A simulation evaluation method and system for urban district emergency logistics distribution, a terminal and a storage medium

Abstract

The application relates to the technical field of data simulation, and discloses a simulation evaluation method and system for urban area emergency logistics distribution, a terminal and a storage medium.The method comprises the following steps: acquiring regional data of a target urban area, and performing simulation modeling to obtain an area simulation model; inputting set area space data, logistics resource parameters and task demand data into the area simulation model to generate a global distribution task list; performing task allocation to obtain a task allocation result; performing emergency logistics simulation distribution according to the set traffic load intensity and the task allocation result to obtain a target distribution result; and performing visual processing on quantitative evaluation indexes in the target distribution result to obtain a simulation evaluation result of logistics distribution.The application can effectively quantitatively evaluate the urban area emergency logistics distribution through human-vehicle mixed dynamic interaction, multi-stage adaptive distribution scheduling and unloading point queuing service, and improves the distribution efficiency.

Description

Technical Field

[0001] This invention relates to the field of data simulation technology, and in particular to a simulation evaluation method, system, terminal, and computer-readable storage medium for emergency logistics distribution in urban areas. Background Technology

[0002] During the transitional resettlement and long-term operation phase following a natural disaster (e.g., an earthquake), affected people are temporarily housed in various shelters within the area. At this time, one of the core tasks is to ensure the continuous and stable delivery of medical aid, living supplies, and rescue equipment to each shelter. However, the area is characterized by a typical mixed traffic environment with both pedestrians and vehicles. In this environment, the efficiency of emergency logistics delivery is constrained by multiple factors: 1. The connectivity and carrying capacity of the road network itself; 2. Dynamic interactions between vehicles and between vehicles and pedestrians (e.g., slowing down and yielding to pedestrians); 3. Queuing due to limited unloading points; 4. Uneven supply demands at different shelters. The coupling of these factors results in a complex nonlinear dynamic in the delivery system. For example, at a certain time, dense crowds on a particular road may cause severe vehicle slowdowns, leading to delays in supplies to multiple shelters and ultimately prolonging the overall emergency response time for the entire area.

[0003] Currently, existing technologies include macro-level emergency logistics planning models, micro-level traffic simulation models, and static facility layout assessment methods. However, macro-level emergency logistics planning models ignore the detailed road network structure and local congestion within a region, and cannot reflect the dynamic speed reduction caused by crowd gathering and mixed pedestrian and vehicle traffic. Furthermore, they do not consider micro-level behaviors such as queuing at unloading points and waiting between vehicles, resulting in a significant deviation between the output total delivery time and the actual situation. Micro-level traffic simulation models ignore the significant interference of large-scale, high-density crowds on vehicle traffic and cannot realistically simulate the problem of vehicles frequently slowing down to avoid pedestrians. Static facility layout assessment methods lack dynamic interaction and time dimension considerations.

[0004] Therefore, existing technologies still need to be improved and developed. Summary of the Invention

[0005] The main objective of this invention is to provide a simulation evaluation method, system, terminal, and storage medium for emergency logistics distribution in urban areas, aiming to solve the problem that existing technologies cannot effectively quantify and evaluate emergency logistics distribution in urban areas, resulting in inaccurate delivery times and low delivery efficiency.

[0006] To achieve the above objectives, the present invention provides a simulation evaluation method for emergency logistics distribution in urban areas, the simulation evaluation method for emergency logistics distribution in urban areas comprising the following steps: Obtain regional data of the target city area, perform simulation modeling of the target city area based on the regional data to obtain the area simulation model, and set the area spatial data, logistics resource parameters, task requirement data and traffic load intensity; The area spatial data, the logistics resource parameters, and the task requirement data are input into the area simulation model, and the area simulation model generates a global delivery task list based on the area spatial data, logistics resource parameters, and task requirement data; Tasks are allocated based on the global delivery task list to obtain task allocation results. Emergency logistics simulation delivery is performed based on the traffic load intensity and the task allocation results to obtain target delivery results. The quantitative evaluation indicators in the target delivery results are then visualized to obtain the simulation evaluation results of logistics delivery.

[0007] Optionally, the simulation evaluation method for emergency logistics distribution in urban areas, wherein acquiring regional data of the target urban area, performing simulation modeling of the target urban area based on the regional data to obtain an area simulation model, and setting area spatial data, logistics resource parameters, task demand data, and traffic load intensity, specifically includes: Obtain regional data for the target city area, including a plan view of the area, the location of delivery points, resettlement site information, road network information, and the location of population activity areas; Construct an initial model of the area based on the area plan, calculate the number of people that each demand point can accommodate based on the area of ​​each demand point in the resettlement point information, and convert the number of people that can be accommodated at all demand points into the number of material delivery times; Construct a corridor road network based on the road segments and nodes of the road network information, and input the location of the delivery point, the information of the resettlement point, the number of material deliveries, the corridor road network, and the location of the population activity area into the initial model of the area to obtain the area simulation model; Obtain the target delivery plan, and set the area spatial data, logistics resource parameters, task requirement data and traffic load intensity according to the target delivery plan.

[0008] Optionally, the simulation evaluation method for emergency logistics distribution in urban areas, wherein the step of allocating tasks based on the global distribution task list to obtain task allocation results specifically includes: Based on the global delivery task list, demand point information is obtained, and the unfinished delivery volume of each demand point in the demand point information is calculated in real time to obtain the calculation result. The delivery distance to each target demand point in the calculation results is calculated to obtain the distance calculation result. The delivery distances of each target demand point in the distance calculation results are sorted by size to obtain the distance sorting result. The target demand point is the demand point that has not been delivered. Based on the distance sorting results, target allocation rules are formulated, and tasks are allocated according to the target allocation rules to obtain task allocation results.

[0009] Optionally, the simulation evaluation method for emergency logistics distribution in urban areas, wherein the step of conducting emergency logistics simulation distribution based on the traffic load intensity and the task allocation results to obtain target distribution results, and visualizing the quantitative evaluation indicators in the target distribution results to obtain the simulation evaluation results of logistics distribution, specifically includes: Based on the traffic load intensity, vehicle behavior rules and pedestrian behavior rules are formulated, and emergency logistics simulation delivery is carried out based on the vehicle behavior rules and pedestrian behavior rules; When a vehicle is detected to have arrived at the target demand point, the interface resource information of the target demand point is obtained, the unloading process is performed according to the interface status of the interface resource information, and the delivery time of the vehicle is recorded. When all demand points in the task allocation result have been delivered, a target delivery result is generated based on the task allocation result, and a quantitative evaluation index of the target delivery result is extracted, wherein the quantitative evaluation index includes delivery efficiency index and process diagnosis index. The delivery efficiency index is visualized to obtain the delivery completion curve, and the process diagnostic index is visualized to obtain the delivery time distribution histogram. The simulation evaluation results of logistics delivery are obtained based on the delivery completion curve and the delivery time distribution histogram.

[0010] Optionally, the simulation evaluation method for emergency logistics distribution in urban areas, wherein the step of conducting emergency logistics simulation distribution based on the vehicle behavior rules and the pedestrian behavior rules specifically includes: When the vehicle starts to deliver goods at the initial speed, dynamic entity detection is performed on the driving path according to the preset time step to obtain the detection results; If the detection result indicates that a target vehicle exists within a first preset distance, the vehicle's speed is reduced according to the vehicle behavior rules to obtain a first speed. When it is detected that there is no target vehicle within the first preset distance, the first speed is increased to the initial speed; If the detection result indicates that a pedestrian exists within a second preset distance, the vehicle's speed is reduced according to the pedestrian behavior rules to obtain a second speed, and the driving path is adjusted accordingly. When no pedestrian is detected within the second preset distance, the second speed is increased to the initial speed.

[0011] Optionally, the simulation evaluation method for emergency logistics distribution in urban areas, wherein the step of formulating vehicle behavior rules and pedestrian behavior rules based on the traffic load intensity, and conducting emergency logistics simulation distribution based on the vehicle behavior rules and pedestrian behavior rules, further includes: Obtain the delivery stages of the emergency logistics simulation delivery, and determine the delivery mode based on the delivery stages; If the delivery stage is the first delivery stage, then the delivery mode is a distributed delivery mode; If the delivery stage is the second delivery stage, then the delivery mode is a centralized delivery mode.

[0012] Optionally, the simulation evaluation method for emergency logistics distribution in urban areas, wherein the step of visualizing the quantitative evaluation indicators in the target distribution result to obtain the simulation evaluation result of logistics distribution, further includes: The delivery time for each resettlement site in the simulation evaluation results is analyzed and processed to obtain the analysis results; If the analysis results indicate that the delivery time to a target resettlement point is greater than the average delivery time, then the target resettlement point is determined to be an abnormal resettlement point, and an anomaly analysis is performed on the abnormal resettlement point to obtain the anomaly analysis results. The anomaly analysis results are optimized to obtain an optimization scheme, which is then input into the area simulation model.

[0013] Optionally, the simulation evaluation method for emergency logistics distribution in urban areas includes a simulation evaluation system comprising: The simulation model construction module is used to acquire regional data of the target city area, perform simulation modeling of the target city area based on the regional data, obtain the area simulation model, and set the area spatial data, logistics resource parameters, task requirement data and traffic load intensity. The delivery task generation module is used to input the area spatial data, the logistics resource parameters and the task requirement data into the area simulation model, and the area simulation model generates a global delivery task list based on the area spatial data, the logistics resource parameters and the task requirement data; The task simulation evaluation module is used to allocate tasks according to the global delivery task list, obtain task allocation results, perform emergency logistics simulation delivery based on the traffic load intensity and the task allocation results, obtain target delivery results, and visualize the quantitative evaluation indicators in the target delivery results to obtain the simulation evaluation results of logistics delivery.

[0014] Furthermore, to achieve the above objectives, the present invention also provides a terminal, wherein the terminal includes: a memory, a processor, and a simulation evaluation program for urban area emergency logistics distribution stored in the memory and executable on the processor, wherein when the simulation evaluation program for urban area emergency logistics distribution is executed by the processor, it implements the steps of the simulation evaluation method for urban area emergency logistics distribution as described above.

[0015] Furthermore, to achieve the above objectives, the present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a simulation evaluation program for emergency logistics distribution in urban areas, and when the simulation evaluation program for emergency logistics distribution in urban areas is executed by a processor, it implements the steps of the simulation evaluation method for emergency logistics distribution in urban areas as described above.

[0016] In this invention, regional data of a target urban area is acquired, and a simulation model of the target urban area is performed based on the regional data to obtain a regional simulation model. Spatial data, logistics resource parameters, task demand data, and traffic load intensity of the area are set. The spatial data, logistics resource parameters, and task demand data of the area are input into the regional simulation model, which generates a global delivery task list based on these data. Tasks are allocated according to the global delivery task list to obtain task allocation results. Emergency logistics simulation delivery is performed based on the traffic load intensity and the task allocation results to obtain the target delivery result. The quantitative evaluation indicators in the target delivery result are visualized to obtain the simulation evaluation result of logistics delivery. This invention effectively quantifies and evaluates emergency logistics delivery in urban areas through dynamic interaction between pedestrians and vehicles, multi-stage adaptive delivery scheduling, and queuing services at unloading points, thereby improving delivery efficiency. Attached Figure Description

[0017] Figure 1 This is a flowchart of a preferred embodiment of the simulation evaluation method for emergency logistics distribution in urban areas according to the present invention; Figure 2 This is a schematic diagram of the spatial environment construction of the area simulation model in a preferred embodiment of the present invention; Figure 3 This is a schematic diagram of the overall process of vehicle simulation delivery in a preferred embodiment of the present invention; Figure 4 This is a schematic diagram illustrating the effect of a distributed delivery mode delivering for 30 minutes in a preferred embodiment of the present invention; Figure 5 This is a schematic diagram illustrating the effect of a centralized delivery mode delivering goods for 230 minutes in a preferred embodiment of the present invention; Figure 6 This is a schematic diagram of the delivery completion curve in a preferred embodiment of the present invention; Figure 7 This is a schematic diagram of the time sequence of a single delivery trip in a preferred embodiment of the present invention; Figure 8 This is a schematic diagram of the overall distribution of single-trip time samples in a preferred embodiment of the present invention; Figure 9 This is a structural diagram of a preferred embodiment of the simulation evaluation system for emergency logistics distribution in urban areas according to the present invention; Figure 10 This is a structural diagram of a preferred embodiment of the terminal of the present invention. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this invention clearer and more explicit, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0019] It should be noted that if the embodiments of the present invention involve directional indicators (such as up, down, left, right, front, back, etc.), the directional indicators are only used to explain the relative positional relationship and movement of the components in a certain specific posture (as shown in the figure). If the specific posture changes, the directional indicators will also change accordingly.

[0020] Furthermore, if the embodiments of this invention involve descriptions such as "first" or "second," these descriptions are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined with "first" or "second" may explicitly or implicitly include at least one of those features. Additionally, the technical solutions of the various embodiments can be combined with each other, but this must be based on the ability of those skilled in the art to implement them. If the combination of technical solutions is contradictory or impossible to implement, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed by this invention.

[0021] The simulation evaluation method for emergency logistics distribution in urban areas according to a preferred embodiment of the present invention, such as... Figure 1 As shown, the simulation evaluation method for emergency logistics distribution in urban areas includes the following steps: Step S10: Obtain regional data of the target city area, perform simulation modeling of the target city area based on the regional data to obtain the area simulation model, and set the area spatial data, logistics resource parameters, task requirement data and traffic load intensity.

[0022] Specifically, existing technologies cannot effectively quantify and evaluate emergency logistics distribution in urban areas, leading to inaccurate delivery times and low delivery efficiency. Therefore, this invention proposes a simulation evaluation method for emergency logistics distribution in urban areas to overcome the shortcomings of existing technologies. This method is implemented through an emergency logistics distribution efficiency evaluation model. The overall architecture of this model consists of three layers: an input layer, a simulation core layer, and an output layer. The input layer includes spatial data of the area (e.g., road network, location and area of ​​resettlement sites), logistics resource parameters (e.g., fleet size, vehicle carrying capacity, unloading time), task requirement data (e.g., total material demand converted from the population capacity of each resettlement site), and traffic load intensity (e.g., the number of people randomly moving within the area).

[0023] The simulation core layer includes task scheduling, vehicle behavior, and pedestrian behavior. Task scheduling is based on a "demand-driven" principle, monitoring the unfinished deliveries at each resettlement point in real time and dynamically allocating vehicles to the next target point. This demand-driven principle means that the allocation of delivery tasks does not depend on fixed routes or preset sequences, but is dynamically triggered and guided by the real-time material shortage status of each resettlement point. Specifically, this includes three layers of meaning: First, using the unfinished delivery volume as the core judgment criterion, the system maintains a dynamic variable for each resettlement point (demand point)—the unfinished delivery volume, i.e., how much material the resettlement point currently lacks (calculated by subtracting the delivered amount from the pre-set total demand); Second… The first layer prioritizes serving nearby resettlement points with unfinished deliveries. Whenever a delivery vehicle completes unloading and returns to the dispatch point, when the next target needs to be assigned, the system scans all resettlement points with unmet needs, identifies the one with the most unfinished deliveries and closest to the dispatch point, and uses this resettlement point as the target for the vehicle's next trip. The third layer is dynamic updating and real-time response. As delivery progresses, the number of unfinished deliveries at each resettlement point changes continuously. Resettlement points with completed deliveries are removed from the candidate list, while unfinished resettlement points are reordered based on their real-time remaining quantity. Each time a vehicle departs, it receives the most urgently needed target at that moment, ensuring the entire system remains in a demand-driven adaptive state. Vehicle behavior involves actions such as vehicle movement within the road network, slowing down when following other vehicles, slowing down to avoid pedestrians, loading, unloading, and queuing. Pedestrian behavior involves generating randomly moving refugee groups, which act as dynamic obstacles affecting vehicle speed. The output layer is used to output core efficiency indicators (i.e. delivery efficiency indicators, including total delivery time) and process diagnostic indicators (including demand completion rate at each demand point, single delivery time distribution, queue length at unloading points, etc.).

[0024] The specific processing procedure involves acquiring regional data for a target urban area (e.g., an area of ​​approximately 2.5 square kilometers, a planned total refuge capacity of 150,000 people, with two delivery points (East Warehouse and West Warehouse) and 25 resettlement sites (distributed across different communities), a planned deployment of 12 delivery vehicles, each with a carrying capacity equivalent to supplies for 2,000 people, a total of 75 deliveries, an unloading time of 2 minutes per vehicle, and a traffic load intensity of 30% (i.e., 45,000 people moving randomly within the area)). The area... The data includes a plan view of the area, the location of delivery points, information on resettlement sites, road network information, and the location of areas where people are active. The delivery point is a material warehouse or emergency management center near the main entrance of the target city area, serving as the starting point for vehicle loading. The resettlement site information includes outdoor refuge areas (e.g., sports fields, squares, centralized green spaces) and indoor refuge buildings (gymnasiums, canteens, hospital lobbies, etc.). The location of areas where people are active is the location of the entire network and resettlement sites where pedestrians are allowed to move randomly, used to simulate a mixed pedestrian and vehicle environment. An initial model of the area is constructed based on the area plan. The capacity of each demand point is calculated based on its area in the resettlement site information, and the capacity of all demand points is converted into delivery times (e.g., one full truckload delivery for every 2000 people). A road network is constructed based on the road segments and nodes in the road network information to restrict vehicles to designated lanes. Parameters such as road width and speed limits are set based on actual surveying. Then, the locations of the delivery points, resettlement sites, delivery times, road network, and population activity areas are input into the initial area model to obtain the area simulation model. The spatial environment of this area simulation model is constructed as follows: Figure 2 As shown. Next, the target delivery plan is obtained, and based on this plan, area spatial data, logistics resource parameters, task requirement data, and traffic load intensity are set. The traffic load intensity is the percentage of active people randomly moving within the target city area relative to the total planned refuge capacity of the area. This percentage can be freely adjusted (e.g., from 0.01% to 59%). The system automatically generates a corresponding number of pedestrians based on the adjusted percentage, and these pedestrians randomly wander on roads and in open spaces. Pedestrian movement follows simple random walking or obstacle avoidance rules, without actively avoiding vehicles, thus simulating the logistics delivery scenario during refugee resettlement.

[0025] Step S20: Input the area spatial data, the logistics resource parameters, and the task requirement data into the area simulation model. The area simulation model generates a global delivery task list based on the area spatial data, the logistics resource parameters, and the task requirement data.

[0026] Specifically, after obtaining the area simulation model, the corresponding simulation process (i.e., task scheduling) needs to be triggered. Before task scheduling, a corresponding global delivery task list needs to be generated. Specifically, the area spatial data, the logistics resource parameters, and the task requirement data are input into the area simulation model. The area simulation model generates a global delivery task list based on the area spatial data, the logistics resource parameters, and the task requirement data. In this list, all vehicles are located at the dispatch point and are in an idle and ready-to-go state.

[0027] Step S30: Assign tasks according to the global delivery task list to obtain task assignment results. Perform emergency logistics simulation delivery based on the traffic load intensity and the task assignment results to obtain target delivery results. Visualize the quantitative evaluation indicators in the target delivery results to obtain the simulation evaluation results of logistics delivery.

[0028] Specifically, after obtaining the global delivery task list, task scheduling is triggered, such as... Figure 3 As shown, after a task is triggered, task allocation is required. The system calculates the unfinished delivery volume for each demand point in real time. As long as there are unfinished demands at a demand point, the vehicle begins operation. Specifically, the system obtains demand point information based on the global delivery task list and calculates the unfinished delivery volume for each demand point in real time. The real-time calculation process is as follows: First, the total demand for each demand point is set during initialization. Before the simulation begins, the system converts the total number of material deliveries required for each resettlement point into the number of people it can accommodate. This total number of material deliveries is used as the initial total demand for that resettlement point and is recorded to ensure that it remains unchanged throughout the simulation. Second, the completed volume is updated after each unloading. Whenever a delivery vehicle completes unloading (i.e., successfully delivers materials to a resettlement point), the system immediately performs the corresponding operation: the completed delivery volume for this resettlement point is increased by one. At the same time, the system automatically recalculates the unfinished delivery volume for that resettlement point. This process is triggered by an event, rather than scanning continuously every second, ensuring real-time processing without consuming excessive computing resources. Finally, when a new task needs to be assigned, the system reads the latest unfinished volume. When the vehicle returns to the dispatch point and requests the next target, the system will iterate through all the settlement points that have not yet completed all deliveries, read the current unfinished delivery volume of all settlement points, and then select the next target based on the delivery volume.

[0029] The delivery distance to each target demand point in the calculation results is calculated to obtain a distance calculation result. The delivery distances of each target demand point in the distance calculation results are then sorted by size to obtain a distance sorting result. Here, the target demand points are those with incomplete deliveries; the distance sorting result is a sorting result from smallest to largest delivery distance. A target allocation rule is formulated based on the distance sorting result, and tasks are allocated according to the target allocation rule to obtain a task allocation result. The target allocation rule of this invention is single and constant: all vehicles always use the nearest (i.e., the shortest delivery distance) placement point (i.e., the target demand point) to their current location that has not yet completed all delivery tasks as the next delivery target, and this process is repeated until all placement points have completed deliveries.

[0030] Afterwards, vehicle delivery is carried out according to the task allocation results. The vehicle travels from the delivery point to the corresponding demand point according to the shortest path algorithm. During the journey, the vehicle senses the vehicles and pedestrians ahead in real time and dynamically adjusts its speed. That is, vehicle behavior rules and pedestrian behavior rules are formulated according to the traffic load intensity. Emergency logistics simulation delivery is carried out according to the vehicle behavior rules and pedestrian behavior rules. Specifically, when the vehicle starts delivery at an initial speed (e.g., 30 km / h), dynamic entity detection is performed on the driving path according to a preset time step (e.g., 0.5 seconds) to obtain the detection results. If the detection results indicate that there is a target vehicle (i.e., the current path) within a first preset distance (e.g., 50 meters ahead), the vehicle will be considered as a target vehicle. If other vehicles are detected, the vehicle's speed is reduced according to the vehicle behavior rules to obtain a first speed (e.g., 10 km / h), and a safe distance is maintained. When no target vehicle is detected within the first preset distance, the first speed is increased to the initial speed. If the detection result indicates that a pedestrian is within a second preset distance (e.g., 30 meters ahead), the vehicle's speed is reduced according to the pedestrian behavior rules to obtain a second speed (e.g., 5 km / h), and the driving path is adjusted, i.e., detouring. When no pedestrian is detected within the second preset distance, the second speed is increased to the initial speed. The delivery scenario operation process is as follows: Figure 4 and Figure 5 As shown, where, Figure 4 This demonstrates the effect of a distributed delivery model in 30 minutes. Figure 5 The demonstration shows the effect of a centralized delivery model that takes 230 minutes to complete.

[0031] Since this invention does not employ a fixed delivery model, it is necessary to determine the delivery model. Specifically, this involves obtaining the delivery stages of the emergency logistics simulation delivery and determining the delivery model based on these stages. If the delivery stage is the first delivery stage (i.e., the pre-delivery stage), the delivery model is a distributed delivery model. In this model, each vehicle goes to a different demand point, prioritizing the closest or most urgent point. The advantage of choosing a distributed delivery model in the pre-delivery stage is that, since the number of unfulfilled demand points exceeds the number of available vehicles, a distributed delivery model allows vehicles to go to different destinations, thereby improving delivery efficiency. If the delivery stage is the second delivery stage (i.e., the post-delivery stage), the delivery model is a centralized delivery model. In this model, multiple vehicles can be assigned to the same demand point to collaboratively complete the task. The advantage of choosing a centralized delivery model in the post-delivery stage is that, since the number of remaining unfulfilled demand points in the delivery cycle is less than the number of available vehicles, a distributed delivery model allows multiple vehicles to naturally converge towards the same destination, thus improving delivery efficiency.

[0032] Subsequently, when a vehicle arrives at a demand point, the interface resource information (i.e., the unloading interface) of that demand point is obtained. Unloading is processed according to the interface status of the interface resource information, and the delivery time of the vehicle is recorded. Specifically, if the interface status is idle, unloading begins through this interface, with a fixed time (e.g., 2 minutes, which can be adjusted according to actual conditions). After releasing the interface resource, the vehicle leaves. If the interface status is busy, vehicles follow a first-come, first-served approach, entering a waiting queue (queue length is unlimited). When the interface resource is released, the next vehicle is woken up according to the queue order, and the waiting time of each vehicle is recorded for subsequent efficiency analysis. After unloading is completed, the vehicle returns to the dispatch point, updating the completed delivery volume for that demand point. If the cumulative delivery volume of all demand points reaches the total demand, the simulation terminates. A target delivery result is generated based on the task allocation result, and quantitative evaluation indicators for the target delivery result are extracted. These quantitative evaluation indicators include delivery efficiency indicators and process diagnostic indicators. The delivery efficiency indicators are visualized to obtain a delivery completion curve, such as... Figure 6 As shown, the shape of the delivery completion curve can indicate whether the system completes at a constant speed or experiences efficiency degradation later on. Visualizing the process diagnostic indicators yields a delivery time distribution histogram. This histogram includes the single-trip delivery time sequence and the overall distribution of single-trip time samples. The corresponding single-trip delivery time sequence is shown below. Figure 7 As shown, the overall distribution of the corresponding single-trip time samples is as follows: Figure 8 As shown, Figure 8The vertical line in the graph represents the average single-trip time for all samples. Finally, the simulation evaluation results of logistics delivery are obtained based on the delivery completion rate curve and the delivery time distribution histogram.

[0033] Furthermore, after obtaining the simulation evaluation results, the delivery time of each resettlement point in the simulation evaluation results is analyzed to obtain analysis results. If the analysis results indicate that the delivery time of a target resettlement point is greater than the average delivery time (for example, the average queue length at the unloading point of the target resettlement point reaches 3.2 vehicles, and its single delivery time is much higher than the average), then the target resettlement point is determined to be an abnormal resettlement point, and anomaly analysis is performed on the abnormal resettlement point to obtain anomaly analysis results (for example, this target resettlement point is located deep in the area, with narrow roads and dense population, and there is only one unloading interface). The anomaly analysis results are optimized to obtain an optimized solution (for example, adding a temporary unloading port near the target resettlement point (i.e., making it a dual unloading port) and widening its connecting road). The optimized solution is then input into the area simulation model to verify its effectiveness. If the optimized solution is effective, it is used. For example, the total time of this optimized solution is reduced from 328 minutes to 246 minutes, improving efficiency by 25%, thus verifying its effectiveness. If the effect of this optimized solution is not significant, a new optimized solution is generated.

[0034] Furthermore, the present invention can also input different delivery schemes (e.g., increasing the number of vehicles, adding unloading points, adjusting the road network structure) into the area simulation model to run the simulation within the same area. Then, the total delivery time and process indicators are compared to select the optimal delivery scheme.

[0035] This invention incorporates the deceleration behavior of vehicles and pedestrians into logistics delivery simulation at the area scale, making the delivery results more realistic. By running simulations under different traffic load intensities, it obtains the curve of total delivery time changing with population density, thereby identifying the critical point (i.e., the inflection point of system resilience) from smooth to congested. Furthermore, by using process data such as queue size at unloading points and single delivery time, it accurately identifies whether road network congestion or insufficient unloading capacity is limiting overall efficiency, providing a clear direction for optimization. It also supports scheme comparison and optimization, thereby selecting the optimal delivery scheme.

[0036] The technical effects of this invention are as follows: (1) A dynamic interaction rule for a mixed pedestrian and vehicle environment is constructed: existing technologies either only consider pure traffic flow or ignore the significant interference of pedestrians to vehicles. This invention allows vehicles to perceive other vehicles and pedestrians in front in real time during driving, automatically decelerate, and restore speed when road conditions permit. This rule truly reflects the typical scenario of a large number of people moving in the area during the resettlement phase and vehicles being forced to pass at low speed, and is a key factor causing the decline in delivery efficiency. (2) A demand-driven, two-stage adaptive delivery scheduling rule was designed: Existing technologies often use fixed routes or simple polling, which cannot be flexibly adjusted according to the progress of the task. This invention allows the system to monitor the unfinished delivery volume of each resettlement point in real time and dynamically switch the delivery mode: In the early stage (distributed delivery mode): when there are many remaining task points and relatively few vehicles, each vehicle goes to different target points to achieve wide coverage; Later stage (centralized delivery mode): When there are few remaining task points and a relatively large number of vehicles, multiple vehicles work together to deliver to the same target point; This adaptive scheduling mechanism can more realistically simulate the natural evolution of emergency logistics from a blanket approach to a targeted approach, thus improving delivery efficiency; (3) Introducing unloading interface queuing: In real-world scenarios, the unloading ports at each resettlement point are usually limited (e.g., only one channel or platform). When multiple vehicles arrive at the same time, they will inevitably queue. This invention models the unloading interface at each resettlement point as a queuing system with a single service counter, first-come-first-served, and fixed service time, and records the waiting time of each vehicle. This enables the model to accurately distinguish between the slowness caused by road network congestion and the problem caused by insufficient unloading capacity, thereby locating the real bottleneck and optimizing it in a timely manner. (4) A stress test variable for continuously adjustable traffic load intensity is proposed. This invention defines traffic load as the percentage of the number of refugees moving randomly within the area to the total planned capacity, and allows users to adjust it within a wide range (e.g., 0.01%~59%). By gradually increasing the population density, the changes in the total logistics delivery time can be observed, and the critical point (i.e., resilience inflection point) of the system from smooth to congested to paralyzed can be identified. This stress test method can quantitatively evaluate the resilience of the logistics system in the area.

[0037] Furthermore, such as Figure 9 As shown, based on the above-mentioned simulation evaluation method for emergency logistics distribution in urban areas, the present invention also provides a simulation evaluation system for emergency logistics distribution in urban areas, wherein the simulation evaluation system for emergency logistics distribution in urban areas includes: The simulation model construction module 51 is used to acquire regional data of the target city area, perform simulation modeling of the target city area based on the regional data, obtain the area simulation model, and set the area spatial data, logistics resource parameters, task requirement data and traffic load intensity. The delivery task generation module 52 is used to input the area spatial data, the logistics resource parameters and the task requirement data into the area simulation model, and the area simulation model generates a global delivery task list based on the area spatial data, the logistics resource parameters and the task requirement data; The task simulation evaluation module 53 is used to allocate tasks according to the global delivery task list, obtain task allocation results, perform emergency logistics simulation delivery according to the traffic load intensity and the task allocation results, obtain target delivery results, and visualize the quantitative evaluation indicators in the target delivery results to obtain the simulation evaluation results of logistics delivery.

[0038] Furthermore, such as Figure 10 As shown, based on the above-mentioned simulation evaluation method for emergency logistics distribution in urban areas, the present invention also provides a terminal, which includes a processor 10, a memory 20 and a display 30. Figure 10 Only some of the terminal components are shown; however, it should be understood that it is not required to implement all of the components shown, and more or fewer components may be implemented instead.

[0039] In some embodiments, the memory 20 may be an internal storage unit of the terminal, such as a hard disk or memory. In other embodiments, the memory 20 may be an external storage device of the terminal, such as a plug-in hard disk, smart media card (SMC), secure digital card (SD), flash card, etc. Further, the memory 20 may include both internal and external storage devices. The memory 20 is used to store application software and various types of data installed on the terminal, such as the program code installed on the terminal. The memory 20 can also be used to temporarily store data that has been output or will be output. In one embodiment, the memory 20 stores a simulation evaluation program 40 for urban area emergency logistics distribution, which can be executed by the processor 10 to implement the simulation evaluation method for urban area emergency logistics distribution in this application.

[0040] In some embodiments, the processor 10 may be a central processing unit (CPU), a microprocessor, or other data processing chip, used to run program code stored in the memory 20 or process data, such as executing the simulation evaluation method for emergency logistics distribution in the urban area.

[0041] In some embodiments, the display 30 may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an OLED (Organic Light-Emitting Diode) touchscreen. The display 30 is used to display information on the terminal and to display a visual user interface.

[0042] In one embodiment, when the processor 10 executes the simulation evaluation program 40 for emergency logistics distribution in urban areas stored in the memory 20, the following steps are performed: Obtain regional data of the target city area, perform simulation modeling of the target city area based on the regional data to obtain the area simulation model, and set the area spatial data, logistics resource parameters, task requirement data and traffic load intensity; The area spatial data, the logistics resource parameters, and the task requirement data are input into the area simulation model, and the area simulation model generates a global delivery task list based on the area spatial data, logistics resource parameters, and task requirement data; Tasks are allocated based on the global delivery task list to obtain task allocation results. Emergency logistics simulation delivery is performed based on the traffic load intensity and the task allocation results to obtain target delivery results. The quantitative evaluation indicators in the target delivery results are then visualized to obtain the simulation evaluation results of logistics delivery.

[0043] The process of acquiring regional data for the target city area, performing simulation modeling of the target city area based on the regional data to obtain a simulation model of the area, and setting spatial data, logistics resource parameters, task requirement data, and traffic load intensity for the area specifically includes: Obtain regional data for the target city area, including a plan view of the area, the location of delivery points, resettlement site information, road network information, and the location of population activity areas; Construct an initial model of the area based on the area plan, calculate the number of people that each demand point can accommodate based on the area of ​​each demand point in the resettlement point information, and convert the number of people that can be accommodated at all demand points into the number of material delivery times; Construct a corridor road network based on the road segments and nodes of the road network information, and input the location of the delivery point, the information of the resettlement point, the number of material deliveries, the corridor road network, and the location of the population activity area into the initial model of the area to obtain the area simulation model; Obtain the target delivery plan, and set the area spatial data, logistics resource parameters, task requirement data and traffic load intensity according to the target delivery plan.

[0044] Specifically, the step of allocating tasks based on the global delivery task list to obtain task allocation results includes: Based on the global delivery task list, demand point information is obtained, and the unfinished delivery volume of each demand point in the demand point information is calculated in real time to obtain the calculation result. The delivery distance to each target demand point in the calculation results is calculated to obtain the distance calculation result. The delivery distances of each target demand point in the distance calculation results are sorted by size to obtain the distance sorting result. The target demand point is the demand point that has not been delivered. Based on the distance sorting results, target allocation rules are formulated, and tasks are allocated according to the target allocation rules to obtain task allocation results.

[0045] Specifically, the step of conducting emergency logistics simulation delivery based on the traffic load intensity and the task allocation results to obtain target delivery results, and visualizing the quantitative evaluation indicators in the target delivery results to obtain the simulation evaluation results of logistics delivery, includes: Based on the traffic load intensity, vehicle behavior rules and pedestrian behavior rules are formulated, and emergency logistics simulation delivery is carried out based on the vehicle behavior rules and pedestrian behavior rules; When a vehicle is detected to have arrived at the target demand point, the interface resource information of the target demand point is obtained, the unloading process is performed according to the interface status of the interface resource information, and the delivery time of the vehicle is recorded. When all demand points in the task allocation result have been delivered, a target delivery result is generated based on the task allocation result, and a quantitative evaluation index of the target delivery result is extracted, wherein the quantitative evaluation index includes delivery efficiency index and process diagnosis index. The delivery efficiency index is visualized to obtain the delivery completion curve, and the process diagnostic index is visualized to obtain the delivery time distribution histogram. The simulation evaluation results of logistics delivery are obtained based on the delivery completion curve and the delivery time distribution histogram.

[0046] Specifically, the emergency logistics simulation delivery based on the vehicle behavior rules and the pedestrian behavior rules includes: When the vehicle starts to deliver goods at the initial speed, dynamic entity detection is performed on the driving path according to the preset time step to obtain the detection results; If the detection result indicates that a target vehicle exists within a first preset distance, the vehicle's speed is reduced according to the vehicle behavior rules to obtain a first speed. When it is detected that there is no target vehicle within the first preset distance, the first speed is increased to the initial speed; If the detection result indicates that a pedestrian exists within a second preset distance, the vehicle's speed is reduced according to the pedestrian behavior rules to obtain a second speed, and the driving path is adjusted accordingly. When no pedestrian is detected within the second preset distance, the second speed is increased to the initial speed.

[0047] The process of formulating vehicle and pedestrian behavior rules based on the traffic load intensity, and conducting emergency logistics simulation delivery based on the vehicle and pedestrian behavior rules, further includes: Obtain the delivery stages of the emergency logistics simulation delivery, and determine the delivery mode based on the delivery stages; If the delivery stage is the first delivery stage, then the delivery mode is a distributed delivery mode; If the delivery stage is the second delivery stage, then the delivery mode is a centralized delivery mode.

[0048] The step of visualizing the quantitative evaluation indicators in the target delivery results to obtain the simulation evaluation results of logistics delivery further includes: The delivery time for each resettlement site in the simulation evaluation results is analyzed and processed to obtain the analysis results; If the analysis results indicate that the delivery time to a target resettlement point is greater than the average delivery time, then the target resettlement point is determined to be an abnormal resettlement point, and an anomaly analysis is performed on the abnormal resettlement point to obtain the anomaly analysis results. The anomaly analysis results are optimized to obtain an optimization scheme, which is then input into the area simulation model.

[0049] The present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a simulation evaluation program for emergency logistics distribution in urban areas, and the simulation evaluation program for emergency logistics distribution in urban areas, when executed by a processor, implements the steps of the simulation evaluation method for emergency logistics distribution in urban areas as described above.

[0050] In summary, this invention provides a simulation evaluation method, system, terminal, and storage medium for emergency logistics distribution in urban areas. The method includes: acquiring regional data of a target urban area; performing simulation modeling of the target urban area based on the regional data to obtain an area simulation model; and setting area spatial data, logistics resource parameters, task demand data, and traffic load intensity; inputting the area spatial data, logistics resource parameters, and task demand data into the area simulation model; generating a global distribution task list based on the area spatial data, logistics resource parameters, and task demand data; allocating tasks according to the global distribution task list to obtain task allocation results; performing emergency logistics simulation distribution based on the traffic load intensity and the task allocation results to obtain target distribution results; and visualizing the quantitative evaluation indicators in the target distribution results to obtain the simulation evaluation results of logistics distribution. This invention effectively quantifies and evaluates emergency logistics distribution in urban areas through dynamic interaction of mixed pedestrian and vehicle traffic, multi-stage adaptive distribution scheduling, and unloading point queuing services, thereby improving distribution efficiency.

[0051] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0052] Of course, those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware (such as a processor, controller, etc.). The program can be stored in a computer-readable storage medium, and when executed, it can include the processes described in the above method embodiments. The computer-readable storage medium can be a memory, magnetic disk, optical disk, etc.

[0053] It should be understood that the application of the present invention is not limited to the examples above. Those skilled in the art can make improvements or modifications based on the above description, and all such improvements and modifications should fall within the protection scope of the appended claims.

Claims

1. A simulation evaluation method for emergency logistics distribution in urban areas, characterized in that, The simulation evaluation method for emergency logistics distribution in urban areas includes: Obtain regional data of the target city area, perform simulation modeling of the target city area based on the regional data to obtain the area simulation model, and set the area spatial data, logistics resource parameters, task requirement data and traffic load intensity; The area spatial data, logistics resource parameters, and task requirement data are input into the area simulation model. The area simulation model generates a global delivery task list based on the area spatial data, logistics resource parameters, and task requirement data, and allocates tasks based on the global delivery task list to obtain the task allocation result. Based on the traffic load intensity, vehicle behavior rules and pedestrian behavior rules are formulated. Emergency logistics simulation delivery is carried out based on the vehicle behavior rules and pedestrian behavior rules. When a vehicle is detected to have arrived at the target demand point, the interface resource information of the target demand point is obtained. Unloading is carried out based on the interface status of the interface resource information. When all target demand points in the task allocation result have completed delivery, the target delivery result is generated based on the task allocation result. The quantitative evaluation indicators in the target delivery result are visualized to obtain the simulation evaluation result of logistics delivery. The emergency logistics simulation delivery based on the vehicle behavior rules and the pedestrian behavior rules specifically includes: When the vehicle starts to deliver goods at the initial speed, dynamic entity detection is performed on the driving path according to the preset time step to obtain the detection results; If the detection result indicates that a target vehicle exists within a first preset distance, the vehicle's speed is reduced according to the vehicle behavior rules to obtain a first speed. When it is detected that there is no target vehicle within the first preset distance, the first speed is increased to the initial speed; If the detection result indicates that a pedestrian exists within a second preset distance, the vehicle's speed is reduced according to the pedestrian behavior rules to obtain a second speed, and the driving path is adjusted accordingly. When no pedestrian is detected within the second preset distance, the second speed is increased to the initial speed; The process of formulating vehicle and pedestrian behavior rules based on the traffic load intensity, and conducting emergency logistics simulation delivery based on the vehicle and pedestrian behavior rules, further includes: Obtain the delivery stages of the emergency logistics simulation delivery, and determine the delivery mode based on the delivery stages; If the delivery stage is the first delivery stage, then the delivery mode is a distributed delivery mode; If the delivery stage is the second delivery stage, then the delivery mode is a centralized delivery mode; The step of visualizing the quantitative evaluation indicators in the target delivery results to obtain the simulation evaluation results of logistics delivery further includes: The delivery time for each resettlement site in the simulation evaluation results is analyzed and processed to obtain the analysis results; If the analysis results indicate that the delivery time to a target resettlement point is greater than the average delivery time, then the target resettlement point is determined to be an abnormal resettlement point, and an anomaly analysis is performed on the abnormal resettlement point to obtain the anomaly analysis results. The anomaly analysis results are optimized to obtain an optimization scheme, which is then input into the area simulation model.

2. The simulation evaluation method for emergency logistics distribution in urban areas according to claim 1, characterized in that, The process involves acquiring regional data for the target city area, performing simulation modeling of the target city area based on the regional data to obtain a regional simulation model, and setting regional spatial data, logistics resource parameters, task requirement data, and traffic load intensity. Specifically, this includes: Obtain regional data for the target city area, including a plan view of the area, the location of delivery points, resettlement site information, road network information, and the location of population activity areas; Construct an initial model of the area based on the area plan, calculate the number of people that each demand point can accommodate based on the area of ​​each demand point in the resettlement point information, and convert the number of people that can be accommodated at all demand points into the number of material delivery times; Construct a corridor road network based on the road segments and nodes of the road network information, and input the location of the delivery point, the information of the resettlement point, the number of material deliveries, the corridor road network, and the location of the population activity area into the initial model of the area to obtain the area simulation model; Obtain the target delivery plan, and set the area spatial data, logistics resource parameters, task requirement data and traffic load intensity according to the target delivery plan.

3. The simulation evaluation method for emergency logistics distribution in urban areas according to claim 1, characterized in that, The process of allocating tasks based on the global delivery task list to obtain task allocation results specifically includes: Based on the global delivery task list, demand point information is obtained, and the unfinished delivery volume of each demand point in the demand point information is calculated in real time to obtain the calculation result. The delivery distance to each target demand point in the calculation results is calculated to obtain the distance calculation result. The delivery distances of each target demand point in the distance calculation results are sorted by size to obtain the distance sorting result. The target demand point is the demand point that has not been delivered. Based on the distance sorting results, target allocation rules are formulated, and tasks are allocated according to the target allocation rules to obtain task allocation results.

4. The simulation evaluation method for emergency logistics distribution in urban areas according to claim 1, characterized in that, The step of visualizing the quantitative evaluation indicators in the target delivery results to obtain the simulation evaluation results of logistics delivery specifically includes: Extract quantitative evaluation indicators for the target delivery results, wherein the quantitative evaluation indicators include delivery efficiency indicators and process diagnostic indicators; The delivery efficiency index is visualized to obtain the delivery completion curve, and the process diagnostic index is visualized to obtain the delivery time distribution histogram. The simulation evaluation results of logistics delivery are obtained based on the delivery completion curve and the delivery time distribution histogram.

5. A simulation evaluation system for emergency logistics distribution in urban areas, characterized in that, The simulation evaluation system for urban area emergency logistics distribution is used to implement the simulation evaluation method for urban area emergency logistics distribution as described in any one of claims 1-4. The simulation evaluation system for urban area emergency logistics distribution includes: The simulation model construction module is used to acquire regional data of the target city area, perform simulation modeling of the target city area based on the regional data, obtain the area simulation model, and set the area spatial data, logistics resource parameters, task requirement data and traffic load intensity. The delivery task generation module is used to input the area spatial data, the logistics resource parameters and the task requirement data into the area simulation model, and the area simulation model generates a global delivery task list based on the area spatial data, the logistics resource parameters and the task requirement data; The task simulation evaluation module is used to allocate tasks according to the global delivery task list, obtain task allocation results, perform emergency logistics simulation delivery based on the traffic load intensity and the task allocation results, obtain target delivery results, and visualize the quantitative evaluation indicators in the target delivery results to obtain the simulation evaluation results of logistics delivery.

6. A terminal, characterized in that, The terminal includes a memory, a processor, and a program stored in the memory and executable on the processor. When the program is executed by the processor, it implements the steps of the simulation evaluation method for emergency logistics distribution in urban areas as described in any one of claims 1-4.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, which stores a simulation evaluation program for emergency logistics distribution in urban areas. When the simulation evaluation program for emergency logistics distribution in urban areas is executed by a processor, it implements the steps of the simulation evaluation method for emergency logistics distribution in urban areas as described in any one of claims 1-4.

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