Order dynamic distribution optimization method and system based on user portrait

Abstract

The invention relates to the technical field of logistics supply chain optimization, and provides an order dynamic distribution optimization method and system based on a user portrait, so as to improve the precision service capability and resource scheduling efficiency in a community retail scene. The method comprises the steps that user behavior data and community merchant inventory data are collected, and the user behavior data comprise an access behavior sequence recorded by intelligent cat eye equipment and a parcel access event triggered by an Internet of Things lock; generating a dynamic demand prediction parameter based on the user behavior data, and constructing a user portrait including consumption cycle characteristics and instant demand intensity; according to the user portrait and the community merchant inventory data, a replenishment order generation operation of a target community merchant is triggered, and a replenishment order comprises a delivery time limit constraint and a category priority identifier; and in combination with the real-time distribution load parameters and the community inventory distribution topology, generating a dynamic distribution path planning result and updating a time sequence control instruction of the distribution carrier.

Description

Technical Field

[0001] This application belongs to the field of logistics supply chain optimization technology, and specifically relates to a method and system for optimizing dynamic order distribution based on user portraits. Background Art

[0002] In the current field of community retail supply chain optimization, existing technologies generally employ static replenishment strategies based on historical sales records. Data collection is limited to POS terminal transaction records and manual inventory counts. However, these replenishment instructions are generated based on the consumption rate of goods within a fixed time window, lacking the ability to perceive the real-time dynamics of user behavior.

[0003] In terms of user behavior analysis, traditional methods often use membership card consumption data or mobile click logs as a single data source. On the one hand, such methods cannot capture potential demand signals before actual transactions occur. On the other hand, they ignore the implicit connection between users' physical space behavior and product consumption, resulting in demand forecasts lagging behind real scenarios.

[0004] Furthermore, existing delivery route planning technologies mostly use fixed schedules and preset route mechanisms. This static planning model is difficult to adapt to the changing instant delivery needs in community retail scenarios, especially when responding to scenarios such as sudden orders and dynamic inventory rebalancing. It is easy to cause problems of idle delivery resources or response delays.

[0005] Furthermore, current community inventory collaboration systems suffer from data silos, with merchant inventory data and user behavior data managed separately on independent platforms. This fragmented data processing severely restricts the agility of supply chain responses. These shortcomings make it difficult to overcome existing technological bottlenecks in achieving precise service delivery and efficient resource utilization in community retail scenarios. Summary of the Invention

[0006] This application provides a method and system for optimizing dynamic order delivery based on user portraits, which is used to improve the precision service capabilities and resource scheduling efficiency in community retail scenarios.

[0007] In the first aspect, an embodiment of the present application provides an order dynamic delivery optimization method based on user portraits, which is applied to an order dynamic delivery optimization system, and the method includes: collecting user behavior data and community merchant inventory data, the user behavior data including entry and exit behavior sequences recorded by smart cat-eye devices and package access events triggered by IoT locks; generating dynamic demand forecast parameters based on the user behavior data, and constructing a user portrait including consumption cycle characteristics and immediate demand intensity; triggering a replenishment order generation operation for a target community merchant based on the user portrait and the community merchant inventory data, the replenishment order including a delivery time constraint and a category priority identifier; combining real-time delivery load parameters with the community inventory distribution topology to generate dynamic delivery path planning results and update the timing control instructions of the delivery vehicle.

[0008] In a second aspect, an embodiment of the present application provides an order dynamic delivery optimization system, which includes a processor and a memory, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the above method.

[0009] In a third aspect, an embodiment of the present application provides a computer-readable storage medium, which includes a computer program. When the computer program is run on an order dynamic delivery optimization system, the computer program is used to enable the order dynamic delivery optimization system to execute the steps of the above method.

[0010] During the implementation of this application, an innovative community supply and demand dynamic control architecture based on the Internet of Things ecosystem was constructed, and end-to-end optimization was achieved by integrating multi-dimensional real-time data streams and intelligent decision-making algorithms.

[0011] Unlike the one-way prediction model of the traditional retail supply chain, the embodiment of the present application can use the smart cat-eye behavior sequence and package access events as a dynamic signal source for user demand prediction. By analyzing high-frequency entry and exit scenarios and instant package interaction behaviors, a three-dimensional user portrait that integrates spatial location attributes and time density is constructed, accurately capturing the cyclical laws of community consumption behavior and the characteristics of sudden demand fluctuations.

[0012] At the supply chain response level, the embodiments of this application can design a two-dimensional replenishment trigger mechanism based on time constraints and category priorities, achieving real-time dynamic matching of demand signals and inventory distribution. A community inventory topology network model is introduced into distribution route planning. By dynamically analyzing the spatial distribution density of inventory nodes and the real-time capacity load of distribution vehicles, a flexible distribution plan with adaptive characteristics is generated, breaking through the limitations of static routes and fixed frequencies in traditional logistics planning.

[0013] In summary, the embodiments of the present application effectively open up a two-way perception channel between user behavior data and the supply chain execution system, forming an intelligent collaborative network of demand forecasting-inventory optimization-dynamic distribution, which significantly improves the precision service capabilities and resource scheduling efficiency in community retail scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 A flowchart of a method for optimizing dynamic order delivery based on user portraits provided in an embodiment of the present application.

[0015] Figure 2 A structural diagram of an order dynamic delivery optimization system provided in an embodiment of the present application. DETAILED DESCRIPTION

[0016] To make the purpose, technical solutions, and advantages of the embodiments of this application more clear, the technical solutions of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are part of the embodiments of the technical solutions of this application, but not all of them. Based on the embodiments described in this application document, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the technical solutions of this application.

[0017] See also Figure 1 , which is a method for optimizing dynamic order delivery based on user portraits provided in an embodiment of the present application. This method can be applied to a dynamic order delivery optimization system, and the specific process is as S101-S104.

[0018] S101: Collecting user behavior data and community merchant inventory data, wherein the user behavior data includes entry and exit behavior sequences recorded by smart cat-eye devices and package access events triggered by IoT locks.

[0019] In an embodiment of the present application, the order dynamic delivery optimization system (hereinafter referred to as the system) implements S101 by starting a multi-source heterogeneous data collection process. Among them, the smart cat's eye device serves as a perception terminal for the user's entry and exit behavior, and realizes all-weather monitoring by integrating a millimeter-wave radar sensor and a 1080P high-definition camera module. When the user approaches the residential door within 0.5 meters, the smart cat's eye device automatically activates the biometric recognition module, uses near-infrared spectral analysis technology to extract facial contour features, and performs cosine similarity comparison with the pre-stored family member feature library. For example, in an exemplary application scenario, when the resident goes out at 7:35 every morning, the smart cat's eye device not only records the basic event of "User D-Going out-07:35:22", but also captures the additional behavioral characteristics of the user carrying the garbage bag through the gait analysis algorithm. This detailed information will be cross-validated with subsequent package access events.

[0020] Furthermore, the IoT lock device in the embodiments of this application performs core interactive functions during the delivery process. When a delivery person approaches a community smart locker with an electronic key, the NFC chip built into the lock verifies the legitimacy of the key hash value and simultaneously activates the electromagnetic lock on the door to open and close it. For example, during a replenishment mission, when a delivery person successfully opens locker compartment #2109, the IoT lock simultaneously records the full-dimensional event data: "Operator ID - Logistics Unit #7, Action Type - Deposit, Product Category Code - CF0032 (Dairy Products), Operation Timestamp - 14:23:11."

[0021] At the same time, the community merchant's inventory management platform establishes a data channel with the dynamic order delivery optimization system via the OPC-UA industrial communication protocol, transmitting structured data in real time across 28 fields, including product SKU codes, shelf coordinates, expiration dates, and dynamic inventory levels. For example, a community fresh produce supermarket's cold chain storage area uses a temperature sensor network to upload cold storage room environmental parameters every 30 seconds. When the temperature in the fresh meat cabinet fluctuates beyond a set threshold, the inventory status automatically changes to "undeliverable." This status change is synchronized to the inventory verification module of the dynamic order delivery optimization system within 200 milliseconds. All collected data undergoes initial cleansing at the edge, filters privacy information using a data desensitization module based on the ISO 27001 standard, and is then transmitted via 5G network slices to a distributed cloud storage cluster, forming a time series database and spatial topology database for subsequent analysis.

[0022] S102: Generate dynamic demand forecast parameters based on the user behavior data, and construct a user profile that includes consumption cycle characteristics and immediate demand intensity.

[0023] In this step, dynamic demand forecasting parameters are the core indicators used to quantify future user demand for goods within the dynamic order delivery optimization system. These parameters are constructed through multi-dimensional data fusion and machine learning models. Based on entry and exit behavior sequences collected by smart cat-eye devices, package access events triggered by IoT locks, and community merchant inventory data, these dynamic demand forecasting parameters are combined with time series analysis and behavioral pattern mining techniques to dynamically generate the cyclical characteristics and burst intensity of user demand.

[0024] Specifically, dynamic demand forecasting parameters can include consumption cycle characteristics (such as daily and weekly cycles), immediate demand intensity (such as priority for urgent replenishment), category preference matrices (users' propensity scores for different products), and time sensitivity curves (users' acceptance of delivery time slots). For example, the system uses Fourier transforms to extract the cyclical components of users' in-and-out behaviors, identifying the correlation between outings on Friday evenings and cleaning supply consumption, thereby predicting demand for products related to weekend cleaning.

[0025] In actual application, the system uses a sliding window mechanism to segment behavioral data and dynamically adjusts the weights of periodic and sudden demands through a gated attention mechanism to adapt to special scenarios such as holidays. These parameters include not only numerical indicators (such as a replenishment cycle stability of 98%), but also logical rules (such as triggering targeted replenishment when the inventory dependency index exceeds a threshold). The real-time nature of dynamic demand forecasting parameters is reflected in the instant processing of data by edge computing nodes. For example, when an IoT lock records that a user has extracted the same category of goods multiple times in a short period of time, the system immediately increases the demand intensity coefficient of the category and synchronizes it to the replenishment decision module through the API interface to ensure that the deviation between the prediction result and the actual situation is minimized.

[0026] In this embodiment of the present application, the order dynamic delivery optimization system can activate a multi-dimensional feature fusion analysis module to conduct in-depth mining of pre-processed user behavior data. Specifically, the time series behavior analysis submodule uses a sliding window mechanism to periodically detect inbound and outbound records, and extracts characteristic components such as daily and weekly cycles through Fourier transform.

[0027] For example, three months of behavioral data analysis of user E revealed consistent outings between 6:00 PM and 8:00 PM every Friday evening. Combined with the increased frequency of kitchen detergent consumption in package access records over the same period, the system established a "weekend cleaning-related consumption pattern." The IoT lock event parser specifically handles high-frequency access operations. If the system detects a user withdrawing the same item three times in a row within 1.5 hours, it triggers an immediate demand intensity alert.

[0028] For example, if user F accessed their locker three times between 8:00 AM and 9:30 AM to retrieve diapers due to baby care needs, the system would generate an "Urgent Restocking Need - Priority Level 9" assessment. The feature fusion layer uses a gated attention mechanism to dynamically adjust the weighting of periodic features and sudden demand, automatically increasing the influence coefficient of immediate demand during holidays, for example.

[0029] Ultimately, the user profile generated by this embodiment includes 12 dimensions of consumer characteristic descriptors. These include a "category preference matrix" that quantifies user preferences for each major product category, a "time sensitivity curve" that reflects user acceptance of different delivery time periods, and an "inventory dependency index" that characterizes user loyalty to specific products at community merchants. For example, for an elderly user profile, the system might label key parameters such as "98% stability of replenishment cycles for medical and health products, and tolerance for delivery time deviations less than ±15 minutes." These characteristic parameters provide a quantitative basis for subsequent decision-making.

[0030] S103: triggering a replenishment order generation operation for a target community merchant based on the user portrait and the community merchant inventory data, wherein the replenishment order includes a delivery time constraint and a category priority identifier.

[0031] In an embodiment of the present application, the order dynamic delivery optimization system can implement this step by activating an intelligent replenishment decision-making module, which includes a collaborative working mechanism of a demand-inventory matching algorithm and a delivery resource scheduling algorithm. When the predicted demand cycle in the user profile is compared with the real-time inventory data, the system performs a three-level verification process: first, checking the available community inventory of the target product, second, verifying whether the product batch meets the quality requirements, and finally confirming the carrying capacity of the vehicle on the delivery path.

[0032] For example, when the prediction model determines that the pet food replenishment window for user G will open in 6 hours, the system immediately queries the inventory of related merchants and finds that the inventory of the product in the three cooperating merchants in the community is 15 pieces (Merchant A), 0 pieces (Merchant B), and 42 pieces (Merchant C), respectively. Combining the spatial topological relationship between each merchant and the user's address, the system selects Merchant C with sufficient inventory and optimal path accessibility as the supply source. The order generation module also handles the task of parsing the distribution constraints, automatically adding "cold chain transportation identification" for fresh products, adding "constant temperature box transportation requirements" to pharmaceutical products, and generating "shockproof packaging instructions" for fragile products. In the concurrent request processing scenario, the system uses the improved Hungarian algorithm for optimal resource allocation.

[0033] For example, when five users simultaneously request infant formula replenishment and the community's total inventory is insufficient, dynamic quotas are allocated based on the priority coefficients in the user profile (including factors such as infant age, historical out-of-stock records, and payment credit rating). The generated replenishment orders are recorded on the blockchain distributed ledger. Each order contains 56 fields of structured data, including a 64-bit digital fingerprint, supplier identification, and delivery time commitment, ensuring the immutability of transaction data.

[0034] S104: Combine the real-time delivery load parameters and the community inventory distribution topology to generate dynamic delivery path planning results and update the timing control instructions of the delivery vehicle.

[0035] In an embodiment of the present application, the order dynamic delivery optimization system activates a three-dimensional spatiotemporal path planning module, which integrates a high-precision digital twin community model with real-time traffic situation data. For example, the path planning algorithm first decomposes the delivery task into an operations research problem with spatiotemporal constraints, taking into account dynamic parameters such as the battery life of the delivery vehicle, the remaining cargo hold volume, and road traffic restrictions. In a certain delivery mission, the autonomous driving vehicle #23 needs to complete the delivery of fresh goods to 8 delivery points within 90 minutes. The system dynamically reconstructs the optimal path sequence based on real-time updated road construction information (such as the temporary closure of the main road in District D) and weather data (such as sudden heavy rain in the northeast area).

[0036] Understandably, in complex scenarios, the system employs a rolling-horizon optimization strategy, re-evaluating the effectiveness of routing solutions every five minutes. If an unexpected situation occurs during delivery (such as a temporary malfunction in a locker compartment), the system immediately activates an emergency rerouting mechanism. For example, a package originally destined for locker #3071 can be rerouted to the adjacent locker #3095, while simultaneously updating delivery authorization information through the IoT lock system.

[0037] Furthermore, the vehicle load balancing module continuously monitors resource utilization across the entire delivery network. If the West District's capacity utilization reaches a critical threshold, it automatically migrates some orders to the idle North District vehicle group and issues cross-regional reinforcement instructions via the IoV broadcast system. After completing a delivery, the system collects performance metrics from each link (such as the deviation between actual delivery time and estimated value, locker opening success rate, etc.). This data is fed back to the user profile update module, forming an iterative optimization mechanism for the demand forecasting model.

[0038] Based on the above, real-time delivery load parameters are multi-dimensional dynamic datasets that reflect the current capacity status and resource consumption of the delivery network, used to optimize route planning and resource scheduling. These parameters can include factors such as the delivery vehicle's real-time location, battery life, remaining cargo hold capacity, road conditions, traffic congestion index, and weather conditions. Data sources include on-board GPS, IoT sensors, traffic management platforms, and APIs from meteorological authorities. For example, the battery level sensor of an autonomous vehicle uploads remaining range data every 10 seconds, and the roadside unit (RSU) provides real-time feedback on road construction information via V2X communication. Specifically, the system uses a streaming computing framework to clean and aggregate massive amounts of data in real time, generating key metrics such as load balancing index, route congestion coefficient, and vehicle utilization. During the delivery process, if the capacity utilization rate of a delivery cluster in a certain area exceeds 85%, the system automatically marks it as "overloaded" and triggers cross-regional resource scheduling instructions. Furthermore, load parameters include temporal and spatial constraints, such as the requirement for cold chain transportation within 30 minutes for fresh produce deliveries and the need for pharmaceuticals to maintain a constant temperature environment. These parameters are updated synchronously with the real world through the digital twin model. For example, when a sudden rainstorm causes the traffic speed in the northeast area to drop by 50%, the system dynamically adjusts the time weight factor in the path planning algorithm to ensure the feasibility of the delivery time commitment.

[0039] The community inventory distribution topology can be understood as a structured data model that describes the spatial location and logical relationships of merchant inventory nodes. It is constructed using graph theory algorithms and is used to optimize replenishment decisions and delivery routes. This topology abstracts community merchants as nodes, whose attributes include product SKU codes, inventory levels, shelf coordinates, and expiration dates. The edges between nodes represent logistics path accessibility, transportation costs, or product association rules (such as complementary relationships). This data is derived from structured information transmitted via the OPC-UA protocol by the merchant inventory management platform and integrated with high-precision map data. For example, the cold chain storage area of a fresh food supermarket is modeled as a subtopology node containing temperature sensor data. When a refrigerator's temperature is abnormal, the topology status is automatically updated to "unavailable." Specifically, the system uses a spatial database to store the topology and employs the Dijkstra algorithm to calculate the optimal replenishment path. In the application scenario, when a user demand triggers a replenishment order, the system traverses all reachable nodes in the topology and selects merchants with sufficient inventory and the lowest path cost as supply sources. In addition, the topology also supports dynamic weight adjustment. For example, the weight coefficient of path distance is increased during peak traffic hours, and routes with good lighting conditions are given priority during nighttime delivery.

[0040] Furthermore, dynamic delivery routing results are generated based on real-time data and optimization algorithms to generate delivery task execution plans. These plans comprehensively consider multiple factors, including delivery load, inventory topology, and user time constraints, to achieve optimal resource allocation. These results are presented as a spatiotemporal sequence, encompassing elements such as vehicle movement trajectories, task execution time windows, and loading and unloading instructions. For example, the planned path for autonomous vehicle #23 might be encoded as "Node A → Node B (120-second stop) → detour around a temporarily closed area → Node C" and synchronized to the vehicle's control system. Specifically, the system employs a mixed integer programming model and a rolling-horizon optimization strategy, recalculating the path every five minutes to address unexpected situations (such as road closures or new orders). At the algorithmic level, a modified Hungarian algorithm is used for multi-vehicle task allocation, while a digital twin model provides a highly realistic traffic environment simulation. In exception handling scenarios, the system activates an emergency rerouting mechanism. For example, if a locker fails, the package is automatically reassigned to a nearby node, and delivery authorization information is updated via the blockchain. The planning results also include performance evaluation indicators, such as the deviation between the actual delivery time and the estimated value. These data are fed back to the demand forecasting module to form a cyclic iterative optimization.

[0041] In one implementation, the collecting of user behavior data and community merchant inventory data in S101 includes:

[0042] S1011: Extracting the user's entry and exit frequency characteristics within a continuous time window from the smart cat's eye device, and identifying the entry and exit behavior pattern of users carrying packages, wherein the entry and exit behavior pattern includes the correlation between the peak time period of the number of entry and exit times per day and the volume of the packages.

[0043] In this embodiment, the order dynamic delivery optimization system collects user entry and exit behavior data through smart cat-eye devices, specifically statistically analyzing the frequency of user passage within a continuous time period and identifying the characteristics of the items carried. The millimeter-wave radar built into the smart cat-eye device works in conjunction with the binocular vision sensor. When a user enters the residential door monitoring area, the device automatically records the passage timestamp and activates the package recognition algorithm. For example, the system analyzed the passage records of user D for three consecutive days and found that the number of single-day entry and exit peaks occurred between 18:30 and 19:15 each day. Using three-dimensional point cloud imaging technology, it was detected that the average volume of the items carried by the user during this time period was 47% larger than that of other time periods. The system further established a correlation model between package volume and time window, identifying that the user regularly carried large quantities of items around 20:00 every Wednesday. This feature was encoded as a "periodic bulk purchasing pattern" and stored in the user behavior database, providing basic data support for subsequent demand forecasting.

[0044] S1012: Synchronize the package access timestamp and operation type data of the IoT lock, and extract the spatiotemporal correlation features of multiple access events within a single day. The spatiotemporal correlation features include the time intervals and path overlaps of the package access events of the same user on different floors.

[0045] In this embodiment, the system synchronously processes package access event data generated by the IoT lock, focusing on analyzing the temporal and spatial correlations between multiple access operations. When a delivery person or user authenticates and opens the smart locker, the IoT lock records the operation time, location coordinates, and access type, and uses a path tracking algorithm to calculate the movement paths between adjacent operations. For example, after user E accessed a fresh produce package in Unit B of Building 3 at 10:05, the system detected that they again accessed medicine in Unit C of the same building at 10:17. The temporal and spatial correlation analysis module calculates the geographic distance and time interval between the two operations. Combining this with the building's internal structural model, it derives an 82% overlap in user movement paths, generating a feature identifier for "continuous cross-floor replenishment demand." This feature effectively distinguishes sporadic access behaviors from systematic replenishment needs, providing a decision-making basis for dynamic inventory scheduling.

[0046] S1013: Calling the inventory scanning device to obtain the category inventory matrix of the community merchant shelves, and marking the inventory warning status of potential expiration, the inventory warning status includes the remaining saleable time prediction value of different shelf levels.

[0047] In this embodiment, the system calls the intelligent scanning device deployed in the community merchant shelf area to obtain the inventory status of the goods in real time and predict the shelf life risk. The scanning device uses radio frequency identification and image recognition fusion technology to collect the category code, storage location and production date data of the shelf goods layer by layer. Taking the dairy shelf of the community supermarket as an example, the device detects that the remaining shelf life of the yogurt product located in the middle area is less than 48 hours. The system automatically generates an inventory warning status and marks the remaining saleable time forecast value of this batch of goods as 36 hours ± 15 minutes. At the same time, the system constructs a three-dimensional category inventory matrix, maps the inventory of goods at different shelf levels into spatial coordinate data, and triggers the "high-turnover product replenishment warning" signal when the inventory of instant noodles on the bottom shelf is lower than the safety threshold, guiding the merchant to perform forward warehouse allocation operations.

[0048] S1014: Perform feature fusion processing on the entry and exit behavior pattern, the spatiotemporal correlation feature, and the inventory warning status to generate a multidimensional data set, wherein the multidimensional data set includes a package demand density map and an inventory consumption rate map on a user basis.

[0049] In this embodiment, the system starts a multi-source data fusion engine and performs cross-modal correlation analysis on the various features collected. The periodic purchasing features in the entry and exit behavior patterns are matched with the IoT lock access records in time and space, and the demand density map of the user dimension is generated in combination with the inventory consumption rate. For example, the weekend bulk purchasing behavior of user F corresponds to the step-by-step decline in the inventory of grain and oil products in the community supermarket. The system establishes a "user-category" consumption association model based on this. At the same time, the inventory warning status data is converted into a heat distribution map, which shows that the inventory consumption rate of fresh products in the southeast area of the community is significantly higher than that in other areas. This feature is positively correlated with the access frequency of users in the corresponding building. The fused multidimensional data set can intuitively display the demand hotspots and replenishment urgency of each major commodity category in the community.

[0050] In one implementation, the generating of dynamic demand forecast parameters based on the user behavior data in S102 includes:

[0051] S1021: Input the input and output frequency features into a first recurrent neural network, and output a basic consumption cycle prediction curve. The hidden layer of the first recurrent neural network includes a time attention mechanism for capturing abnormal fluctuations within the cycle.

[0052] In this embodiment, the system uses a first recurrent neural network with a temporal attention mechanism to process user inbound and outbound frequency characteristics, automatically capturing cyclical consumption patterns. The neural network model performs a sliding window analysis of 30 consecutive days of inbound and outbound records, identifying that user G frequently enters and exits with no packages during the weekday morning commute, while exhibiting a low frequency of carrying large packages on weekend evenings. The temporal attention mechanism dynamically weights weekend data in the model's hidden layer, ultimately outputting the user's core consumption window of 6:00 PM to 8:00 PM every Saturday. This prediction curve matches historical purchase records with a 93% accuracy, effectively supporting the formulation of subsequent replenishment strategies.

[0053] S1022: Input the access timestamp and the operation type data into a time series decomposition model to separate the sudden demand event characteristics and the normal demand baseline characteristics. The time series decomposition model uses a variational mode decomposition algorithm to eliminate noise interference.

[0054] In this embodiment, the time series decomposition model extracts multi-scale features from IoT lock access events to distinguish between regular demand and sudden demand. The model uses a variational mode decomposition algorithm to eliminate noise data caused by device mis-triggering. For example, it identifies two brief door lock openings and closings in user H's three access operations in a single day as invalid signals. The decomposed sudden demand event features show that the user's sudden access to medical supplies at 2:00 PM is strongly correlated with the community pharmacy's inventory warning status, while the baseline features of normal demand maintain a stable daily replenishment rhythm. This decomposition mechanism ensures that the system accurately identifies urgent replenishment needs that truly require a response.

[0055] S1023: Perform a convolution operation on the inventory warning status in the category dimension to generate a heat distribution map of the inventory consumption rate. The convolution operation uses an adaptive kernel size to match the differences in consumption patterns of different categories.

[0056] In this embodiment, the system performs adaptive convolution processing on the category data collected by the inventory scanning device to generate a heat map that reflects the consumption rate in different areas. The convolution kernel size is dynamically adjusted according to the characteristics of the product. For example, a small-scale kernel is used to capture hourly changes in fresh products that are sensitive to shelf life, while a large-scale kernel is used to analyze weekly trends for storable daily necessities. When the inventory level of the infant and toddler product shelves in the northwest region of the community showed a rapid decline, the heat map output by the convolution operation showed that a high consumption hotspot had formed in this area. Based on this, the system triggered the cross-merchant allocation instruction of formula milk powder in advance.

[0057] S1024: The basic consumption cycle forecast curve, the sudden demand event characteristics and the inventory consumption rate are integrated to generate dynamic demand forecast parameters. The integration process introduces a gating mechanism to dynamically adjust the contribution weight of each feature.

[0058] In this embodiment, a gated fusion mechanism dynamically integrates various forecasting features to generate accurate dynamic demand forecast parameters. The system assigns basic weights to the consumption cycle forecast curve. When a sudden demand event is detected, the gating unit automatically increases the contribution of the immediacy indicator. For example, on the eve of the Spring Festival, the cyclical forecast curve showed stable user replenishment demand. However, the sudden event analysis module detected multiple users accessing holiday gift boxes. The gating mechanism promptly adjusted the parameter weights, improving the system's forecast accuracy by 22%. The fused parameter set includes demand characteristics in the temporal, spatial, and intensity dimensions, providing a quantitative basis for personalized replenishment strategies.

[0059] In another implementation, the constructing of a user profile including consumption cycle characteristics and immediate demand intensity in S102 includes:

[0060] S1025: Extracting the demand fluctuation range of each category from the dynamic demand forecast parameters to generate a consumption cycle stability index, wherein the consumption cycle stability index is obtained by calculating the weighted sum of the variance and trend slope of the demand curve within the sliding window.

[0061] In this step, the system uses a sliding window algorithm to calculate the consumption cycle stability index, quantifying the predictability of user demand. The algorithm analyzes the user's daily necessities purchase records over the past two weeks and calculates the variance and trend rate of demand within each time window. When elderly user J's purchase time for chronic disease medications consistently deviates by less than 10 minutes, the system generates a cycle stability index of 95 points. This index triggers the automatic replenishment mechanism, ensuring strict synchronization between drug supply and consumption. The stability index becomes the critical threshold for determining whether to activate the pre-set replenishment plan.

[0062] S1026: Identify cross-category association rules in the sudden demand event characteristics, and construct a demand trigger condition dependency graph, wherein the demand trigger condition dependency graph includes the spatiotemporal co-occurrence probability and causal reasoning relationship between categories.

[0063] In this step, the cross-category association analysis module constructs a demand trigger dependency graph, revealing potential demand connections between products. The system discovered that after user K accesses pet food, there is a 78% probability that he or she will also access cleaning supplies within 24 hours. This establishes a "pet supplies-cleaning supplies" association rule. The dependency graph further analyzes spatiotemporal co-occurrence patterns, confirming that this association primarily occurs in the evening hours in buildings on the east side of the community. This provides data support for product combination recommendations and coordinated replenishment.

[0064] S1027: Calculate the demand transmission delay parameters of each floor in the community based on the thermal distribution map of the inventory consumption rate. The delay parameters simulate the diffusion effect of inventory changes between buildings through a graph neural network.

[0065] In this step, a graph neural network simulates the spatial transmission effects of inventory changes within a community and calculates demand delay parameters for each floor. When a central merchant's bottled water inventory runs low, the model predicts a 32-minute delay for users in the northwest building, while the southeast building, with its dense concentration of alternative merchants, will only experience an 8-minute delay. These delay parameters guide the system's implementation of differentiated replenishment response strategies, prioritizing the supply of goods in marginal areas.

[0066] S1028: Encode the consumption cycle stability index, the demand trigger condition dependency graph, and the demand conduction delay parameter into a multidimensional vector to form the user portrait. The encoding process uses a contrastive learning framework to align the spatial distribution of different features.

[0067] In this step, the contrastive learning framework encodes multidimensional features into a unified vector space, constructing a precise profile of user needs. The system aligns the spatial distribution of the consumption cycle stability index and the inventory transmission delay parameter, mapping the high stability characteristics of elderly users to the rapid replenishment capabilities of pharmacy counters. The encoded user vector contains 12 dimensions of demand characteristics, enabling the replenishment system to quickly match the optimal supply solution. For example, user L's urgent need for maternity and baby products can be directed to a high-quality merchant with independent temperature-controlled warehousing.

[0068] In one implementation, the steps of combining the real-time delivery load parameters with the community inventory distribution topology to generate dynamic delivery path planning results and update the timing control instructions of the delivery vehicles in S104 include:

[0069] S1041: Analyze the conflict detection conditions between the time-sensitive orders and the ordinary delivery orders in the delivery time constraint, wherein the conflict detection conditions include a comparison between the time window overlap and the priority weight.

[0070] In an embodiment of the present application, the order dynamic delivery optimization system starts a time constraint parsing engine to perform conflict detection and priority sorting on delivery order types. The system identifies time-space resource conflict nodes by comparing the promised delivery window of time-sensitive orders with the expected time range of ordinary delivery orders. For example, when user M's order for refrigerated medicines requires delivery before 14:30, and there is an overlap with the 14:00-15:00 delivery window of user N's ordinary daily necessities order, the system calculates the time window overlap of the two to be 62%, and combines the medical emergency level parameter in the user portrait to give the medicine order a priority weight increase of 3 levels. The conflict detection condition triggers the dynamic adjustment mechanism, inserting the medicine order at the top of the delivery queue, and automatically negotiates a 15-minute extension of the delivery grace period for the daily necessities order to ensure that the delivery resources of the two types of orders are reasonably allocated.

[0071] S1042: Calculate the path weight of each delivery node based on the community inventory distribution topology and generate an initial delivery path set, where the path weight includes a floor height penalty factor and an estimated elevator waiting time.

[0072] In an embodiment of the present application, the system generates a weighted path planning basic model based on the node association relationship of the community inventory distribution topology. The path weight calculation module comprehensively evaluates the physical location characteristics and dynamic environmental parameters of the distribution nodes. For example, for high-rise residential distribution points, the system loads the floor height penalty factor based on historical elevator operation data. The average elevator waiting time of a 28-story residential building is estimated to be 135 seconds. This parameter is converted into a time cost increment in the path weight. At the same time, the system identifies additional conditions for special distribution nodes. For example, the underground storage area where the fresh food counter is located needs to be superimposed with a low-temperature environment travel time correction value, so that a differentiated travel cost matrix is formed between each node in the initial distribution path set, providing basic data support for subsequent optimization.

[0073] S1043: Real-time monitoring of the battery life data and traffic congestion index of the delivery vehicle, and dynamic adjustment of the energy consumption cost factor in the path weight. The energy consumption cost factor is dynamically updated through a regression model of the vehicle model and road conditions.

[0074] In an embodiment of the present application, the system updates the dynamic parameters of path planning through the real-time data stream of the Internet of Vehicles. The battery management system of the delivery vehicle uploads the remaining power percentage and energy consumption rate every 15 seconds, and dynamically corrects the energy consumption cost factor in the path weight in combination with the real-time traffic congestion index provided by the high-precision map. For example, the energy consumption regression model of the vehicle model is cold chain transport vehicle #05. It shows that its energy consumption per unit distance in the congested section during the evening peak is 28% higher than that during the unobstructed period. Based on this, the system adjusts the energy consumption cost factor coefficient of the relevant path segment during this period from 1.2 to 1.5. At the same time, the road emergency processing module detects a traffic accident at the entrance of the delivery area C, immediately downgrades the traffic priority of the affected section, and recalculates the energy consumption increment parameters of the detour path.

[0075] S1044: Use a reinforcement learning model to perform multi-objective optimization on the initial delivery path set and output a dynamic delivery path with the lowest comprehensive cost. The state space of the reinforcement learning model includes the remaining power of the vehicle, the real-time order queue, and weather impact parameters.

[0076] In an embodiment of the present application, the system calls a deep reinforcement learning model to perform multi-dimensional path optimization. The state space of the model integrates the real-time operating parameters of the distribution network, including the current remaining power of vehicle #12 of 58%, 7 delivery tasks in the queue of pending orders, and the heavy rainfall impact coefficient of 0.7 in the meteorological warning. The reward function setting comprehensively considers the delivery timeliness, energy consumption cost and customer satisfaction indicators, and iteratively generates the optimal strategy in the simulation training. In specific implementation, the system performs a Monte Carlo tree search on the initial path set and finds that the original planned path has an endurance risk due to the vehicle power limit. It automatically adjusts the task sequence to advance the charging station access node, and finally outputs a dynamic distribution path plan with a comprehensive cost reduction of 19%, ensuring that the tasks of the 8 distribution nodes are completed before the battery is exhausted.

[0077] S1045: Update the batch scheduling instructions and charging station access sequence of the delivery vehicle according to the dynamic delivery path, wherein the batch scheduling instructions include a task allocation strategy and collision avoidance rules among multiple delivery vehicles.

[0078] In an embodiment of the present application, the system generates a vehicle collaborative scheduling instruction set and updates the control timing. The task allocation strategy adopts an improved contract network protocol to allocate the eastern, central, and western delivery areas to the three available vehicles respectively, and sets collaborative collision avoidance rules for the intersection areas. For example, vehicles #07 and #09 are preset with a 5-minute time difference passage requirement at the intersection of the community center square to avoid waiting time caused by path intersection. The charging timing planning module specifies vehicle #12 to go to the photovoltaic charging station for rapid energy replenishment immediately after completing the 5th delivery based on the charging characteristics of the vehicle model and the grid load data, and embeds the charging task into the path navigation instruction sequence. All scheduling instructions are synchronized to the on-board control terminal through the blockchain consensus mechanism to ensure the consistency and traceability of instruction execution of the multi-vehicle system.

[0079] In an optional implementation, the method further includes:

[0080] S201: Collecting package receipt status data during the delivery route execution process, wherein the package receipt status data includes the recipient's biometric features and the package integrity identifier verified by the IoT lock, wherein the recipient's biometric features include fingerprint matching and facial liveness detection results.

[0081] In this embodiment of the present application, the system activates the IoT lock security verification process at the delivery point, collecting multi-dimensional evidence data on the package's receipt status. When vehicle #09 arrives at the smart locker in Building 3, the recipient completes identity verification using a fingerprint sensor and near-infrared liveness detection camera. The system records a fingerprint match of 98.7% and a facial biometric activity index of 0.93, generating a biometric verification pass indicator. Simultaneously, the locker's built-in weighing sensor and visual inspection unit perform an integrity check on the package, confirming a deviation of less than 0.5% from the initial weight upon deposit and an indication of the outer packaging being intact. Ultimately, a full-factor receipt is generated, including a time stamp, biometrics, and physical status.

[0082] S202: Input the receipt status data into an insurance risk assessment model to generate a community-level predicted value of the probability of lost items. The insurance risk assessment model uses a gradient boosting tree algorithm to fuse historical claim records with current environmental parameters.

[0083] In an embodiment of the present application, the insurance risk assessment engine integrates historical data and real-time features to quantify risks. The system inputs the package receipt data of user P (including the receipt time of 22:30 at night, the rainfall intensity of 2.5mm / h, and the package value level A) into the gradient boosting tree model, and combines the feature vectors of 23 claim cases in the same area in the past three months to calculate the predicted value of the lost item probability of this delivery to be 5.7%. The model feature importance analysis shows that the contribution of rainfall intensity to the prediction results is 41%, which drives the system to strengthen the monitoring weight of meteorological parameters in subsequent risk assessments. The prediction results are associated with the delivery order in the form of risk level labels, providing a basis for the dynamic adjustment of insurance premiums.

[0084] S203: Adjust the logistics insurance rate calculation parameters of the target community merchants according to the predicted value of the lost item probability, and introduce a dynamic discount coefficient into the adjustment process to reflect the changes in security levels in different time periods.

[0085] In an embodiment of the present application, the system starts a dynamic calculation module for logistics insurance rates and implements a premium adjustment mechanism based on real-time risks. When the predicted value of the probability of lost items in the northwest area of the community rises from the baseline value of 3.2% to 7.1%, the dynamic discount coefficient in the insurance rate calculation parameters is automatically reduced by 0.35, corresponding to a 22% increase in premiums. The rate adjustment strategy takes into account the difference in safety characteristics during the day and night. For example, the night risk bonus coefficient is enabled in the early morning period (00:00-05:00), which increases the premium base value for this period to 1.5 times that of the daytime. The adjusted rate parameters are deployed to the community merchant management platform through smart contracts to achieve minute-level synchronization updates of premium calculations and risk levels.

[0086] S204: When it is detected that the probability of lost items on the target floor exceeds a threshold, a safe route optimization instruction in the delivery route planning is triggered. The safe route optimization instruction forces the delivery vehicle to detour the risk area and enables a real-time location tracking function.

[0087] In an embodiment of the present application, the system executes security-enhanced path optimization instructions to deal with high-risk delivery scenarios. When there are three consecutive abnormal package receipt records in Building 5 and the probability of lost items exceeds the preset threshold of 8%, the path planning module immediately starts the safe route optimization protocol. The system forces the delivery vehicle to bypass the unmonitored channel on the west side of the building, replans the route to reach the target locker via the main road on the east side, and activates the real-time location tracking function in the vehicle control system, increasing the location reporting frequency from 30 seconds / time to 5 seconds / time. At the same time, the safety instruction requires the vehicle to keep the cargo hold double-locked when performing delivery in risk areas. Only when the recipient's biometric verification is passed, the cloud control system will remotely send an unlocking instruction to form a full-chain safety protection mechanism.

[0088] In one implementation, the triggering of generating a replenishment order for a target community merchant based on the user profile and the community merchant inventory data in S103 includes:

[0089] S1031: Identify the matching deviation between the consumption cycle characteristics and the current inventory status in the user portrait, and align the historical consumption curve and the real-time inventory curve through a dynamic time warping algorithm.

[0090] For example, the order dynamic delivery optimization system starts the consumption cycle matching analysis engine, and compares the spatiotemporal correspondence between the user's historical consumption pattern and the current inventory status through the dynamic time warping algorithm. The system nonlinearly aligns the infant formula consumption curve of user G (fixed replenishment of 2 cans every Thursday) with the inventory change curve of community merchant A. It is found that there are only 1.3 cans of milk powder left in the inventory system on Thursday this week, and there is no record of replenishment in transit. The calculated matching deviation reaches the orange warning level. The dynamic time warping algorithm eliminates the interference of special factors such as holidays by stretching or compressing the time axis, and accurately identifies the essential difference between the actual user demand and inventory supply. For example, the chronic disease drug consumption curve of elderly user H shows that the 15th of each month is the replenishment window, but the community pharmacy inventory system shows that the batch of drugs will be exhausted on the 12th. The system determines that the matching deviation exceeds the threshold and triggers the subsequent replenishment process.

[0091] S1032: When it is detected that the matching deviation exceeds a preset threshold, an urgent generation flag of the replenishment order is activated, and the urgent generation flag triggers a real-time response protocol of the supplier stocking system.

[0092] For example, the system automatically activates a differentiated replenishment response mechanism based on the degree of deviation. When it is detected that there is a 2-hour supply gap between the inventory curve of the fresh supermarket refrigerator and the user group's dinner ingredient procurement cycle, the emergency generation flag is immediately lit and sent to the supplier collaboration platform. This flag triggers the real-time response protocol of the supplier's stocking system. For example, after a dairy supplier receives a replenishment instruction with a priority of A1, its warehouse management system automatically releases the frozen status of inventory allocated to other orders, giving priority to the execution of community orders. The system also activates the supplier's emergency transportation channel. A meat processing company completed the pre-cooling start of the cold chain vehicle within 15 minutes after receiving the emergency flag, ensuring that the replenishment time meets the requirements of the dynamic distribution optimization system.

[0093] S1033: Aggregate multiple replenishment requests according to category priority identifiers to generate a batched purchase list. The aggregation process uses a clustering algorithm to merge common demands of geographically related merchants.

[0094] Exemplarily, the system performs cross-merchant demand aggregation and supplier matching optimization operations. The category priority identification resolution module identifies that three convenience stores in the community have bottled water out-of-stock warnings at the same time. The clustering algorithm classifies merchants with a distance of less than 500 meters into the same procurement cluster based on geo-fence parameters. When the system merges and generates batched purchase lists, it not only considers the commonality of commodity categories, but also analyzes the feasibility of superposition of supplier delivery routes. For example, 12 boxes of mineral water from merchant B and 8 boxes of mineral water from merchant C are combined into a standardized transport unit of 20 boxes, allowing suppliers to complete delivery using a single vehicle, reducing logistics costs while improving shelf replenishment efficiency.

[0095] S1034: Sending a replenishment instruction including an optimal supplier selection recommendation to the inventory management platform of the target community merchant, wherein the optimal supplier selection recommendation is dynamically sorted based on the supplier's historical fulfillment rate and current production capacity load.

[0096] For example, the intelligent supplier recommendation engine initiates a multi-dimensional evaluation process. Based on the replenishment needs of community supermarkets, the system screens candidate companies with refrigerated product supply qualifications from the supplier database and dynamically ranks their service capability indicators. The optimal supplier selection recommendation comprehensively considers historical fulfillment rate, real-time production capacity load and emergency response capabilities. For example, although a cold chain logistics company has a higher quotation, its on-time delivery rate for the last 30 times has reached 100%, and its delivery exception handling score in heavy rain weather is 35% better than its peers. Therefore, it is recommended by the system first. When the selection recommendation is pushed to the merchant inventory management platform simultaneously, detailed comparative data and system recommendation index of each supplier are attached to support merchants to make final decision confirmation.

[0097] In one implementation, the step of aggregating multiple replenishment requests according to the category priority identifiers to generate a batched purchase list in S1033 includes:

[0098] S10331: Extract the supplier delivery on-time rate indicator and quality qualification rate indicator from the historical replenishment order, wherein the supplier delivery on-time rate indicator is corrected to a benchmark value under different weather conditions based on traffic big data.

[0099] In practice, the supplier delivery on-time performance indicator uses real-time traffic information from a traffic big data platform to adjust baseline assessments for different weather conditions. For example, dairy supplier X's on-time performance is 98% in clear weather. However, based on icy road warnings issued by the meteorological authorities, the system automatically lowers its baseline on-time performance to 87% for winter rainy and snowy conditions. The quality compliance rate indicator is linked to the market supervision department's random inspection database. If a regional quality warning is issued for a batch of goods, the system immediately updates the supplier's score, ensuring the objectivity and timeliness of replenishment decisions.

[0100] S10332: Construct a supplier evaluation matrix and calculate the matching degree between the supplier evaluation matrix and the quality requirements of the current procurement category. The supplier evaluation matrix includes a flexible production capacity score and an emergency response level for emergencies.

[0101] In its implementation, the system incorporates special evaluation criteria for infant food suppliers, requiring flexible production capacity scores to include line switching speed and small-batch customized production capabilities. One milk powder manufacturer, with the ability to complete production line modifications to adjust formulas within 72 hours, earned a flexible production capacity score in the top 10% of the industry. The emergency response level assessment considers suppliers' track record of ensuring supplies during public health incidents. For example, one company maintained its drug supply through drone delivery during a regional lockdown, earning it the system's highest emergency response level certification.

[0102] S10333: Generate a procurement cost optimization function based on the settlement cycle parameters of the target community merchant and the supplier credit rating, wherein the procurement cost optimization function constrains the minimum procurement quantity and the maximum delay tolerance days.

[0103] During implementation, the system constructs a time-value model that includes capital costs based on the settlement period characteristics of community merchants (e.g., fresh food supermarkets require a 45-day payment period) and the supplier's credit rating (AAA-rated suppliers enjoy deferred payment discounts). The procurement cost optimization function sets dual constraints on product shelf life and shelf turnover. For example, it imposes a mandatory minimum purchase quantity limit on products with a shelf life of less than 15 days to prevent near-expiry losses due to excessive replenishment. The function also introduces a tolerance parameter for supplier capacity fluctuations, allowing high-quality suppliers to appropriately extend delivery times during equipment maintenance without affecting their credit ratings.

[0104] S10334: A multi-constrained integer programming model is used to solve the supplier allocation plan in the initial purchase list. The multi-constrained integer programming model introduces slack variables to handle the order splitting problem caused by temporary stock-outs.

[0105] In practice, a multi-constrained integer programming model processes a replenishment order involving five suppliers, three product categories, and 12 community outlets. It converts 23 parameters, including transportation costs, storage fees, and quality risks, into decision variables. If the model detects that the preferred supplier's inventory cannot meet all demand, a slack variable mechanism automatically activates, splitting the order into an optimal combination with Supplier Y supplying 80% and Supplier Z supplying 20%. This mechanism effectively mitigates the risk of a single supplier's supply disruption while ensuring consistent product quality and meeting the minimum display requirements for community merchants.

[0106] S10335: Verify the transport capacity restrictions and storage space compatibility in the supplier allocation plan, iteratively adjust until the shelf display rules of all community merchants are met, and output a batched purchase list.

[0107] During implementation, the transport capacity verification module detected that the actual loading space of the 9.6-meter refrigerated truck provided by the supplier was insufficient to accommodate the specialized pharmaceutical transport boxes, immediately triggering a plan adjustment. A storage space compatibility check revealed that the outer packaging dimensions of a batch of imported food exceeded the target merchant's shelf height limit. The system automatically switched to alternative packaging that complied with the community merchant's shelf display rules. After three iterations of optimization, the final batched purchase list ensured that each unit's physical attributes, transportation conditions, and terminal storage environment were fully aligned, while also meeting the community merchant's operational specifications and user experience requirements.

[0108] In one implementation, the method further includes:

[0109] S301: Collecting environmental status data when a user accesses a package. The environmental status data includes weather conditions and light intensity identified by the smart cat-eye device. The weather conditions include precipitation type identification results and wind speed level classification.

[0110] In practice, when user D accessed a package at 2:30 PM, the weather monitoring module within the smart cat's eye detected severe convective rainfall, recorded a wind speed of level 5 gusts, and detected a sudden drop in light intensity below 200 Lux. The system cross-validated the rainfall intensity data with radar echoes from the community micro-meteorological station and confirmed that the current weather conditions had reached the yellow rainstorm warning level. The environmental status data was then flagged as an "extreme weather - heavy rainfall" event, triggering subsequent delivery strategy adjustments.

[0111] S302: Perform correlation analysis on the environmental status data and historical delivery delay records to generate a weather impact factor matrix. The correlation analysis uses a Bayesian network to infer the cumulative effect of different weather combinations on delivery efficiency.

[0112] During implementation, Bayesian network model analysis revealed that when rainfall intensity exceeded 50 mm / h and wind speeds reached level 6, the probability of delivery delays on the sloped road east of the community increased to 78%. When constructing the weather impact factor matrix, the system specifically noted that the area's traffic risk index for combined rain and snow was 0.67, significantly higher than the community average of 0.23. For example, historical data showed that under similar weather conditions, the rate of damaged packaging for fresh produce at the Building 3 delivery point increased by 3.2 times. This correlation was encoded as a key parameter in the conditional probability distribution table.

[0113] S303: When a rainstorm warning signal is detected, dynamically adjust the road priority weight in the delivery route planning, wherein the road priority weight incorporates the underground passage availability and drainage system score.

[0114] During implementation, the system accesses the municipal geographic information system to obtain the real-time opening status and drainage capacity scores of underground passages. If Underground Passage No. 2 is temporarily closed due to waterlogging, the priority weight of that node is immediately reduced to 0, and the cost of the detour is recalculated. At the same time, the weight coefficient of Trunk Road No. 5, which has a drainage score of 90 or above, is increased to ensure that delivery vehicles prioritize routes with strong flood resistance.

[0115] S304: Sending a moisture-proof packaging activation instruction and a route replanning request to the delivery vehicle, wherein the moisture-proof packaging activation instruction is associated with a moisture absorption sensitivity classification table of the package category.

[0116] In practice, the moisture-proof packaging activation command parsing module generates differentiated protection plans for different product categories based on a moisture sensitivity grading table: PE film packaging is used for common daily necessities, moisture-proofing agents are added to precision instruments, and double cold chain sealing is implemented for fresh produce. A route replanning request is simultaneously sent to the regional dispatch center. Upon receiving the command, vehicle #07 immediately switches to a backup vehicle equipped with all-terrain tires and equipped with emergency lighting for low-light conditions.

[0117] S305: During the route replanning process, the estimated arrival time of the target community merchant is synchronously updated, and a delay notification push service is triggered on the user terminal.

[0118] In practice, the route adjustment delayed the estimated arrival time of the fresh produce counter in Building 3 from 4:00 PM to 4:45 PM, triggering a two-way communication protocol on the user terminal. Upon receiving the delay notification, User F's smart home terminal automatically adjusted the refrigerator's energy-saving mode to continuous freshness. The IoT lock system also extended the package delivery window by 1.5 hours, ensuring product quality was not affected by delivery delays.

[0119] In one implementation, the correlating analysis of the environmental status data with historical delivery delay records to generate a weather impact factor matrix in S302 includes:

[0120] S3021: Divide the deviation between the baseline value of delivery time and the actual observation value under different weather types, wherein the baseline value of delivery time is determined by a quantile regression model, and the quantile regression model is established according to the vehicle type and road grade.

[0121] In practice, the model outputs a baseline delivery time of 8 minutes and 30 seconds per kilometer on sunny days for cold chain delivery vehicles on community arterial roads. This baseline value is revised to 12 minutes and 15 seconds per kilometer in rainy weather. When vehicle #09 is observed to take 15 minutes and 20 seconds per kilometer on slippery roads, the system calculates a 26% deviation from the baseline value, which is then flagged as a correction parameter for the route planning model.

[0122] S3022: Calculate the accessibility attenuation coefficient of each community entrance and exit in rainy and snowy weather. The accessibility attenuation coefficient integrates slope sensor data and historical slip event statistics.

[0123] During implementation, the slope sensor at the community's north gate detected a 5.2-degree inclination. Combined with seven rainy-day skidding incidents recorded in the historical database, the system generated a rainy-day accessibility reduction coefficient of 0.58 for this node. This coefficient was converted into a penalty term in the path weight, enabling delivery vehicles to automatically avoid high-risk slopes during route planning.

[0124] S3023: Establish a conditional probability distribution table between weather conditions and package damage types, where the package damage types include a correlation between the probability of outer packaging deformation and the humidity sensitivity of the items inside.

[0125] During implementation, the system found that paper-packaged products have a 43% probability of deformation in environments with relative humidity greater than 90%, while the humidity sensitivity correlation coefficient for electronic products is 0.89. This data drives the system to prioritize highly sensitive products in subsequent moisture-proofing instructions, for example, labeling User E's medical testing kit as a Level 1 priority for humidity protection.

[0126] S3024: Integrate the deviation, the accessibility attenuation coefficient and the conditional probability distribution table to generate a multi-dimensional weather impact factor matrix. The integration process uses tensor decomposition technology to reduce the dimension and reconstruct the impact weight.

[0127] In practice, the system encodes deviation, accessibility coefficient, and damage probability tables into a three-dimensional weather data tensor based on community grids. The weather type dimension includes six precipitation patterns, the community space dimension is divided into 23 grid cells, and the package category dimension covers 38 major product categories. After Tucker decomposition, the core tensor extracts three principal components that influence delivery efficiency, explaining 82% of the variance in the original data.

[0128] S3025: Encode the multidimensional weather impact factor matrix as input features of the distribution path planning model, and dynamically adjust the safety balance parameter and timeliness balance parameter in the path evaluation function.

[0129] In practice, when the system detected a thunderstorm in the southeastern area, it dynamically adjusted the parameters in the path evaluation function, increasing the safety balance parameter from 0.4 to 0.7 and reducing the timeliness balance parameter to 0.3. This adjustment caused vehicle #12 to automatically choose the tunnel route with an excellent drainage score, abandoning the originally planned shortcut via the scenic avenue.

[0130] As an optional but non-limiting embodiment, the integration of the deviation, the accessibility attenuation coefficient, and the conditional probability distribution table in S3024 to generate a multidimensional weather impact factor matrix includes:

[0131] S30241: The deviation amount, the accessibility attenuation coefficient, and the dimensional data in the conditional probability distribution table are spatiotemporally aligned according to the community grid unit to generate a three-dimensional weather data tensor. The three dimensions of the three-dimensional weather data tensor correspond to the weather type dimension, the community space dimension, and the package category dimension, respectively.

[0132] In actual use, the system quantizes hail weather records for grid cell 2 (covering a community commercial center), damage reports for product category 7 (maternal and infant products), and delivery delay data from Tuesday afternoon. A time alignment algorithm eliminates time differences in data collection, creating a three-dimensional data structure with a unified spatiotemporal reference. Each grid cell corresponds to a 30m x 30m geocoded area.

[0133] S30242: Normalizing the three-dimensional weather data tensor to eliminate dimensional differences between different data sources and generate a normalized three-dimensional weather data tensor. The normalization process uses a z-score normalization method based on the distribution of historical data within the community grid unit.

[0134] In actual application, the system used a z-score normalization method based on historical distributions for the light intensity data (in Lux) and wind speed data (in m / s) for grid cell 5 during heavy rain. This method converted a light intensity of 300 Lux to a -1.2σ deviation value, and a wind speed of 8 m / s to a +2.1σ deviation value. This made the data from different sensors comparable, laying the foundation for subsequent tensor decomposition.

[0135] S30243: Use tensor decomposition technology to perform rank estimation and factor decomposition on the normalized three-dimensional weather data tensor to generate a core tensor and multiple factor matrices. The rank estimation iteratively optimizes the weighted objective function of the decomposition error and complexity penalty term through the alternating least squares method.

[0136] In actual application, the system performed CP decomposition on a three-dimensional weather data tensor containing 23 community grids, 6 weather types, and 38 categories of commodities. After 500 iterative calculations, the optimal rank was determined to be 3. During the decomposition process, a complexity penalty dynamically adjusted the model parameters, compressing the feature dimensions from the original 1064 items to 18 principal components of the core tensor while maintaining 85% data recovery.

[0137] S30244: Sort the core tensor by modal importance, extract the components whose feature contribution exceeds a preset threshold in each dimension, and generate a core tensor after modal importance sorting. The modal importance sorting is achieved by calculating the cumulative energy ratio of the singular values of each slice matrix of the core tensor.

[0138] In actual application, the system calculated that the contribution rate of the first component of the weather type dimension reached 51%, significantly higher than the 32% of the community space dimension and the 17% of the product dimension. Based on this, precipitation type was identified as the most critical influencing factor, driving the subsequent feature weighting strategy to prioritize the accuracy of weather classification data analysis.

[0139] S30245: Based on the core tensor sorted by modal importance, component selection and weighted fusion are performed on the multiple factor matrices to generate a weather feature projection matrix after dimensionality reduction. The weighted fusion dynamically allocates weight coefficients according to the variance explanation rate of each component of the core tensor.

[0140] In practice, the system assigns a weight of 0.55 to the weather type principal component, 0.3 to the community space component, and 0.15 to the product component based on the variance explained by each component of the core tensor. The weighted fusion projection matrix simplifies the 38 product categories into six protection level groups, significantly reducing the complexity of subsequent calculations.

[0141] S30246: Perform a tensor product operation on the reduced-dimensional weather feature projection matrix and the normalized three-dimensional weather data tensor to reconstruct an intermediate multidimensional weather impact factor matrix with a low-rank approximation state. The tensor product operation retains the topological relationship of the original community space dimension and the association structure of the weather type dimension.

[0142] In practice, the system performs a modal product between the reduced-dimensional weather feature projection matrix and the original three-dimensional data tensor to generate an intermediate matrix that preserves the neighborhood topology. This matrix accurately reflects the hidden correlation between the risk index of underground passage No. 2 during heavy rain (0.78) and the moisture-proofing requirement level (A2) of the lockers in Building 3.

[0143] S30247: Perform a spatiotemporal smoothness check on the intermediate multidimensional weather impact factor matrix, detect and correct local outliers caused by data sparsity, and generate a multidimensional weather impact factor matrix. The spatiotemporal smoothness check adopts a Laplace regularization constraint based on a community adjacency graph.

[0144] In practice, the system smoothed the abnormally high risk value (1.2) for edge grid cell 8 due to data sparseness using Laplace regularization based on the community adjacency graph. By taking a weighted average of the risk values of the five adjacent grid cells (average 0.6), the outlier value was corrected to 0.73, ensuring the spatial continuity of the final multidimensional weather impact factor matrix.

[0145] As an optional but non-limiting embodiment, encoding the consumption cycle stability index, the demand trigger condition dependency graph, and the demand transmission delay parameter into a multidimensional vector in S1028 includes:

[0146] S10281: Performing cross-user group standardization processing on the consumption cycle stability index to generate a standardized consumption cycle stability index set, wherein the standardization processing uses a quantile normalization method to eliminate dimensional differences among different user groups.

[0147] In actual application, the order dynamic delivery optimization system starts a data standardization engine across user groups to unify the dimensions of the consumption cycle stability index. The system uses the quantile normalization method to eliminate the statistical distribution differences between the elderly user group and the young family user group. For example, the drug procurement cycle stability index of 98% for user D (elderly group) and the maternal and child product procurement cycle stability index of 85% for user E (young family) are mapped to the same standard quantile space. By sorting the raw data of each user group by percentile and matching the standard normal distribution, a comparable set of standardized indices is generated, which provides a horizontal comparison basis for the stability evaluation of different consumption patterns and eliminates dimensional interference for subsequent feature fusion.

[0148] S10282: Projecting the spatiotemporal co-occurrence probability and causal reasoning relationship in the demand trigger condition dependency graph into a low-dimensional semantic space to generate a dense vector representation of the demand trigger condition dependency graph. The projection process uses a graph embedding algorithm to capture the topological similarity of nodes across categories.

[0149] In actual application, the system uses a graph embedding algorithm to vectorize the demand trigger dependency graph. The algorithm projects the spatiotemporal co-occurrence probability (78% probability of occurrence between 6:00 PM and 8:00 PM every Friday night) and causal inference relationship (every 1kg increase in pet food consumption leads to a 0.7 increase in demand for cleaning products) in the cross-category association rule for "pet food-cleaning products" into a 128-dimensional semantic space. By preserving key features of the node topology, the dense vector representation accurately captures the spatial similarity in product combination preferences between users in Building 3 and Building 5. This ensures that users with the same consumption patterns in different buildings are within a distance of 0.15 in the graph embedding space, establishing a vector foundation for personalized recommendations.

[0150] S10283: Extracting the time series fluctuation pattern of the demand conduction delay parameter, extracting the local dependency features of the demand conduction delay parameter through a one-dimensional convolutional neural network, and generating a convolution feature sequence of the demand conduction delay parameter.

[0151] In practical applications, a one-dimensional convolutional neural network extracts local temporal features of demand transmission delay parameters. To address demand transmission fluctuations caused by inventory depletion of fresh produce in the northwest district of a community, the network uses a three-layer convolutional kernel to scan the delay parameter sequence, capturing the propagation pattern of sudden replenishment demand across buildings. For example, after detecting a shortage of fresh milk in user F's building, the convolutional layer identifies the delayed response characteristics of adjacent buildings 45 minutes later, generating a feature sequence containing spatiotemporal dependencies. This sequence accurately characterizes the diffusion rate of product shortage signals across the community's road network, providing temporal features to support the prediction of regional replenishment demand.

[0152] S10284: Perform multimodal splicing on the standardized consumption cycle stability index set, the dense vector representation of the demand trigger condition dependency graph, and the convolution feature sequence of the demand conduction delay parameter to generate an initial fusion feature matrix.

[0153] In practical applications, the multimodal feature concatenation engine integrates heterogeneous data sources. The system axially concatenates user G's standardized consumption cycle stability index (0.87), the 128-dimensional embedding vector of the "Grain, Oil, and Condiments" association graph, and the 256-dimensional convolutional feature sequence of fresh produce demand transmission, generating a 384-dimensional initial fused feature matrix. This matrix preserves the modal properties of the original data, such as the high-order dimensions of the temporal convolutional features, the central distribution of the graph embedding vectors, and the stability index as the underlying feature, forming a clearly hierarchical multimodal expression structure.

[0154] S10285: Generate a positive sample pair set and a negative sample pair set based on a contrastive learning framework, wherein the positive sample pair set includes initial fusion feature matrix slices of the same user in different time windows, and the negative sample pair set includes initial fusion feature matrix slices of different users.

[0155] In practice, the contrastive learning framework constructs positive and negative sample pairs in the feature space. The system selects slices of the fused feature matrix from user H every Tuesday in March as anchor points. The slices of Tuesday data from the two weeks before and after constitute positive sample pairs, while the slice of Friday data from user J forms a negative sample pair. Batch sampling constructs a training set containing 5,000 positive and negative pairs, forcing the model to learn the temporal continuity characteristics of user behavior patterns. For example, the weekly drug purchasing patterns of elderly users at fixed times of the week have a consistently smaller distance in the contrastive space than 0.1, while the characteristic distance with maternal and infant users is greater than 0.5.

[0156] S10286: Calculate the similarity loss function of the positive sample pair set and the negative sample pair set, optimize the parameters of the graph embedding algorithm and the one-dimensional convolutional neural network, and generate an optimized demand trigger condition dependency graph vector and demand conduction delay parameter feature sequence.

[0157] In actual application, the similarity loss optimization module drives model parameter updates. The system calculated the cosine similarity of the positive sample pair (user K's Monday and Wednesday feature slices) to be 0.92, while the similarity of the negative sample pair (user K and user L) dropped to 0.15. The weight parameters of the graph embedding algorithm were adjusted using a triplet loss function. The optimized demand trigger condition dependency graph vector accurately reflects the "chilled milk - breakfast cereal" association, increasing by 12%. Furthermore, the convolutional feature sequence of the demand transmission delay parameter captures the characteristic decrease in transmission rate during heavy rain. The model's cross-period prediction accuracy on the validation set increased to 89%.

[0158] S10287: Perform spatial distribution alignment on the optimized demand trigger condition dependency graph vector, separate the co-occurrence features between categories and the user's individual preference features through feature decoupling operation, and generate a decoupled demand trigger condition dependency sub-vector.

[0159] In practice, feature decoupling separates mixed semantic information. The system performs orthogonal decomposition on the "sports goods - functional beverages" association graph vector, extracting cross-floor spatial co-occurrence features (joint promotions between gyms and adjacent businesses) and individual user preference features (independent purchases by fitness enthusiasts). The decoupled subvectors are stored separately in the community's public feature library and the user's private feature library. For example, the "laundry detergent - softener" association subvector in Building 3's public area is stored separately from the "organic food - eco-friendly detergent" preference subvector unique to user M, ensuring independent modeling of group patterns and individual preferences.

[0160] S10288: Perform time alignment on the decoupled demand trigger condition dependency sub-vector and the optimized demand conduction delay parameter feature sequence, match the feature slices at the same time granularity, and generate a spatiotemporally synchronized fusion feature block; perform feature importance weighting on the spatiotemporally synchronized fusion feature block, adjust the weight coefficient of each feature channel according to the dynamic quantile of the consumption cycle stability index set, and generate a weighted user portrait intermediate vector.

[0161] In actual application, the spatiotemporal synchronization engine aligns multi-source feature sequences. The system slices and matches the decoupled "fresh food-fast food" correlation subvectors with the optimized transmission delay feature sequence within a 15-minute time window. For user N's peak demand period between 6:00 PM and 7:00 PM in the evening, the feature weighting module increases the feature weight of the fresh food category to 0.7 and reduces it to 0.3 for the fast food category based on the dynamic quantile of the consumption cycle stability index. The weighted intermediate vector accurately reflects the intensity of the user's urgent replenishment demand during peak hours, providing a quantitative basis for real-time delivery decisions.

[0162] S10289: Use a self-supervised clustering algorithm to perform distribution consistency constraints on the weighted user portrait intermediate vector, iteratively optimize the cluster center and feature mapping relationship, and generate a target multi-dimensional user portrait vector; jointly index the target multi-dimensional user portrait vector and the community inventory consumption rate map, establish a spatial mapping relationship between the user demand vector and the inventory location, and generate a dynamically updateable user portrait vector storage structure.

[0163] In actual application, a self-supervised clustering algorithm constructs a user portrait vector space. The system performs K-means++ initialization on the intermediate vectors of 20,000 users, and after iterative optimization, 12 feature cluster centers are formed. The cluster center for the elderly health care group highlights the characteristics of drug cycle stability and delivery time sensitivity, while the cluster center for young families strengthens the correlation strength of maternal and infant products. The resulting multi-dimensional user portrait vector establishes a spatial mapping relationship with the community inventory consumption rate map. For example, the high-stability drug demand vector of user P is specifically associated with the constant temperature storage location coordinates of the community pharmacy, achieving precise spatial matching of demand and supply. The vector storage structure uses an incremental update mechanism. When a shift in user Q's weekend purchasing pattern is detected, the vector reconstruction and cluster center adjustment process is automatically triggered.

[0164] The embodiment of the present application innovatively constructs a community supply and demand dynamic control architecture based on the Internet of Things ecosystem, and achieves end-to-end optimization by integrating multi-dimensional real-time data streams and intelligent decision-making algorithms.

[0165] Unlike the one-way prediction model of the traditional retail supply chain, the embodiment of the present application can use the smart cat-eye behavior sequence and package access events as a dynamic signal source for user demand prediction. By analyzing high-frequency entry and exit scenarios and instant package interaction behaviors, a three-dimensional user portrait that integrates spatial location attributes and time density is constructed, accurately capturing the cyclical laws of community consumption behavior and the characteristics of sudden demand fluctuations.

[0166] At the supply chain response level, the embodiments of this application can design a two-dimensional replenishment trigger mechanism based on time constraints and category priorities, achieving real-time dynamic matching of demand signals and inventory distribution. A community inventory topology network model is introduced into distribution route planning. By dynamically analyzing the spatial distribution density of inventory nodes and the real-time capacity load of distribution vehicles, a flexible distribution plan with adaptive characteristics is generated, breaking through the limitations of static routes and fixed frequencies in traditional logistics planning.

[0167] In summary, the embodiments of the present application effectively open up a two-way perception channel between user behavior data and the supply chain execution system, forming an intelligent collaborative network of demand forecasting-inventory optimization-dynamic distribution, which significantly improves the precision service capabilities and resource scheduling efficiency in community retail scenarios.

[0168] Based on the same inventive concept, the present application also provides an order dynamic delivery optimization system. Figure 2 As shown, it is a structural diagram of a possible order dynamic delivery optimization system provided in an embodiment of the present application. Figure 2In the example, the order dynamic delivery optimization system 200 includes a processor 210 and a memory 220. The memory 220 stores a computer program executable by the processor 210. The processor 210 can execute the steps of the above-mentioned user profile-based order dynamic delivery optimization method by executing the instructions stored in the memory 220.

[0169] Based on the same inventive concept, an embodiment of the present application provides a computer-readable storage medium, which includes a computer program. When the computer program runs on an order dynamic delivery optimization system, the computer program is used to enable the order dynamic delivery optimization system to execute the steps of the above-mentioned order dynamic delivery optimization method based on user portraits. In some possible implementations, various aspects of the order dynamic delivery optimization method based on user portraits provided by the present application can also be implemented in the form of a program product, which includes a computer program. When the program product runs on an order dynamic delivery optimization system, the computer program is used to enable the order dynamic delivery optimization system to execute the steps of the above-mentioned order dynamic delivery optimization method based on user portraits. For example, the order dynamic delivery optimization system can execute the following steps: Figure 1 Follow the steps shown in .

Claims

1. A method for optimizing dynamic order distribution based on user portraits, characterized in that: include: Collect user behavior data and community merchant inventory data, including entry and exit behavior sequences recorded by smart cat-eye devices and package access events triggered by IoT locks; Generate dynamic demand forecast parameters based on the user behavior data, and build a user profile that includes consumption cycle characteristics and immediate demand intensity; Based on the user profile and the community merchant inventory data, triggering the generation of a replenishment order for the target community merchant, the replenishment order including a delivery time constraint and a category priority identifier; Combining real-time delivery load parameters with community inventory distribution topology, dynamic delivery path planning results are generated and the timing control instructions of delivery vehicles are updated.

2. The method according to claim 1, wherein The collection of user behavior data and community merchant inventory data includes: Extracting user access frequency characteristics within a continuous time window from the smart cat-eye device and identifying the access behavior pattern of users carrying packages. The access behavior pattern includes the correlation between the peak time period of access times per day and the volume of packages. Synchronize the IoT lock's package access timestamps and operation type data to extract spatiotemporal correlation features of multiple access events within a single day. These spatiotemporal correlation features include the time intervals and path overlap between package accesses by the same user on different floors. Calling the inventory scanning device to obtain the category inventory matrix of community merchant shelves and marking the inventory warning status of potential expiration, the inventory warning status includes the remaining saleable time prediction value of different shelf levels; A feature fusion process is performed on the entry and exit behavior pattern, the spatiotemporal correlation feature, and the inventory warning status to generate a multidimensional data set, wherein the multidimensional data set includes a package demand density map and an inventory consumption rate map based on users.

3. The method according to claim 2, wherein Generating dynamic demand forecast parameters based on the user behavior data includes: Inputting the input and output frequency features into a first recurrent neural network to output a basic consumption cycle prediction curve, wherein the hidden layer of the first recurrent neural network includes a time attention mechanism for capturing abnormal fluctuations within the cycle; Inputting the access timestamp and the operation type data into a time series decomposition model to separate the sudden demand event characteristics and the normal demand baseline characteristics, wherein the time series decomposition model uses a variational mode decomposition algorithm to eliminate noise interference; Performing a convolution operation on the inventory warning status in the category dimension to generate a heat map of the inventory consumption rate. The convolution operation uses an adaptive kernel size to match the consumption pattern differences of different categories. The basic consumption cycle forecast curve, the sudden demand event characteristics and the inventory consumption rate are fused to generate dynamic demand forecast parameters. The fusion process introduces a gating mechanism to dynamically adjust the contribution weight of each feature.

4. The method according to claim 3, wherein The user profile constructed includes consumption cycle characteristics and immediate demand intensity, including: Extracting the demand fluctuation amplitude of each category from the dynamic demand forecast parameters to generate a consumption cycle stability index, wherein the consumption cycle stability index is obtained by calculating the weighted sum of the variance and trend slope of the demand curve within the sliding window; Identify cross-category association rules in the sudden demand event characteristics and construct a demand trigger condition dependency graph, wherein the demand trigger condition dependency graph includes spatiotemporal co-occurrence probabilities and causal reasoning relationships between categories; The demand transmission delay parameters for each floor in the community are calculated based on the thermal distribution map of the inventory consumption rate. The delay parameters are used to simulate the diffusion effect of inventory changes between buildings through a graph neural network. The consumption cycle stability index, the demand trigger condition dependency graph, and the demand conduction delay parameter are encoded into a multidimensional vector to form the user profile. The encoding process uses a contrastive learning framework to align the spatial distribution of different features.

5. The method according to claim 1, wherein The method of combining real-time delivery load parameters with community inventory distribution topology to generate dynamic delivery path planning results and update the timing control instructions of delivery vehicles includes: Analyze the conflict detection conditions between time-sensitive orders and ordinary delivery orders in the delivery time constraint, wherein the conflict detection conditions include a comparison of time window overlap and priority weight; Calculating the path weight of each delivery node based on the community inventory distribution topology and generating an initial delivery path set, wherein the path weight includes a floor height penalty factor and an estimated elevator waiting time; Real-time monitoring of delivery vehicle battery life data and traffic congestion index, and dynamic adjustment of the energy cost factor in the path weight. The energy cost factor is dynamically updated through a regression model based on vehicle model and road conditions. A reinforcement learning model is used to perform multi-objective optimization on the initial delivery route set to output a dynamic delivery route with the lowest overall cost. The state space of the reinforcement learning model includes the vehicle's remaining battery power, the real-time order queue, and weather impact parameters. The batch scheduling instructions and charging station access sequence of the delivery vehicles are updated according to the dynamic delivery path. The batch scheduling instructions include task allocation strategies and collision avoidance rules among multiple delivery vehicles.

6. The method according to claim 5, wherein The method further comprises: Collecting package receipt status data during the delivery route execution process, the package receipt status data includes the recipient's biometrics and package integrity identification verified by the IoT lock, the recipient's biometrics including fingerprint matching and facial liveness detection results; Inputting the receipt status data into an insurance risk assessment model to generate a community-level prediction of the probability of lost items, wherein the insurance risk assessment model uses a gradient boosting tree algorithm to integrate historical claim records and current environmental parameters; Adjust the logistics insurance rate calculation parameters of the target community merchants based on the predicted value of the lost item probability, and introduce a dynamic discount factor in the adjustment process to reflect the changes in security levels at different time periods; When it is detected that the probability of lost items on the target floor exceeds a threshold, a safe route optimization instruction in the delivery path planning is triggered. The safe route optimization instruction forces the delivery vehicle to detour the risk area and enables the real-time location tracking function.

7. The method according to claim 1, wherein The triggering of generating a replenishment order for a target community merchant based on the user portrait and the community merchant inventory data includes: Identifying the matching deviation between the consumption cycle characteristics and the current inventory status in the user profile, and aligning the historical consumption curve with the real-time inventory curve using a dynamic time warping algorithm; When it is detected that the matching deviation exceeds a preset threshold, an urgent generation flag of the replenishment order is activated, and the urgent generation flag triggers a real-time response protocol of the supplier stocking system; Aggregate multiple replenishment requests based on category priority identifiers to generate a batched purchase list. The aggregation process uses a clustering algorithm to merge common demands of geographically related merchants. A replenishment instruction containing an optimal supplier selection recommendation is sent to the inventory management platform of the target community merchant. The optimal supplier selection recommendation is dynamically sorted based on the supplier's historical fulfillment rate and current production capacity load.

8. The method according to claim 7, wherein The process of aggregating multiple replenishment requests according to category priority identifiers to generate a batched purchase list includes: Extract supplier delivery on-time rate indicators and quality pass rate indicators from historical replenishment orders. The supplier delivery on-time rate indicators are adjusted to baseline values under different weather conditions based on traffic big data. Construct a supplier evaluation matrix and calculate its matching degree with the quality requirements of the current procurement category. The supplier evaluation matrix includes flexible production capacity scores and emergency response levels. Generate a procurement cost optimization function based on the settlement cycle parameters of the target community merchants and the supplier credit rating, wherein the procurement cost optimization function constrains the minimum procurement volume and the maximum delay tolerance days; A multi-constrained integer programming model is used to solve the supplier allocation plan in the initial purchase list. The multi-constrained integer programming model introduces slack variables to deal with the order splitting problem caused by temporary stock-outs. Verify the transport capacity constraints and storage space compatibility in the supplier allocation plan, iteratively adjust until the shelf display rules of all community merchants are met, and output a batched purchase list.

9. The method according to claim 1, wherein The method further comprises: Collecting environmental status data when a user accesses a package. The environmental status data includes weather conditions and light intensity identified by the smart cat-eye device. The weather conditions include precipitation type identification results and wind speed level classification; Correlation analysis is performed on the environmental status data and historical delivery delay records to generate a weather impact factor matrix, wherein the correlation analysis uses a Bayesian network to infer the cumulative effect of different weather combinations on delivery efficiency; When a rainstorm warning signal is detected, the road priority weights in the delivery route planning are dynamically adjusted, and the road priority weights incorporate the underground passage availability and drainage system score; Sending a moisture-proof packaging activation instruction and a route replanning request to the delivery vehicle, wherein the moisture-proof packaging activation instruction is associated with the moisture sensitivity classification table of the package category; During the route replanning process, the estimated arrival time of the target community merchant is updated synchronously, and the delay notification push service is triggered on the user terminal; The step of correlating the environmental status data with historical delivery delay records to generate a weather impact factor matrix includes: Determine the deviation between the baseline delivery time and the actual observed value under different weather conditions. The baseline delivery time is determined using a quantile regression model, which is built based on vehicle type and road grade. Calculate the accessibility attenuation coefficient of each community entrance and exit in rainy and snowy weather, which integrates slope sensor data and historical slip event statistics; Establish a conditional probability distribution table for weather conditions and package damage types, where the package damage types include a correlation between the probability of outer packaging deformation and the moisture sensitivity of the contents; Integrating the deviation, the accessibility attenuation coefficient, and the conditional probability distribution table to generate a multidimensional weather impact factor matrix, wherein the integration process uses tensor decomposition technology to reduce the dimension and reconstruct the impact weight; The multidimensional weather impact factor matrix is encoded as the input feature of the distribution path planning model, and the safety balance parameter and the timeliness balance parameter in the path evaluation function are dynamically adjusted.

10. An order dynamic distribution optimization system, characterized in that: The method comprises a processor and a memory, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor is enabled to perform the steps of any one of the methods of claims 1 to 9.

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