An emergency scheduling method for burst order multi-constraint distribution enhanced by meta-learning

Abstract

The application provides a meta-learning enhanced burst order multi-constraint distribution emergency scheduling method, comprising: acquiring the time distribution mode and intensity change trend of continuous burst order flow through historical order data collection, and determining the regularity difference capture result; according to the regularity difference capture result, using meta-learning model training to obtain evolution trend judgment parameters of subsequent demand prediction; if the evolution trend judgment parameters exceed the preset threshold, starting resource allocation optimization to obtain an acceleration processing means allocation scheme under a dynamic coordination mechanism; for the preliminary scheduling sequence, using genetic algorithm iteration to determine the minimum path of the chain delay situation, and obtaining the optimization output of order mode analysis; through the final execution plan, using particle swarm optimization algorithm fusion to determine the compensation measures of resource allocation lag, and obtaining the complete sequence of overall distribution emergency scheduling.

Description

Technical Field

[0001] This invention relates to the field of information technology, and in particular to a meta-learning-enhanced method for emergency dispatching of sudden orders with multiple constraints.

[0002] Terminology Explanation Dynamic coordination mechanism: refers to a scheduling rule system that dynamically adjusts resource allocation ratios, task execution order, and compensation measures based on real-time order flow changes, resource status, and constraints. It includes load balancing strategies, priority ranking rules, and fault tolerance mechanisms.

[0003] Accelerated processing measures refer to resource optimization measures taken to cope with sudden order peaks, including temporary allocation of transportation capacity, flexible adjustment of constraints, and utilization of third-party crowdsourcing resources.

[0004] Pattern difference capture results (also known as "pattern difference capture results"): refers to the set of characteristic differences (such as duration and average intensity) and trend changes between different pattern categories obtained by analyzing the time distribution pattern and intensity change trend of sudden order flow.

[0005] Evolution trend judgment parameter (hereinafter referred to as "evolution trend parameter"): refers to the parameter obtained by training through meta-learning model, which is used to quantify the intensity of subsequent demand evolution. The value range is 0-1, and the larger the value, the more intense the demand evolution.

[0006] Chain-delayed orders: refers to a set of orders that cannot be started on time due to the delay of the preceding orders, and may cause delays in subsequent orders. It is determined by the order dependency relationship and the propagation of the delay amount. Background Technology

[0007] In the logistics and distribution sector, rapid response to sudden orders has become a core requirement for ensuring service quality and customer satisfaction.

[0008] With the frequent occurrence of e-commerce promotions, extreme weather, or emergencies, delivery systems often face the pressure of a large number of orders flooding in within a short period of time. This continuous surge in orders not only tests the ability to process orders in real time, but also poses a severe challenge to overall resource scheduling and constraint management.

[0009] Many current delivery scheduling methods perform adequately when dealing with single emergencies, but when orders arrive in waves, existing response strategies often become passive: while the system is processing the current order peak, it is difficult to accurately predict the arrival rhythm and scale of subsequent orders, resulting in a significant lag in resource allocation. Before the emergency scheduling of the previous wave of orders is completed, the next wave of orders has already begun to pile up, leading to a chain of delays and a situation where service commitments cannot be fulfilled.

[0010] The continuous nature of this interaction between orders makes the inadequacy of traditional methods in predicting time a major bottleneck.

[0011] The essence of continuous sudden orders lies in the obvious regular differences in their arrival time, intensity changes, and duration.

[0012] High-intensity bursts in a short period of time often require the immediate release of a large amount of transportation capacity, while medium-intensity fluctuations over a longer period require the gradual penetration and flexible adjustment of resources.

[0013] If the system only reacts to the current order volume, it cannot simultaneously balance immediate efficiency and the controllability of subsequent demand under limited resources.

[0014] This directly leads to a core contradiction: when faced with a continuous stream of orders, how to quickly absorb the current peak while avoiding insufficient or excessive resource consumption for subsequent orders has become a critical challenge that emergency delivery dispatch must confront.

[0015] Therefore, how to deeply capture the time distribution patterns, intensity trends, and duration patterns of continuous burst order flows, and predict the evolution of subsequent demands in advance during the current processing, thereby dynamically coordinating the adjustment of processing methods and various constraints, has become the core problem that needs to be solved in the meta-learning-enhanced emergency dispatch method for multi-constraint delivery of burst orders. Summary of the Invention

[0016] This invention provides a meta-learning-enhanced emergency dispatch method for multi-constraint delivery of sudden orders, mainly comprising: By collecting historical order data, the temporal distribution patterns and intensity trends of continuous surge orders are obtained, and the results of capturing patterns and differences are determined. Based on the captured patterns and differences, a meta-learning model is trained to obtain parameters for predicting the evolution of subsequent demand. If the parameters for predicting the evolution of demand exceed a preset threshold, resource allocation optimization is initiated to obtain an allocation scheme for accelerated processing methods under a dynamic coordination mechanism. Constraint adjustment variables are extracted from the allocation scheme for accelerated processing methods, and their matching degree with real-time processing efficiency is judged to obtain a preliminary scheduling sequence with controllable and balanced demand. For the preliminary scheduling sequence, a genetic algorithm is used iteratively to determine the minimum path for cascading delays, obtaining the optimized output of order pattern analysis. Based on the optimized output, real-time updated data on intensity change trends are obtained to determine whether further constraint adjustments are needed, resulting in the final execution plan of the dynamic coordination mechanism. Through the final execution plan, a particle swarm optimization algorithm is used for fusion to determine compensation measures for resource allocation lags, obtaining a complete sequence of overall emergency delivery scheduling.

[0017] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects: This invention discloses a dynamic emergency scheduling method for addressing the chain-reaction delays and resource lags caused by continuous surges in order flows during logistics and distribution. Through in-depth mining of historical order data, the method accurately captures the differences in the temporal distribution patterns and intensity evolution of surge orders, and obtains dynamic evolution trend judgment parameters based on a meta-learning model. When these parameters exceed a threshold, a resource allocation optimization process is immediately triggered, generating an accelerated processing allocation scheme under a dynamic coordination mechanism. Subsequently, key constraint adjustment variables are extracted from the scheme, and their matching degree with real-time processing efficiency is evaluated to form a preliminary scheduling sequence with controllable demand balance. This sequence is further iteratively optimized using a genetic algorithm to obtain an optimized path that minimizes chain-reaction delays. Based on this path, the intensity change trend is updated in real time, and necessary adjustments to the constraints are dynamically judged and completed, ultimately forming an executable dynamic coordination mechanism plan. Finally, through particle swarm optimization, effective compensation measures are designed for resource allocation lag links, outputting a complete sequence of overall emergency dispatch for distribution. This invention achieves end-to-end closed-loop adaptive control from accurate identification of surge order patterns and intelligent prediction of demand trends to dynamic resource optimization and delay minimization, significantly improving the emergency response capability and overall operational stability of the logistics system in high-uncertainty surge scenarios. Attached Figure Description

[0018] Figure 1 This is a flowchart of the emergency dispatching method for sudden orders with multiple constraints, which is based on meta-learning enhancement.

[0019] Figure 2 This is a schematic diagram of the emergency dispatching method for sudden orders with multiple constraints, which is based on meta-learning enhancement of the present invention.

[0020] Figure 3 This is a schematic diagram of the system hierarchy of the meta-learning-enhanced emergency dispatch method for multi-constraint delivery of sudden orders.

[0021] Figure 4 This is a schematic diagram illustrating the rapid adaptation mechanism of the meta-learning model of this invention.

[0022] Figure 5 This is a curve comparing the accuracy of the demand prediction for this invention.

[0023] Figure 6 This is a line graph comparing the optimization effects of the chain delay time of the present invention.

[0024] Figure 7 This is the data flow graph for the genetic algorithm-particle swarm optimization collaborative scheduling of this invention.

[0025] Figure 8 This is a three-dimensional layout diagram of the distribution dispatch center of this invention.

[0026] Figure 9 This is a topology diagram showing the distribution of logistics and warehousing nodes according to the present invention. Detailed Implementation

[0027] To further understand the content of this invention, a detailed description of the invention is provided in conjunction with the accompanying drawings and embodiments. The specific embodiments described herein are for illustrative purposes only and are not intended to limit the invention. It should also be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings.

[0028] like Figure 1 As shown, the meta-learning-enhanced emergency dispatch method for sudden orders with multiple constraints provided by this invention includes the following steps: First, step S101 is executed to collect historical order data, obtain the time distribution pattern and intensity change trend, and determine the result of capturing the pattern differences; then, step S102 is executed to train a meta-learning model to obtain evolution trend judgment parameters; next, step S103 is executed to determine whether the parameters exceed the threshold. If not, monitoring continues and step S102 is returned. If so, step S104 is executed to extract constraint adjustment variables, judge the matching degree, and obtain a preliminary dispatch sequence; then, step S105 is executed to iterate using a genetic algorithm to determine the path that minimizes chain delays; then, step S106 is executed to obtain real-time updated data, determine whether further adjustments are needed, and obtain an execution plan; finally, step S107 is executed to fuse data using a particle swarm optimization algorithm to determine compensation measures and obtain a complete dispatch sequence. Through the above process, rapid response to sudden orders and optimization of delivery routes under multiple constraints are achieved.

[0029] like Figure 2As shown, the meta-learning-enhanced emergency dispatch method for sudden orders with multiple constraints in delivery according to the present invention includes six functional layers and eight core modules. In the data input layer, the historical order database module 101 is responsible for storing and managing historical delivery order data, providing a data foundation for subsequent analysis. In the pattern analysis layer, the pattern difference capture module 102 performs time distribution pattern analysis and intensity change trend analysis on historical data to extract order pattern features. In the meta-learning layer, the meta-learning model training module 103 trains the model based on the extracted pattern features and outputs evolution trend judgment parameters to predict the development trend of sudden orders. The resource scheduling layer includes a resource allocation optimization module 104 and a constraint adjustment module 105. The former achieves optimal resource allocation through dynamic coordination mechanisms and accelerated processing methods, while the latter performs matching degree judgment and generates a preliminary scheduling sequence. The optimization algorithm layer uses a genetic algorithm optimization module 106 to minimize chain delays and a particle swarm optimization module 107 to compensate for resource lag. Both work together to receive resource allocation, preliminary sequence, and constraint inputs, respectively. Finally, at the output layer, the complete scheduling sequence output module 108 receives the optimized sequence after genetic algorithm optimization and the compensation scheme of particle swarm optimization, and outputs the final scheduling sequence after multiple optimizations for use by the delivery execution system.

[0030] like Figure 3 As shown, the meta-learning-enhanced emergency dispatch system for sudden orders with multiple constraints in this invention adopts a six-layer pyramid architecture. The top layer 201 is the overall control layer of the emergency dispatch system for sudden orders with multiple constraints, responsible for the unified coordination and management of the system. The data layer 202 includes three data source modules: historical order data, real-time order flow, and resource status data, providing data support for the system. The analysis layer 203 sets up a pattern difference capture module and a meta-learning model prediction module to identify order patterns and make intelligent predictions. The optimization layer 204 includes three optimization modules: resource allocation optimization, genetic algorithm iteration, and particle swarm optimization, realizing multi-strategy collaborative optimization of the dispatch scheme. The execution layer 205 sets up three execution modules: dynamic coordination mechanism, constraint adjustment, and compensation measure execution, ensuring the dynamic adaptability of the dispatch scheme. The output layer 206 generates a complete dispatch sequence, completing the output of the final dispatch result. The layers are connected by data flow arrows, realizing top-down hierarchical dispatch; simultaneously, a feedback mechanism from the output layer 206 to the data layer 202 (shown by the dashed arrow in the figure) is set up to achieve closed-loop optimization control, enabling the system to continuously improve the dispatch strategy based on the execution results.

[0031] like Figure 9As shown, the logistics warehousing system of the present invention adopts a tree-like topology structure, divided into five levels from top to bottom to form a multi-level distribution network. This network includes: a central distribution center 601 located at the top core of the network, responsible for overall scheduling and bulk cargo storage; regional distribution centers A 602 and B 603 distributed as secondary nodes on both sides of the central distribution center, responsible for regional cargo distribution and transshipment; front-end warehouses 1 604, 2 605, and 3 606 as tertiary nodes, where front-end warehouse 1 belongs to region A, front-end warehouse 3 belongs to region B, and front-end warehouse 2 is located in the middle and can serve two regions simultaneously, achieving rapid response and nearby delivery; a cluster of last-mile delivery stations 607 including multiple small stations, undertaking the transshipment function of last-mile delivery; and a customer terminal 608 located at the bottom layer of the network, represented by a human-shaped icon, serving as the final receiving node. The nodes are connected by main trunk lines (thick solid lines) to the central distribution center and regional distribution centers, by branch lines (thin solid lines) to the regional distribution centers and forward warehouses, and by terminal lines (dashed lines) to the forward warehouses and the last delivery stations, and the last delivery stations and the customer terminals. This topology fully reflects the hierarchical and radiating characteristics of the logistics network, facilitating hierarchical management and efficient delivery.

[0032] Specifically, this embodiment of a meta-learning-enhanced emergency dispatch method for bursty orders with multiple constraints may include: S101. By collecting historical order data, obtain the time distribution pattern and intensity change trend of continuous burst order flow, and determine the results of capturing the regular differences.

[0033] Obtain the order occurrence timestamp sequence from historical order data. Calculate the time interval sequence between adjacent orders based on the order timestamp sequence. Identify consecutive order segments with intervals less than a preset threshold using the time interval sequence, obtaining a set of consecutive burst order flow segments. Count the number of orders in each consecutive burst order flow segment to obtain the burst traffic intensity sequence for each segment. Calculate the intensity difference sequence between adjacent segments based on the burst traffic intensity sequence. If there is a point in the intensity difference sequence that exceeds a preset difference threshold, it is identified as an intensity abrupt change location. Segment the burst traffic intensity sequence by intensity abrupt change locations to obtain multiple relatively stable subsequences. Extract the duration and average intensity of each relatively stable subsequence to obtain the distribution pattern characteristics of the subsequence. Group and cluster all subsequence distribution pattern characteristics to obtain a set of pattern categories for burst order flows. Statistically analyze the frequency distribution of each category based on the pattern category set to obtain the time distribution pattern. Determine the intensity change trend by observing the frequency changes of each category within different time windows. By comparing the distribution patterns and intensity trends of each category, we can obtain the results of capturing pattern differences. Here, "comparison" refers to cross-comparison, that is, analyzing the intensity trends of the same category in different time windows, and the differences in distribution patterns of different categories in the same time window.

[0034] For example, the preset interval threshold can be set to 15-60 seconds according to the delivery scenario type (e.g., 30 seconds for e-commerce retail scenarios and 15 seconds for fresh food delivery scenarios), and its setting is based on the average interval statistical value of historical burst order flows minus 2 standard deviations; the preset difference threshold is set to 30%-50% (e.g., 30% for daily scenarios and 50% for promotional scenarios), and is determined based on the 95% confidence interval of the intensity fluctuation of adjacent segments; the time window is divided into hours (1 hour / window) to ensure the timeliness of the intensity change trend.

[0035] For example, in an e-commerce platform's order processing system, the process of obtaining the order occurrence timestamp sequence from historical order data can be implemented as follows: Assume the platform stores millions of order records, each containing an order ID, a user ID, and a timestamp accurate to the second, such as 2023-10-01 12:00:05. The system first queries the database, sorts all orders in ascending order of timestamp, and extracts the timestamp sequence, for example, [t1=12:00:05, t2=12:00:10, t3=12:00:20...]. This helps in subsequent analysis of order burst patterns.

[0036] In one possible implementation, when calculating the time interval sequence of adjacent orders for the order timestamp sequence, the difference can be calculated pairwise.

[0037] Specifically, for the sequence [t1, t2, t3...], the interval sequence is [d1=t2-t1=5 seconds, d2=t3-t2=10 seconds...]. If there are thousands of orders in the sequence, the system uses loop traversal or vector operations to efficiently calculate and ensure that the interval unit is uniformly in seconds to capture the density of orders.

[0038] For example, the process of identifying consecutive order segments shorter than a preset interval threshold through a time interval sequence to obtain a set of consecutive burst order flow segments requires setting a threshold such as 30 seconds. Then, the interval sequence is traversed to find consecutive segments where d_i < 30 seconds. For example, if d1=5, d2=10, d3=40, d4=15, then the first three orders form a burst segment. The set records multiple such segments, which reflects the burst flow of orders during peak periods.

[0039] In one possible implementation, the number of orders for each continuous burst order flow segment is counted to obtain the burst flow intensity sequence for each segment.

[0040] For example, one segment has 5 orders with a strength of 5; another has 10 orders with a strength of 10, as in the sequence [5, 10, 7...]. The strength is defined as the number of orders within the segment, reflecting the quantification of traffic peaks.

[0041] For example, when calculating the intensity difference sequence between adjacent segments based on the burst flow intensity sequence, for [5,10,7], the difference sequence is [5(10-5), -3(7-10)], which captures the intensity fluctuations.

[0042] In one possible implementation, if there is a point in the intensity difference sequence that exceeds a preset difference threshold, it is determined to be a location of an intensity abrupt change. For example, if the threshold is 4, and the difference is 5 > 4, then the location is the first difference point, representing a sudden change from low intensity to high intensity.

[0043] For example, the process of dividing a burst flow intensity sequence by intensity mutation location to obtain multiple relatively stable subsequences is as follows: if the sequence [5,10,7,12] has a mutation between 5 and 10, it is divided into [10,7,12], but if there is no large difference in [10,7,12], it is considered a stable subsequence.

[0044] In one possible implementation, the duration and average intensity of each relatively stable subsequence are extracted to obtain the distribution characteristics of the subsequence.

[0045] For example, for [10,7], assuming the total duration of the corresponding segment is 60 seconds, the average intensity is (10+7) / 2=8.5, and the characteristic is (duration 60, intensity 8.5), which summarizes the pattern of the subsequence.

[0046] For example, when grouping and clustering based on the distribution patterns of all subsequences to obtain a set of pattern categories for sudden order flows, the K-means algorithm can be used to cluster the feature vectors into three categories, such as short-term high intensity, medium-term medium intensity, and long-term low intensity, which is helpful for pattern classification.

[0047] In one possible implementation, the time distribution pattern is obtained by statistically analyzing the frequency distribution of each pattern category based on the set of pattern categories.

[0048] For example, in one day, the high-intensity category appeared 10 times and the medium-intensity category appeared 5 times, with the distribution showing that the peak occurred at noon, revealing a temporal pattern.

[0049] For example, the process of determining the trend of intensity change by the frequency of occurrence of each category in different time windows is as follows: divide the window into hours, calculate the high intensity category from 2 times in the morning to 8 times in the afternoon, the trend is upward, indicating that the intensity is increasing.

[0050] In one possible implementation, the distribution patterns and intensity trends of each category are compared to obtain the results of capturing pattern differences.

[0051] For example, comparing the short-term characteristics of high-intensity categories with the upward trend shows that they occur more frequently during promotional periods. By capturing differences such as sudden pattern variations caused by promotions, business operations can optimize inventory management and improve response efficiency.

[0052] In one possible implementation, if historical order data is missing (missing rate ≤ 20%), the K-nearest neighbor (K=5) interpolation method is used to supplement the missing data; if the missing rate > 20%, historical data from similar scenarios (same promotion type, same weather conditions) is called for migration and supplementation to ensure the accuracy of the analysis of time distribution patterns and intensity change trends; if there is no similar scenario data, the initial analysis is started using default parameters (interval threshold of 30 seconds, difference threshold of 40%).

[0053] S102. Based on the results of capturing the differences in patterns, a meta-learning model is used for training to obtain the parameters for judging the evolution of subsequent demand predictions.

[0054] Demand records for each time period in historical data sequences are obtained. By comparing demand records from adjacent time periods, a pattern difference sequence is calculated. This pattern difference sequence is input into a meta-learning model for training, obtaining initial parameters adapted to different evolution patterns. The initial parameters obtained from training are used to quickly adjust the latest pattern differences, obtaining parameters for judging the current evolution trend. The current evolution trend judgment parameters are compared with a preset judgment threshold. If the current evolution trend judgment parameters are greater than the preset judgment threshold, it is determined to be an accelerating upward trend. If the current evolution trend judgment parameters are less than or equal to the preset judgment threshold, it is determined to be a stable or declining trend. The accelerating upward trend, stable, or declining trend is used as the input basis for subsequent demand prediction, outputting a demand level label for the next stage. The evolution trend judgment parameters obtained from the meta-learning model training are the sole triggering condition for resource allocation optimization. The core optimization objective of the genetic algorithm iteration is to minimize the chain delay time. The key to the fusion of the particle swarm optimization algorithm is multi-dimensional compensation for the lagging links in resource allocation (including capacity allocation, crowdsourcing, and priority adjustment). Each step is progressive and forms a closed-loop adaptive control, ensuring a balance between immediate efficiency and subsequent demand controllability.

[0055] The preset judgment threshold is set to 0.5 (range 0-1), determined by statistically analyzing the dividing point between "accelerated upward trend" and "stable / downward trend" in historical data; the demand level labels include four categories: "extremely high", "high", "stable", and "low", with corresponding evolution trend judgment parameter ranges of [0.8, 1.0], [0.5, 0.8], [0.2, 0.5], and [0, 0.2], respectively.

[0056] The meta-learning model employs a 3-layer fully connected network structure. The input layer dimension is the length of the regular difference sequence (by default, the difference values ​​of the most recent 10 time periods are taken), the number of hidden layer nodes are 64 and 32 respectively, and the output layer dimension is 1. The activation function is the ReLU function, and the loss function is the cross-entropy loss. The task division criteria for meta-training are: divided into 6 types of tasks according to promotion type (large promotion, medium promotion, small promotion, no promotion) and 4 types of tasks according to weather conditions (sunny, cloudy, rainy, snowy). The gradient iteration step size is set to 0.005, and parameter fine-tuning can be completed in only 1-3 iterations.

[0057] The meta-learning model employs a task-gradient-based learning framework. Specifically, the model identifies historical order fluctuations under different promotional periods and weather conditions as independent tasks. Through meta-training on multiple historical tasks, it learns a set of initial weight parameters with high generalization ability. When a sudden surge in orders arrives, the model only needs to perform 1-3 gradient iterations using a small amount of observation data from the most recent 5-10 time periods to quickly fine-tune the initial weights to judgment parameters suitable for the current evolution, thereby achieving second-level real-time classification and prediction of demand level labels (e.g., extremely high, high, stable).

[0058] For example, in the inventory management system of an e-commerce platform, the first step is to extract historical order data from the database over the past year and divide it into multiple time periods by hour, such as each time period corresponding to the demand records for one hour. In this way, a sequence can be obtained, such as the demand for 50 items from 9:00 to 10:00 AM, 60 items from 10:00 to 11:00 AM, etc., forming a complete historical demand sequence, providing basic data support for subsequent analysis.

[0059] Specifically, by comparing the demand in these adjacent time periods—for example, subtracting the 50 items from 9:00 to 10:00 from the 60 items from 10:00 to 11:00 to get a difference of 10; then comparing the 70 items from 11:00 to 12:00 with the previous difference of 10, and so on—the pattern of differences throughout the entire sequence is calculated. This sequence reflects the fluctuation pattern of demand; for example, if the difference value continues to increase positively, it indicates that demand is gradually rising, helping to identify potential trend changes.

[0060] like Figure 4As shown, the rapid adaptation mechanism of the meta-learning model consists of two core parts: the meta-training stage and the rapid adaptation stage. In the meta-training stage, multiple historical tasks 301 (including promotional tasks, weather tasks, and holiday tasks) are trained through meta-parameter learning via cross-task gradient aggregation to obtain initial weights T0 with good generalization ability. In the rapid adaptation stage, the current sudden order flow 302 is used as input, and based on the initial weights T0, it undergoes rapid fine-tuning through a small number of gradient iterations 303 (1-3 times) to obtain fine-tuned weights T' adapted to the current task. These fine-tuned weights T' are input into a 3-layer fully connected neural network, which includes an input layer, two hidden layers (containing 64 and 32 neurons respectively), and an output layer with one output neuron. The final network output 304 is an evolutionary trend judgment parameter in the range of 0 to 1, which is divided into four levels according to the parameter values ​​using task classification labels 305: extremely high corresponding to the [0.8-1.0] interval, high corresponding to the [0.5-0.8] interval, stable corresponding to the [0.2-0.5] interval, and low corresponding to the [0-0.2] interval. This mechanism enables the model to quickly adapt to new tasks through a meta-learning strategy, significantly reducing the amount of training data and the number of iterations required for the model to adapt to unexpected scenarios.

[0061] In one embodiment, a meta-learning model is an advanced machine learning framework that rapidly adapts to new tasks by learning common patterns across multiple tasks. Specifically, using the aforementioned pattern of differences as input data, the model first processes historical difference data during the training phase. For example, it uses a small number of samples to learn the evolution patterns of different seasons, such as holidays and daily routines, and outputs initial parameters. These parameters act like a preset "starting point," enabling the model to converge faster when faced with new data, thereby adapting to different evolution patterns, such as a surge in demand due to sudden promotions, and achieving more efficient predictive adjustments.

[0062] For example, the process of quickly adjusting the latest pattern differences using these initial parameters can be understood as inputting the difference value of the most recent hour in real-time monitoring, such as a recent difference of 15. After fine-tuning based on the initial parameters, the model outputs a current evolution trend judgment parameter value of 0.8. This parameter quantifies the dynamic changes in demand; a higher value indicates a more drastic evolution, providing a quantitative basis for judging the trend.

[0063] Specifically, this judgment parameter is compared with a preset threshold, such as 0.5. If it is greater than 0.5, it is judged as an accelerating upward trend. For example, if the parameter reaches 0.9 during the Double Eleven shopping festival, it indicates that demand is growing rapidly. Conversely, if the parameter is 0.3, it is judged as a stable or declining trend, such as when demand fluctuates little on weekdays. This judgment helps to adjust inventory strategies in a timely manner and avoid stockouts or overstocking.

[0064] In one embodiment, these trends are used as input for subsequent demand forecasting. For example, an accelerating upward trend is input into the prediction model, and combined with historical data, a label indicating the next stage of demand level, such as a "high demand" label, is output to prompt the platform to increase supply chain response speed. This method can improve forecast accuracy and reduce losses in business operations. For instance, in practical applications, this technology has increased the accuracy of demand forecasting from 70% to 90%, thereby optimizing resource allocation.

[0065] like Figure 5 As shown, this invention underwent comparative testing to verify the accuracy of demand prediction. The horizontal axis represents the number of test rounds (1-10), and the vertical axis represents the prediction accuracy (%). The solid line in the figure represents the prediction accuracy curve of the method of this invention, while the dashed line represents the prediction accuracy curve of the traditional method. The test results show that the accuracy of the method of this invention is 75% in the initial stage. With the increase in test rounds, the accuracy gradually improves, stabilizing after the 5th-6th round, and finally reaching a relatively high level of around 90%. In contrast, the accuracy of the traditional method fluctuates between 65% and 75%, exhibiting significant instability and a lower overall accuracy level. The test results demonstrate that the method of this invention has a significant advantage over the traditional method in terms of demand prediction accuracy, improving the accuracy by approximately 15-20 percentage points, and exhibiting better stability, providing more reliable predictive support for subsequent decision-making.

[0066] S103. If the evolution trend judgment parameter exceeds the preset threshold, resource allocation optimization is initiated to obtain an allocation scheme for accelerated processing methods under the dynamic coordination mechanism.

[0067] If the evolutionary trend parameters exceed a preset threshold, the system's operational data is acquired through the information acquisition module. This data undergoes preliminary filtering to obtain real-time records of the evolutionary trend. Based on these records, a data comparison tool is used to analyze the deviation between the parameter thresholds and the current trend parameters, determining if the deviation consistently exceeds a preset range and obtaining the deviation analysis results. If the deviation analysis results show a continuous exceedance of the range, the resource allocation module is triggered to dynamically evaluate the current resource usage and obtain a priority ranking for resource allocation. Based on the priority ranking, the scheduling rules of the dynamic coordination mechanism are obtained. These rules are then matched with the current resource allocation status to determine if the conditions for accelerated processing are met. If the conditions for accelerated processing are met, the allocation ratio is adjusted through the resource scheduling interface. The adjusted resource distribution is monitored in real-time to obtain the execution status of the processing measures. Based on the execution status, a support vector machine algorithm is used to optimize the allocation scheme. The optimized scheme is then simulated and verified to determine the final allocation scheme. Using the final allocation scheme, feedback data from the system operation is acquired, recorded, and stored to determine if the system has returned to a stable state.

[0068] The preset threshold is consistent with the preset judgment threshold in S102 (0.5); the preset range is set as "the deviation exceeds the threshold for three consecutive data acquisition cycles (10 seconds per cycle); the accelerated processing conditions include: resource utilization rate is less than 80%, reserve resource reserve is ≥20%, and scheduling interface response delay is ≤1 second.

[0069] The support vector machine uses the RBF kernel function, with a penalty parameter C=1.0 and a kernel function parameter γ=0.1; positive samples are "historical stable allocation mode" (system recovery time ≤10 minutes), and negative samples are "historical failure mode" (system recovery time >30 minutes); when the stability probability score output by the model is ≥80%, the scheme passes simulation verification.

[0070] For example, in one possible implementation, the information acquisition module will initiate a data capture process when the evolutionary state parameters exceed a preset threshold.

[0071] Specifically, this module is typically designed as a real-time monitoring system. It collects operational data through sensors or API interfaces deployed at various nodes of the system. For example, in a supply chain management system, it collects indicators such as current inventory levels, order processing speed, and fluctuations in user demand. This data is recorded in timestamps to ensure that the system's real-time status is captured, thus providing a foundation for subsequent analysis.

[0072] In one possible implementation, a filtering algorithm is applied to remove noisy data during the initial screening of the collected data.

[0073] For example, in the resource management system of an e-commerce platform, the screening process involves checking the integrity and relevance of data, such as removing invalid log entries and retaining only key indicators related to the demand situation, ultimately obtaining real-time change records, which may include the demand growth rate curve every minute.

[0074] Specifically, when using data comparison tools to analyze deviations, this tool can be a comparison module based on statistical software, which compares a preset threshold, such as a 10% growth threshold, with the current parameter point by point.

[0075] For example, in a production scheduling system, if the current situation parameters show a demand increase of 15%, the tool will calculate the deviation value and track its duration. If it exceeds the deviation for three consecutive cycles, an analysis result will be generated to confirm the persistent deviation.

[0076] For example, if the deviation analysis results show that the deviation is consistently outside the acceptable range, the resource allocation module is triggered to perform a dynamic evaluation. This module scans the current CPU usage, memory usage, and other conditions to generate a priority ranking, such as allocating high-priority tasks to tasks with peak demand.

[0077] like Figure 8 As shown, the delivery dispatch center adopts a functional zoning layout with an overall rectangular spatial structure. The dispatch command console 501 is located in the center, equipped with a display screen array for real-time monitoring and command of dispatch operations. The server rack area 502 is located in the rear area, containing multiple side-by-side racks for system computing and data processing tasks. The large-screen monitoring wall 503 is located at the front, using a multi-screen splicing method to display maps, data statistics, and real-time delivery status information. Operator workstations 504 are distributed on the left and right sides, with multiple independent workstations on each side for dispatch personnel to operate. The network switching equipment area 505 is located in the right rear corner, responsible for network communication and data exchange for the entire dispatch center. The emergency communication equipment area 506 is located near the exit to ensure communication support in emergencies. The data storage array 507 is located adjacent to the server rack area and is used to store delivery orders, route planning, and historical data. The UPS power supply equipment 508 is installed in the bottom area, providing uninterrupted power protection for the entire dispatch center and ensuring stable system operation. This layout fully considers the collaborative work needs between equipment and the convenience of personnel operation, achieving efficient operation of the dispatch center.

[0078] In one possible implementation, when obtaining the scheduling rules of the dynamic coordination mechanism based on priority, these rules may be a predefined set of algorithms. For example, in a cloud service environment, the rules include load balancing strategies. Then, the current situation is matched to determine whether acceleration is needed, such as when the resource utilization rate exceeds 80%.

[0079] Specifically, after adjusting the allocation ratio through the resource scheduling interface, the execution status is obtained through real-time monitoring.

[0080] For example, in data center management, API calls are used to reallocate virtual machine resources, and metrics such as response time changes are monitored to ensure that the adjustments are effective.

[0081] For example, when using the support vector machine algorithm to optimize the allocation scheme, this algorithm is a supervised learning method that classifies and optimizes paths by constructing a hyperplane to separate data points.

[0082] In this step, Support Vector Machine (SVM) is used as a 'stability evaluator' for the delivery scheme. The system constructs a classification hyperplane in a high-dimensional feature space, using historically validated successful resource allocation patterns as positive samples and patterns that lead to system crashes or severe delays as negative samples. After a preliminary scheme is generated by a genetic algorithm or manual rules, the SVM quickly provides a probability score that the scheme will enable the system to recover to a stable state by calculating the distance from the corresponding feature points to the hyperplane. If the score is lower than a set threshold, it is fed back to the scheduling interface to readjust the resource allocation ratio until a final scheme that meets the stability requirements is obtained.

[0083] Specifically, during the optimization process, the current execution state data, such as resource load and response latency, is used as input features. The support vector opportunity model is trained to find the optimal separation boundary, thereby adjusting the scheme parameters. For example, in network traffic control, simulations are used to verify whether the optimization scheme reduces bottlenecks, and finally the scheme is determined.

[0084] In one possible implementation, the system stability is determined by obtaining feedback data through the final allocation scheme, recording and storing it.

[0085] For example, in a smart manufacturing system, feedback data includes production efficiency indicators. If the system returns to normal, it is marked as stable. This helps the system to optimize in the long term and improve response efficiency.

[0086] In one possible implementation, a distributed data acquisition architecture is adopted, which retrieves data from the order management system, logistics scheduling system, and real-time traffic monitoring system through a RESTful API interface. The collected indicators include: current order backlog, number of available vehicles, driver on-duty status, road congestion index, and warehouse inventory. The data update frequency is 10 seconds / time, and the Kalman filter algorithm is used to remove data noise.

[0087] In one possible implementation, if the resource scheduling interface fails, a backup scheduling channel (an asynchronous scheduling mechanism based on message queues) is activated. The manual intervention trigger condition for interface failure is "failure duration exceeds 5 minutes" or "order backlog exceeds 30% of the current processing capacity". If the backup resource reserve is less than 20%, the third-party crowdsourcing resource pool is automatically activated to supplement the transportation capacity at a 1:1 ratio.

[0088] S104. Extract constraint adjustment variables from the accelerated processing method allocation scheme, determine their matching degree with the real-time processing efficiency, and obtain a preliminary scheduling sequence with controllable demand balance. Here, "its" refers to the constraint adjustment variables extracted from the accelerated processing method allocation scheme.

[0089] Constraint data is obtained from the accelerated processing scheme, and the specific restrictions of the constraints are categorized and organized to obtain a preliminary condition set. Based on the preliminary condition set, the range and priority of adjustment variables are analyzed, and the influence weight of each variable is ranked using the variable analysis module to determine the list of key variables. For the list of key variables, real-time efficiency data streams are obtained. If the real-time efficiency is lower than a preset threshold, the key variables are dynamically adjusted, and the efficiency improvement after adjustment is judged. The trend of the degree of fit is analyzed based on the efficiency improvement after adjustment, and the degree of fit is quantitatively evaluated using data comparison tools to obtain a fit score. Based on the fit score and the allocation rules of demand balancing, an initial scheduling logical framework is generated, and the priority ranking of the scheduling sequence is determined. For the priority ranking of the scheduling sequence, the real-time status of resource allocation is obtained. If the resource allocation does not match the priority ranking, the allocation ratio is adjusted through logical operations to obtain the final scheduling sequence.

[0090] The preset instant efficiency threshold is set to 80% (calculated as a weighted average of order processing completion rate and resource utilization rate); the matching score adopts a 100-point system, with 85 points and above being "highly matching", 60-84 points being "mediumly matching", and below 60 points being "lowly matching". Only highly matching scheduling sequences can be used as initial output.

[0091] The constraint adjustment variables include, but are not limited to: a flexible relaxation value for the maximum daily working hours of delivery personnel, a tolerance range for the delivery time window (e.g., allowing non-urgent orders to be delayed by 30-60 minutes), and a dynamic downward adjustment range for the vehicle load factor threshold. In the matching degree judgment, the system obtains the real-time traffic efficiency of the current delivery route through a real-time monitoring API; if the real-time efficiency is below 80%, the system will automatically increase the 'delivery time window' variable and recalculate the matching degree score to ensure that the generated preliminary scheduling sequence is actually executable during peak demand periods, avoiding scheduling failures due to overly tight constraints.

[0092] For example, in a resource management system, when obtaining constraint data from an accelerated processing solution, specific data such as CPU utilization limits, memory allocation boundaries, and network bandwidth limits can be extracted first. This constraint data originates from the solution's core configuration file. By classifying and organizing them, they are divided into hard constraints such as absolute thresholds and soft constraints such as adjustable ranges, thus forming a preliminary set of conditions.

[0093] Specifically, this classification process involves using data parsing tools to scan the solution documents, identify the attributes of each condition, such as classifying the CPU utilization limit as a performance constraint and network bandwidth as a transmission constraint, and finally summarizing them into a structured list to support subsequent analysis.

[0094] In one possible implementation, when adjusting the scope and priority of variables based on the initial set of conditions, the influence weight of each variable is calculated through the variable analysis module.

[0095] For example, suppose the system processes a task queue, and the variables include task priority, resource availability, and response time range. This module uses a weighted average algorithm to sort the variables, such as setting the weight of response time to 0.6 and resource availability to 0.3, thereby determining the list of key variables, in which response time is listed first due to its high weight.

[0096] For example, after obtaining a real-time data stream of immediate efficiency for a list of key variables, if the efficiency is below a threshold such as 80%, the variables are dynamically adjusted.

[0097] For example, in a cloud computing environment, real-time data streams come from monitoring APIs, showing that the current efficiency is 75%. The task priority variable is then adjusted from low to high, and the improvement is calculated by comparing the previous and current performance. For example, if the efficiency increases from 75% to 85%, it is determined whether the expected efficiency is met.

[0098] In one possible implementation, when analyzing the changing trend of the degree of fit by examining the adjusted efficiency improvement, a data comparison tool is used for quantitative evaluation.

[0099] For example, the tool compares data points before and after the adjustment, calculates the trend slope, and if the slope is positive, it indicates an upward trend, thus obtaining a fit score such as 90 points, which reflects the degree of matching between the variable adjustment and the system requirements.

[0100] For example, when generating the logical framework for the initial scheduling based on the fit score and the allocation rules for demand balancing, the priority order of the scheduling sequence will be determined.

[0101] Specifically, the rules may include load balancing principles. If the score is higher than 85, the framework will place high-priority tasks at the front of the sequence. For example, in data center scheduling, urgent computing tasks will be processed first, followed by regular backups, thus forming an ordered sequence.

[0102] In one possible implementation, after obtaining the real-time status of resource allocation based on the priority sorting of the scheduling sequence, if there is a mismatch, the ratio is adjusted through logical operations.

[0103] For example, if the real-time status shows a CPU allocation of 40% but the sequence requires 60%, then the ratio is recalculated using AND / OR logical operations to obtain a final scheduling sequence that increases CPU usage to 55%, ensuring overall resource optimization. This adjustment can effectively improve system response speed and reduce bottlenecks.

[0104] In one possible implementation, the Analytic Hierarchy Process (AHP) is used to calculate the influence weights of variables. The criteria layer includes "efficiency improvement contribution", "constraint compliance risk" and "resource consumption cost", with weights of 0.5, 0.3 and 0.2 respectively. The list of key variables is taken from the top 3 variables in terms of weight (which by default include: delivery timeliness window tolerance, vehicle load factor threshold and standby capacity call ratio).

[0105] In one possible implementation, Boolean algebra rules are used: "AND operation" verifies the matching between resource allocation and priority order (tasks with priority 1 need to match ≥50% of core resources), and "OR operation" determines alternative adjustment schemes (when core resources are insufficient, alternative resources can be called or time constraints can be relaxed).

[0106] In one possible implementation, the adjustment boundaries and priorities of the constraint adjustment variables are as follows: Adjusting boundaries: The maximum daily working hours for delivery personnel can be flexibly increased by 10%-20% (not exceeding 12 hours, in accordance with labor law); the delivery time window tolerance range is 30-60 minutes (only applicable to non-urgent orders, with a tolerance of ≤15 minutes for urgent orders such as fresh produce and medicine); the minimum vehicle load factor threshold can be lowered to 50% (to avoid excessively high empty-running rates).

[0107] Prioritize adjustments (from highest to lowest): ① Adjust the tolerance range for delivery timeliness; ② Adjust the vehicle load factor threshold; ③ Relax the maximum daily working hours limit for delivery personnel; prioritize adjustments that have the least impact on compliance risks and service quality.

[0108] S105. For the initial scheduling sequence, a genetic algorithm is used iteratively to determine the path that minimizes the chain delay situation and obtain the optimized output of the order pattern analysis.

[0109] Obtain the planned start time and actual available resource time for each order in the initial scheduling sequence. Calculate the initial delay for each order based on the deviation between the planned start time and the actual available resource time, and mark cascading delayed orders. For the marked cascading delayed orders, construct a directed graph structure with pre- and post-order constraints. Use a genetic algorithm to encode the order sequence in the directed graph to generate an initial population. Iteratively update the population through selection, crossover, and mutation operations, calculating the total delay time corresponding to each chromosome. Determine if the total delay time of the optimal chromosome in the current population meets the convergence condition; if it does, terminate the iteration; otherwise, continue executing the selection, crossover, and mutation operations. Obtain the converged optimal chromosome and decode it into an adjusted order sequence. Based on the adjusted order sequence, reallocate the start and end times of each order to determine the optimized production mode.

[0110] The convergence condition is set as "the total delay time fluctuation of the best chromosome for 5 consecutive generations is less than 5%" or "the number of iterations reaches 100 generations"; the initial population size is set to 50 to ensure the comprehensiveness of the algorithm search.

[0111] The encoding method uses real number encoding. The length of each chromosome is equal to the number of chain-delayed orders. The value of each gene position (0-1) corresponds to the order execution priority (the larger the value, the higher the priority). The selection operator uses tournament selection (selection pressure = 2), the crossover operator uses single-point crossover (crossover probability = 0.7), and the mutation operator uses Gaussian mutation (mutation probability = 0.03). The total delay time is calculated as "the sum of the delays of each order + the chain-delay diffusion coefficient × 2" (the diffusion coefficient is the number of affected subsequent orders).

[0112] For example, when processing the initial scheduling sequence, a specific production workshop scenario can be used to understand how to obtain the planned start time and actual available resource time for each order. Suppose that in an automotive parts production workshop, there are 5 orders to be processed, each corresponding to a different part production task. There are 3 critical pieces of equipment in the workshop, but due to equipment maintenance and previous order occupancy, the actual available resource time varies. According to system records, the planned start time for order 1 is 8:00 AM, but the actual available equipment time is 9:00 AM, resulting in a 1-hour deviation. Similarly, other orders may also experience time deviations due to equipment occupancy or material delays. Collecting this deviation data lays the foundation for subsequent analysis. Next, in calculating the initial delay and marking cascading delayed orders, we can assume that the 1-hour delay of order 1 causes order 2 to be unable to start on time, because order 2 depends on the semi-finished product processing of order 1. The system will mark order 1 and order 2 as cascading delayed orders, recording delays of 1 hour and 0.5 hours respectively. This marking method helps identify the propagation path of delays, providing a basis for subsequent optimization.

[0113] For example, when constructing a directed graph structure with pre- and post-order constraints, order 1 can be directed to order 2, indicating that order 2 can only begin after order 1 is completed, forming a directed edge. In this way, the pre- and post-order dependencies of all orders within the workshop are constructed into a complete graph structure, intuitively demonstrating the logical chain of delay propagation. When using a genetic algorithm to encode the order sequences in the directed graph and generate the initial population, each order sequence can be viewed as a chromosome. For example, order 1-2-3-4-5 can be used as a permutation, and multiple such sequences can be randomly generated as the initial population. By simulating the process of natural selection, the system will select and retain sequences with shorter total delay times.

[0114] For example, when iteratively updating the population through selection, crossover, and mutation operations, two chromosomes with shorter delay times can be crossovered, such as swapping parts of the sequences of order 1-2-3 and order 4-5-1 to generate a new sequence, order 1-5-3. This operation simulates biological evolution, gradually optimizing sequence combinations. When determining whether the total delay time of the optimal chromosome meets the convergence condition, a target value can be set, such as a total delay time of less than 2 hours. If the total delay time of the current optimal sequence is 1.5 hours, the iteration terminates; otherwise, optimization continues. This judgment mechanism ensures the algorithm's efficiency.

[0115] For example, after decoding the optimal chromosome and adjusting the order sequence, the start and end times of the optimized sequence orders 1-5-2-3-4 can be reallocated. For instance, order 1 could be adjusted to start at 8:30 AM, with order 5 following immediately after. This adjustment ensures maximum resource utilization. Finally, when determining the optimized production mode, a new production plan can be formulated based on the adjusted sequence and the actual operating status of the workshop equipment, such as prioritizing the use of key equipment for orders 1 and 5. This mode supports the efficient operation of the workshop.

[0116] like Figure 6 The figure shows a comparison of the proposed method with traditional genetic algorithms and static allocation schemes in terms of cascading delay time. The horizontal axis represents order batches (batch 1 to batch 10), and the vertical axis represents cascading delay time (unit: minutes). Solid lines with circular markers represent the proposed method, dashed lines with square markers represent traditional genetic algorithms, and dashed lines with triangle markers represent static allocation schemes. As can be seen from the figure, the cascading delay time of the proposed method consistently remains within the range of 20-40 minutes, the delay time of the traditional genetic algorithm is in the range of 50-70 minutes, while the static allocation scheme has the highest delay time, reaching the range of 80-110 minutes. Experimental data shows that the proposed method reduces cascading delay time by approximately 65% ​​compared to the static allocation scheme and also shows a significant improvement over the traditional genetic algorithm. This result verifies that the dynamic scheduling strategy adopted in this invention can effectively reduce cascading delays caused by order changes or unforeseen circumstances, thereby improving overall logistics and delivery efficiency.

[0117] S106. Based on the optimized output, obtain real-time updated data on the intensity change trend, determine whether further constraint adjustments are needed, and obtain the final dynamic coordination mechanism execution plan.

[0118] By acquiring trend data on intensity changes from the monitoring system and performing preliminary comparisons using preset thresholds, an initial fluctuation state assessment result is obtained. Based on this initial assessment, a deeper analysis of the dynamic fluctuation situation is conducted, employing time series analysis to determine whether the fluctuation exceeds a preset range. If the fluctuation exceeds the preset range, the trend data is segmented to obtain the change characteristics within each time period, determining whether an adjustment mechanism for the constraints needs to be triggered. The triggering criterion is "the current intensity change characteristics match historical data with a degree of less than 80%" (matching degree calculated using Euclidean distance). Through further comparison of the change characteristics, combined with historical data records, the matching degree between the current intensity change and past trends is analyzed to obtain the basis for adjustment judgments. Based on the basis for adjustment judgments, an update strategy for the constraints is generated, determining the final coordinated execution direction. If there is a deviation between the generated update strategy and the current execution plan, the execution plan is dynamically revised to obtain the latest execution plan content. By integrating the latest execution plan content, a complete execution flow for the dynamic coordination mechanism is generated, determining the final implementation steps.

[0119] The preset threshold is set to the intensity change rate ±10%; the preset range is set to the intensity fluctuation exceeding ±15%; the time series analysis method uses the ARIMA model (p=2, d=1, q=2), and the model parameters are determined by the AIC criterion.

[0120] For example, in the monitoring system of power transmission network, trend data of conductor strength changes are obtained from real-time sensors. This data includes time-series records of indicators such as temperature, wind speed and load. By comparing these data with preset thresholds, such as the maximum allowable fluctuation range of 10%, an initial fluctuation status assessment result is obtained. For example, if the strength fluctuation of a certain conductor exceeds the threshold, it is marked as a high-risk state, thus providing a basis for subsequent analysis.

[0121] Specifically, based on the initial fluctuation status assessment results, the dynamic fluctuation situation is analyzed in depth, and time series analysis methods such as the ARIMA model are used to predict future trends and determine whether the fluctuation exceeds the preset range. Here, the ARIMA model is an autoregressive integral moving average model, which models the time series by analyzing the stationarity of the data, differencing, and parameter estimation. For example, when processing conductor strength data, the stationarity of the original data is first checked. If it is not stationary, first-order differencing is performed, and then the autoregressive and moving average parameters are estimated. Finally, it is determined whether the predicted fluctuation exceeds the safe range, such as the upper limit of 15%. If it does, further processing is triggered.

[0122] In one embodiment, if the fluctuation exceeds the preset range, the trend data is segmented to obtain the change characteristics in each time period. For example, the monitoring data of a day is divided into three segments: morning, noon and evening. The average intensity change rate and standard deviation of each segment are calculated to determine whether the constraint adjustment mechanism needs to be triggered. For example, when the volatility of the evening segment is more than twice that of the daytime segment, the load distribution constraint needs to be adjusted to avoid overload.

[0123] For example, by further comparing the characteristics of change and combining historical data records such as similar fluctuation events in the past year, the matching degree between the current intensity change and the past trend can be analyzed to obtain the basis for adjustment judgment. The specific process includes using similarity algorithms such as Euclidean distance to calculate the closeness between the current data vector and the historical vector. If the matching degree is less than 80%, it indicates that the current fluctuation is abnormal and an update strategy needs to be generated. This helps to improve the robustness of the system and can bring more stable power supply effect in business.

[0124] Specifically, based on the basis of the adjustment judgment, an update strategy for the constraints is generated to determine the final direction of coordinated execution. For example, if the matching degree is low, the strategy may include reducing the load limit of the conductor segment from 100% to 80% and prioritizing the scheduling of backup lines to ensure the overall network balance.

[0125] In one embodiment, if there is a deviation between the updated strategy and the current execution plan, the execution plan is dynamically corrected to obtain the latest execution plan content. For example, the original plan was to run at full load during peak hours, but now it is corrected to reduce load in different time periods. After integration, a plan containing specific time points and load values ​​is generated.

[0126] For example, by integrating the latest implementation plan, a complete execution process of the dynamic coordination mechanism is generated, and the final implementation steps are determined, such as first notifying the control center to adjust parameters, then monitoring the execution effect and providing feedback loop. This forms a closed-loop optimization process, which can effectively reduce the failure rate and optimize resource utilization in power transmission services.

[0127] S107. By using the final execution plan and particle swarm optimization algorithm for fusion, compensation measures for resource allocation lag are determined to obtain the complete sequence of overall emergency dispatch for delivery.

[0128] Obtain the initial delivery task list and current resource status data. Analyze resource allocation records to determine the specific task set for lagging stages. For the task set of lagging stages, use particle swarm optimization to calculate a compensation and adjustment scheme. Based on the compensation and adjustment scheme, update the task execution time and resource matching relationship for lagging stages. Merge the updated lagging stage data with the initial delivery task list to obtain the adjusted overall task sequence. Perform a sequence check on the adjusted overall task sequence; if conflicts exist, return to the compensation and adjustment scheme stage for recalculation; otherwise, output the complete emergency delivery scheduling sequence.

[0129] The criteria for determining the delay is "the task execution time exceeds the planned time by 30 minutes or more"; the criteria for determining the conflict is "the same resource (vehicle / personnel) is assigned to 2 or more tasks in the same time period".

[0130] The particle dimension is set to 3, corresponding to "the proportion of idle transportation capacity allocation (0-1)", "the number of crowdsourced resource calls (0-10)" and "the number of high-priority orders jumping the queue (0-5)" respectively; the fitness function is F=0.7×T+0.3×C (T is the reduction in total delay time, and C is the resource cost control rate); the inertia weight adopts a linear decreasing strategy of 0.4-0.9 (0.9 in the early stage of iteration, 0.4 in the later stage), and the learning factor c1=c2=2; the number of iterations is set to 50 times to ensure rapid convergence to the optimal solution.

[0131] The compensation and adjustment scheme calculated by the particle swarm optimization algorithm includes multi-dimensional resource replenishment measures: First, for tasks in the lagging stages, idle capacity is dynamically allocated from adjacent delivery nodes with a load rate below 60% for support; second, a pre-set third-party crowdsourced delivery resource pool is activated to outsource non-core packages in the lagging sequence; third, high-priority orders such as fresh produce and pharmaceuticals in the lagging tasks are "jumped into the queue," and the earliest time slice in the optimal path is reallocated. The algorithm iteratively searches for the optimal combination of the above measures to ensure that the overall delivery task sequence minimizes the delay time accumulated due to allocation lag without resource conflicts.

[0132] For example, in the field of logistics and distribution, the first step is to obtain an initial delivery task list from the scheduling system, such as a list containing 10 orders, each specifying the type of goods, destination, and expected delivery time. At the same time, current resource status data, such as the number of available vehicles, driver status, and real-time traffic information, must be obtained.

[0133] Specifically, this data can be obtained through integrated database queries, ensuring that the inventory covers the entire chain of tasks from warehouse to customer.

[0134] In one embodiment, the specific set of tasks in the lagging stage can be determined by analyzing resource allocation records.

[0135] For example, suppose the logs show that three trucks were delayed due to traffic congestion. The system will then scan all allocation logs and identify two tasks involving fresh food delivery and one task involving electronics delivery as a lag set. The delay time of these tasks has exceeded a preset threshold of 30 minutes, thus forming a targeted set for subsequent optimization.

[0136] For example, for a set of tasks with these lagging components, particle swarm optimization (PSO) can be used to calculate compensation and adjustment schemes. PSO is a biomimetic algorithm that simulates the foraging behavior of bird flocks, finding the optimal solution by iteratively updating the position and velocity of particles in the search space.

[0137] Specifically, this process first initializes a particle swarm, with each particle representing a possible resource adjustment scheme, such as reallocating vehicles or adjusting routes. Then, the merits of each scheme are evaluated by calculating a fitness function, which is based on total delay time, minimizing additional costs, and maximizing resource utilization. Next, the particles update their speed and position based on the global optimum and their individual optima, iterating multiple times until convergence. For example, after 50 iterations, a suggested scheme is to allocate a spare electric vehicle to the fresh produce task and detour along an alternative route to compensate for the 15-minute delay. The advantage of this algorithm is its ability to quickly handle multidimensional optimization problems, avoiding getting trapped in local optima, thus providing efficient compensation for emergency scenarios.

[0138] In one embodiment, the task execution time and resource matching relationship of the lagging link are updated according to the compensation adjustment scheme.

[0139] For example, the execution time of the fresh produce task was adjusted from the original 10:00 AM to 10:15 AM, and a backup truck with a load capacity of 5 tons was matched. At the same time, the matching relationship record was updated to ensure that the driver information was synchronized with the new route.

[0140] For example, the updated data on lagging links can be merged with the initial delivery task list to obtain the adjusted overall task sequence.

[0141] Specifically, this involves a data fusion step, where the three compensated delayed tasks are inserted into the appropriate positions in the original list, for example, by sorting them by timestamps and placing them after the non-delayed tasks, forming a sequence of 13 tasks, ensuring that the sequence logically starts execution from the earliest task.

[0142] In one embodiment, a sequence check is performed on the adjusted overall task sequence. If a conflict exists, the system returns to the compensation adjustment phase for recalculation. For example, if the check finds that two tasks require the same vehicle at the same time, the system will mark the conflict and roll back to the particle swarm optimization phase, inputting conflict constraints and re-iteratio calculation. If there is no conflict, such as when all task times do not overlap and resources are sufficient, a complete emergency delivery scheduling sequence is output, such as a detailed list including the start time, end time, vehicle allocation, and route details for each task. This ensures that the entire delivery process is completed efficiently under emergency conditions. This sequence can also be archived as a log to support subsequent auditing.

[0143] In one embodiment, if there are conflicts in the overall task sequence after adjustment, the orders are sorted by priority (priority from high to low: pharmaceutical orders > fresh food orders > expedited e-commerce orders > regular e-commerce orders > other orders). The execution time of high-priority orders is retained first, the resource matching relationship of low-priority orders is adjusted (such as changing vehicles or delaying execution time), and the process is returned to the compensation and adjustment plan stage for recalculation until there are no conflicts.

[0144] like Figure 7 As shown in the diagram, the genetic algorithm-particle swarm optimization collaborative scheduling data flow diagram illustrates the complete data processing flow of the two-stage hybrid optimization. The initial scheduling sequence is input into the genetic algorithm module, passing through the following processing nodes in sequence: encoding (401), initial population (402), selection (403), crossover (404), mutation (405), fitness calculation (406), and convergence judgment (407). If the convergence condition is not met, the process returns to node 403 via a feedback loop to re-perform the selection operation; if convergence has been achieved, the optimal sequence is passed to the particle swarm optimization module. In the particle swarm optimization module, the data passes through the following processing nodes in sequence: particle initialization (408), velocity / position update (409), fitness evaluation (410), and global optimal update (411). Then, the process enters the conflict check node (412). If a scheduling conflict is detected, the process returns to node 409 to recalculate the velocity and position; if no conflict is detected, the complete delivery scheduling sequence is output. This dual-module collaborative architecture fully leverages the global search capability of the genetic algorithm and the local fine-tuning characteristics of particle swarm optimization, ensuring the reliability of the optimization results at each stage through two independent feedback mechanisms.

[0145] The technical effects of this invention have been verified through the following tests: Test dataset: Contains 100,000 historical order data (2022-2023), covering 3 types of promotional scenarios (large, medium and small), 5 types of weather conditions (sunny, cloudy, rainy, snowy and foggy), involving 10 delivery areas, 50 delivery vehicles, and 8 types of orders (including fresh food, medicine, e-commerce retail, etc.).

[0146] Comparison schemes: traditional genetic algorithm scheduling scheme and static resource allocation scheme.

[0147] Test results: The proposed solution reduces cascading delay time by 40% compared to the traditional genetic algorithm and by 65% ​​compared to the static resource allocation solution; resource utilization is improved by 25% compared to the traditional genetic algorithm and by 40% compared to the static resource allocation solution; demand prediction accuracy is improved from 70% to 90% compared to the traditional solution; in the Double Eleven promotion scenario, the on-time order delivery rate is improved from 82% to 95% compared to the traditional solution, effectively solving the problem of resource lag and cascading delays caused by continuous and sudden order flows.

[0148] Supplementary explanations regarding the algorithms, models, and thresholds related to this invention: 1. Specific implementation of the meta-learning model 1.1 Model Structure This invention employs a three-layer fully connected neural network: the input layer receives 10 numerical values ​​(representing the regular differences in the most recent 10 time periods); the first hidden layer contains 64 processing units and uses a modified linear activation method; the second hidden layer contains 32 processing units and also uses a modified linear activation method; the output layer contains 1 processing unit and uses a sigmoid activation function to output continuous values ​​between 0 and 1, which are the evolution trend judgment parameters.

[0149] 1.2 Training Process Meta-training phase: Historical orders are grouped into 24 task groups based on promotion type (4 categories) and weather conditions (4 categories). An adaptive moment estimation optimizer is trained for 50 epochs on each task group. The gradient change trends of all tasks are summarized, the average gradient is calculated, and a general initial weight is generated.

[0150] Rapid adaptation phase: Input the regular difference sequence of the current 10 time periods. Based on the initial weights mentioned above, only 1 to 3 rounds of mini-batch gradient fine-tuning are needed (learning rate is fixed at 0.005). Loss calculation uses cross-entropy: For each sample, the probability value output by the model is compared with the true label (0 or 1). The loss value is formed by calculating the weighted average of the logarithmic difference between the predicted probability and the true state, and is used for parameter updates.

[0151] 1.3 Basis for Threshold Setting Preset judgment threshold (0.5): Based on statistical analysis of 100,000 historical order data. Data shows that 95% of the judgment parameters for samples with an "accelerating upward trend" are above 0.6, while 95% of the parameters for samples with a "stable / declining trend" are below 0.4. Using 0.5 as the dividing point allows for effective differentiation at a 95% confidence level.

[0152] Demand level range: determined by the mapping relationship between parameter values ​​in historical data and actual demand growth rate. Extremely High: 0.8-1.0 (corresponding to demand growth of over 20% per hour) High: 0.5-0.8 (corresponding to demand growth of 10%-20% per hour) Stable: 0.2-0.5 (corresponding to hourly demand changes of -5% to 10%) Low: 0-0.2 (corresponding to a demand decrease of more than 5% per hour) 1.4 Implementation and Verification In historical data from the Double Eleven shopping festival, 95% of the "accelerated rise" sample parameters fell within the range of 0.7-0.95. After multiple rounds of backtesting, the 0.5 threshold consistently triggered alerts across various promotional scenarios, with a false alarm rate of less than 8%.

[0153] 2. Specific implementation of genetic algorithms 2.1 Encoding and Initialization The chromosome uses real number encoding and its length is equal to the number of chained delayed orders to be optimized.

[0154] Each gene position takes a real number between 0 and 1, and the value represents the execution priority of the corresponding order.

[0155] Initially, 50 chromosomes are generated, with gene values ​​randomly and uniformly generated within the range of 0-1.

[0156] 2.2 Evolutionary Operations Selection: Tournament selection method. Two individuals are randomly selected each time to compare fitness, and the better one is retained. This process is repeated until 50 parents are selected.

[0157] Crossover: Single-point crossover, 70% probability. A crossover point is randomly selected, and all genes after that point in the two parents are swapped to generate two offspring.

[0158] Mutation: Gaussian mutation, probability 3%. For the gene loci to be mutated, a random perturbation value with a mean of 0 and a standard deviation of 0.1 is superimposed, and the result is constrained back to the 0-1 interval.

[0159] 2.3 Fitness and Termination Criteria Fitness is defined as "the reciprocal of the total delay time" (total delay time includes the delay of each order itself and twice the number of chain-affected orders, plus a very small constant to prevent division by zero).

[0160] Termination condition: The total delay time fluctuation of the best individuals for 5 consecutive generations is less than 5%, or the upper limit of 100 iterations is reached.

[0161] 2.4 Implementation Results In an optimization test of 15 chain delayed orders in a logistics center, the initial average total delay was 120 minutes. After 50 generations, the optimal solution was reduced to 35 minutes, which met the convergence condition and output a stable priority sequence.

[0162] 3. Specific implementation of the particle swarm optimization algorithm 3.1 Particle Design Each particle contains 3 decision dimensions: the proportion of idle transportation capacity allocation (0-1), the number of crowdsourced resources called (0-10 integers), and the number of high-priority orders that jump the queue (0-5 integers).

[0163] The inertia weight is linearly decreased from 0.9 to 0.4; the learning factor is set to 2.0; the population size is 50; and the maximum number of iterations is 50.

[0164] 3.2 Assessment and Updates Fitness = Total delay reduction × 0.7 + Resource cost saving rate × 0.3. Where, the total delay reduction is the difference before and after optimization; Resource cost saving rate = (Original cost - Optimized cost) ÷ Original cost × 100%.

[0165] The velocity update consists of three parts: the current velocity inertial component, the component learned from its own historical best position, and the component learned from the global best position. The position is updated by accumulating the velocity and is forcibly constrained to the legal range of each dimension.

[0166] 3.3 Implementation Results In the fresh food delivery scenario, the adaptability of three delayed tasks improved from 0.65 to 0.88 after optimization. The solution was to allocate 70% of idle capacity, call on three crowdsourced personnel, and allow two high-priority orders to jump the queue. The total delay was reduced by 45 minutes, and the cost savings rate was increased by 15%.

[0167] 4. Specific implementation of the Support Vector Machine model 4.1 Model Configuration The kernel function uses radial basis functions and performs nonlinear mapping by calculating the exponentially decaying similarity of feature distances between samples, with the internal coefficient set to 0.1.

[0168] The penalty parameter is set to 1.0.

[0169] Sample labeling: System recovery time ≤ 10 minutes is labeled as "stable mode" (positive sample); > 30 minutes is labeled as "failure mode" (negative sample).

[0170] The input features include 5 items: resource utilization rate, reserve resource amount, scheduling interface response delay, order backlog, and timeliness window tolerance.

[0171] 4.2 Training and Validation 2,000 valid samples (600 positive samples and 1,400 negative samples) were selected from 100,000 historical data points, and the features were standardized.

[0172] The model was fine-tuned using 10-fold cross-validation, and the final model achieved an accuracy of 92.3%, a precision of 91.5%, and a recall of 93.8% on the test set.

[0173] 4.3 Application Examples For a scheme with input features of [75%, 25%, 0.8 seconds, 150 orders, 45 minutes], the model outputs a "stable mode" probability of 85.7%, which is higher than the 80% judgment threshold. The scheme is validated and adopted.

[0174] 5. Logic and range for determining key thresholds 5.1 Preset interval threshold Logic: Calculate the average and fluctuation range of historical sudden order intervals, and take the average minus twice the standard deviation as the threshold to ensure coverage of 95% of normal fluctuations.

[0175] Example range: 30 seconds for e-commerce scenarios (mean 45 seconds, standard deviation 7.5 seconds); 15 seconds for fresh food scenarios (mean 25 seconds, standard deviation 5 seconds); 45 seconds for general logistics (mean 60 seconds, standard deviation 7.5 seconds).

[0176] 5.2 Preset Difference Threshold Logic: Analyze historical data on order intensity fluctuations in adjacent time periods and take the upper limit of the 95% confidence interval.

[0177] Example range: 30% for daily scenarios (confidence interval 25%-35%); 50% for promotional scenarios (45%-55%); 60% for emergency scenarios (55%-65%).

[0178] 5.3 Instantaneous efficiency threshold Logic: Dynamically weighted order completion rate and resource utilization rate according to scenario. General scenario (completion rate weight 0.6, utilization rate 0.4); peak scenario (0.5 each); emergency scenario (completion rate 0.4, utilization rate 0.6).

[0179] Example values: Normal 80%, Peak 75%, Emergency 70%.

[0180] 5.4 Constraint Adjustment Boundary Delivery personnel working hours: Within the 12-hour limit allowed by labor law, the standard working hours can be flexibly increased by 10%-20% (e.g., if the standard is 10 hours, it can be adjusted to 11-12 hours).

[0181] Time tolerance: 30-60 minutes for non-urgent orders; ≤15 minutes for urgent orders such as fresh produce / medicine (customer surveys show that the complaint rate increases significantly when the delivery time exceeds 15 minutes).

[0182] Vehicle load factor: can be adjusted to a minimum of 50% (below this value, transportation costs will increase by more than 20%, determined after cost-benefit analysis).

[0183] 6. Threshold Validation Method Statistical validation: Calculate confidence intervals based on historical data to confirm that the thresholds cover a reasonable range of business fluctuations.

[0184] Real-world testing: Test the impact of different thresholds on metrics such as delay time and customer satisfaction in a small-scale business environment, and select the value with the best overall effect.

[0185] Sensitivity analysis: Adjust the threshold within ±10% range, observe the stability of key system indicators, and select the setpoint with the smallest fluctuation.

[0186] Industry benchmarking: By comparing with industry-standard solutions or competing products, the rationality and advancement of the threshold setting of this invention can be verified.

[0187] The above description is merely a preferred embodiment of this application and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of the invention involved in this application is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the concept of this application. For example, technical solutions formed by substituting the above features with (but not limited to) technical features with similar functions disclosed in this application.

Claims

1. A meta-learning-enhanced method for emergency dispatching of sudden orders with multiple constraints, characterized in that, The method includes: By collecting historical order data, the temporal distribution patterns and intensity trends of continuous surge orders are obtained, and the results of capturing patterns and differences are determined. Based on the captured patterns and differences, a meta-learning model is trained to obtain parameters for predicting the evolution of subsequent demand. If the parameters for predicting the evolution of demand exceed a preset threshold, resource allocation optimization is initiated to obtain an allocation scheme for accelerated processing methods under a dynamic coordination mechanism. Constraint adjustment variables are extracted from the allocation scheme for accelerated processing methods, and their matching degree with real-time processing efficiency is judged to obtain a preliminary scheduling sequence with controllable and balanced demand. For the preliminary scheduling sequence, a genetic algorithm is used iteratively to determine the minimum path for cascading delays, obtaining the optimized output of order pattern analysis. Based on the optimized output, real-time updated data on intensity change trends are obtained to determine whether further constraint adjustments are needed, resulting in the final execution plan of the dynamic coordination mechanism. Through the final execution plan, a particle swarm optimization algorithm is used for fusion to determine compensation measures for resource allocation lags, obtaining a complete sequence of overall emergency delivery scheduling.

2. The method according to claim 1, characterized in that, The process of collecting historical order data to obtain the time distribution patterns and intensity trends of continuous bursts of orders, and determining the results of capturing these patterns and differences, includes: Obtain the order occurrence timestamp sequence from historical order data; Calculate the time interval sequence between adjacent orders based on the order timestamp sequence; By identifying consecutive order segments smaller than a preset interval threshold through time interval sequences, a set of consecutive burst order flow segments is obtained; For each continuous burst order flow segment, the number of orders is counted to obtain the burst flow intensity sequence of each segment; Calculate the intensity difference sequence between adjacent segments based on the burst flow intensity sequence; If there are points in the intensity difference sequence that exceed the preset difference threshold, they are determined to be intensity abrupt change locations; By segmenting the burst flow intensity sequence at the location of abrupt changes in intensity, multiple relatively stable subsequences are obtained; For each relatively stable subsequence, its duration and average intensity are extracted to obtain the distribution pattern characteristics of the subsequence; Based on the distribution characteristics of all subsequences, group and cluster them to obtain a set of pattern categories for burst order flows; By statistically analyzing the frequency distribution of each pattern category, the temporal distribution pattern can be obtained. The trend of intensity change was determined by the change in the frequency of occurrence of each category within different time windows; By comparing the distribution patterns and intensity trends of each category, the results of capturing pattern differences are obtained.

3. The method according to claim 1, characterized in that, The method of capturing results based on patterns and differences, and then training a meta-learning model to obtain parameters for predicting the evolution of subsequent demand trends, includes: Retrieve demand records for each time period in a historical data sequence; By comparing demand records from adjacent time periods, a pattern of differences can be calculated. By inputting the regular difference sequence into the meta-learning model for training, initial parameters adapted to different evolution patterns are obtained; The initial parameters obtained from training are used to quickly adjust the latest differences in the patterns, thereby obtaining parameters for judging the current evolution trend; The current evolution trend is judged by comparing the parameters with the preset judgment threshold. If the parameters are greater than the preset judgment threshold, it is judged as an accelerating upward trend. If the current evolution trend judgment parameter is less than or equal to the preset judgment threshold, it is judged as a stable or declining trend. The trend of accelerating upward, stabilizing, or declining demand will be used as the input for predicting subsequent demand, and the next stage demand level label will be output.

4. The method according to claim 1, characterized in that, If the evolution trend judgment parameter exceeds a preset threshold, resource allocation optimization is initiated to obtain an accelerated processing method allocation scheme under a dynamic coordination mechanism, including: If the evolution trend parameters exceed the preset threshold, the current system operation data is obtained through the information acquisition module, and the acquired data is initially screened to obtain a real-time record of the evolution trend changes. Based on real-time change records, data comparison tools are used to analyze the deviation between parameter thresholds and current situation parameters, determine whether the deviation continues to exceed the preset range, and obtain the deviation analysis results. If the deviation analysis results show that the deviation is continuously outside the range, the resource allocation module is triggered to dynamically evaluate the current resource usage and obtain the priority ranking of resource allocation. Based on priority sorting, obtain the scheduling rules of the dynamic coordination mechanism, match the scheduling rules with the current resource allocation status, and determine whether the conditions for accelerated processing are met. If the conditions for accelerated processing are met, the allocation ratio is adjusted through the resource scheduling interface, and the adjusted resource distribution is monitored in real time to obtain the execution status of the processing measures. Based on the execution status, the allocation scheme is optimized using the support vector machine algorithm. The optimized scheme is then simulated and verified to determine the final allocation scheme. By using the final allocation scheme, feedback data from the system operation is obtained, recorded, and stored to determine whether the system has recovered to a stable state.

5. The method according to claim 1, characterized in that, The step of extracting constraint adjustment variables from the accelerated processing method allocation scheme, determining their matching degree with real-time processing efficiency, and obtaining a preliminary scheduling sequence with controllable demand balance includes: Constraint data is obtained from the accelerated processing scheme, and the specific restrictions of the constraints are classified and organized to obtain a preliminary set of conditions. Based on the initial set of conditions, the scope and priority of the adjustment variables are analyzed, and the influence weight of each variable is sorted through the variable analysis module to determine the list of key variables; For the list of key variables, obtain real-time data streams of immediate efficiency. If the immediate efficiency is lower than a preset threshold, dynamically adjust the key variables and determine the extent of efficiency improvement after adjustment. By analyzing the change trend of the degree of fit after the adjustment of efficiency improvement, data comparison tools are used to quantitatively evaluate the degree of fit and obtain a degree of fit score. Based on the fit score and the allocation rules for demand balancing, an initial scheduling logical framework is generated, and the priority order of the scheduling sequence is determined. Based on the priority sorting of the scheduling sequence, the real-time status of resource allocation is obtained. If the resource allocation does not match the priority sorting, the allocation ratio is adjusted through logical operations to obtain the final scheduling sequence.

6. The method according to claim 1, characterized in that, The process involves using a genetic algorithm iteratively to determine the path that minimizes cascading delays in the initial scheduling sequence, thereby obtaining the optimized output of the order pattern analysis, including: Obtain the planned start time and actual available resource time for each order in the initial scheduling sequence; Based on the deviation between the planned start time and the actual available resource time, calculate the initial delay for each order and mark the chain of delayed orders; For the marked chain of delayed orders, construct a directed graph structure containing pre- and post-order constraints; A genetic algorithm is used to encode the order sequence in the directed graph to generate an initial population; The population is iteratively updated through selection, crossover, and mutation operations, and the total delay time corresponding to each chromosome is calculated. Determine whether the total delay time of the best chromosome in the current population meets the convergence condition. If it does, terminate the iteration. If it does not, continue to perform selection, crossover and mutation operations. The converged optimal chromosome is obtained and decoded into an adjusted order sequence. Based on the adjusted order sequence, the start and end times of each order are reallocated to determine the optimized production mode.

7. The method according to claim 1, characterized in that, The step of obtaining real-time updated data on the intensity change trend based on the optimized output, determining whether further constraint adjustments are needed, and obtaining the final dynamic coordination mechanism execution plan includes: By acquiring trend data of intensity changes from the monitoring system and using preset thresholds for preliminary comparison, an initial assessment result of the fluctuation state is obtained. Based on the initial fluctuation status assessment results, an in-depth analysis of the dynamic fluctuation situation is conducted, and time series analysis is used to determine whether the fluctuation exceeds the preset range. If the fluctuation exceeds the preset range, the trend data is segmented to obtain the change characteristics within each time period and determine whether the constraint adjustment mechanism needs to be triggered. By further comparing the characteristics of change and combining historical data records, we can analyze the degree of matching between the current intensity change and past trends, and obtain the basis for adjustment judgment. Based on the basis of the adjustment judgment, an update strategy for the constraints is generated, and the final direction of coordinated execution is determined. If the updated strategy deviates from the current execution plan, the execution plan will be dynamically corrected to obtain the latest execution plan. By integrating the latest implementation plan, a complete execution process for the dynamic coordination mechanism is generated, and the final implementation steps are determined.

8. The method according to claim 1, characterized in that, The complete sequence for obtaining the overall emergency dispatch of delivery includes: Obtain the initial delivery task list and current resource status data; By analyzing resource allocation records, the specific set of tasks in the lagging stages can be determined; For the set of tasks with lag, the particle swarm optimization algorithm is used to calculate the compensation and adjustment scheme. According to the compensation adjustment plan, update the task execution time and resource matching relationship of the lagging links; The updated data on lagging links are merged with the initial delivery task list to obtain the adjusted overall task sequence; Perform a sequence check on the adjusted overall task sequence. If there is a conflict, return to the compensation and adjustment scheme stage to recalculate. If there is no conflict, output the complete emergency delivery scheduling sequence.

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