Intelligent logistics scheduling method based on Beidou positioning

Abstract

The invention discloses an intelligent logistics scheduling method based on Beidou positioning, and the method comprises the steps: obtaining the real-time position information of a vehicle, and constructing a traffic state matrix in combination with road congestion data; judging a weather influence area based on the weather change trend, and obtaining a weather risk area in combination with the traffic state matrix; obtaining inventory state data, and generating a resource demand priority list in combination with the weather risk area; adopting a multi-objective optimization algorithm, taking the resource demand priority list, the traffic state matrix and the weather risk area as input, obtaining an optimal path set, and generating a preliminary scheduling scheme; if a dynamic task insertion request is received, the task insertion feasibility is judged, the resource demand priority list is adjusted, the multi-objective optimization algorithm is operated again, and a corrected scheduling scheme is generated. Through multi-dimensional data fusion and intelligent algorithm optimization, the real-time performance, flexibility and reliability of logistics scheduling are improved, and an effective solution is provided for resource allocation in a complex environment.

Description

Technical Field

[0001] The present invention belongs to the technical field of logistics scheduling, and particularly relates to an intelligent logistics scheduling method based on Beidou positioning. Background Art

[0002] In intelligent logistics scheduling methods, the core of the technical problem lies in how to achieve efficient multi-objective collaborative scheduling in a dynamic and complex logistics environment.

[0003] Specifically, the logistics scheduling system needs to simultaneously process various dynamic data such as traffic conditions, weather information, and warehouse status. These data have highly uncertain and real-time changing characteristics. For example, traffic congestion, sudden weather changes, or temporary shortages of warehouse resources may all affect the execution efficiency of logistics tasks. In addition, the system also needs to support dynamic task insertion and resource reallocation, which means that the original scheduling plan may be frequently interrupted and adjusted, making the scheduling process complex and difficult to optimize.

[0004] In view of the above problems, there is an urgent need to propose an intelligent logistics scheduling method based on Beidou positioning. Summary of the Invention

[0005] To solve the above technical problems, the present invention proposes an intelligent logistics scheduling method based on Beidou positioning to solve the problems existing in the above prior art.

[0006] To achieve the above object, the present invention provides an intelligent logistics scheduling method based on Beidou positioning, including the following steps:

[0007] Obtain the real-time position information of vehicles based on the Beidou positioning system, and combine it with road congestion data to construct a traffic state matrix;

[0008] Based on the weather change trend, judge the weather influence area, and combine it with the traffic state matrix to obtain the weather risk area;

[0009] Obtain the inventory status data, extract the available resource information, and combine it with the weather risk area to generate a resource demand priority list;

[0010] Adopt a multi-objective optimization algorithm, use the resource demand priority list, traffic state matrix, and weather risk area as inputs, obtain the optimal path set, and generate a preliminary scheduling plan;

[0011] If a dynamic task insertion request is received, judge the feasibility of task insertion, adjust the resource demand priority list, and re-run the multi-objective optimization algorithm to generate a revised scheduling plan.

[0012] Optionally, the process of obtaining the real-time position information of vehicles based on the Beidou positioning system and combining it with road congestion data to construct a traffic state matrix includes:

[0013] Based on traffic condition sensors to collect road congestion data, combine the real-time vehicle position information with the road congestion data for processing to obtain traffic condition data; based on the traffic condition data, use a clustering algorithm to classify the traffic state to obtain traffic state data; if the congestion degree in the traffic state data exceeds a preset threshold, then use a path planning algorithm to optimize the vehicle path to obtain optimized path data, based on the optimized path data, update the real-time vehicle position information, regenerate the traffic state data, and further generate the final traffic state matrix.

[0014] Optionally, the process of judging the weather impact area based on the weather change trend and combining with the traffic state matrix to obtain the weather risk area includes:

[0015] Use a clustering algorithm to classify the traffic state in the weather impact area, judge the risk level of each area according to the traffic state classification result, and determine the weather risk area.

[0016] Optionally, the process of obtaining inventory status data, extracting available resource information, and combining with the weather risk area to generate a resource demand priority list includes:

[0017] Obtain inventory status data, extract the inventory quantity, resource quantity, and status value, and generate an inventory data table; perform clustering analysis on the inventory data table and the weather risk area distribution map to obtain the material distribution characteristics in different weather risk areas; based on the material distribution characteristics, use an association rule algorithm to judge the resource demand quantity and priority; generate a resource demand priority list according to the resource demand quantity and priority.

[0018] Optionally, the process of using a multi-objective optimization algorithm with the resource demand priority list, traffic state matrix, and weather risk area as inputs to obtain an optimal path set and generate a preliminary scheduling plan includes:

[0019] Obtain the resource quantity and priority data in the resource demand priority list, combine with the traffic volume information in the traffic state matrix to judge the initial path of resource distribution; adjust the initial path according to the weather value and risk degree data in the weather risk area to obtain a preliminary path set; use a multi-objective optimization algorithm to optimize the path set and scheduling table data in the preliminary path set to generate an optimized path set; combine with the area classification value in the area distribution map to judge the area coverage of the optimized path set and determine the preliminary scheduling plan.

[0020] Optionally, if a dynamic task insertion request is received, the process of judging the feasibility of task insertion and adjusting the resource demand priority list includes:

[0021] Adopt a task parsing algorithm to extract task attribute information from dynamic task insertion requests, and obtain task urgency and resource requirement data; combine the resource allocation status and path planning data in the current preliminary scheduling plan to judge the feasibility of task insertion; if task insertion is feasible, adopt a priority adjustment algorithm to recalculate the resource allocation priority, and then adjust the resource requirement priority list.

[0022] Optionally, after generating the corrected scheduling plan, it further includes:

[0023] Compare the corrected scheduling plan with the real-time vehicle position information through the collaborative management platform to judge whether there is a risk of deviation. If there is a deviation, trigger the Beidou short message warning mechanism;

[0024] In areas without network, use the Beidou short message function to receive vehicle status information, combine it with the corrected scheduling plan, generate an emergency path plan, and achieve the continuity of task execution.

[0025] The present invention also provides a computer device, including a memory, a processor, and a computer program stored on the memory. The processor executes the computer program to implement the steps of the method.

[0026] The present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements the steps of the method.

[0027] The present invention also provides a computer program product, including a computer program. When the computer program is executed by a processor, it implements the steps of the method.

[0028] Compared with the prior art, the present invention has the following advantages and technical effects:

[0029] The present invention discloses an intelligent logistics scheduling method based on Beidou positioning. The method combines real-time vehicle position, road congestion conditions, and weather prediction information to generate a dynamic traffic state matrix and identify potential risk areas. Generate a resource requirement priority list according to the inventory data of the warehouse management system. Adopt a multi-objective optimization algorithm, comprehensively consider the above factors, calculate the optimal path, and generate a scheduling plan. The present invention can flexibly respond to dynamic task insertion and ensure scheduling efficiency by re-optimizing the path. In addition, the system also has a real-time monitoring function, which can detect the risk of deviation in time and trigger an alarm. In areas without network, use the Beidou short message function to implement emergency path planning to ensure continuous task execution. Through multi-dimensional data fusion and intelligent algorithm optimization, the present invention significantly improves the real-time performance, flexibility, and reliability of logistics scheduling, and provides an effective solution for resource allocation in complex environments. Description of the Drawings

[0030] The accompanying drawings, which form a part of this application, are used to provide a further understanding of this application. The schematic embodiments and descriptions thereof of this application are used to explain this application and do not constitute an improper limitation to this application. In the drawings:

[0031] Figure 1 It is a flowchart of the method according to an embodiment of the present invention. Detailed implementation manners

[0032] It should be noted that, without conflict, the embodiments in this application and the features in the embodiments may be combined with each other. The following will refer to the accompanying drawings and combine with embodiments to detail this application.

[0033] It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that here.

[0034] Embodiment 1

[0035] As Figure 1 shown, in this embodiment, an intelligent logistics scheduling method based on Beidou positioning is provided, including the following steps:

[0036] Obtain the real-time position information of the vehicle based on the Beidou positioning system, and combine the road congestion data to construct a traffic state matrix;

[0037] Based on the weather change trend, judge the weather impact area, and combine the traffic state matrix to obtain the weather risk area;

[0038] Obtain the inventory status data, extract the available resource information, and combine the weather risk area to generate a resource demand priority list;

[0039] Adopt a multi-objective optimization algorithm, use the resource demand priority list, traffic state matrix and weather risk area as inputs, obtain the optimal path set, and generate a preliminary scheduling plan;

[0040] If a dynamic task insertion request is received, judge the feasibility of task insertion, adjust the resource demand priority list, and re-run the multi-objective optimization algorithm to generate a revised scheduling plan.

[0041] As a specific implementation manner, it includes the following steps:

[0042] S101. The process of obtaining the real-time position information of the vehicle provided by the Beidou positioning system and combining the road congestion data collected by the traffic condition sensor to generate the current traffic state matrix includes:

[0043] Obtain the real-time vehicle position information provided by the Beidou positioning system to get vehicle position data. Based on the traffic condition sensors to collect road congestion data, combine and process the real-time vehicle position information with the road congestion data to obtain traffic condition data; based on the traffic condition data, use the clustering algorithm to classify the traffic state to obtain traffic state data; if the congestion degree in the traffic state data exceeds the preset threshold, then use the path planning algorithm to optimize the vehicle path to obtain optimized path data, based on the optimized path data, update the real-time vehicle position information, regenerate the traffic state data, and then generate the final traffic state matrix.

[0044] Implementable, the Beidou positioning system obtains real-time vehicle position data through satellite navigation and positioning, including information such as longitude and latitude coordinates, driving speed, and driving direction. For example, the real-time position of a certain bus on the Tsinghua Road section is 40 degrees 10 minutes north latitude and 116 degrees 20 minutes east longitude, the driving speed is 30 kilometers per hour, and the driving direction is due north. The road congestion data mainly comes from various sensors deployed on the road, including equipment such as loop detectors and cameras. These devices can count information such as traffic flow, vehicle speed, and vehicle density. For example, through sensors, it is detected that the average vehicle speed during the morning rush hour is 15 kilometers per hour and the vehicle density is 80 vehicles per kilometer, from which it can be determined that this road section is in a relatively congested state. Combining the vehicle position data with the congestion degree data can obtain more comprehensive traffic condition data. For example, in the Zhongguancun Road section, by combining the vehicle distribution shown by the Beidou positioning and the traffic flow density collected by the sensors, it can be determined that there is local congestion in this road section and the vehicle passing speed has decreased significantly. When using the clustering algorithm to classify the traffic state, the traffic condition can be divided into four levels: unobstructed, slightly congested, moderately congested, and severely congested. For example, in the Xierqi Bridge Road section, according to the data of the average vehicle speed of 20 kilometers per hour and the vehicle density of 70 vehicles per kilometer, it can be classified as a moderately congested state. The state matrix generation model can integrate the traffic state data of each road section into a matrix form. If congestion occurs in multiple road sections of a certain main road and the congestion degree exceeds the preset threshold of 75%, path optimization needs to be started. The path planning algorithm will plan the optimal driving route for the vehicle according to the real-time traffic condition. For example, when it is found that the College Road is congested, it can suggest that the vehicle take the Zhichun Road instead. Although the distance is slightly longer, the travel time is shorter. The system will update the optimized driving route in real time and continuously monitor the traffic conditions of the new route. After the optimized path is determined, the vehicle drives along the new route and its position data is also updated accordingly. The system continues to collect and analyze the real-time traffic conditions of each road section, dynamically adjusts the state matrix, and ensures that the driving route is always in the optimal state. Through this continuous optimization process, traffic congestion can be effectively alleviated and the road traffic efficiency can be improved.

[0045] S102. The process of determining the potentially affected area range in the future period based on the weather change trend output by the weather prediction model and combining with the traffic state matrix to determine the potential risk area includes:

[0046] Obtain the weather change trend data output by the weather prediction model and extract the weather change characteristics in the future period. According to the weather change characteristics, use the area division algorithm to determine the potentially affected area range. Combine the traffic state data in the traffic state matrix and use the clustering algorithm to classify the traffic states of the area range. According to the traffic state classification results, judge the risk levels of each area and determine the potential risk area. Use the path planning algorithm to optimize the vehicle paths in the potential risk area and generate the optimized path data. According to the optimized path data, update the traffic state data in the traffic state matrix. Through iterative calculation, dynamically adjust the range and risk level of the potential risk area.

[0047] Implementable. The weather prediction model uses multi-dimensional information such as meteorological station data, satellite cloud images, and atmospheric circulation data to predict meteorological elements such as precipitation, temperature change, wind direction, and wind speed in the future period. For example, a certain city predicts that there will be heavy rainfall in the next four hours, with the precipitation reaching 30 millimeters per hour, and there will be strong winds of force 8 at the same time. These weather change characteristics will have a significant impact on traffic operation. The area division algorithm is based on the weather change characteristics and combines geographical information and road network characteristics to determine the affected area range. For example, in the case of predicting heavy rainfall, the system will focus on low-lying areas, overpass areas, and waterlogging-prone sections and divide the key monitoring areas. Through the analysis of historical data, it is found that the overpass group in the southern area of the city is prone to waterlogging when the rainfall exceeds 20 millimeters. The traffic state classification uses the clustering algorithm to classify the road sections in the area according to indicators such as traffic flow, average vehicle speed, and congestion degree. For example, the road section states are divided into four categories: unobstructed, slightly congested, moderately congested, and severely congested. The average vehicle speed of a certain main road drops from the original 50 kilometers per hour to 30 kilometers per hour in rainy weather, and the congestion degree is significantly improved. The risk level judgment comprehensively considers the weather conditions, traffic states, and geographical characteristics. For example, in the case of heavy rainfall, the waterlogging depth in the low-lying area increases by 10 centimeters per hour. When the waterlogging depth exceeds 20 centimeters, the area is determined to be a high risk level. The system will focus on monitoring the road network around the high-risk area.

[0048] Furthermore, the path optimization algorithm dynamically adjusts the navigation plan according to the risk level. Assuming that the system detects that the water accumulation in a certain overpass area in the eastern part of the city has reached 15 centimeters and is still rising, it will plan alternative routes for vehicles passing through the area in advance. By calculating the travel time and safety factor of the detour plan, the optimal alternative route is selected. The traffic status data is updated to reflect the optimized road conditions. When a large number of vehicles follow the optimized route, the traffic volume on the originally congested main road is reduced by 30%, while the traffic volume on the alternative route is increased but still remains within a reasonable range. The system continuously monitors the actual traffic conditions of each section of the road and adjusts the traffic flow allocation plan in time. Dynamic iterative calculation continuously evaluates and updates the risk area. As the rainfall intensity changes and the water accumulation situation evolves, the system adjusts the risk level and control measures in real time. When the rainfall weakens and the water accumulation subsides, the traffic control is gradually lifted and the normal traffic order is restored. Through the dynamic response mechanism, the safety and efficiency of the transportation system in bad weather are ensured.

[0049] S103. The process of extracting available resource information based on the inventory status data fed back by the warehouse management system and generating a resource demand priority list in combination with the risk area distribution includes:

[0050] Obtain the inventory status data in the warehouse management system, extract the inventory quantity, resource quantity and status value, and generate an inventory data table. According to the inventory data table and the weather risk area distribution map, use the association rule algorithm to determine the resource demand and priority; based on the resource demand and priority, generate a resource demand priority list. Update the resource allocation data table in the warehouse management system through the resource demand priority list. Use the path optimization algorithm, combined with the resource allocation data table and the risk area distribution map, to optimize the resource distribution path. According to the optimized distribution path, dynamically adjust the inventory status data in the warehouse management system.

[0051] Implementable. The inventory status data collected by the warehouse management system includes information such as the inventory quantity of materials, storage location resources, and status values. For example, a certain warehouse center stores categories such as medical supplies, daily necessities, and rescue equipment, and each category of materials has specific inventory quantities, storage location numbers, and status identifiers. Organizing these data into an inventory data table can intuitively reflect the storage situation of various materials. When performing clustering analysis based on the inventory data table and the weather risk area distribution map, a density-based clustering method can be used. For example, if the warehouse area is divided into three levels of high, medium, and low according to the risk level, and the distribution density of the material storage locations is also considered, the distribution characteristics of materials in different risk level areas can be obtained. Through clustering analysis, it is found that important emergency materials are often concentrated in high-risk areas. The association rule algorithm can effectively judge the resource demand pattern. For example, by analyzing historical data, it is found that when a flood disaster occurs, the demand for drinking water and life-saving equipment in a certain area will increase significantly. By mining such association relationships, the material demand trends in different types of disaster situations can be predicted, so as to reasonably determine the priority of resource allocation. The generation of the resource demand priority list needs to comprehensively consider factors such as risk level, material importance, and timeliness. For example, for medical supplies in high-risk areas, due to their high rescue value and strong timeliness requirements, a higher distribution priority should be given. Dynamically adjust the resource allocation plan according to the priority list to ensure that important materials are delivered first. Optimizing the delivery route requires weighing multiple factors, including transportation distance, road traffic conditions, delivery timeliness, etc. For example, in the case of urban waterlogging, it is necessary to avoid waterlogged sections and choose a detour route. Through the route optimization algorithm, the most reasonable delivery route under the current road conditions can be found, which not only ensures the delivery efficiency but also reduces the transportation risk.

[0052] Furthermore, the dynamic adjustment mechanism enables the system to respond flexibly according to the actual situation. When the materials in a certain warehouse area are consumed too quickly, the system automatically issues a replenishment warning. During the replenishment process, adjacent warehouse areas can complement each other through material transfer to maintain the overall inventory balance. Through this dynamic adjustment method, not only the timely supply of emergency materials is ensured, but also the utilization efficiency of warehouse resources is improved. The whole process forms a closed loop, and each link supports each other to jointly ensure the scientific scheduling and efficient delivery of emergency materials.

[0053] S104. The process of using a multi-objective optimization algorithm, taking the resource demand priority list, traffic state matrix, and weather risk area as inputs, calculating the optimal path set, and generating a preliminary scheduling plan includes:

[0054] Obtain the resource quantity and priority data in the resource demand priority list, and combine the traffic volume information in the traffic state matrix to judge the initial path of resource distribution. According to the weather value and risk degree data in the weather risk area map, adjust the initial path to obtain a preliminary path set. Use a multi-objective optimization algorithm to optimize the path set and scheduling table data in the preliminary path set to generate an optimized path set. Combine the area classification values in the area map to judge the area coverage in the optimized path set and determine the final scheduling plan. According to the optimized values in the final scheduling plan, update the resource demand quantity data in the resource demand priority list. Use a clustering algorithm to classify the updated resource demand quantity data and area classification values to generate a new area distribution map. Dynamically adjust the traffic volume information in the traffic state matrix through the new area distribution map and the optimized path set.

[0055] Feasible. The resource requirement priority list contains resource quantity and priority data, which reflect the resource shortage degree and distribution requirements of each region. For example, in the distribution of epidemic prevention materials in a certain city, the demand quantities of medical masks, protective clothing and other materials are 1,000 pieces and 500 pieces respectively, and the priorities are 9.5 and 8.5 respectively. Combining these data with information such as traffic flow and travel time in the traffic state matrix, an initial distribution route can be planned. The traffic state matrix records the real-time traffic conditions of each section of the road network, including indicators such as traffic flow density and average vehicle speed. For example, the traffic flow density on the main road reaches 80 vehicles per kilometer during the morning and evening rush hours, and the average vehicle speed drops to 20 kilometers per hour. These data can be used to evaluate the traffic efficiency of different routes. The weather risk area map reflects the weather conditions and potential risks of different regions. For example, in case of heavy rain, the water depth in some low-lying areas exceeds 30 centimeters, and the risk level reaches 8.0. This requires adjusting the original distribution route to avoid these high-risk areas and forming a new set of alternative routes. The multi-objective optimization algorithm comprehensively considers multiple factors such as distribution time, cost and risk. For example, during the distribution process, it is necessary to balance the two goals of the shortest transportation distance and avoiding congested sections. By setting different weights, multiple optimization schemes can be obtained. Although the distance of a certain distribution route increases by 2 kilometers, it avoids three congested intersections, and the overall distribution time is shortened by 15 minutes instead. The regional classification value reflects the characteristics and importance of different regions. For example, the classification value of the medical institution cluster area is 9.0, and that of the commercial area is 7.0. These data are used to evaluate the regional coverage effect of the optimized route. The final scheduling plan needs to ensure that key areas can receive material supplies in a timely manner. The updated resource demand reflects the latest demand status of each region after distribution. Through cluster analysis, groups of regions with similar demands can be identified. For example, it is found that the demand for medical materials in three adjacent regions is more than 500 pieces each, and they can be divided into the same distribution block to optimize the subsequent distribution plan. The new regional distribution map shows the spatial distribution characteristics of resource demands. For example, high-demand areas show a concentric distribution pattern, and the demand gradually decreases from the city center outwards. This distribution characteristic interacts with the change law of traffic flow to form a dynamically balanced distribution network.

[0056] S105. If the system receives a dynamic task insertion request, extract the task attribute information, combine it with the current scheduling plan, and judge the feasibility of task insertion. The process of adjusting the resource allocation priority includes:

[0057] Adopt a task parsing algorithm to extract task attribute information from dynamic task insertion requests, and obtain task urgency and resource requirement data. Combine the resource allocation status and path planning data in the current scheduling scheme to judge the feasibility of task insertion. If task insertion is feasible, adopt a priority adjustment algorithm to recalculate the resource allocation priority. Update the resource allocation status data according to the adjusted priority. Adopt a path optimization algorithm to generate a new scheduling scheme in combination with the updated resource allocation status and path planning data. Judge the resource coverage after dynamic task insertion according to the new scheduling scheme. Update the task attribute information and record the resource allocation status after task insertion.

[0058] Feasible. The task parsing algorithm extracts attribute information from dynamic task insertion requests, including key data such as task urgency and resource requirements. For example, in an emergency rescue scenario, when a sudden event occurs in a certain area and medical supplies are needed for support, the task urgency can be rated as extremely urgent, urgent, and routine at three levels according to factors such as the number of wounded and the severity, and the resource requirement data includes specific information such as drug types and quantities. The resource allocation status in the current scheduling scheme reflects the material reserves at each distribution point, and the path planning data contains the existing distribution routes. Taking the medical supply distribution in a certain city as an example, there are three main medical supply distribution centers in the urban area, and each center is responsible for the distribution tasks in different regions. When judging the feasibility of task insertion, it is necessary to evaluate whether the inventory of the nearest distribution center can meet the demand and whether the existing distribution routes can cover the new task locations. The priority adjustment algorithm will recalculate the resource allocation priority. The system will increase its priority according to factors such as the number of infected people and the risk level in this community, which may exceed the originally lower-priority routine distribution tasks. When updating the resource allocation status data, it is necessary to consider the dynamic changes of resources. For example, a distribution center originally planned to distribute masks to five communities, with 10,000 masks for each community. After inserting an emergency task, it may be necessary to adjust the distribution plan and allocate part of the inventory to the newly added task points with higher priority. When the path optimization algorithm generates a new plan in combination with the updated status, it is necessary to ensure the timely delivery of resources while ensuring efficiency. For example, originally the delivery vehicles delivered in sequence according to the shortest path. After inserting an emergency task, the delivery order may need to be changed to give priority to completing the emergency task delivery. Judging the resource coverage needs to consider two dimensions: space and time. Taking the flood control material scheduling as an example, when a flood disaster occurs in a certain area and sandbags need to be urgently allocated. The system will evaluate whether the existing scheduling scheme can ensure the completion of material delivery before the flood situation arrives and ensure that there are still sufficient flood control materials reserved in the surrounding areas. Updating and recording the task attribute information is necessary for subsequent optimization. For example, in the distribution of disaster relief materials, recording the specific situation of each emergency task, including the occurrence time, location, demand characteristics, etc., helps the system to continuously improve its emergency response ability and improve the efficiency of resource scheduling.

[0059] S106. The process of re - running the multi - objective optimization algorithm according to the adjusted resource allocation priority, updating the optimal path set, and generating the corrected scheduling plan includes:

[0060] Adopt a path optimization algorithm, combine the updated resource allocation status and path planning data to generate a new scheduling plan. According to the new scheduling plan, update the task attribute information and record the resource allocation status after the task is inserted.

[0061] Implementable, the resource allocation priority data is the core element of the scheduling system. The multi - objective optimization algorithm forms an optimal path set by comprehensively considering factors such as resource utilization efficiency and task completion time. The corrected scheduling plan records the resource allocation status, including the usage of various resources, remaining available quantities, etc. Taking a logistics distribution center as an example, the scheduling plan will record information such as the real - time location of distribution vehicles, load status, route arrangements, etc. When a new order is inserted, the system first extracts task attribute information such as the delivery location, cargo weight, and expected delivery time of the order. The task urgency and resource demand data directly affect the scheduling decision, and the priority adjustment algorithm will re - allocate priorities according to factors such as task urgency and resource utilization rate. The path optimization algorithm combines the updated resource status to generate a new scheduling plan. Finally, the system synchronizes the updated scheduling plan to the task attribute information and records the allocation status of each resource to ensure that the scheduling process is traceable and monitorable.

[0062] S107. The process of comparing the corrected scheduling plan with the real - time vehicle location information through the collaborative management platform to determine whether there is a risk of deviation. If there is a deviation, triggering the Beidou short message warning mechanism includes:

[0063] Use the collaborative management platform to obtain the real - time vehicle location information, combine the corrected scheduling plan, and extract the path planning data. According to the path planning data and the real - time vehicle location information, calculate the deviation distance between the vehicle and the planned path. If the deviation distance is greater than the preset threshold, obtain the current resource status data and determine whether it meets the warning trigger condition. According to the warning trigger condition, call the Beidou short message warning mechanism to generate a warning message. Associate the warning message with the resource status data and update the warning record of the collaborative management platform. According to the updated warning record, recalculate the matching degree between the deviation distance and the path planning. If the matching degree is lower than the preset standard, adopt a path optimization algorithm to generate a new scheduling plan and update the collaborative management platform data.

[0064] Implementable. The collaborative management platform collects vehicle location information in real time through in-vehicle terminals. A delivery vehicle departs from Haidian District, Beijing and heads to Chaoyang District for delivery. The system receives location data every thirty seconds. The revised scheduling plan shows that the planned route should be to take the Fourth Ring Road, but the real-time location shows that the vehicle has entered the Third Ring Road. The system compares the real-time collected vehicle location with the planned route and calculates the deviation value. If the vehicle deviates from the planned route by more than 500 meters and the duration exceeds five minutes, the warning mechanism is triggered. In this case, the deviation distance after the vehicle enters the Third Ring Road reaches 2,000 meters, and the system immediately determines that a warning needs to be issued. The current resource status shows that the vehicle load is eight tons, the loading rate is 90%, and the urgency of the delivery task is high. The system combines these status data for comprehensive judgment. If it is found that the vehicle is overloaded or the delivery timeliness will be affected, the Beidou short message warning is triggered. The Beidou short message system can still ensure the reliability of information transmission in the absence of mobile communication network coverage. The warning information includes key information such as vehicle number, current location, deviation distance, remaining delivery time, etc. The system associates and stores this information with resource status data such as the current vehicle load status and road condition information to form a complete warning record. For example, the system records that the vehicle deviated from the original route during the morning rush hour, resulting in a 40-minute delay in the expected arrival time.

[0065] Furthermore, based on the updated warning record, the system re-evaluates the feasibility of the current route. By calculating the matching degree between the actual driving trajectory and the planned route, if the matching degree is lower than 70%, a new route needs to be planned. In this embodiment, due to the current congestion on the Third Ring Road, the system recommends that the vehicle take the Fifth Ring Road instead and adjusts the arrival time sequence of the subsequent delivery points. The route optimization algorithm comprehensively considers factors such as the travel time of the new route, road conditions, and the current location of the vehicle. For example, the system finds that although the Fifth Ring Road has a longer distance, due to less traffic flow, the overall travel time is expected to be shortened by 20 minutes. The optimized new scheduling plan will be pushed to the in-vehicle terminal in real time to guide the driver to adjust the driving route. At the same time, the collaborative management platform updates relevant data, including the expected arrival time, route planning data, vehicle status, etc., to ensure that the information in each link is updated synchronously. This dynamic monitoring and warning mechanism can timely detect and handle abnormal situations during transportation, improving the accuracy and efficiency of logistics distribution.

[0066] S108. The process of receiving vehicle status information using the Beidou short message function in a network-free area, combining with the revised scheduling plan, and generating an emergency route plan to ensure the continuity of task execution includes:

[0067] In areas without network coverage, the Beidou short message function is used to receive vehicle status information. According to the received vehicle status information, the corrected scheduling plan is obtained. Combining the vehicle status information and the corrected scheduling plan, the path planning data is extracted. The path optimization algorithm is used to generate the emergency path planning. According to the emergency path planning, the scheduling plan data is updated. The Beidou short message function is used to transmit the updated scheduling plan. According to the updated scheduling plan, the task path planning is executed.

[0068] Implementable, the Beidou short message function is a key means to realize vehicle status monitoring in blind areas of communication network coverage. For example, a transport vehicle departs from Wulanchabu, Inner Mongolia to Hohhot to deliver goods. When passing through a mountainous area without communication network coverage, the on-vehicle terminal reports the status information such as location, speed, and load to the dispatching center every five minutes through the Beidou short message. These status information include key data such as vehicle number, timestamp, longitude and latitude coordinates, instantaneous speed, and remaining fuel. After receiving the vehicle status information, the dispatching center immediately compares it with the corrected scheduling plan. For example, the original planned driving route of this transport vehicle was the expressway from Erenhot to Hohhot, but the received location shows that the vehicle has entered the provincial road. The system detects that the vehicle deviates from the planned path by more than one kilometer and triggers the emergency response process. The path planning data includes information such as road network topology, traffic flow, speed limits, and traffic restrictions. In case of an emergency, the system comprehensively considers multiple alternative paths from the current location to the destination. The path optimization algorithm will weigh factors such as the length of the journey, the estimated travel time, and the road conditions to generate the optimal emergency path. For example, the system finds that although the provincial road is longer, due to fewer traffic restrictions and better road conditions, the total travel time is actually shorter. The updated scheduling plan includes information such as the new path planning, the estimated arrival time, and key nodes. The dispatching center sends the updated scheduling plan to the on-vehicle terminal through the Beidou short message. After receiving the instruction, the navigation system of the on-vehicle terminal automatically updates the path, and the screen displays the new driving route and the estimated time to guide the driver to adjust the driving direction. When executing the task path planning, the system continuously tracks the vehicle running track and evaluates the execution situation. If it is found that the vehicle still deviates from the new planned path or encounters a new unexpected situation, the emergency plan is triggered again. For example, after the vehicle enters the new planned path, it encounters a sudden heavy rain in the mountainous area. The system automatically calculates the alternative detour route and issues a new dispatching instruction through the Beidou short message. The whole process forms a closed-loop dynamic monitoring and emergency response mechanism to ensure that abnormal situations can be detected and handled in a timely manner in areas without network coverage, and to ensure the safe and efficient completion of the transportation task.

[0069] Embodiment 2

[0070] This embodiment also provides a computer device, including a memory, a processor, and a computer program stored on the memory. The processor executes the computer program to implement the steps of the method.

[0071] Example 3

[0072] This embodiment also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the method are implemented.

[0073] Example 4

[0074] This embodiment also provides a computer program product, including a computer program. When the computer program is executed by a processor, the steps of the method are implemented.

[0075] The above is only the preferred specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by those skilled in the art within the technical scope disclosed by the present application should be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. An intelligent logistics scheduling method based on Beidou positioning, characterized in that, It includes the following steps: Based on the Beidou positioning system, obtain the real-time vehicle position information, and combine it with the road congestion data to construct a traffic state matrix; Based on the weather change trend, judge the weather influence area, and combine it with the traffic state matrix to obtain the weather risk area; Obtain the inventory status data, extract the available resource information, and combine it with the weather risk area to generate a resource demand priority list; Adopt a multi-objective optimization algorithm, take the resource demand priority list, traffic state matrix, and weather risk area as inputs, obtain the optimal path set, and generate a preliminary scheduling plan; If a dynamic task insertion request is received, judge the feasibility of task insertion, adjust the resource demand priority list, re-run the multi-objective optimization algorithm, and generate a revised scheduling plan.

2. The method according to claim 1, characterized in that The process of obtaining the real-time vehicle position information based on the Beidou positioning system and combining it with the road congestion data to construct a traffic state matrix includes: Collect road congestion data based on traffic condition sensors, combine the real-time vehicle position information with the road congestion data for processing to obtain traffic condition data; based on the traffic condition data, use a clustering algorithm to classify the traffic state to obtain traffic state data; if the congestion degree in the traffic state data exceeds a preset threshold, use a path planning algorithm to optimize the vehicle path to obtain optimized path data, based on the optimized path data, update the real-time vehicle position information, re-generate the traffic state data, and then generate the final traffic state matrix.

3. The method according to claim 1, characterized in that The process of judging the weather influence area based on the weather change trend and combining it with the traffic state matrix to obtain the weather risk area includes: Use a clustering algorithm to classify the traffic state of the weather influence area, and based on the traffic state classification result, judge the risk level of each area to determine the weather risk area.

4. The method according to claim 1, characterized in that The process of obtaining the inventory status data, extracting the available resource information, and combining it with the weather risk area to generate a resource demand priority list includes: Obtain the inventory status data, extract the inventory quantity, resource quantity, and status value to generate an inventory data table; conduct a clustering analysis on the inventory data table and the weather risk area distribution map to obtain the material distribution characteristics in different weather risk areas; based on the material distribution characteristics, use an association rule algorithm to judge the resource demand quantity and priority; generate a resource demand priority list according to the resource demand quantity and priority.

5. The method according to claim 1, characterized in that The process of adopting a multi-objective optimization algorithm, taking the resource demand priority list, traffic state matrix, and weather risk area as inputs, obtaining the optimal path set, and generating a preliminary scheduling plan includes: Obtain the resource quantity and priority data in the resource demand priority list, and combine the traffic volume information in the traffic status matrix to determine the initial path of resource distribution; according to the weather values and risk degree data in the weather risk area, adjust the initial path to obtain a preliminary path set; use a multi-objective optimization algorithm to optimize the path set and scheduling table data in the preliminary path set to generate an optimized path set; combine the area classification values in the area step-by-step diagram to judge the area coverage in the optimized path set and determine the preliminary scheduling plan.

6. The method according to claim 1, wherein If a dynamic task insertion request is received, judge the feasibility of task insertion. The process of adjusting the resource demand priority list includes: Adopt a task parsing algorithm to extract task attribute information from the dynamic task insertion request, and obtain task urgency and resource demand data; combine the resource allocation status and path planning data in the current preliminary scheduling plan to judge the feasibility of task insertion; if the task insertion is feasible, adopt a priority adjustment algorithm to recalculate the resource allocation priority, and then adjust the resource demand priority list.

7. The method according to claim 1, wherein After generating the corrected scheduling plan, it further includes: Compare the corrected scheduling plan with the real-time vehicle position information through the collaborative management platform to judge whether there is a risk of deviation. If there is a deviation, trigger the Beidou short message warning mechanism; In an area without network, use the Beidou short message function to receive vehicle status information, and combine the corrected scheduling plan to generate an emergency path plan to achieve the continuity of task execution.

8. A computer device, comprising a memory, a processor, and a computer program stored on the memory, characterized in that, The processor executes the computer program to implement the steps of the method according to any one of claims 1-7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the method according to any one of claims 1-7.

10. A computer program product comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the method according to any one of claims 1-7.

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