based on the risky segment identification information, analyzing the spatial distribution characteristics of the risky segments in the expected delivery path, identifying path vulnerable sections where the risky segments appear continuously and densely, and obtaining a path risk distribution pattern; based on the path risk distribution pattern, analyzing the cumulative process and probability of damage to flowers material when delivery passes through the path vulnerable sections, and quantifying and generating the order fulfillment risk information used to predict the order completion quality and loss level.

[0014] Optionally, the step of dynamically adjusting order assignment and route planning in real time based on the order fulfillment risk information to minimize skill mismatch and transportation losses, and outputting a dynamic order scheduling log, includes: based on the order fulfillment risk information and the skill matching information set, analyzing the trade-off between the skill resources required for risk avoidance and the current predetermined assignment scheme, obtaining the resource adjustment space under risk avoidance constraints; based on the resource adjustment space and the route evaluation information set, analyzing a set of strategies for replanning delivery routes to simultaneously reduce transportation losses, obtaining a feasible set of optimized routes; based on the feasible set of optimized routes, performing collaborative optimization analysis to generate a dynamic adjustment strategy that can simultaneously satisfy the dual objectives of skill matching improvement and route risk reduction; based on the dynamic adjustment strategy, updating the order assignment and route planning, and simultaneously recording the logical basis and expected goal achievement of updating the dynamic adjustment strategy, obtaining the dynamic order scheduling log.

[0015] Secondly, this application provides a dynamic order scheduling system for cross-store and franchised flower delivery collaboration. The system includes: a skill matching module, used to acquire real-time order data and store information sets, and based on the real-time order data and the store information sets, analyze the matching information between the order's floral needs and the expertise of florists in different stores to obtain a skill matching information set; a path evaluation module, used to analyze the impact of the evolution of multi-physics field effects on floral materials under the expected delivery path based on the skill matching information set to obtain a path evaluation information set; an order fulfillment module, used to analyze path risk factors that lead to a decline in order completion quality and an increase in delivery losses based on the path evaluation information set to obtain order fulfillment risk information; and an order scheduling module, used to dynamically adjust order assignment and path planning in real time according to the order fulfillment risk information to minimize skill mismatch and transportation losses, and output a dynamic order scheduling log. Attached Figure Description

[0016] 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.

[0017] Figure 1 This is a schematic diagram illustrating an application scenario provided in one embodiment of this application;

[0018] Figure 2 A flowchart illustrating a dynamic order scheduling method for cross-store and franchised delivery services, provided as an embodiment of this application;

[0019] Figure 3 This is a schematic diagram of a dynamic order scheduling system structure that allows for collaboration between stores and franchised delivery services, as provided in one embodiment of this application. Detailed Implementation

[0020] 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.

[0021] 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.

[0022] The embodiments of this application will now be described in further detail with reference to the accompanying drawings.

[0023] In the process of cross-store and franchised flower delivery collaborative scheduling, the current flower retail industry's order scheduling mostly relies on fixed zones or simple proximity rules; in the direct-operated and franchised mixed network, only order recording and forwarding are realized, and the resource status data between stores are isolated from each other, forming data silos. Delivery mostly relies on external transportation capacity for extensive order dispatch, and the industry as a whole lacks the ability to coordinate and optimize scheduling of real-time resources and dynamic demands across the entire network.

[0024] Based on this, this application provides a dynamic order scheduling method and system for cross-store and franchised flower delivery collaboration, which accurately matches floral needs with florists' expertise, ensuring the quality of the work and meeting user expectations; optimizes delivery routes based on multi-physics field analysis to reduce flower material transportation losses and reduce operating costs; adapts to various emergencies through a dynamic adjustment mechanism to improve order fulfillment efficiency; optimizes resource allocation across stores and franchise systems, and combines scheduling log data to feed back into operational optimization, continuously strengthening the company's core competitiveness.

[0025] Figure 1 This application provides an illustration of an application scenario. In the process of cross-store and franchised flower delivery collaborative scheduling, the method provided in this application is applied to comprehensively ensure the quality of floral services, reduce costs, improve operational efficiency, and strengthen the core competitiveness of enterprises through demand and expertise matching, delivery route optimization, dynamic adjustment of fulfillment, resource allocation upgrades, and log data support.

[0026] Specifically, the method provided in this application can be applied to any server. The server interacts with the Huadi online operation platform and the Huadi store management platform to obtain real-time order data provided by the Huadi online operation platform and store information set provided by the Huadi store management platform. It accurately matches floral needs with florists' expertise, generates order fulfillment risk information for store managers, dynamically adjusts mechanisms to adapt to various unexpected changes, improves order fulfillment rate, and outputs dynamic order scheduling logs to headquarters store operation and maintenance personnel to optimize resource allocation across stores and franchise systems and improve operational efficiency.

[0027] For specific implementation details, please refer to the following examples.

[0028] Figure 2 This is a flowchart illustrating a dynamic order scheduling method for cross-store and franchised delivery services, provided as an embodiment of this application. The method of this embodiment can be applied to servers in the above scenarios. Figure 2 As shown, the method includes:

[0029] S201. Obtain real-time order data and store information set. Based on the real-time order data and store information set, analyze the matching information between the floral arrangement requirements of the order and the expertise of the florists in different stores to obtain the skill matching information set.

[0030] Real-time order data can be a dataset submitted by users through online platforms (such as the Flower Delivery mini-program), containing information such as floral arrangement type, style, flower types and quantities, delivery address, and delivery time requirements, with the Flower Delivery online operations platform as the data source. Store information can be a collection of basic information from all stores across the Flower Delivery system and its franchisees, with the Flower Delivery store management platform as the data source. Floral arrangement requirements can be the specific floral arrangement requirements explicitly stated in the order. Florist expertise can be information on a florist's proficiency in creating specific floral arrangements, presenting styles, or applying techniques. Skill matching information can be a set of information reflecting the degree of compatibility between the order's floral arrangement requirements and the expertise of florists at each store.

[0031] Specifically, in the large-scale operation model of cross-store and franchised flower delivery, if the order and florist matching process is only based on distance or delivery capacity and the deep matching of order requirements (such as "artistic structured bouquets") and florists' expertise (such as being proficient only in existing etiquette flower arrangements) is ignored, it will directly lead to the finished product being seriously inconsistent with customer expectations, resulting in a high rate of rework, redoing, or customer complaints. This not only wastes flowers and labor time, but also damages the brand's reputation.

[0032] S202. Based on the skill matching information set, analyze the impact of the time evolution of multiple physical fields on the floral materials under the expected delivery route, and obtain the route evaluation information set.

[0033] Floral materials can be the basic plant materials used in creating floral arrangements. Multiphysics time-dependent evolution refers to the combined effects of various physical factors, such as vibration, on floral materials during delivery. The route assessment information set is a comprehensive analysis of the degree to which floral materials are affected by multiphysics time-dependent evolution under different anticipated delivery routes, forming an assessment of their adaptability to various routes.

[0034] Specifically, in the large-scale operation model of cross-store and franchised flower delivery, if the shortest distance or time is the only goal when planning the delivery route, and the time-sensitive damage to delicate flowers (such as hydrangeas) caused by bumps and vibrations during transportation is ignored, the bouquets will have already suffered irreversible damage such as dehydration, petal loss, or disarray when they arrive, resulting in economic losses and a substantial decline in customer experience.

[0035] S203. Based on the route evaluation information set, analyze the route risk factors that lead to a decline in order completion quality and an increase in delivery losses, and obtain order fulfillment risk information.

[0036] Route risk factors can be any route-related factors that lead to decreased order completion quality and increased delivery losses. Order fulfillment risk information can be a comprehensive set of information that integrates all route risk factors, reflecting potential quality problems and loss risks during order fulfillment.

[0037] Specifically, in the large-scale operation model of cross-store and franchise delivery, when integrating production and delivery information, if we only look at the results of skill matching and route evaluation in isolation without analyzing the systemic risks of order fulfillment formed by the combination of the two (such as high-matching orders needing to be delivered through bad routes), we cannot proactively identify the order solutions most likely to fail, resulting in short-sighted scheduling decisions, resources being misallocated to high-risk orders, and overall unstable fulfillment quality.

[0038] S204. Based on order fulfillment risk information, dynamically adjust order assignment and route planning in real time to minimize skill mismatch and transportation losses, and output dynamic order scheduling logs.

[0039] Dynamic order assignment and route planning refers to the process of adjusting the target stores and corresponding delivery routes for orders in real time based on order fulfillment risk information, real-time order status, changes in store resources, and dynamic route environment. A dynamic order scheduling log can be a log file that records all key information throughout the entire process of an order's dispatch, from receipt to completion.

[0040] Specifically, in the large-scale operation model of cross-store and franchised delivery services, if static or batch processing rules are used when executing scheduling decisions, it is impossible to dynamically redistribute and replan the routes of high-risk orders based on real-time changes in order flow, road conditions and store status. In this case, the system will rigidly execute the original high-risk plan and will not be able to proactively avoid the impending quality degradation and loss, thus rendering the aforementioned risk analysis results worthless and the overall scheduling optimization goal unsuccessful.

[0041] The method provided in this embodiment can accurately match floral design needs with florists' expertise, ensuring the quality of the finished product and meeting user expectations; it can also optimize delivery routes by combining multi-physics analysis to reduce flower material transportation losses and save costs; the dynamic adjustment mechanism can adapt to various unexpected changes and improve order fulfillment rates; it can optimize resource allocation across stores and franchise systems to improve operational efficiency; and it can also provide data support for subsequent optimization through scheduling logs, thereby enhancing the company's core competitiveness.

[0042] In some embodiments, real-time order data includes the order delivery address and the order's floral arrangement requirements; the store information set includes store location information and a store florist information set; based on the order delivery address and combined with the store location information, the geographical accessibility of service orders from each store is analyzed to obtain geographical matching information including distance factors and estimated delivery time; based on the order's floral arrangement requirements and combined with the store florist information set, the matching information between the florist's expertise and the production techniques corresponding to the floral arrangement requirements of the order is analyzed to obtain skill matching information; based on the geographical matching information and combined with the skill matching information, a geographical-skill balance is performed on each store to prioritize matching the florist resources with the highest skill matching within an acceptable service range, resulting in a skill matching information set.

[0043] The order delivery address can be the location specified by the user for receiving the bouquet. Order floral arrangement requirements can be the user's personalized requests for the bouquet. Store location information can be the specific spatial location of the store. Store florist information set can be a collection of professional information related to all florists in the store. Geographical accessibility can be the store's service coverage capability to the order delivery address, characterized by distance factors and estimated delivery time. Geographic matching information can be store service suitability data derived from the analysis of the order delivery address and store location information. Distance factor can be the actual spatial distance between the store and the order delivery address. Estimated delivery time can be the estimated time from when the bouquet is completed at the store to when it is delivered to the delivery address. Skill matching information can be technique suitability data derived from the analysis of the order floral arrangement requirements and the florist's expertise. Geographic-skill trade-off can be a decision-making process that integrates geographic matching information and skill matching information, prioritizing orders based on their differentiated emphasis on delivery timeliness and floristry skills.

[0044] Specifically, in the process of scheduling orders for cross-store franchise flower delivery, if a dual analysis of geography and skills is not performed by combining real-time order data and store information sets, focusing only on distance can easily lead to a mismatch between the florist's skills and order requirements, resulting in works that do not meet user expectations. Focusing only on skills can easily lead to the selection of distant stores, causing flowers to wilt and break due to delivery delays. It can also lead to resource mismatch due to differences in order demand preferences, significantly increasing the return and exchange rate and operating costs. To address the aforementioned issues: First, multi-source data fusion is employed: order delivery addresses (e.g., "Building B, XX Science and Technology Park") and structured descriptions of floral arrangements (e.g., "Forest-themed wedding arch, main flowers: hydrangeas and orchids") are extracted in real-time from the order flow. Simultaneously, store location information including latitude and longitude and a set of store florists' information including skill matrices are obtained from the store data center. Next, using the path planning API of Geographic Information System (GIS) combined with real-time traffic flow data, the driving distance and estimated delivery time from each candidate store to the delivery address are calculated (e.g., Store A is 3.5 kilometers away and is expected to take 25 minutes), generating quantified geographic matching information. At the skill analysis level, Natural Language Processing (NLP) technology is used to extract keywords and identify intent from the order request text, and semantic similarity is calculated with skill tags (e.g., "proficient in structural floral arrangements," "proficient in European classical style") and feature vectors of historical works in the florist's information. Furthermore, this is combined with the florist's... Historical ratings of similar orders (e.g., an average of 4.8 stars) and the success rate of complex works are used to quantify the skill matching degree of each florist through a weighted scoring model (e.g., florist Zhang San's skill matching degree rating for this order is 92 / 100), thereby generating skill matching information. Finally, a geographical-skill trade-off is performed: it is not a simple comparison of static values, but rather the construction of a dynamic priority rule engine. For example, for orders explicitly marked "high requirements for styling", the engine will first filter out all stores with skill matching degrees higher than a preset threshold (e.g., 85 points), and then select the closest one for assignment; for orders marked "urgent order, delivery as soon as possible", it will first identify all available stores within the promised fastest delivery time (e.g., 30 minutes), and then assign the one with the highest skill rating. This synchronous traversal and logical decision-making based on rules and multi-objective evaluation ultimately outputs an optimal assignment scheme that balances timeliness and quality, namely the skill matching information set.

[0045] The method provided in this embodiment accurately matches store and florist resources, ensuring that orders that emphasize skills receive highly suitable works, while also meeting the need for fast delivery for orders that emphasize timeliness. This effectively reduces order returns and exchanges due to skill mismatch and flower material losses caused by excessive delivery time, optimizes resource allocation, and improves order completion quality and user satisfaction.

[0046] In some embodiments, based on the floral arrangement requirements of the order, the types and styles of floral works required for the order are analyzed to obtain the technical information required for the order; based on the information set of store florists, the historical work data and skill certification data of the florists are analyzed to obtain the information on the florists' strengths; based on the technical information required for the order, combined with the information on the florists' strengths, the correspondence between the two in terms of technical categories, technical complexity, and historical application effects of the techniques is analyzed to obtain the technical fit for each florist; based on the technical fit, all candidate florists are sorted and screened from high to low to obtain skill matching information.

[0047] The required techniques for an order can include the specific methods, procedures, and craftsmanship required to complete the floral arrangement. The florist's past work data includes records of their previous floral arrangements. Skill certification data provides evidence of the florist's expertise in a particular area of ​​floral art. Information on the florist's areas of strength includes their expertise in specific types, styles, and techniques of floral arrangements. Technique compatibility is a quantifiable indicator of how well the required techniques match the florist's strengths.

[0048] Specifically, in the process of order scheduling across stores and franchised flower delivery companies, relying solely on distance or experience for assignment can easily lead to skill mismatch. For example, assigning an order that requires advanced structural techniques to a florist who is only good at traditional bouquets can directly result in the finished product being seriously inconsistent with the customer's expectations, leading to a high probability of rework, complaints, and even order loss. This not only wastes materials and time but also continuously damages the brand's professional reputation. To address the aforementioned issues: First, natural language processing and image recognition technologies are used to analyze the "order floral design requirements." For example, from a customer's description of a "forest-style natural hand-held flower basket, requiring a moss base and an asymmetrical drooping effect," keywords and reference image features are extracted. The structured output is then presented as "required technique information for the order," including the type of work (hand-held flower basket), style (forest-style natural), core techniques (moss base construction, asymmetrical drooping shape), and complexity level (e.g., intermediate). Simultaneously, historical order data and skill profiles of the target florist are retrieved from the "store florist information set." Data mining techniques are used to analyze the keyword tags, customer ratings, completion time, and uploaded image features of their past works. Combined with their skill certification records (e.g., whether they hold a "structural florist" certificate), a multi-dimensional, dynamic "florist's expertise" profile is constructed, quantifying their skills in various aspects. The algorithm first assesses a florist's proficiency, style preference, and historical performance within the same skill category. Then, it employs a hybrid matching algorithm based on rules and collaborative filtering to map and compare the order's skill information with each florist's expertise across multiple dimensions. For example, the algorithm examines the frequency and quality of tags such as "forest style," "moss," and "asymmetrical design" in the florist's past works, and evaluates the correlation between their certificates and the skills required for the order (such as structural design). A quantitative "skill matching score" (ranging from 0 to 1, with 0.9 indicating a high degree of matching) is calculated. Finally, all candidate florists are sorted in descending order based on this matching score, and a filtering threshold is set according to business rules (e.g., only florists with a matching score higher than 0.75 are displayed). This automatically generates a recommended list sorted by matching score, which is the final "skill matching information," providing accurate capability-based decision-making for subsequent stages.

[0049] The method provided in this embodiment provides a structured analysis of order technique requirements and florist skill profiles, and a quantitative calculation of the multi-dimensional fit between the two. This enables the precise selection of the optimal solution from a vast amount of resources, fundamentally eliminating quality problems caused by skill mismatch, ensuring the artistic presentation level of each order, significantly improving customer satisfaction, and providing high-quality capability dimension input for subsequent global scheduling decisions.

[0050] In some embodiments, based on geographic matching information and skill matching information, the mutually exclusive relationship between the increase or decrease of physical distance and the rise or fall of florist skill compatibility is analyzed; based on the order's floral arrangement requirements, the differentiated emphasis of the current order on delivery timeliness and craftsmanship is analyzed; based on the mutually exclusive relationship and the differentiated emphasis, a dynamic priority fusion rule is constructed: for orders emphasizing craftsmanship, priority is given to ensuring the nearest store with a skill compatibility not lower than a set threshold; for orders emphasizing timeliness, priority is given to ensuring the store with the highest skill compatibility within the fastest delivery range; according to the dynamic priority fusion rule, the candidate stores and corresponding florist resources that meet the basic conditions are simultaneously traversed and logically judged to obtain a target assignment scheme that simultaneously carries the optimal geographic attributes and the optimal skill attributes, so as to complete the geographic-skill trade-off.

[0051] Physical distance can be the actual spatial distance between the candidate store and the order's delivery address. Mutual exclusion can be the inverse relationship between increases or decreases in physical distance and increases or decreases in the florist's skill matching. Delivery time can be the estimated delivery time from when the floral arrangement is completed at the store to the order's delivery address. Craftsmanship skills can be the craftsmanship skills required to complete the order's floral arrangement requirements. Differentiation preference can be the order's priority preference between the two core requirements of delivery time and craftsmanship skills. Dynamic priority fusion rules can be decision rules that balance geographical and skill attributes based on the order's differentiation preference. Skill-focused orders are those with craftsmanship skills as the core requirement within the differentiation preference. The set threshold can be the minimum skill matching standard preset to ensure the quality of floral arrangement production. The nearest store can be the candidate store with the closest physical distance to the order's delivery address, provided the skill matching is not lower than the set threshold. Time-focused orders can be those with delivery time as the core requirement within the differentiation preference. Fastest delivery range can be the longest acceptable delivery time range preset to meet timeliness requirements. The highest skill-matched store can be the candidate store with the highest florist skill matching within the fastest delivery range. Basic conditions can be the minimum entry standards that candidate stores must meet. Synchronous traversal and logical decision-making can be a process of comprehensively screening candidate stores and florist resources, and making logical judgments and decisions based on dynamic priority fusion rules. The target assignment scheme can be an order allocation scheme derived through synchronous traversal and logical decision-making that simultaneously incorporates the optimal geographical attributes and optimal skill attributes.

[0052] Specifically, in the process of scheduling cross-store flower delivery orders, if a geographical-skill balance is not considered, and only florists with mismatched skills are assigned based on distance, the quality of the work will be substandard. Conversely, if only skill is considered, distant stores may be selected, extending delivery time and increasing flower wastage. This can lead to customer complaints, order cancellations, and misallocation of store resources, severely impacting the platform's reputation and operational efficiency. To address these issues: First, by statistically analyzing historical assignment data, a negative correlation between physical distance and the average skill rating of matchable florists is quantified, i.e., a "mutually exclusive relationship" model. Then, Natural Language Processing (NLP) technology and a rule engine are used to analyze order requirements: For example, if the order description contains keywords such as "replica" or "artistic styling," or the customer tag is "premium member," it is determined to be "skill-oriented"; if the order is marked as "urgent" or the requirement is "simple bouquet," it is determined to be "time-oriented." Based on this, a dynamic priority fusion rule is constructed: for orders emphasizing skill, the rule is set as "skill compatibility ≥ 0.8." The constraint "5" serves as a hard constraint. Then, among the candidate stores that meet this constraint, the shortest path algorithm is used to select the geographically closest one (e.g., store A is calculated to be 3 kilometers away). For orders prioritizing timeliness, the rule first defines a geofence (e.g., an area with an estimated delivery time ≤ 30 minutes and a radius of approximately 5 kilometers). Within this "fastest delivery range," a sorting algorithm selects the florist with the highest skill fit (e.g., the florist at store B has a skill fit of 0.92). Finally, all candidate stores and florists are simultaneously traversed and multi-objective decisions are made, i.e., the matching degree of each option to the above rules is calculated in parallel, and the optimal overall target assignment scheme is output, completing this trade-off.

[0053] The method provided in this embodiment balances geographical and skill factors through dynamic priority rules, ensuring that orders that emphasize craftsmanship are produced with high quality and that orders that emphasize timeliness are delivered quickly. This avoids problems such as substandard quality and delivery delays, optimizes the allocation of store resources, reduces flower material waste, improves user satisfaction, and enhances the flexibility and reliability of platform order fulfillment, thus consolidating the operational foundation.

[0054] In some embodiments, based on the skill matching information set, the starting delivery store location and the order delivery address are determined to obtain the expected delivery route; based on the expected delivery route, the vibration field intensity distribution of traffic flow data acting on the transport vehicle during the delivery period is analyzed to obtain the multi-physics field interaction spectrum; the traffic flow data includes road surface smoothness and traffic density, used to quantify the level of physical disturbance that causes mechanical damage to the flowers during the delivery process; based on the floral arrangement requirements of the order, the tolerance threshold of different flower categories to physical vibration is analyzed to obtain the vulnerability information of flower categories; based on the multi-physics field interaction spectrum, combined with the vulnerability information of flower categories, the impact of the multi-physics field interaction spectrum on the quality degradation of flowers during the expected delivery route time is analyzed to obtain the route evaluation information set.

[0055] The projected delivery route can be the flower transportation route planned based on the starting delivery store location and order delivery address determined by the skill matching information set. Multiphysics time-dependent evolution can be the process of gradual quality changes in flowers during delivery due to physical fields such as vibration from the transport vehicle over time. The multiphysics spectrum can be a spectral diagram used to quantify the risk of mechanical damage to flowers during delivery. Traffic flow data can be parameters reflecting traffic conditions along the delivery route, including road surface smoothness and traffic density. The transport vehicle can be the vehicle used to carry the flowers for delivery. Flower category vulnerability information can be the risk information of different flower categories wilting, breakage, and shedding under physical vibration. Physical vibration can be the mechanical motion such as bumps and swaying generated by the transport vehicle during operation. The tolerance threshold can be the maximum intensity or duration of physical vibration that flowers can withstand; exceeding this threshold will lead to quality deterioration.

[0056] Specifically, in the process of cross-store and franchised flower delivery collaborative scheduling, only the skill matching was completed without analyzing the impact of flower transportation. Physical effects such as vibrations along the delivery route can cause flowers to wilt and break. Moreover, different flowers have different tolerances, and changes in traffic flow can exacerbate losses, directly reducing order fulfillment quality, causing user complaints, and increasing flower loss costs, resulting in resource waste. To address the above issues: First, extract the specific starting delivery store location (such as flagship store A in the city center) and the order delivery address (such as a residence in the eastern part of the city) assigned to the current order from the skill matching information set. Then, plan the expected delivery route (such as a route with a total length of 15 kilometers, including main urban roads and some auxiliary roads) through the integrated map service API. Next, the system utilizes real-time traffic data platforms and historical road databases to obtain road surface smoothness indices (e.g., using the International Roughness Index (IRI) as a measure, with a value of 3.5 m / km for a certain road segment) and real-time traffic density data (e.g., 30 vehicles per minute passing through a certain road segment) for each segment within the expected delivery period. Using this data, a vibration transmission analysis method based on vehicle dynamics principles is employed to simulate the vibration field intensity spectrum (e.g., a spectrum containing different frequency and amplitude distributions) generated inside the delivery vehicle (e.g., a specific model of electric tricycle) as it travels in this complex traffic environment. Simultaneously, the system analyzes the specific details of the floral arrangement requirements in the order. The system generates a list of flower materials (such as hydrangeas and lisianthus), queries a pre-built knowledge base of the physical properties of flower materials to obtain the mechanical vulnerability parameters of various flower materials. For example, hydrangea petals have a low tolerance threshold for high-frequency vibrations (such as the 150-250Hz range). Finally, through a spatiotemporal coupling analysis algorithm, the evolution of the vibration field intensity spectrum along the path within the expected time (such as 45 minutes) is compared and accumulated with the vulnerability threshold of the flower materials being transported. This predicts key risk sections (such as a 500-meter-long uneven auxiliary road) and the possible quality degradation level (such as slight dehydration of petals), thereby generating a structured path assessment information set to provide a quantitative basis for subsequent decision-making.

[0057] The method provided in this embodiment accurately analyzes the risk of quality deterioration of floral materials during the delivery route, providing data support for subsequent risk assessment and scheduling adjustments. It can avoid high-loss routes in advance, reduce floral material damage, ensure the quality of delivered floral arrangements, improve user satisfaction, and reduce the company's floral material loss costs.

[0058] In some embodiments, based on the expected delivery route, and in conjunction with road surface smoothness and traffic flow density, the vehicle foundation vibration information caused by the combined effects of smoothness differences and traffic flow density fluctuations is analyzed to obtain the foundation vibration spectrum for each road segment. Based on the foundation vibration spectrum, according to the inherent vibration isolation properties of the transport vehicle used for delivery, the dynamic response of the vehicle to foundation vibration and the vibration transmission process are analyzed to obtain the vibration field intensity distribution. Based on the vibration field intensity distribution, and in conjunction with the floral arrangement requirements of the order, the sensitivity characteristics and damage threshold of the floral materials to vibration frequency are analyzed. Based on the sensitivity characteristics and damage threshold, and in conjunction with the expected delivery route, the cumulative and changing information of floral material damage during the delivery process is analyzed to generate a multiphysics field interaction spectrum for quantifying the risk of mechanical damage to floral materials during the delivery process.

[0059] Traffic flow density reflects the degree of traffic congestion on the delivery route and its impact on the vibration of the transport vehicle. The basic vibration spectrum is a spectrum formed by analyzing the vehicle vibration information caused by the combined effect of the road surface smoothness and traffic flow density along the expected delivery route; it is used to characterize the initial vibration state of each road segment. Inherent vibration isolation properties refer to the inherent characteristics of the transport vehicle itself that weaken the transmission of external vibrations. Vibration field intensity distribution describes the intensity distribution of vibration within the transport vehicle and the flower storage space. Vibration frequency sensitivity refers to the degree to which the flowers respond to vibrations of different frequencies, i.e., whether the flowers are easily damaged at a specific vibration frequency. The damage threshold is the maximum vibration intensity or frequency upper limit that the flowers can withstand; exceeding this threshold can easily cause wilting, breakage, and other damage.

[0060] Specifically, in the process of cross-store flower delivery orders, if a multi-physics field spectrum is not constructed, it will be impossible to quantify the impact of road surface smoothness (such as potholes), traffic density (such as congested periods), and vehicle vibration isolation properties on the vibration of flowers. Flowers are prone to wilting and breakage due to unknown vibration risks, resulting in a decline in order fulfillment quality, a surge in loss rate, and also causing user complaints, damaging brand reputation, and directly affecting the stability and profitability of cross-store collaborative scheduling. To address the aforementioned issues: First, the data extraction stage needs to achieve refined quantification: extract historical IRI data for the past 3 months from the urban road database, subdivided into 10-meter segments (e.g., the average IRI of segment A is 2.3 m / km, belonging to the moderate bump level), combined with real-time data updated every 5 minutes by roadside radar sensors; traffic flow density is obtained from the intelligent transportation database through time-segmented statistical values ​​(e.g., during the evening peak from 17:00 to 19:00, the hourly traffic flow of a certain segment is 2800 vehicles, and the density is 180 vehicles / km), and substituted into the Green Shields velocity-density model v=v_f(1-k / k_j) (v is the actual driving speed, v_f is the free flow velocity, k is the real-time density, and k_j is the congestion density), which is transformed into the dynamic driving speed of vehicles passing through each segment, providing time dimension parameters for vibration simulation. The basic vibration spectrum simulation is based on the three-degree-of-freedom model of vehicle dynamics (sprung mass, unsprung mass, tire stiffness), using road surface unevenness as the vibration excitation source, and constructing the original basic vibration spectrum through power spectral density (PSD). The specific function is G_a(f) = 4π 2 f 2 G_z(f), where G_z(f) is the power spectrum of road surface roughness (fitted using ISO8608 standard, G_z(f)=G_z(f0)(f / f0)^(-w), f0 is the reference frequency, and w is the spectral index), and f is the vibration frequency (1-100Hz). This formula quantifies the distribution characteristics of vibration intensity with frequency by relating frequency to road surface roughness. For example, the base vibration acceleration PSD value of a moderately bumpy road section at a frequency of 10Hz can reach 0.02g. 2 / Hz (g is the acceleration due to gravity). The establishment of the vibration transfer function requires coupling with the inherent properties of the vehicle: The suspension stiffness k = 120 N / mm and damping coefficient c = 8 N·s / mm of the target electric tricycle are retrieved from the vibration isolation parameter library. The transfer function H(f) = F_out(f) / F_in(f) is constructed, and the vertical vibration differential equation is transformed into a frequency domain expression using Laplace transform: H(f) = (k + j2πfc) / [(m_sω) / 2πfc ... 2 -k)+j 2[πfc] (m_s is the sprung mass, ω is the angular frequency). Simultaneously, the finite element method is used to divide the cargo platform into a 20×20mm grid, calculating the vibration acceleration response of each grid. Combined with the platform stiffness distribution, a three-dimensional vibration field intensity cloud map is generated, accurately presenting the vibration attenuation / amplification effect at different locations within the packaging box (e.g., the vibration intensity at the corners of the box is 15% higher than at the center). Matching the sensitive characteristics of floral materials with the damage threshold requires refined physiological parameters: the sensitive frequency range of roses (5-15Hz, mid-frequency) is retrieved from the database; the damage threshold for loosened petals is a peak acceleration a_th = 0.8g, and the number of continuous vibration cycles N = 10. 4 The sensitive frequency of tulip stems is 3-8 Hz, the bending damage threshold a_th = 0.5 g, and N = 5 × 10⁻⁶. 3 The cumulative damage effect of vibration on flowers is quantified using the Miner linear cumulative damage criterion D=Σ(n_i / N_i) (where n_i is the actual number of vibration cycles, calculated from the travel time and vibration frequency of the road segment). In the final spatiotemporal overlay analysis, the delivery route is divided into 100-meter segments. The travel time for each segment is calculated based on real-time traffic flow. The peak vibration acceleration within each segment is dynamically compared with the a_th value of the flowers to calculate the cumulative D value. For example, in the 3.2-3.3 km segment (IRI=3.1 m / km, evening peak traffic flow 2500 vehicles), the vibration acceleration transmitted to the box is 0.6g (8Hz), the travel time is 2 minutes, n_i=8×60×2=960 cycles, and the D value for roses is 960 / 10. 4 =0.096, Tulip D=960 / 5×10 3 =0.192. A risk heatmap (horizontal axis represents the path segment, vertical axis represents the flower type, and color depth corresponds to the D value) and data curve were generated using Matlab, clearly marking the key risk peak segments (such as the 5.7-5.8 km segment, where roses have D=0.92, close to the damage threshold).

[0061] The method provided in this embodiment accurately captures vibration risks throughout the entire delivery process, clarifies the cumulative pattern of floral material damage, provides a scientific basis for route assessment, can avoid high-risk road sections and transportation conditions in advance, reduce floral material loss, ensure the quality of floral arrangements, improve user satisfaction, and provide data support for subsequent order assignment and route adjustment, thereby enhancing the accuracy and efficiency of cross-store collaborative scheduling.

[0062] In some embodiments, based on the floral arrangement requirements of the order, the categories and morphological characteristics of the flowers included in the order are analyzed to obtain a set of physical structure information of the flowers; based on the set of physical structure information of the flowers, the differences in petal thickness, stem lignification degree and flower structure compactness of the transported flower categories are analyzed to obtain inherent mechanical strength information; based on the set of physical structure information of the flowers, combined with the inherent mechanical strength information, the risk information of wilting, breakage and falling off of the transported flower categories under the action of vibration intensity and duration during transportation is analyzed and quantified to obtain the vulnerability information of the flower categories.

[0063] The types of flowers used in floral arrangements can refer to the various flower varieties that make up the arrangement. The physical structure information set of the flowers can include information on morphological characteristics such as petal thickness, stem lignification, and flower structure density. Inherent mechanical strength information refers to the flowers' inherent ability to resist mechanical damage. Wilting refers to the wilting of leaves and petals caused by vibration leading to obstructed water transport or cell damage. Breakage refers to the breakage or damage to stems and petals caused by vibration exceeding their inherent tolerance. Shedding refers to petals and sepals detaching from the flower due to vibration causing loosening of the connecting parts.

[0064] Specifically, in the process of cross-store flower delivery, if the acquisition of information on the fragility of flower categories is skipped, the differences in vibration tolerance of different flowers (such as roses and baby's breath) will be ignored. During delivery, fragile flowers are prone to wilting and breakage due to vibration exceeding the threshold, resulting in a decline in order quality. It will also make subsequent route assessment lack a basis, making it impossible to identify high-risk sections, ultimately increasing delivery losses and affecting user experience and brand reputation. To address the aforementioned issues: First, Natural Language Processing (NLP) technology is used to parse the order text and extract a list of flowers, such as "10 white roses and 5 multi-headed lilies." Then, a pre-built "Flower Knowledge Graph Database" is invoked. This database integrates floriculture data, supplier specifications, and historical order data, mapping a set of structural parameters to each flower category (e.g., "white roses" corresponds to the category "cut roses - Snow Mountain series"). For example, the database shows that "Snow Mountain roses" have a petal thickness of "medium-thick," a stem lignification level of "medium," and a flower head compactness of "high." "Multi-headed lilies," on the other hand, have petal thickness of "thin," a stem lignification level of "low," and a flower structure of "loose cluster." Based on this set of physical structural information, a rule-based and case-based reasoning (CBR) model is used to output a quantified "inherent mechanical strength index" for each flower. This model matches and weights physical parameters (e.g., associating "stem lignification level - medium" with historical data on bending resistance during transportation) with known strength levels. Finally, a "vibration-damage" correlation mapping model is used to simulate the obtained mechanical strength index and the "vibration field intensity distribution spectrum" of the path to be evaluated. This simulation does not solve complex dynamic equations, but rather performs logical deduction and risk accumulation calculation based on a preset "intensity-duration-damage" relationship lookup table (e.g., inputting the "thin petal" feature and "a certain medium vibration intensity spectrum", outputting "the probability of petal falling off is high"). This ultimately generates a structured and quantifiable "flower material category vulnerability information" report, which clearly indicates the risk level and main damage factors of wilting, breakage or falling off of each flower material in the order (e.g., lilies compared to roses) under the current path conditions. This provides direct and detailed input for subsequent accurate risk segment identification and global scheduling decisions.

[0065] The method provided in this embodiment accurately quantifies the vibration tolerance threshold of different floral materials, identifies damage risk points, and provides targeted data support for path assessment and risk analysis. This helps subsequent stages avoid high-risk paths, optimize order assignment, reduce damage to floral materials during transportation from the source, ensure the quality of delivered floral works, reduce delivery losses, and at the same time improve the scientificity and accuracy of dynamic scheduling, thereby enhancing user satisfaction.

[0066] In some embodiments, based on the route assessment information set and combined with the vulnerability information of flower categories, the vibration field intensity of each segment in the transportation route is compared with the tolerance threshold of the currently transported flowers to screen out risky segments with excessive vibration intensity and obtain risky segment identification information; based on the risky segment identification information, the spatial distribution characteristics of risky segments in the expected delivery route are analyzed to identify path vulnerable sections where risky segments appear continuously and densely, and obtain the route risk distribution pattern; based on the route risk distribution pattern, the cumulative process and probability of damage to flowers when they are delivered through path vulnerable sections are analyzed to quantify and generate order fulfillment risk information for predicting the quality of order completion and the degree of loss.

[0067] Risky sections are those along the delivery route where the vibration field intensity exceeds the tolerance threshold of the corresponding floral materials. Risky section identification information includes numbering, location markings, and descriptions of the degree of exceedance for the selected risky sections. Vulnerable sections are areas along the expected delivery route where risky sections appear consecutively and densely. The route risk distribution pattern describes the spatial distribution, density, and correlation of risky sections along the entire expected delivery route, reflecting the overall risk distribution pattern of the route.

[0068] Specifically, in the process of cross-store and franchised flower delivery coordination, if the risk sections and continuous vulnerable sections of the delivery route that exceed the vibration limit are not accurately located, the cumulative effect of flower material damage will be ignored, leading to increased losses such as wilting and breakage of flowers, a significant drop in the quality of completed orders, which will not only cause user complaints, but also increase the merchant's operating costs such as replenishment and after-sales compensation, and damage the brand reputation. To address the aforementioned issues: First, a threshold comparison technique is employed. This involves performing real-time, segment-by-segment matching calculations on the path assessment information set (containing multiphysics field spectra generated from vibration sensor data and path models, such as vibration acceleration values ​​for specific road sections) and floral material vulnerability information (derived from a floral material knowledge base, for example, tulip petals are fragile, and their preset vibration tolerance threshold is 0.18g). A logical judgment module then identifies all road sections where vibration intensity exceeds the corresponding floral material tolerance threshold, such as the measured vibration of the middle section of Zhongshan North Road (0.25g > 0.18g). This generates a set of risk segment identification information with geographic coordinates. Subsequently, geospatial analysis techniques (such as density-based spatial clustering algorithms, like DBSCAN) are introduced to analyze the spatial pattern of these discrete risk points. The algorithm can automatically identify clusters of risky road sections that are spatially continuous and densely distributed, based on preset proximity distances (e.g., 500 meters) and minimum point number thresholds. For example, it can identify a continuous range of approximately 3.2 kilometers from "City Center Square" to "Overpass Entrance" containing more than a preset number of high-risk points, thus classifying it as a complete "vulnerable section of the path" and generating a path risk distribution pattern. Finally, for the identified vulnerable section, it calls probabilistic statistics and damage accumulation models (such as fatigue damage models based on random vibration theory or Monte Carlo simulations), combining the intensity spectrum and duration of vibration within the section with the damage response function of the flowers, to simulate and calculate the cumulative probability and expected severity of damage events such as petal drop and stem bending when flowers (e.g., tulips) pass through the section. This model quantitatively outputs a comprehensive risk index, such as "the expected quality degradation rate is a certain percentage," which serves as order fulfillment risk information that can be directly used for scheduling decision-making.

[0069] The method provided in this embodiment accurately identifies the risk distribution of routes and the probability of damage to floral materials, providing a clear basis for subsequent scheduling adjustments. It can specifically avoid high-risk routes or optimize resource allocation, significantly reduce transportation losses, ensure the quality of floral product delivery, reduce ineffective scheduling and cost waste, improve cross-store collaboration efficiency, enhance user experience and brand trust, and help the business develop steadily.

[0070] In some embodiments, based on order fulfillment risk information and a skills matching information set, the trade-off between the skills resources required for risk avoidance and the current predetermined assignment scheme is analyzed to obtain the resource adjustment space under risk avoidance constraints. Based on the resource adjustment space and a path evaluation information set, a set of strategies for replanning delivery routes to simultaneously reduce transportation losses is analyzed to obtain a path optimization feasibility set. Based on the path optimization feasibility set, collaborative optimization analysis is performed to generate a dynamic adjustment strategy that can simultaneously meet the dual objectives of skills matching improvement and path risk reduction. Based on the dynamic adjustment strategy, the order assignment and path planning are updated, and the logical basis and expected goal achievement of the updated dynamic adjustment strategy are recorded simultaneously to obtain a dynamic order scheduling log.

[0071] The resource adjustment space under risk aversion constraints can be the boundary of adjustable florist and store skill resources to mitigate order fulfillment risks without significantly reducing service quality. The feasible set of optimized routes can be the set of all strategies that meet basic delivery requirements after replanning the original delivery routes to reduce transportation losses. Collaborative optimization analysis can be an analytical method that combines and optimizes the resource adjustment space and the feasible set of optimized routes, simultaneously considering the dual objectives of improving skill matching and reducing route risks. Dynamic adjustment strategies can be specific schemes generated through collaborative optimization analysis for updating order assignment (such as changing stores or florists) and route planning. Order assignment can be the decision result of allocating orders to specific stores and corresponding florists. Route planning can be the planning of the delivery route from the assigned store to the delivery address, including route segment selection and time estimation.

[0072] Specifically, in the process of cross-store and franchised flower delivery collaborative scheduling, the initial order assignment and route planning are easily affected by dynamic factors such as sudden changes in florists, sudden changes in road traffic, and weather. If adjustments are not made in real time, it will lead to a decrease in skill matching (such as suitable florists being unable to perform their duties) and a surge in flower material loss (such as excessive vibration causing a surge in breakage rate), directly resulting in substandard order quality, frequent user complaints, damage to brand reputation, and seriously affecting the stability of contract fulfillment. To address the aforementioned issues: The process begins with triggering a response to "order fulfillment risk information." For example, if analysis reveals a section of the existing optimal route to a customer marked as "high-risk" due to continuous high-intensity vibration (poor road surface smoothness and heavy traffic), the first step is to perform a "resource adjustment space" analysis. This involves retrieving the "skill matching information set" to identify all other stores and their florists capable of providing services for the order. The changes in skill compatibility for each alternative (e.g., from 95% to 85%) and the new geographical distance are calculated, creating a trade-off list. Next, a "route optimization feasibility set" analysis is conducted. For each alternative store's location, the route evaluation method is re-applied to plan several alternative delivery routes that avoid the original high-risk section. The "multiphysics spectrum" and estimated loss rate of each new route are evaluated (e.g., from the original route's estimated loss rate of 15% to the alternative route). (5%), then initiate "collaborative optimization analysis", using a constrained multi-objective genetic algorithm as a technical means to combine the above-mentioned candidate stores and candidate routes into multiple scheduling schemes. With "maximizing skill fit" and "minimizing path loss risk" as dual objective functions, and "longest delivery time" as a hard constraint, the system performs automated search and evaluation, and finally generates a balanced solution, namely "dynamic adjustment strategy". This strategy will instruct the order to be reassigned to a store whose skill fit is still higher than the acceptable threshold (e.g., 80%) and which takes a new route that detours a part of the distance but significantly reduces loss. Then, the order attribution and delivery navigation route in the database are updated, and the decision logic of this adjustment, the risk sections avoided, and the comparison data of the discarded and adopted schemes are fully recorded in the "dynamic order scheduling log", completing a closed-loop dynamic scheduling.

[0073] By employing the methods provided in this embodiment, fulfillment risks can be precisely mitigated, ensuring that orders are always matched with optimal skill resources and low-risk delivery routes. This minimizes skill mismatches and transportation losses, while accumulating real data for subsequent scheduling optimization. This promotes the formation of a closed-loop iteration of the collaborative system, improves the level of scheduling intelligence, safeguards user experience, and strengthens the brand's core competitiveness in the floral delivery field.

[0074] Figure 3 A schematic diagram of a dynamic order scheduling system for cross-store and franchised delivery services, provided as an embodiment of this application, is shown below. Figure 3As shown, the dynamic order scheduling system 300 for cross-store and franchised delivery collaboration in this embodiment includes: a skill matching module 301, a route evaluation module 302, an order fulfillment module 303, and an order scheduling module 304.

[0075] The skill matching module 301 is used to acquire real-time order data and store information sets. Based on the real-time order data and the store information sets, it analyzes the matching information between the floral arrangement requirements of the order and the expertise of florists in different stores to obtain a skill matching information set. The path evaluation module 302 is used to analyze the impact of the multi-physics field time-effect evolution of floral materials on the expected delivery path based on the skill matching information set to obtain a path evaluation information set. The order fulfillment module 303 is used to analyze the path risk factors that lead to a decrease in order completion quality and an increase in delivery losses based on the path evaluation information set to obtain order fulfillment risk information. The order scheduling module 304 is used to dynamically adjust order assignment and path planning in real time according to the order fulfillment risk information to minimize skill mismatch and transportation losses, and output a dynamic order scheduling log.

[0076] Optionally, when the skill matching module 301 analyzes the matching information between the order's floral arrangement requirements and the expertise of different store florists based on the real-time order data and the store information set to obtain a skill matching information set, it is specifically used for: the real-time order data including the order delivery address and the order's floral arrangement requirements; the store information set including store location information and store florist information set; based on the order delivery address and the store location information, analyzing the geographical accessibility of each store's service orders to obtain geographical matching information including distance factors and estimated delivery time; based on the order's floral arrangement requirements and the store florist information set, analyzing the compatibility information between the florist's expertise and the corresponding production techniques for the floral arrangement requirements of the order to obtain skill matching information; based on the geographical matching information and the skill matching information, performing a geographical-skill trade-off for each store to prioritize matching the florist resources with the highest skill compatibility within an acceptable service range, thereby obtaining the skill matching information set.

[0077] Optionally, the skill matching module 301, during the construction of the skill matching information, is specifically used for: analyzing the type and style of floral works required for the order based on the order's floral requirements, to obtain the required technique information; analyzing the florist's historical work data and skill certification data based on the store florist information set, to obtain the florist's expertise information; analyzing the correspondence between the required technique information and the florist's expertise information in terms of technique category, technique complexity, and historical application effect, to obtain the technique fit for each florist; and sorting and filtering all candidate florists from high to low based on the technique fit, to obtain the skill matching information.

[0078] Optionally, when the skill matching module 301 implements the geographical-skill trade-off for each store based on the geographical matching information and the skill matching information, it is specifically used for: analyzing the mutually exclusive relationship between the increase or decrease of physical distance and the rise or fall of florist skill compatibility based on the geographical matching information and the skill matching information; analyzing the differentiated emphasis of the current order on delivery timeliness and craftsmanship based on the order's floral requirements; constructing a dynamic priority fusion rule based on the mutually exclusive relationship and the differentiated emphasis: for orders emphasizing skills, prioritizing the nearest store with a skill compatibility not lower than a set threshold; for orders emphasizing timeliness, prioritizing the store with the highest skill compatibility within the fastest delivery range; and, according to the dynamic priority fusion rule, synchronously traversing and logically adjudicating the candidate stores and corresponding florist resources that meet the basic conditions to obtain a target assignment scheme that simultaneously carries the optimal geographical attributes and the optimal skill attributes, thereby completing the geographical-skill trade-off.

[0079] Optionally, when the path evaluation module 302 analyzes the impact of the multi-physics field time-dependent evolution on the floral materials under the expected delivery path based on the skill matching information set to obtain the path evaluation information set, it is specifically used for: determining the starting delivery store location and the order delivery address based on the skill matching information set to obtain the expected delivery path; analyzing the vibration field intensity distribution of traffic flow data acting on the transport vehicle during the delivery period based on the expected delivery path to obtain the multi-physics field action spectrum; the traffic flow data includes road surface smoothness and traffic density, used to quantify the physical disturbance level that causes mechanical damage to the floral materials during the delivery process; analyzing the tolerance threshold of different floral material categories to physical vibration based on the floral arrangement requirements of the order to obtain the vulnerability information of the floral material categories; and analyzing the impact of the multi-physics field action spectrum on the quality deterioration of the floral materials within the expected delivery path time, based on the multi-physics field action spectrum and the vulnerability information of the floral material categories, to obtain the path evaluation information set.

[0080] Optionally, the path evaluation module 302, during the construction of the multiphysics field interaction spectrum, is specifically used for: based on the expected delivery route, combined with the road surface smoothness and the traffic flow density, analyzing the vehicle foundation vibration information caused by the combined effect of smoothness differences and traffic flow density fluctuations, and obtaining the foundation vibration spectrum of each road segment; based on the foundation vibration spectrum, according to the inherent vibration isolation properties of the transport vehicle used for delivery, analyzing the dynamic response and vibration transmission process of the vehicle to foundation vibration, and obtaining the vibration field intensity distribution; based on the vibration field intensity distribution, combined with the order floral requirements, analyzing the sensitivity characteristics and damage threshold of floral materials to vibration frequency; based on the sensitivity characteristics, the damage threshold, and combined with the expected delivery route, analyzing the cumulative and changing information of floral material damage during the delivery process, and generating the multiphysics field interaction spectrum used to quantify the risk of mechanical damage to floral materials during the delivery process.

[0081] Optionally, when the path evaluation module 302 analyzes the tolerance threshold of different flower categories to physical vibration based on the order's floral arrangement requirements to obtain the flower category vulnerability information, it is specifically used for: analyzing the category and morphological characteristics of the flowers included in the order based on the order's floral arrangement requirements to obtain a set of flower material physical structure information; analyzing the differences in petal thickness, stem lignification degree, and flower structure compactness of the transported flower categories based on the flower material physical structure information set to obtain inherent mechanical strength information; and analyzing and quantifying the risk information of wilting, breakage, and shedding of the transported flower categories under the influence of vibration intensity and duration during transportation based on the flower material physical structure information set and the inherent mechanical strength information to obtain the flower category vulnerability information.

[0082] Optionally, when the order fulfillment module 303 analyzes the path risk factors that lead to a decline in order completion quality and an increase in delivery losses based on the path assessment information set to obtain order fulfillment risk information, it specifically performs the following: based on the path assessment information set and combined with the vulnerability information of the flower material category, it compares the vibration field intensity of each segment in the transportation path with the tolerance threshold of the currently transported flower material, filters out risky segments with vibration intensity exceeding the limit, and obtains risky segment identification information; based on the risky segment identification information, it analyzes the spatial distribution characteristics of the risky segments in the expected delivery path, identifies the path vulnerable sections where the risky segments appear continuously and densely, and obtains the path risk distribution pattern; based on the path risk distribution pattern, it analyzes the cumulative process and probability of damage to the flower material when it is delivered through the path vulnerable section, and quantifies and generates the order fulfillment risk information used to predict the order completion quality and the degree of loss.

[0083] Optionally, when the order scheduling module 304 dynamically adjusts order assignment and route planning in real time based on the order fulfillment risk information to minimize skill mismatch and transportation losses, and outputs a dynamic order scheduling log, it is specifically used for: analyzing the trade-off between the skill resources required to be adjusted to achieve risk avoidance and the current predetermined assignment scheme based on the order fulfillment risk information and the skill matching information set, to obtain the resource adjustment space under risk avoidance constraints; analyzing the set of strategies for replanning the delivery route to simultaneously reduce transportation losses based on the resource adjustment space and the route evaluation information set, to obtain a feasible set of route optimization; performing collaborative optimization analysis based on the feasible set of route optimization to generate a dynamic adjustment strategy that can simultaneously meet the dual objectives of skill matching improvement and route risk reduction; and updating the order assignment and route planning based on the dynamic adjustment strategy, and simultaneously recording the logical basis and expected goal achievement of updating the dynamic adjustment strategy to obtain the dynamic order scheduling log.

[0084] The system in this embodiment can be used to execute the methods of any of the above embodiments, and its implementation principle and technical effect are similar, so they will not be described again here.

Claims

1. A dynamic order scheduling method for cross-store and franchised delivery collaboration, characterized in that, include: Obtain real-time order data and store information sets. Based on the real-time order data and the store information sets, analyze the matching information between the floral arrangement requirements of the orders and the expertise of florists in different stores to obtain a skill matching information set. Based on the aforementioned skill matching information set, the impact of multi-physics time-dependent evolution on floral materials under the expected delivery route is analyzed to obtain a route evaluation information set, including: Based on the skill matching information set, the starting delivery store location and the order delivery address are determined, and the expected delivery route is obtained; Based on the predicted delivery route, the vibration field intensity distribution of the transportation vehicle under the action of traffic flow data during the delivery period is analyzed to obtain the multiphysics field interaction spectrum. Traffic flow data includes road surface smoothness and traffic density, which are used to quantify the level of physical disturbance that causes mechanical damage to the flowers during the delivery process; Based on the floral arrangement requirements of the order, the tolerance threshold of different flower material categories to physical vibration is analyzed to obtain the vulnerability information of flower material categories; Based on the multi-physics field interaction spectrum and combined with the vulnerability information of the flower material categories, the impact of the multi-physics field interaction spectrum on the quality deterioration of the flower materials within the expected delivery route time is analyzed to obtain the route evaluation information set. The process of constructing the multiphysics interaction spectrum includes: Based on the predicted delivery route, combined with the road surface smoothness and the traffic flow density, the vehicle basic vibration information caused by the combined effect of smoothness differences and traffic flow density fluctuations is analyzed to obtain the basic vibration spectrum of each road segment. Based on the aforementioned basic vibration spectrum, and according to the inherent vibration isolation properties of the transport vehicle used for delivery, the dynamic response of the vehicle to the basic vibration and the vibration transmission process are analyzed to obtain the vibration field intensity distribution. Based on the vibration field intensity distribution and the order floral requirements, the sensitivity characteristics and damage threshold of floral materials to vibration frequency are analyzed. Based on the aforementioned sensitivity characteristics and damage threshold, and combined with the predicted delivery route, the cumulative and changing information of floral material damage during the delivery process is analyzed to generate the multiphysics field interaction spectrum used to quantify the risk of mechanical damage to floral materials during the delivery process. Based on the aforementioned path evaluation information set, path risk factors leading to decreased order completion quality and increased delivery losses are analyzed to obtain order fulfillment risk information, including: Based on the path assessment information set and combined with the vulnerability information of the flower material categories, the vibration field intensity of each section of the transportation path is compared with the tolerance threshold of the current transported flowers material to screen out the risk sections with excessive vibration intensity and obtain risk section identification information. Based on the risk segment identification information, the spatial distribution characteristics of the risk segments in the expected delivery route are analyzed, and the vulnerable sections of the route where the risk segments appear continuously and densely are identified, thus obtaining the route risk distribution pattern. Based on the risk distribution pattern of the route, the cumulative process and probability of damage to the flowers when they are delivered through the vulnerable sections of the route are analyzed, and the order fulfillment risk information used to predict the quality of order completion and the degree of loss is quantified and generated. Based on the order fulfillment risk information, the order assignment and route planning are dynamically adjusted in real time to minimize skill mismatch and transportation losses, and a dynamic order scheduling log is output.

2. The method according to claim 1, characterized in that, Based on the real-time order data and the store information set, the matching information between the floral arrangement requirements of the orders and the expertise of florists in different stores is analyzed to obtain a skill matching information set, including: The real-time order data includes the order's delivery address and floral arrangement requirements. The store information set includes store location information and store florist information set; Based on the order delivery address and the store location information, the geographical accessibility of service orders for each store is analyzed to obtain geographical matching information including distance factor and estimated delivery time. Based on the order's floral arrangement requirements, and combined with the store's florist information set, the matching information between the florist's expertise and the corresponding production techniques required by the order's floral arrangement requirements is analyzed to obtain skill matching information. Based on the geographic matching information and the skill matching information, a geographic-skill balance is performed on each store to prioritize matching florists with the highest skill matching within an acceptable service range, thereby obtaining the skill matching information set.

3. The method according to claim 2, characterized in that, The process of constructing the skill matching information includes: Based on the floral arrangement requirements of the order, the types and styles of floral arrangements required for the order are analyzed to obtain the technical information required for the order. Based on the aforementioned store florist information set, we analyze the florists' historical work data and skill certification data to obtain information on the florists' areas of expertise. Based on the required techniques for the order, and combined with the florist's expertise, the correspondence between the two in terms of technique category, technique complexity, and historical application effect is analyzed to obtain the technique fit for each florist. Based on the compatibility of the production techniques, all candidate florists are sorted and screened from high to low to obtain the skill matching information.

4. The method according to claim 2, characterized in that, The specific implementation of the geographic-skill trade-off for each store based on the geographic matching information and the skill matching information includes: Based on the geographic matching information and the skill matching information, the mutually exclusive relationship between the increase or decrease of physical distance and the rise or fall of the florist's skill compatibility is analyzed. Based on the aforementioned floral arrangement requirements, this paper analyzes the current order's emphasis on delivery time and craftsmanship. Based on the mutual exclusion relationship and the differentiated emphasis, a dynamic priority fusion rule is constructed: For orders that emphasize skills, priority will be given to ensuring that the nearest store has a skill matching rate that is no lower than a set threshold. For orders that prioritize timeliness, priority will be given to ensuring delivery to the most skilled stores within the fastest delivery range; Based on the dynamic priority fusion rule, the candidate stores and corresponding florists that meet the basic conditions are synchronously traversed and logically judged to obtain a target assignment scheme that simultaneously carries the optimal geographical attributes and the optimal skill attributes, so as to complete the geographical-skill trade-off.

5. The method according to claim 4, characterized in that, Based on the floral arrangement requirements of the order, the tolerance threshold of different flower categories to physical vibration is analyzed to obtain the vulnerability information of flower categories, including: Based on the floral arrangement requirements of the order, the types and morphological characteristics of the flowers included in the order are analyzed to obtain a set of physical structure information of the flowers; Based on the physical structure information set of the flower materials, the differences in petal thickness, stem lignification degree and flower structure compactness of the transported flower material categories are analyzed to obtain inherent mechanical strength information; Based on the physical structure information set of the flower materials and the inherent mechanical strength information, the risk information of wilting, breakage and falling off of the transported flower materials under the influence of vibration intensity and duration during transportation is analyzed and quantified, so as to obtain the vulnerability information of the flower materials.

6. The method according to claim 5, characterized in that, The process involves dynamically adjusting order assignment and route planning in real time based on the order fulfillment risk information to minimize skill mismatch and transportation losses, and outputting a dynamic order scheduling log, including: Based on the order fulfillment risk information and the skill matching information set, the trade-off between the skill resources that need to be adjusted to achieve risk avoidance and the current predetermined assignment scheme is analyzed, and the resource adjustment space under risk avoidance constraints is obtained. Based on the resource adjustment space and the path evaluation information set, a set of strategies for replanning delivery routes to simultaneously reduce transportation losses is analyzed, resulting in a feasible set of path optimization. Based on the aforementioned feasible set of path optimization, collaborative optimization analysis is performed to generate a dynamic adjustment strategy that can simultaneously satisfy the dual objectives of skill matching improvement and path risk reduction. Based on the dynamic adjustment strategy, the order assignment and the route planning are updated, and the logical basis for updating the dynamic adjustment strategy and the degree of achievement of the expected goals are recorded simultaneously to obtain the dynamic order scheduling log.

7. A dynamic order scheduling system for cross-store and franchised delivery services, characterized in that, The method applied to any one of claims 1-6 includes: The skill matching module is used to acquire real-time order data and store information sets. Based on the real-time order data and the store information sets, it analyzes the matching information between the floral arrangement requirements of the orders and the expertise of florists in different stores to obtain a skill matching information set. The path evaluation module is used to analyze the impact of the multi-physics field time-effect evolution on the floral materials under the expected delivery path based on the skill matching information set, and obtain the path evaluation information set; The order fulfillment module is used to analyze the path risk factors that lead to a decline in order completion quality and an increase in delivery losses based on the path evaluation information set, and to obtain order fulfillment risk information. The order scheduling module is used to dynamically adjust order assignment and route planning in real time based on the order fulfillment risk information, so as to minimize skill mismatch and transportation loss, and output dynamic order scheduling logs.

Images