Multi-type logistics scene customized multi-period warehouse site selection method based on real-time data fusion

Abstract

The invention relates to the technical field of logistics warehouse site selection calculation, and particularly discloses a real-time data fused multi-type logistics scene customized multi-period warehouse site selection method, which comprises the steps of constructing a multi-type logistics scene classification system and a real-time data system, matching a unique scene label for a project to be subjected to site selection, and performing site selection on the project to be subjected to site selection; accessing a real-time data source and a data preprocessing rule matched with the scene label; based on the scene label and the real-time data system, scene-based space-time layering parameters are generated, and the scene-based space-time layering parameters comprise a time layering rule based on demand fluctuation coefficient division and a space layering rule based on demand density and road network density division. According to the method, by establishing the scene label system and the scene exclusive index function, the limitation of a general model is broken through, oriented optimization can be carried out on specific pain points such as temperature control energy consumption of a cold chain and cross-border combined transport connection, and it is ensured that a site selection scheme is highly matched in business logic.

Description

Technical Field

[0001] This invention relates to the field of logistics warehouse location calculation technology, and in particular to a real-time data fusion method for customized multi-phase warehouse location selection for various logistics scenarios. Background Technology

[0002] Most existing logistics warehouse location technologies employ general mathematical models based on static historical data, typically with the lowest total cost over the entire lifecycle as the sole optimization objective. However, this "one-size-fits-all" static location model suffers from severe lag and incompatibility when facing the ever-changing modern logistics landscape.

[0003] For example, in special scenarios such as cold chain logistics or cross-border logistics, warehouse location selection depends not only on land price and distance, but also on strong constraints such as temperature control and energy efficiency, and multimodal transport efficiency. Traditional general models cannot perceive the key indicators specific to these scenarios, resulting in warehouses with low construction costs but hidden losses in subsequent operation, such as spoilage of medicines and huge cargo delays. More importantly, location schemes based on long-term static planning cannot cope with short-term demand fluctuations (such as e-commerce promotions), causing a conflict between the rigidity of long-term infrastructure construction and the drastic fluctuations in short-term business demand. This leads to warehouse resources alternating between idleness and overcrowding, making it impossible to achieve dynamic optimization.

[0004] Furthermore, in extreme emergency logistics and high-frequency real-time data applications, existing technologies face more profound problems of logical failure and system instability.

[0005] On the one hand, in the early stages of a disaster, traditional algorithms often rely solely on whether the physical road network is broken to determine the reliability of a path, while ignoring the phenomenon of traffic oversaturation caused by panic escape. This conflict between the connectivity of the physical road network and the actual paralysis at the functional level often misleads relief supplies into paths that are "theoretically accessible but actually deadlocked".

[0006] On the other hand, directly introducing high-frequency streaming data in pursuit of real-time site selection can easily cause system oscillations. Even slight fluctuations in the data can cause the algorithm to frequently trigger suggestions to open or close the warehouse. This conflict between the high requirement for data sensitivity and the stability of the decision-making scheme leads the system into a "parameter ping-pong" effect, consuming a lot of computing power but failing to produce a stable site selection scheme that can be implemented. Summary of the Invention

[0007] This invention aims to at least partially address one of the technical problems in related technologies. Therefore, the purpose of this invention is to propose a real-time data fusion method for customized multi-phase warehouse location selection across multiple logistics scenarios, in order to achieve superior location selection results with the help of big data analysis.

[0008] To achieve the above objectives, a first aspect of the present invention proposes a method for customized multi-phase warehouse location selection for multiple logistics scenarios based on real-time data fusion, comprising the following steps:

[0009] S1. Construct a multi-type logistics scenario classification system and real-time data system, match a unique scenario label for the project to be selected, and connect to the real-time data source and data preprocessing rules that are compatible with the scenario label;

[0010] S2. Based on the scene tags and real-time data system, generate scene-based spatiotemporal stratification parameters. The scene-based spatiotemporal stratification parameters include time stratification rules based on demand fluctuation coefficients and spatial stratification rules based on demand density and road network density.

[0011] S3. Construct a scenario-based multi-period, multi-objective model, which includes a service quality maximization objective function, a cost minimization objective function, a carbon footprint minimization objective function, and scenario-specific indicator functions; the function type of the scenario-specific indicator functions and the weight coefficients of each objective function are uniquely determined based on the scenario label.

[0012] S4. Use a real-time data-driven adaptive heuristic algorithm to solve the scenario-based multi-phase multi-objective model and output a scenario-based multi-phase warehouse location scheme.

[0013] S5. Deploy a real-time anomaly response closed loop, monitor the operation status of the scenario-based multi-phase warehouse location scheme based on real-time data, and execute hierarchical response rules when the scenario-based anomaly monitoring indicators are triggered.

[0014] To achieve the above objectives, a second aspect of the present invention proposes a real-time data fusion-based customized multi-phase warehouse location selection system for various logistics scenarios, comprising:

[0015] The scenario classification and data access module is used to build a multi-type logistics scenario classification system and a real-time data system, match a unique scenario label for the project to be selected, and access the real-time data source and data preprocessing rules that are compatible with the scenario label.

[0016] The spatiotemporal stratification parameter generation module is used to generate scenario-based spatiotemporal stratification parameters based on the scenario labels and real-time data system. The scenario-based spatiotemporal stratification parameters include time stratification rules based on demand fluctuation coefficients and spatial stratification rules based on demand density and road network density.

[0017] A multi-objective model construction module is used to construct a scenario-based, multi-period, multi-objective model. The model includes a service quality maximization objective function, a cost minimization objective function, a carbon footprint minimization objective function, and scenario-specific indicator functions. The function type of the scenario-specific indicator functions and the weight coefficients of each objective function are uniquely determined based on the scenario label, and the weight coefficients satisfy normalization constraints. ,in: For service quality weighting, As a cost weight, For carbon footprint weighting, Specific indicator weights for each scenario;

[0018] The model solving module is used to solve the scenario-based multi-phase multi-objective model using a real-time data-driven adaptive heuristic algorithm, and output the scenario-based multi-phase warehouse location scheme.

[0019] The anomaly response module is used to deploy a real-time anomaly response closed loop, monitor the operation status of the scenario-based multi-phase warehouse location scheme based on real-time data, and execute hierarchical response rules when scenario-based anomaly monitoring indicators are triggered.

[0020] To achieve the above objectives, a third aspect of the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory. When the computer program is executed by the processor, it implements the above-mentioned method for customized multi-phase warehouse location selection for multi-type logistics scenarios based on real-time data fusion.

[0021] The real-time data fusion method for customized multi-phase warehouse location selection in various logistics scenarios of this invention breaks through the limitations of general models by establishing a scenario labeling system and scenario-specific indicator functions. It can perform targeted optimization for specific pain points such as temperature control and energy consumption in the cold chain and cross-border intermodal transport connections, ensuring that the location selection scheme is highly compatible with business logic. Furthermore, in response to the hidden risks of seemingly connected but actually congested road networks, this invention introduces flow acceleration impedance logic and multimodal escape mechanism, which can identify functional blockages caused by traffic surges and avoid deadlock paths in advance when the physical road network is not interrupted, significantly improving the delivery probability of emergency supplies.

[0022] Furthermore, to address the model oscillation problem caused by real-time data, this invention innovatively employs an asymmetric hysteresis locking strategy. By setting asymmetric trigger thresholds and cooling-off locking windows, it intelligently filters out interference from high-frequency noise data, updating the model only when a definite long-term trend is captured. This not only significantly reduces computational power consumption, but more importantly, it ensures the continuity and executability of the output scheme while maintaining the system's sensitivity to significant changes, achieving a balance between data-driven approaches and engineering implementation. Attached Figure Description

[0023] Figure 1 This is a flowchart illustrating the multi-stage warehouse location selection method for various logistics scenarios based on real-time data fusion provided by the present invention.

[0024] Figure 2 This is a schematic diagram illustrating the implementation of the real-time data fusion-based customized multi-phase warehouse location selection system for various logistics scenarios provided by this invention.

[0025] Figure 3 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0026] Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention.

[0027] The following description, with reference to the accompanying drawings, outlines a method, system, and electronic device for real-time data fusion in customized multi-phase warehouse location selection for various logistics scenarios based on multiple types of logistics scenarios.

[0028] Example 1:

[0029] This embodiment targets the ambient temperature e-commerce logistics scenario, specifically labeled Ambient Temperature E-commerce Logistics S01, and is applied in the multi-phase warehouse location selection process. This method, through real-time data fusion and scenario-customized processing, solves key problems in ambient temperature e-commerce logistics such as drastic demand fluctuations, high on-time delivery requirements, and dynamic optimization of resource allocation. The steps of this embodiment will be described in detail below:

[0030] For example, in step S1, a multi-type logistics scenario classification system and a real-time data system are constructed. A unique scenario label is matched for the project to be selected, namely ambient temperature e-commerce logistics S01, and real-time data sources and data preprocessing rules adapted to this scenario label are connected. Real-time data sources include, but are not limited to, order flow data, traffic congestion data, inventory level data, and historical demand data. This data is collected in real time through IoT devices, API interfaces, or cloud databases to ensure data timeliness.

[0031] Data preprocessing rules include sliding window filtering and scenario-based normalization. The sliding window filtering configuration is as follows: since the scenario label is non-emergency logistics, a sliding window of second length is used to filter the real-time data. This second length is greater than the first length used in emergency logistics scenarios, which helps smooth data fluctuations and reduce noise interference. The scenario-based normalization configuration is as follows: for congestion coefficient data, a higher maximum congestion threshold is set based on scenario label S01 for normalization calculation. This maximum congestion threshold is set higher than the maximum congestion threshold for emergency logistics S03, thus adapting to common traffic congestion situations in e-commerce logistics and improving the accuracy of data processing.

[0032] Optionally, the real-time data system can also integrate weather forecast data or social media trend data to further optimize demand forecasting.

[0033] For example, step S2 generates scenario-based spatiotemporal stratification parameters based on scenario labels and real-time data systems. These parameters include time stratification rules based on demand fluctuation coefficients and spatial stratification rules based on demand density and road network density.

[0034] Specifically, the time-based stratification rule is as follows: For ambient temperature e-commerce logistics S01, periods where the demand fluctuation coefficient exceeds the preset promotion threshold are defined as core periods, and the remaining periods are defined as stable periods. The preset promotion threshold can be dynamically adjusted based on historical e-commerce activity data. For example, during promotional periods, when the demand fluctuation coefficient is high, the system automatically marks these periods as core periods to prioritize resource allocation.

[0035] The spatial stratification rule is as follows: Define the grid granularity of the first spatial level, where the grid granularity of ambient temperature e-commerce logistics S01 is larger than that of cold chain logistics S02 and emergency logistics S03. This means that in spatial partitioning, ambient temperature e-commerce logistics uses coarser grid cells to accommodate its wide geographical coverage and uniform demand distribution, while reducing computational complexity.

[0036] Optionally, spatial layering can also be combined with road network density data to refine high-density areas in order to optimize the matching of warehouse locations with transportation routes.

[0037] For example, in step S3, a scenario-based multi-period, multi-objective model is constructed. This model includes an objective function for maximizing service quality, an objective function for minimizing cost, an objective function for minimizing carbon footprint, and scenario-specific indicator functions.

[0038] Among them, the function type of the scene-specific indicator function and the weight coefficient of each objective function are uniquely determined based on the scene label S01, and the weight coefficient configuration satisfies the following normalization constraints: ;

[0039] in, For service quality weighting, As a cost weight, For carbon footprint weighting, Specific indicator weights for each scenario.

[0040] The specific value of the weight coefficient is determined by the preset scenario type corresponding to scenario label S01. For example, in ambient temperature e-commerce logistics, the service quality weight... The setting may be relatively high to reflect its emphasis on on-time delivery rate. The specific calculation logic of the service quality maximization objective function switches based on the scenario label S01: when the scenario label is ambient temperature e-commerce logistics S01, the service quality maximization objective function uses the on-time delivery rate as the core indicator, calculating the ratio of the actual delivery time to the maximum allowable time. This ratio is dynamically updated through real-time transportation data to ensure that the model can quickly respond to logistics delays.

[0041] The cost minimization objective function comprehensively considers warehouse construction costs, operating costs, and transportation costs, while the carbon footprint minimization objective function calculates carbon emissions based on real-time energy consumption data to support sustainable development goals. Scenario-specific indicator functions can be defined as inventory turnover rate optimization functions in ambient temperature e-commerce logistics, but their specific form can be customized according to business needs. The model uses weighting coefficients... To balance its impact.

[0042] Optionally, the model can also integrate machine learning algorithms to automatically adjust the objective function parameters based on real-time data to improve prediction accuracy.

[0043] For example, step S4 uses a real-time data-driven adaptive heuristic algorithm to solve the scenario-based multi-phase multi-objective model and output a scenario-based multi-phase warehouse location scheme. The adaptive heuristic algorithm includes genetic algorithm, simulated annealing algorithm, or particle swarm optimization algorithm, which dynamically adjusts the search strategy according to real-time data fluctuations.

[0044] For example, during peak demand periods, the algorithm prioritizes exploring high-service-quality areas to ensure on-time delivery. The solution process is iterative until the model converges to a Pareto optimal solution, and the output includes warehouse location, capacity planning, and time-phased scheduling.

[0045] Optionally, the algorithm can also be combined with visualization tools to provide users with an interactive interface for adjusting solutions, thereby enhancing decision support.

[0046] For example, step S5 deploys a real-time anomaly response closed loop, monitors the operational status of scenario-based multi-phase warehouse location schemes based on real-time data, and executes tiered response rules when scenario-based anomaly monitoring indicators are triggered.

[0047] The tiered response rules specifically include: for ambient temperature e-commerce logistics S01, monitoring the order coverage gap indicator; if the order coverage gap exceeds the first threshold, a second-level abnormal response is triggered, and an inventory cycle adjustment strategy is executed.

[0048] Inventory cycle adjustment strategies may include dynamically reallocating inventory, activating backup warehouses, or adjusting shipping frequency to fill coverage gaps. Anomaly monitoring is based on real-time data streams, such as order flow sensors or GPS tracking devices, to ensure rapid detection of anomalies.

[0049] After executing an abnormal response, the system continuously monitors data for a preset number of periods. If the indicators return to the normal range, the response is terminated; otherwise, the response level is upgraded.

[0050] Optionally, the response rules can also be integrated with an early warning mechanism to notify managers in advance when indicators approach thresholds, in order to prevent potential problems.

[0051] For example, this embodiment also includes a dynamic parameter update mechanism driven by real-time data. This mechanism specifically includes: establishing real-time data fluctuation monitoring; when the fluctuation amplitude of real-time data exceeds a preset threshold and the duration reaches a preset period, triggering model parameter updates. For the scenario labeled ambient temperature e-commerce logistics S01, when the real-time order peak exceeds a preset proportion of historical peaks, automatically reducing the grid granularity in the spatial stratification rules and increasing the service quality weight. The numerical values ​​enable the model to adapt to sudden fluctuations in demand, such as during major e-commerce promotions, by refining the spatial grid and prioritizing service quality to optimize warehouse layout and resource allocation.

[0052] The dynamic update mechanism also involves carbon footprint weighting. and scenario-specific indicator weights The update mechanism is adjusted to ensure the model maintains a balance among multiple objectives. Optionally, the update mechanism can be combined with an artificial intelligence model to predict long-term trends and adjust parameters in advance to enhance robustness.

[0053] For example, the dynamic parameter update mechanism also includes an asymmetric hysteresis locking strategy to prevent model oscillations. This strategy specifically includes setting a parameter upgrade threshold. With parameter degradation threshold ,in The corresponding trigger sensitivity is higher than This means the system is more sensitive to increasing parameter changes but more conservative to decreasing ones, in order to avoid frequent adjustments. Set a cooling lock window. When model parameters are updated due to fluctuations in real-time data, the system immediately enters a period of [duration missing]. The system is in a locked state. While locked, the system refuses to respond to parameter callback requests that are opposite to the current update direction, unless a scene label change to the highest priority interruption signal (Emergency Logistics S03) is detected in the real-time data.

[0054] Cooling lock window The length is associated with the scene tag, where S01 is an ambient temperature e-commerce logistics tag. Length less than cold chain logistics S02 The length reflects the need for rapid response in e-commerce logistics, while reducing system latency through a shorter lockout window.

[0055] Optionally, the locking strategy can also be dynamically adjusted based on data quality metrics. Length, to further optimize stability.

[0056] For example, this embodiment achieves efficient warehouse location selection for ambient temperature e-commerce logistics scenarios through the above steps. In time layering, the division into core and stable segments allows the system to prioritize peak demand and reduce resource waste. In spatial layering, a larger grid granularity reduces computational overhead, while a dynamic update mechanism automatically refines the grid during demand surges to ensure accuracy. The service quality maximization objective function, with on-time delivery rate as its core, directly improves user experience and customer satisfaction. The anomaly response closed-loop and hierarchical rules provide flexible fault handling capabilities, while the asymmetric hysteresis locking strategy balances data sensitivity and system stability, preventing model oscillations.

[0057] Overall, this embodiment not only improves the accuracy and real-time nature of warehouse location selection, but also optimizes operational efficiency through scenario customization, providing a scalable solution for ambient temperature e-commerce logistics.

[0058] Optionally, this embodiment can also be integrated with other logistics systems, such as supply chain management platforms or intelligent transportation systems, to achieve end-to-end optimization.

[0059] For example, to further illustrate the implementation effect of this embodiment, consider an application scenario:

[0060] An e-commerce company used this method for warehouse location selection during the Double Eleven shopping festival. Real-time data sources were integrated with the order system and transportation network. Data preprocessing smoothed fluctuations using sliding window filtering, and standardization employed a high congestion threshold to adapt to e-commerce logistics. Temporal stratification defined the promotional period as the core segment, while spatial stratification used a coarser grid to cover the entire country.

[0061] In a multi-objective model, service quality weights The system is configured with a high objective function to calculate on-time delivery rate. An adaptive heuristic algorithm outputs the optimal warehouse location, and an anomaly response monitors for order coverage gaps, triggering inventory adjustments when the gap exceeds a threshold. A dynamic update mechanism shrinks the grid and improves efficiency during peak order periods. The asymmetric hysteresis locking strategy prevented model fluctuations caused by data jitter. As a result, the enterprise achieved improved warehouse utilization and reduced transportation delays, demonstrating the practicality and effectiveness of this method.

[0062] Example 2:

[0063] This embodiment targets a cold chain logistics scenario, specifically labeled as Cold Chain Logistics S02, and is applied in the multi-phase warehouse site selection process. This method, through real-time data fusion and scenario-customized processing, solves key problems in cold chain logistics such as high energy consumption for temperature control, volatile pharmaceutical products, and sensitivity to transportation environments. The following details each step of this embodiment:

[0064] For example, in step S1, a multi-type logistics scenario classification system and a real-time data system are constructed. A unique scenario label, namely cold chain logistics S02, is matched for the project to be selected, and real-time data sources and data preprocessing rules adapted to this scenario label are connected. Real-time data sources include, but are not limited to, temperature sensor data, humidity monitoring data, drug order data, traffic flow data, and energy consumption record data. These data are collected in real time through IoT devices, cold chain monitoring systems, or cloud platforms to ensure high-frequency updates and accuracy.

[0065] Data preprocessing rules include sliding window filtering and scenario-based normalization. The sliding window filtering configuration is as follows: since the scenario label is non-emergency logistics, a sliding window of second length is used to filter the real-time data. This second length is greater than the first length used in emergency logistics scenarios. This helps smooth out temperature fluctuations and random noise in energy consumption data, improving data stability. The scenario-based normalization configuration is as follows: for congestion coefficient data, an appropriate maximum congestion threshold is set based on scenario label S02 for normalization calculation. This threshold may be lower than that of ambient temperature e-commerce logistics S01 but higher than that of emergency logistics S03, to meet the higher requirements for transportation timeliness in cold chain logistics.

[0066] Optionally, the real-time data system can also integrate meteorological data or cold chain equipment status data to further optimize temperature control forecasting and energy consumption management.

[0067] For example, step S2 generates scenario-based spatiotemporal stratification parameters based on scenario tags and a real-time data system. These parameters include time stratification rules based on demand fluctuation coefficients and spatial stratification rules based on demand density and road network density. Specifically, for cold chain logistics S02, the entire construction cycle is defined as the core segment. This means that throughout the entire warehouse construction and operation cycle, the system continuously monitors temperature fluctuations and energy consumption changes to ensure that resource allocation always prioritizes the integrity of the cold chain.

[0068] Specifically, the spatial stratification rule defines the grid granularity of the first spatial level, where the grid granularity of cold chain logistics S02 is smaller than that of ambient temperature e-commerce logistics S01, but larger than that of emergency logistics S03. This reflects the need for medium-precision spatial division in cold chain logistics to balance geographical coverage and temperature control accuracy, while avoiding excessive refinement that would waste computing power.

[0069] Optionally, spatial stratification can also be combined with road network density data to highlight areas with high cold chain demand, such as pharmaceutical parks or food distribution centers, in order to optimize the matching of warehouse locations with cold chain transportation routes.

[0070] For example, in step S3, a scenario-based multi-period, multi-objective model is constructed. This model includes a service quality maximization objective function, a cost minimization objective function, a carbon footprint minimization objective function, and scenario-specific indicator functions. The function type of the scenario-specific indicator functions and the weight coefficients of each objective function are uniquely determined based on the scenario label S02. The weight coefficient configuration satisfies the normalization constraints in Example 1.

[0071] For example, in cold chain logistics, the weight of scenario-specific indicators. The setting may be relatively high to emphasize the importance of temperature control energy consumption optimization. The scenario-specific indicator function is defined differently based on the scenario label: when the scenario label is cold chain logistics S02, the scenario-specific indicator function is a temperature control energy consumption minimization function, whose optimization objective is to reduce temperature difference energy consumption during transportation. This function calculates energy efficiency using real-time temperature data to ensure that cold chain equipment maintains the standard temperature with minimal energy consumption. The cost minimization objective function, when the scenario label is cold chain logistics S02, also specifically includes temperature control energy consumption cost items and pharmaceutical loss cost items. The calculation of the temperature control energy consumption cost item is based on real-time temperature control data. The calculation formula involves the product of transportation time, the absolute value of the temperature difference between the actual temperature and the standard temperature, and the energy consumption coefficient per unit temperature difference. The mathematical expression can be:

[0072] ;

[0073] The calculation of drug loss costs is based on the drug order volume and loss coefficient. The loss coefficient is positively correlated with the duration of temperature exceeding the threshold during transportation, meaning that the longer the temperature exceeds the threshold, the higher the risk of drug spoilage, and the higher the loss cost. The service quality maximization objective function uses on-time delivery rate and temperature stability as core indicators to ensure that goods are delivered within the scheduled time and that temperature fluctuations are within acceptable limits. The carbon footprint minimization objective function calculates carbon emissions based on real-time energy consumption data, supporting green logistics goals. Optionally, the model can also integrate multi-objective optimization algorithms, such as the weighted sum method or the epsilon constraint method, to balance conflicts between different objectives.

[0074] For example, step S4 employs a real-time data-driven adaptive heuristic algorithm to solve the scenario-based multi-phase, multi-objective model, outputting a scenario-based multi-phase warehouse location scheme. The adaptive heuristic algorithm includes genetic algorithms, simulated annealing algorithms, or ant colony optimization algorithms, which dynamically adjust their search strategies based on real-time temperature data and energy consumption fluctuations. For instance, during periods of abnormal temperature, the algorithm prioritizes exploring low-energy-consumption warehouse locations to minimize temperature control costs. The solution process iterates until the model converges to a Pareto optimal solution, and the output scheme includes warehouse location, temperature control equipment configuration, drug storage zoning, and time-phased arrangement.

[0075] Optionally, the algorithm can also be combined with digital twin technology to construct a virtual cold chain environment for simulation testing to verify the robustness and feasibility of the solution.

[0076] For example, step S5 deploys a real-time anomaly response closed loop, monitors the operational status of scenario-based multi-phase warehouse location schemes based on real-time data, and executes tiered response rules when scenario-based anomaly monitoring indicators are triggered.

[0077] The tiered response rules specifically include: for cold chain logistics S02, monitoring temperature exceedance indicators and drug loss rate indicators. If the temperature exceeds the warning threshold or the loss rate exceeds the preset limit, the corresponding level of abnormal response is triggered, and strategies such as adjusting transportation routes, activating backup refrigeration equipment, or reallocating inventory are implemented. Abnormal monitoring is based on real-time sensor data, such as temperature loggers or humidity detectors, to ensure rapid detection of environmental changes. After executing an abnormal response, the system continuously monitors data for a preset number of periods. If the indicators return to the normal range, the response terminates; otherwise, the response level is escalated.

[0078] Optionally, the response rules can also integrate an early warning mechanism to notify managers in advance when the temperature approaches the threshold, in order to prevent the risk of drug deterioration.

[0079] For example, this embodiment also includes a dynamic parameter update mechanism driven by real-time data, which specifically includes:

[0080] Establish real-time data fluctuation monitoring. When the fluctuation amplitude of real-time data exceeds a preset threshold and the duration reaches a preset period, trigger model parameter updates. For the scenario labeled "Cold Chain Logistics S02," when the real-time monitored temperature exceeds the warning temperature, automatically increase the weight of scenario-specific indicators. The numerical values ​​are adjusted, and the transportation time constraints in the model are tightened. This allows the model to respond quickly to temperature anomalies, reducing the risk of drug spoilage by prioritizing temperature control energy consumption optimization and shortening allowable transportation time. The dynamic update mechanism also involves cost weights. and carbon footprint weight Adjustments are made to ensure that the model maintains a balance among multiple objectives.

[0081] Optionally, the update mechanism can be combined with a machine learning model to predict long-term temperature trends and adjust parameters in advance to enhance the system's adaptability.

[0082] For example, the dynamic parameter update mechanism also includes an asymmetric hysteresis locking strategy to prevent model oscillations, which specifically includes:

[0083] Set parameter upgrade threshold With parameter degradation threshold ,in The corresponding trigger sensitivity is higher than This means the system is more sensitive to increases in parameter values ​​but more conservative to decreases, in order to avoid frequent adjustments due to small temperature fluctuations. (Set a cooling lock window.) When model parameters are updated due to fluctuations in real-time data, the system immediately enters a period of [duration missing]. The system is in a locked state. While locked, the system refuses to respond to parameter callback requests that are opposite to the current update direction, unless a scene label change to the highest priority interruption signal (Emergency Logistics S03) is detected in the real-time data. Cooling the locked window. The length is associated with the scene label, where cold chain logistics S02 is... Length greater than ambient temperature e-commerce logistics S01 The length reflects the high requirements for stability in cold chain logistics. A longer locking window reduces parameter fluctuations and ensures the continuity and feasibility of the solution.

[0084] Optionally, the locking strategy can also be dynamically adjusted based on data quality metrics. Length, for example, extending the lock window when data noise is high, to further optimize system performance.

[0085] For example, this embodiment achieves efficient warehouse site selection in cold chain logistics scenarios through the above steps. In the time layering, the entire construction cycle is used as the core segment to ensure continuous monitoring and resource optimization, reducing the risk of temperature runaway. In the spatial layering, medium grid granularity balances computational efficiency and control accuracy, and a dynamic update mechanism automatically adjusts parameters when temperatures are abnormal, ensuring minimal energy consumption. The scenario-specific index function focuses on minimizing temperature control energy consumption, directly reducing operating costs and environmental impact.

[0086] The cost terms for temperature control and pharmaceutical waste in the cost minimization objective function provide refined economic optimization, while the abnormal response closed-loop and hierarchical rules enhance the system's fault tolerance. The asymmetric hysteresis locking strategy effectively prevents model oscillations and ensures the stability of the output scheme.

[0087] Overall, this embodiment not only improves the accuracy and reliability of cold chain warehouse site selection, but also optimizes energy efficiency and drug preservation rate through scenario customization, providing a sustainable solution for cold chain logistics.

[0088] Optionally, this embodiment can also be integrated with other systems, such as a pharmaceutical supply chain platform or an environmental monitoring system, to achieve end-to-end cold chain management.

[0089] For example, to further illustrate the implementation effect of this embodiment, consider an application scenario:

[0090] A pharmaceutical company used this method for vaccine warehouse site selection. Real-time data sources were connected to temperature sensors and the transportation network. Data preprocessing smoothed temperature fluctuations using sliding window filtering, and standardization applied appropriate congestion thresholds to accommodate cold chain transportation. Temporal stratification defined the entire construction cycle as the core segment, while spatial stratification used a medium-sized grid to cover key areas. In the multi-objective model, scenario-specific indicator weights were used. The settings are relatively high, the objective function calculates the minimum temperature control energy consumption, and the cost function includes the temperature control energy consumption cost item and the drug loss cost item;

[0091] The adaptive heuristic algorithm outputs the optimal warehouse location, and anomaly response detects temperature exceeding the threshold, triggering route adjustments when the temperature exceeds the limit. The dynamic update mechanism improves performance during temperature alerts. Furthermore, by shortening transportation time, an asymmetric lag locking strategy prevented model fluctuations caused by data noise. As a result, the company achieved reduced energy consumption and vaccine loss, demonstrating the practicality and effectiveness of this method.

[0092] Example 3:

[0093] This embodiment targets an emergency logistics scenario, specifically labeled Emergency Logistics S03, and is applied in the multi-phase warehouse site selection process. This method, through real-time data fusion and scenario-customized processing, solves unique problems in emergency logistics such as functional road network disruptions, extremely high requirements for transportation reliability, and critical timeliness of material delivery. The following details each step of this embodiment:

[0094] For example, in step S1, a multi-type logistics scenario classification system and a real-time data system are constructed. A unique scenario label, namely emergency logistics S03, is matched for the project to be selected, and real-time data sources and data preprocessing rules adapted to this scenario label are connected. Real-time data sources include, but are not limited to, disaster early warning data, road network status data, population flow data, material demand data, and meteorological monitoring data. These data are collected in real time through government emergency platforms, satellite remote sensing systems, IoT sensors, and mobile communication networks to ensure the timeliness and accuracy of the data.

[0095] Data preprocessing rules include sliding window filtering and scenario-based standardization. The sliding window filtering configuration is as follows: when the scenario label is Emergency Logistics S03, a sliding window of the first length is used to filter the real-time data. This first length is shorter than the second length used for non-emergency logistics, which helps preserve the instantaneous characteristics of the data and quickly capture sudden signals caused by disasters. The scenario-based standardization configuration is as follows: for congestion coefficient data, different maximum congestion thresholds are set according to the scenario label S03 for normalization calculation. The maximum congestion threshold for Emergency Logistics S03 is set lower than that for Normal Temperature E-commerce Logistics S01, reflecting the extreme sensitivity of emergency logistics to road traffic conditions; any degree of congestion can affect rescue efficiency.

[0096] Optionally, the real-time data system can also integrate social media sentiment data or drone reconnaissance data to further optimize disaster impact assessment and demand forecasting.

[0097] For example, step S2 generates scenario-based spatiotemporal stratification parameters based on scenario tags and real-time data systems. These parameters include time stratification rules based on demand fluctuation coefficients and spatial stratification rules based on demand density and road network density. Specifically, the time stratification rules define the disaster warning period as the core segment for emergency logistics S03. This means that the system concentrates resources on high-priority tasks to ensure rapid allocation of relief supplies for a period before, during, and after a disaster. The spatial stratification rules define the grid granularity of the first spatial layer, where the grid granularity of emergency logistics S03 is smaller than that of cold chain logistics S02, and the grid granularity of cold chain logistics S02 is smaller than that of ambient temperature e-commerce logistics S01. Simultaneously, the first spatial layer coverage of emergency logistics S03 is limited to a preset distance around the disaster risk area. This reflects the need for highly refined spatial division in emergency logistics, focusing computational resources on the disaster-stricken area to avoid resource dispersion, and ensuring warehouse locations are close to the risk area through distance restrictions to shorten response time.

[0098] Optionally, spatial layering can also be combined with terrain data or historical disaster data to divide high-risk areas into multi-layer grids in order to optimize the matching of warehouse layout and rescue routes.

[0099] For example, in step S3, a scenario-based multi-period, multi-objective model is constructed. This model includes a service quality maximization objective function, a cost minimization objective function, a carbon footprint minimization objective function, and scenario-specific indicator functions. The function type of the scenario-specific indicator functions and the weight coefficients of each objective function are uniquely determined based on the scenario label S03, and the weight coefficient configuration satisfies the normalization constraints in Example 1.

[0100] The specific value of the weighting coefficient is determined by the preset scenario type corresponding to scenario label S03. For example, in emergency logistics, the service quality weighting... and scenario-specific indicator weights The setting may be relatively high to reflect its extreme pursuit of transportation reliability and material delivery probability. The scenario-specific indicator function is defined differently based on the scenario label: When the scenario label is Emergency Logistics S03, the scenario-specific indicator function is the transportation reliability maximization function, whose optimization objective is to improve the probability of material delivery under road network blockage risk. This function calculates path reliability using real-time road network data and disaster impact models to ensure that materials can be delivered to their destination via the optimal path. The specific calculation logic of the service quality maximization objective function is switched based on the scenario label S03: When the scenario label is Emergency Logistics S03, the service quality maximization objective function uses weighted reliability as the core indicator. Weighted reliability is obtained by weighted summation of transportation on-time rate and road network connectivity reliability, where road network connectivity reliability is inversely proportional to the real-time road closure length. Mathematically expressed as:

[0101] ;

[0102] Among them, road network connectivity reliability , This represents a proportionality coefficient. While cost minimization may be relatively less important in emergency logistics, warehouse construction and transportation costs still need to be considered. In contrast, carbon footprint minimization calculates carbon emissions based on real-time energy consumption data, supporting the concept of green relief.

[0103] Optionally, the model can also integrate game theory methods to simulate the decision-making behavior of multiple rescue entities, thereby improving the model's practicality and adaptability.

[0104] For example, step S4 employs a real-time data-driven adaptive heuristic algorithm to solve the scenario-based multi-phase, multi-objective model, outputting a scenario-based multi-phase warehouse location scheme. The adaptive heuristic algorithm includes genetic algorithms, simulated annealing algorithms, or ant colony optimization algorithms. These algorithms dynamically adjust their search strategies based on real-time disaster data and road network conditions. For instance, when the risk of road network blockage is high, the algorithm prioritizes exploring highly reliable paths and combines a multimodal escape mechanism to find alternative solutions. The solution process iterates until the model converges to a Pareto optimal solution, and the output scheme includes temporary warehouse locations, material allocation plans, transportation route arrangements, and time-phased strategies.

[0105] Optionally, the algorithm can also be combined with reinforcement learning techniques to train the model using historical disaster data, thereby improving decision-making capabilities in complex environments.

[0106] For example, step S5 deploys a real-time anomaly response closed loop, monitoring the operational status of the scenario-based multi-phase warehouse location plan based on real-time data, and executing tiered response rules when scenario-based anomaly monitoring indicators are triggered. The tiered response rules specifically include: for emergency logistics S03, monitoring transportation reliability indicators and material delivery delay indicators. If transportation reliability falls below the second threshold or delivery delay exceeds the third threshold, a Level 1 anomaly response is triggered, and a backup grid activation strategy is executed. The backup grid activation strategy may include activating temporary warehouses, adjusting transportation modes, or mobilizing additional rescue forces to quickly fill transportation gaps. Anomaly monitoring is based on real-time sensor data, such as road network cameras or satellite imagery, to ensure rapid detection of environmental changes. After executing the anomaly response, a preset number of data cycles are continuously monitored. If the indicators return to normal range, the response is terminated; otherwise, the response level is escalated.

[0107] Optionally, the response rules can also integrate smart contract technology to automatically execute emergency protocols via blockchain, improving response speed and transparency.

[0108] For example, this embodiment also includes a dynamic parameter update mechanism driven by real-time data. This mechanism specifically includes: establishing real-time data fluctuation monitoring; when the fluctuation amplitude of real-time data exceeds a preset threshold and the duration reaches a preset period, triggering a model parameter update. For the scenario label "Emergency Logistics S03," when the real-time road network blockage length exceeds the warning value or the disaster level escalates, the service quality weight is automatically increased. and scenario-specific indicator weights The model adjusts the numerical values ​​and the calculation parameters for road network connectivity reliability. This enables the model to respond quickly to disaster evolution by prioritizing transport reliability and optimizing route selection, thereby reducing the risk of delivery failures.

[0109] Furthermore, the dynamic update mechanism also involves cost weighting. and carbon footprint weight Adjustments are made to ensure that the model maintains a balance among multiple objectives.

[0110] Optionally, the update mechanism can be combined with artificial intelligence models to predict disaster development trends and adjust parameters in advance to enhance the system's foresight and robustness.

[0111] For example, when the scenario label is Emergency Logistics S03, the calculation of road network connectivity reliability also integrates flow acceleration impedance logic and a multimodal escape mechanism. The flow acceleration impedance logic is configured as follows: real-time monitoring of the traffic density change rate of a road segment; when the traffic density change rate of a certain road segment exceeds a preset panic threshold, regardless of whether the physical road closure length of that road segment is zero, a virtual blocking penalty coefficient is applied to that road segment in the service quality maximization objective function, forcibly reducing the connectivity reliability of that road segment to zero. This solves the limitation of traditional algorithms that only determine path reliability based on whether the physical road network is broken, and can identify the phenomenon of traffic oversaturation caused by panic escape, preventing materials from entering theoretically unobstructed but actually deadlocked paths. The mathematical expression can be represented as:

[0112] ;

[0113] The multimodal escape mechanism is configured as follows: when the number of paths satisfying the connectivity reliability threshold in the calculated path schemes is lower than the preset minimum number of survivable paths, the constraints of unconventional transportation nodes are automatically unlocked in the scenario-based multi-phase multi-objective model, allowing drone take-off and landing points or two-wheeled vehicle docking points to participate in site selection calculations as temporary transit warehouses. This expands the range of rescue route selection, provides alternative solutions when traditional road networks fail, and significantly improves the probability of material delivery.

[0114] Optionally, the multimodal escape mechanism can also integrate unmanned boat or helicopter landing sites to adapt to rescue needs in different geographical environments.

[0115] For example, the dynamic parameter update mechanism also includes an asymmetric hysteresis locking strategy to prevent model oscillations. This strategy specifically includes setting a parameter upgrade threshold. With parameter degradation threshold ,in The corresponding trigger sensitivity is higher than This means the system is more sensitive to increases in parameter values ​​but more conservative to decreases, in order to avoid frequent adjustments due to small fluctuations in data. (Set a cooling lock window.) When model parameters are updated due to fluctuations in real-time data, the system immediately enters a period of [duration missing]. The system is in a locked state. While locked, the system refuses to respond to parameter callback requests that are opposite to the current update direction, unless a scene label change to the highest priority interruption signal (Emergency Logistics S03) is detected in the real-time data. Cooling the locked window. The length is associated with the scene label, where Emergency Logistics S03 is... The length may be shorter to reflect its need for rapid response, while balancing system stability through intelligent locking.

[0116] Optionally, the lockdown strategy can also be dynamically adjusted according to the disaster level. Length, for example, shortening the lockout window to speed up the response during a major disaster.

[0117] For example, this embodiment achieves efficient warehouse location selection in emergency logistics scenarios through the above steps. In the temporal layering, the disaster warning period, as the core segment, ensures priority allocation of resources and reduces response delays. In the spatial layering, the minimum grid granularity and distance constraints focus computational resources on risk areas, improving positioning accuracy. The service quality maximization objective function, with weighted reliability as its core, directly improves the probability of material delivery. The calculation of road network connectivity reliability integrates flow acceleration impedance logic and a multimodal escape mechanism, solving the functional blockage problem and avoiding deadlock paths. The abnormal response closed-loop and hierarchical rules provide flexible emergency handling capabilities, while the asymmetric hysteresis locking strategy effectively prevents model oscillations, ensuring the continuity and executability of the output scheme.

[0118] Overall, this embodiment not only improves the accuracy and reliability of emergency warehouse site selection, but also optimizes rescue efficiency and resource utilization through scenario customization, providing strong technical support for emergency logistics.

[0119] Optionally, this embodiment can also be integrated with other systems, such as disaster management platforms or international relief networks, to achieve end-to-end emergency response.

[0120] For example, to further illustrate the implementation effect of this embodiment, consider an application scenario:

[0121] This method was used to select sites for relief warehouses in an area affected by an earthquake. Real-time data sources were integrated with the earthquake monitoring system and road network sensors. Data preprocessing used short sliding window filtering to retain abrupt changes in signal intensity, and standardization employed a low congestion threshold to adapt to emergency needs. Temporal stratification defined the disaster warning period as the core segment, while spatial stratification used a minimum grid to cover the area surrounding the epicenter. In the multi-objective model, service quality weights were considered. and scenario-specific indicator weights The objective function is set to be highly reliable and weighted, and the flow acceleration impedance logic and multi-modal escape mechanism are integrated.

[0122] The adaptive heuristic algorithm outputs the optimal warehouse location, monitors transportation reliability in response to anomalies, and triggers backup grid activation when the location falls below a threshold. A dynamic update mechanism increases weight parameters when the road network is blocked, and an asymmetric hysteresis locking strategy prevents model fluctuations caused by data noise. As a result, relief supplies are delivered quickly via reliable routes, avoiding deadlock areas, demonstrating the practicality and effectiveness of this method.

[0123] Example 4:

[0124] This embodiment targets a cross-border logistics scenario, specifically labeled Cross-border Logistics S04, and is applied to the multi-phase warehouse site selection process. This method, through real-time data fusion and scenario-customized processing, solves key problems in cross-border logistics such as low efficiency of multimodal transport connections, long cargo demurrage times at ports, and high customs clearance uncertainty. The following details each step of this embodiment:

[0125] For example, in step S1, a multi-type logistics scenario classification system and a real-time data system are constructed. A unique scenario label, namely cross-border logistics S04, is matched for the project to be selected, and real-time data sources and data preprocessing rules adapted to this scenario label are connected. Real-time data sources include, but are not limited to, customs clearance data, port operation data, multimodal transport timetable data, international trade policy data, and cross-border transport flow data. These data are collected in real time through the General Administration of Customs system, port management platform, international logistics tracking system, and cross-border e-commerce platform to ensure the internationality and timeliness of the data. Data preprocessing rules include sliding window filtering and scenario-based standardization. The sliding window filtering is configured to use a second-length sliding window suitable for cross-border logistics to filter real-time data. This length is greater than that of emergency logistics but less than or equal to that of normal-temperature e-commerce logistics, in order to adapt to the characteristics of cross-border data that are relatively stable but still require timely response. The scenario-based standardized processing configuration is designed for cross-border congestion coefficient data, such as port waiting time or customs processing time. Based on the scenario label S04, a specific maximum congestion threshold is set for normalization calculation. This threshold is usually between emergency logistics and ambient temperature e-commerce logistics to balance customs clearance efficiency and operating costs.

[0126] Optionally, the real-time data system can also integrate exchange rate fluctuation data or geopolitical risk indicators to further optimize risk assessment and long-term planning for cross-border warehouse site selection.

[0127] For example, step S2 generates scenario-based spatiotemporal stratification parameters based on scenario tags and real-time data systems. These parameters include time-based stratification rules based on demand fluctuation coefficients and spatial stratification rules based on demand density and road network density. Specifically, the time-based stratification rules define peak international trade seasons or periods of policy change as core segments, such as cross-border e-commerce promotional periods or tariff policy adjustment periods. During these periods, the system prioritizes resource allocation to ensure smooth logistics. The spatial stratification rules define the grid granularity of the first spatial level. The grid granularity of cross-border logistics S04 is typically larger than that of emergency logistics but may be smaller than or equal to that of normal-temperature e-commerce logistics. This reflects the need for cross-border logistics to balance broad geographical coverage with refined management of key nodes.

[0128] Optionally, spatial stratification can also be combined with international trade route data or cross-border economic zone planning to refine the grid division of areas surrounding important ports in order to optimize the connection between warehouse locations and international transportation networks.

[0129] For example, in step S3, a scenario-based multi-period, multi-objective model is constructed. This model includes a service quality maximization objective function, a cost minimization objective function, a carbon footprint minimization objective function, and scenario-specific indicator functions. The function type of the scenario-specific indicator functions and the weight coefficients of each objective function are uniquely determined based on the scenario label S04, and the weight coefficient configuration satisfies the normalization constraints in Example 1.

[0130] For example, the specific value of the weighting coefficient is determined by the preset scenario type corresponding to scenario label S04. Typically, in cross-border logistics, scenario-specific indicator weights are used. Service quality weight The target value will be set relatively high to reflect the emphasis on connection efficiency and transportation reliability. The scenario-specific indicator function is defined based on scenario label differentiation: when the scenario label is cross-border logistics S04, the scenario-specific indicator function is a function to minimize multimodal transport connection time, with the optimization objective of shortening the waiting time between warehouses and ports or border crossings. This function calculates the connection efficiency of each link using real-time customs clearance data, transport timetables, and cargo tracking information, and uses this to optimize warehouse location to reduce overall logistics time. The service quality maximization objective function in cross-border logistics typically uses international transport on-time rate and cargo integrity rate as core indicators, while the cost minimization objective function focuses on cross-border transport costs, tariff costs, and opportunity costs. The carbon footprint minimization objective function calculates environmental impact based on international transport carbon emission data, supporting the development of green trade.

[0131] Optionally, the model can also integrate risk management algorithms to assess the impact of geopolitical factors or trade barriers on multi-objective optimization.

[0132] For example, step S4 employs a real-time data-driven adaptive heuristic algorithm to solve the scenario-based multi-phase, multi-objective model, outputting a scenario-based multi-phase warehouse location scheme. Adaptive heuristic algorithms include genetic algorithms, simulated annealing algorithms, or particle swarm optimization algorithms. These algorithms dynamically adjust their search strategies based on real-time customs clearance status, port throughput, and transportation availability. For instance, during periods of high customs clearance delays, the algorithm prioritizes exploring warehouse locations with fast clearance channels and optimizes route selection by incorporating multimodal transport data. The solution process approximates the Pareto optimal solution through multiple iterations, and the output scheme includes cross-border warehouse location selection, capacity planning, multimodal transport node configuration, and a phased implementation plan.

[0133] Optionally, the algorithm can also be combined with digital twin technology to construct a virtual cross-border logistics network for stress testing to verify the robustness of the solution under different international trade scenarios.

[0134] For example, step S5 deploys a real-time anomaly response closed loop, monitoring the operational status of scenario-based multi-phase warehouse location selection schemes based on real-time data, and executing tiered response rules when scenario-based anomaly monitoring indicators are triggered. Specifically, for cross-border logistics S04, the tiered response rules monitor multimodal transport connection time and cargo demurrage rate. If the connection time exceeds a preset threshold or the demurrage rate abnormally increases, the corresponding level of anomaly response is triggered, and strategies such as adjusting the transport mode, activating alternative ports, or optimizing customs clearance processes are implemented. Anomaly monitoring is based on real-time port operation data, customs clearance status, and international transport tracking information to ensure rapid identification of bottlenecks. After executing an anomaly response, the system continuously monitors a preset number of data periods; if the indicators return to normal, the response terminates; otherwise, the response level is escalated.

[0135] Optionally, the response rules can also be integrated with an intelligent early warning mechanism to adjust warehouse operation strategies in advance of changes in trade policies or port congestion, in order to prevent potential disruption risks.

[0136] For example, this embodiment also includes a dynamic parameter update mechanism driven by real-time data. This mechanism specifically includes establishing real-time data fluctuation monitoring. When key cross-border logistics indicators, such as port waiting time or exchange rate fluctuations, exceed preset thresholds and the duration reaches a preset period, model parameter updates are triggered. For scenario labeled cross-border logistics S04, when real-time monitoring shows a continuous deterioration in multimodal transport connection time, the weight of scenario-specific indicators is automatically increased. The model calculates the numerical values ​​and optimizes the transportation time constraint in the objective function of maximizing service quality. This allows the model to dynamically adapt to changes in the international trade environment, reducing the risk of cargo delays by prioritizing connection efficiency and adjusting transportation routes. The dynamic update mechanism also involves cost weights. and carbon footprint weight Coordinated adjustments are needed to ensure that the model strikes a balance between economic benefits and environmental responsibility.

[0137] Optionally, the update mechanism can be combined with artificial intelligence prediction models to analyze long-term trade trends and optimize warehouse network layout in advance.

[0138] For example, the dynamic parameter update mechanism also includes an asymmetric hysteresis locking strategy to prevent model oscillations. This strategy specifically involves setting a parameter upgrade threshold. With parameter degradation threshold ,in The corresponding trigger sensitivity is higher than This means the system is more sensitive to increases in parameters but more conservative to decreases, in order to avoid frequent adjustments due to short-term fluctuations in international trade data. A cooling-off window is set. When model parameters are updated due to fluctuations in real-time data, the system immediately enters a period of [duration missing]. The system is in a locked state. While locked, the system refuses to respond to parameter callback requests that are in the opposite direction of the current update, unless a high-priority interruption signal, such as a trade embargo or port closure, is detected in the real-time data. Cooling the locked window. The length is associated with the scene tag, where cross-border logistics S04 is... The length is typically longer than that of emergency logistics but shorter than that of cold chain logistics, reflecting its dual need for moderate sensitivity to policy changes and operational stability.

[0139] Optionally, the locking strategy can also be dynamically adjusted based on the international trade risk index. Length, to maintain system adaptability in complex environments.

[0140] For example, this embodiment achieves efficient warehouse location selection in cross-border logistics scenarios through the above steps. In the temporal layering, defining peak trade seasons and periods of policy change as core segments ensures prioritized resource allocation and reduces the risk of logistics disruptions. In the spatial layering, a suitable grid granularity balances the needs of global coverage and key node management. Scenario-specific indicator functions, with minimizing multimodal transport connection time as their core, directly improve the overall efficiency of cross-border logistics and customer satisfaction. Anomaly response closed-loop and hierarchical rules provide the ability to flexibly cope with uncertainties in international trade, while the asymmetric lag locking strategy effectively prevents model oscillations caused by data fluctuations, ensuring the continuity and executability of the output solution.

[0141] Overall, this embodiment not only improves the accuracy and economy of cross-border warehouse site selection, but also optimizes the efficiency of multimodal transport connections and risk response capabilities through scenario customization, providing reliable technical support for cross-border logistics.

[0142] Optionally, this embodiment can also be integrated with blockchain technology to achieve tamper-proof and full-process traceability of cross-border logistics data, further enhancing the transparency and trustworthiness of the system.

[0143] For example, to further illustrate the implementation effect of this embodiment, consider an application scenario:

[0144] A multinational corporation used this methodology for selecting cross-border warehouse locations in the Asia-Pacific region. Real-time data sources were integrated with the customs clearance system, port operation platforms, and multimodal transport timetables. Data preprocessing smoothed trade data fluctuations using sliding window filtering, and standardization employed a dedicated congestion threshold to adapt to the characteristics of cross-border logistics. Temporal stratification defined peak e-commerce promotional periods as the core segment, while spatial stratification used a moderate grid to cover major port economic zones. In the multi-objective model, scenario-specific indicator weights were used. Service quality weight The objective function is set to minimize the multimodal transport connection time.

[0145] The adaptive heuristic algorithm outputs the optimal warehouse location, and the anomaly response monitoring system tracks connection time metrics, triggering transportation mode adjustments when thresholds are exceeded. This dynamic update mechanism improves efficiency during port congestion. The weighted, asymmetric hysteresis locking strategy prevents model fluctuations caused by data noise. As a result, the company achieved improved warehouse turnover and optimized cross-border logistics costs, demonstrating the practicality and effectiveness of this method.

[0146] Example 5:

[0147] like Figure 2 As shown, corresponding to the above method embodiments, the present invention also proposes a real-time data fusion-based customized multi-phase warehouse location system for multiple logistics scenarios, including:

[0148] The scenario classification and data access module is used to build a multi-type logistics scenario classification system and a real-time data system, match a unique scenario label for the project to be selected, and access the real-time data source and data preprocessing rules that are compatible with the scenario label.

[0149] The spatiotemporal stratification parameter generation module is used to generate scenario-based spatiotemporal stratification parameters based on the scenario labels and real-time data system. The scenario-based spatiotemporal stratification parameters include time stratification rules based on demand fluctuation coefficients and spatial stratification rules based on demand density and road network density.

[0150] A multi-objective model construction module is used to construct a scenario-based, multi-period, multi-objective model. The model includes a service quality maximization objective function, a cost minimization objective function, a carbon footprint minimization objective function, and scenario-specific indicator functions. The function type of the scenario-specific indicator functions and the weight coefficients of each objective function are uniquely determined based on the scenario label, and the weight coefficients satisfy normalization constraints. ,in: For service quality weighting, As a cost weight, For carbon footprint weighting, Specific indicator weights for each scenario;

[0151] The model solving module is used to solve the scenario-based multi-phase multi-objective model using a real-time data-driven adaptive heuristic algorithm, and output the scenario-based multi-phase warehouse location scheme.

[0152] The anomaly response module is used to deploy a real-time anomaly response closed loop, monitor the operation status of the scenario-based multi-phase warehouse location scheme based on real-time data, and execute hierarchical response rules when scenario-based anomaly monitoring indicators are triggered.

[0153] The system's workflow begins with the scenario classification and data access module, which assigns a unique scenario label to the project to be located, such as ambient temperature e-commerce logistics, cold chain logistics, etc., and accesses the corresponding real-time data source while executing data preprocessing rules.

[0154] Subsequently, the spatiotemporal stratification parameter generation module generates time stratification rules based on scene labels and real-time data, such as dividing core segments and stable segments based on demand fluctuation coefficients, as well as spatial stratification rules, such as dividing grid granularity based on demand density and road network density.

[0155] The multi-objective model building module uses these parameters to construct a scenario-based multi-period multi-objective model, including maximizing service quality, minimizing cost, minimizing carbon footprint, and scenario-specific indicator functions. It also determines the weight coefficients of each objective function through scenario labels to ensure that normalization constraints are met.

[0156] The model solving module uses real-time data-driven adaptive heuristic algorithms, such as genetic algorithms or simulated annealing algorithms, to solve the model and output multi-period warehouse location schemes.

[0157] Finally, the anomaly response module continuously monitors the operational status of the solution, checks scenario-based anomaly monitoring indicators based on real-time data, executes tiered response rules when an anomaly is triggered, and dynamically updates model parameters or re-initializes processes as needed to maintain the adaptability and stability of the solution.

[0158] Example 6:

[0159] Corresponding to the above embodiments, the present invention also proposes an electronic device.

[0160] like Figure 3 The diagram shows a structural schematic of an electronic device according to the present invention. The electronic device 100 includes a processor 101 and a memory 103. The processor 101 and the memory 103 are connected, for example, via a bus 102. Optionally, the electronic device 100 may further include a transceiver 104. It should be noted that in practical applications, the transceiver 104 is not limited to one unit, and the structure of this electronic device 100 does not constitute a limitation on the embodiments of the present invention.

[0161] Processor 101 may be a CPU, a general-purpose processor, a DSP, an ASIC, an FPGA, or other programmable logic device, transistor logic device, hardware component, or any combination thereof. It may implement or execute the various exemplary logic blocks, modules, and circuits described in connection with this disclosure. Processor 101 may also be a combination that implements computational functions, such as including one or more microprocessor combinations, a combination of a DSP and a microprocessor, etc.

[0162] Bus 102 may include a pathway for transmitting information between the aforementioned components. Bus 102 may be a PCI bus or an EISA bus, etc. Bus 102 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 3 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0163] The memory 103 stores a computer program corresponding to a real-time data fusion method for customized multi-phase warehouse location selection in various logistics scenarios according to the above embodiments of the present invention. This computer program is controlled and executed by the processor 101. The processor 101 executes the computer program stored in the memory 103 to implement the content shown in the aforementioned method embodiments.

[0164] Among them, electronic devices 100 include, but are not limited to: mobile terminals such as laptops and PADs (tablet computers) and fixed terminals such as desktop computers. Figure 3 The electronic device 100 shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments of the present invention.

[0165] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A multi-type logistics scenario customized multi-period warehouse location selection method of real-time data fusion, characterized in that, The method comprises the following steps: S1, constructing a multi-type logistics scene classification system and a real-time data system, matching a unique scene label for a to-be-sited project, and accessing a real-time data source and data preprocessing rules adapted to the scene label; S2, generating scene-based spatiotemporal hierarchical parameters based on the scene label and the real-time data system, wherein the scene-based spatiotemporal hierarchical parameters comprise time hierarchical rules based on demand fluctuation coefficients and spatial hierarchical rules based on demand density and road network density; S3, constructing a scene-based multi-period multi-objective model, wherein the model comprises a service quality maximization objective function, a cost minimization objective function, a carbon footprint minimization objective function, and a scene-specific index function; the function type of the scene-specific index function and the weight coefficients of each objective function are uniquely determined based on the scene label; S4, solving the scene-based multi-period multi-objective model using a real-time data-driven adaptive heuristic algorithm, and outputting a scene-based multi-period warehouse site selection scheme; S5, deploying a real-time exception response closed loop, monitoring the running state of the scene-based multi-period warehouse site selection scheme based on real-time data, and executing hierarchical response rules when a scene-based exception monitoring index is triggered.

2. The method of claim 1, wherein, The weight coefficient configuration of the scene-specific index function satisfies the following normalization constraint condition: ； wherein: is a quality of service weight; is a cost weight; is a carbon footprint weight; is a scenario-specific indicator weight; the specific value of the weight coefficient is determined by a preset scenario type corresponding to the scenario label; The preset scene types corresponding to the scene label at least include normal-temperature e-commerce logistics S01, cold-chain logistics S02, emergency logistics S03, and cross-border logistics S04; The scene-specific index function is defined based on the scene label as follows: When the scene label is cold-chain logistics S02, the scene-specific index function is a minimum energy consumption function of temperature control, and the optimization objective is to reduce the temperature difference energy consumption in the transportation process; When the scene label is emergency logistics S03, the scene-specific index function is a maximum reliability function of transportation, and the optimization objective is to improve the probability of material delivery under the risk of road network blockage; When the scene label is cross-border logistics S04, the scene-specific index function is a minimum function of multi-modal transport connection time, and the optimization objective is to shorten the waiting time of flow between the warehouse and the port or the port.

3. The method of claim 2, wherein, The data preprocessing rules in step S1 include sliding window filtering processing and scene-based standardization processing: The sliding window filtering processing is configured as follows: when the scene label is emergency logistics S03, a first length sliding window is used to filter real-time data; When the scene label is non-emergency logistics, a second length sliding window is used to filter real-time data, wherein the second length is greater than the first length; The scene-based standardization processing is configured as follows: for congestion coefficient data, different maximum congestion thresholds are set for normalization calculation according to the scene label, wherein the maximum congestion threshold of normal-temperature e-commerce logistics S01 is set higher than that of emergency logistics S03.

4. The method of claim 2, wherein, The specific generation logic of the scene-based spatiotemporal hierarchical parameters in step S2 is as follows: The time hierarchical rules: for normal-temperature e-commerce logistics S01, define the period with a demand fluctuation coefficient exceeding a preset big promotion threshold as a core segment, and the rest as a stable segment; For cold chain logistics S02, the whole construction cycle is defined as the core section; for emergency logistics S03, the disaster early warning period is defined as the core section; The spatial layering rule defines the grid granularity of the first layer of spatial hierarchy, wherein the grid granularity of emergency logistics S03 is smaller than that of cold chain logistics S02, and the grid granularity of cold chain logistics S02 is smaller than that of normal temperature e-commerce logistics S01; The first layer of spatial coverage range of emergency logistics S03 is limited within a preset distance range around the disaster risk area.

5. The method of claim 2, wherein, The specific calculation logic of the service quality maximization objective function in step S3 is switched based on the scenario label: When the scenario label is normal temperature e-commerce logistics S01, the service quality maximization objective function takes the transportation punctuality rate as the core index, and calculates the ratio of the actual transportation time to the maximum allowed time; When the scenario label is emergency logistics S03, the service quality maximization objective function takes the weighted reliability as the core index, which is obtained by weighted sum of the transportation punctuality rate and the road network connectivity reliability, wherein the road network connectivity reliability is inversely proportional to the real-time road closure length.

6. The method of claim 2, wherein, The cost minimization objective function in step S3 further includes a temperature control energy consumption cost term and a drug loss cost term when the scenario label is cold chain logistics S02: The calculation of the temperature control energy consumption cost term is based on real-time temperature control data, and the calculation formula involves the product of transportation time, the absolute value of the temperature difference between actual temperature and standard temperature, and the unit temperature difference energy consumption coefficient; The calculation of the drug loss cost term is based on the quantity of drug orders and the loss coefficient, which is positively correlated with the temperature exceeding time during transportation.

7. The method of claim 2, wherein, The step S3 further includes a dynamic parameter updating mechanism based on real-time data driving, which specifically includes: Real-time data fluctuation monitoring is established, and when the fluctuation amplitude of real-time data exceeds a preset threshold and the duration reaches a preset period, model parameter updating is triggered; When the scene label is normal-temperature e-commerce logistics S01 and the real-time order peak exceeds the historical peak preset ratio, the grid granularity in the space layering rule is automatically reduced, and the service quality weight is increased a value of the number; When the scene tag is cold chain logistics S02 and the real-time monitored temperature exceeds the early warning temperature, automatically increase the weight of the scene-specific index , and tighten the transport duration constraint condition in the model.

8. The method of claim 2, wherein, The hierarchical response rule in step S5 specifically includes: For normal temperature e-commerce logistics S01, the order coverage gap index is monitored, and if it exceeds the first threshold, a secondary abnormal response is triggered to perform inventory cycle adjustment strategy; For emergency logistics S03, the transportation reliability index and the material delivery delay index are monitored, and if the transportation reliability is lower than the second threshold or the delivery delay exceeds the third threshold, a primary abnormal response is triggered to perform backup grid activation strategy; After executing the abnormal response, a preset number of data periods are continuously monitored, and if the index returns to the normal range, the response is terminated, otherwise the response level is upgraded.

9. The method of claim 5, wherein, When the scenario label is emergency logistics S03, the calculation of the road network connectivity reliability further integrates the traffic acceleration impedance logic and the multi-modal escape mechanism: The traffic acceleration impedance logic is configured to monitor the traffic density change rate of the road section in real time, and when the traffic density change rate of a road section exceeds a preset panic threshold, a virtual block penalty coefficient is applied to the road section in the service quality maximization objective function regardless of whether the physical road closure length of the road section is zero or not, so that the connectivity reliability of the road section is forced to zero. The multi-modal escape mechanism is configured to: when the number of paths meeting the connectivity reliability threshold in the calculated path scheme is lower than the preset minimum survival path number, automatically unlock the constraint condition of the unconventional transportation node in the scenario-based multi-period multi-objective model, allowing the unmanned aerial vehicle landing point or the two-wheel vehicle connection point to participate in site selection calculation as a temporary transit warehouse.

10. The method of claim 7, wherein, Comprise: The dynamic parameter updating mechanism further comprises an asymmetric hysteresis locking strategy to prevent model oscillation, which specifically comprises: Setting a parameter upgrade threshold with a parameter downgrade threshold wherein the corresponding trigger sensitivity is higher than ; Setting a cooling lock window When the model parameter update is triggered due to real-time data fluctuation, the system immediately enters a lock state with a duration of . In the locked state, the system refuses to respond to parameter callback requests opposite to the current updating direction, unless a scenario tag change is detected in real-time data to the highest priority interrupt signal of emergency logistics S03; The cooling lock window The length is associated with the scene tag, wherein the length of the cold chain logistics S02 is greater than the length of the normal temperature e-commerce logistics S01 .

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