Intelligent scheduling method based on Spring Cloud micro-service architecture

Abstract

The invention provides an intelligent scheduling method based on Spring Cloud micro-service architecture, and the method comprises the steps: collecting vehicle scheduling information, and summarizing the vehicle scheduling information to a central data processing service through micro-service for standardization and preprocessing; performing priority evaluation on the vehicles based on the preprocessed vehicle scheduling information to obtain vehicle priority scores; evaluating the operating cost of the vehicle; based on the vehicle priority scores and the operation cost of the vehicles, comprehensive scores are calculated for the vehicles with the preceding priority scores, and an optimal vehicle allocation task is screened out based on the comprehensive scores; generating an optimal scheduling decision based on the optimal vehicle allocation task, and pushing the optimal scheduling decision to each vehicle and driver side; and monitoring the states of each vehicle and each driver in real time in a scheduling execution process, and adjusting a scheduling plan based on a comparison result of real-time data and predicted data. According to the method, multi-dimensional data analysis, an optimization algorithm and a dynamic adjustment mechanism are combined, and cooperative scheduling of the owned motorcade and the social transport capacity is realized.

Description

Technical Field

[0001] The present invention relates to the field of intelligent traffic management and data processing, and specifically to an intelligent scheduling method based on Spring Cloud microservice architecture. Background Art

[0002] As the demand for corporate vehicles increases, many corporate platforms meet the travel needs of employees by combining their own fleets with social transportation capacity. However, when dealing with complex and diverse vehicle requests, existing dispatch systems often face the following problems and shortcomings:

[0003] 1. Inefficient vehicle dispatching: Existing dispatching systems usually rely on fixed rules or simple dispatching strategies (such as first-come, first-served, pre-set priorities, etc.). This dispatching method lacks flexibility and cannot fully consider real-time changing environmental factors (such as road conditions, driver status, vehicle location, etc.), resulting in low vehicle utilization, long periods of idle driving or repeated dispatching.

[0004] 2. Poor economic efficiency: Many dispatch systems only allocate tasks based on the availability of vehicles and drivers, and fail to fully consider economic factors in the dispatch process, such as fuel costs, maintenance costs, price fluctuations of social transportation capacity, etc. This makes it difficult for the dispatch system to optimize the overall travel cost of the enterprise while meeting the demand for vehicles.

[0005] 3. Insufficient dynamic adjustment capabilities: Existing technologies lack effective real-time adjustment mechanisms during the dispatch process. Once the initial dispatch decision is made, the system rarely adjusts according to changes in actual conditions (such as sudden traffic congestion, vehicle failure, temporary driver unavailability, etc.), resulting in inflexible dispatch plans, which in turn affects user experience and enterprise operational efficiency.

[0006] 4. Weak multi-dimensional data processing capabilities: Enterprise vehicle dispatching involves multi-dimensional data analysis and processing, including vehicle location, driver status, vehicle demand, geographic information, road conditions, etc. Existing systems are unable to effectively process these multi-dimensional data in a short period of time, and thus are unable to quickly generate the optimal dispatching plan. Summary of the invention

[0007] In view of the above problems, the present invention aims to propose an intelligent scheduling method based on the Spring Cloud microservice architecture. Through multi-dimensional data optimization and dynamic adjustment algorithms, it solves the problems of low scheduling efficiency, poor economy, insufficient dynamic adjustment capability, etc. in the existing system, improves the utilization efficiency of enterprise fleets and social transportation capacity, and reduces the total operating cost.

[0008] An intelligent scheduling method based on Spring Cloud microservice architecture includes the following steps:

[0009] S1. Collect vehicle dispatch information through Spring Cloud microservices. The vehicle dispatch information includes vehicle request data, vehicle status data, driver information data, and real-time traffic data. The vehicle dispatch information is aggregated to the central data processing service through microservices for standardization and preprocessing;

[0010] S2: Evaluate the priority of vehicles based on the preprocessed vehicle dispatch information to obtain a vehicle priority score;

[0011] S3: Evaluate the operating cost of the vehicle using the cost function;

[0012] S4: Based on the vehicle priority score and the vehicle operating cost, a comprehensive score is calculated for the vehicles with the highest priority score, and the optimal vehicle allocation task is selected based on the comprehensive score;

[0013] S5: Generate the optimal dispatch decision based on the optimal vehicle allocation task, and push the optimal dispatch decision to each vehicle and driver through the message queue;

[0014] S6: During the dispatch execution process, the status of each vehicle and driver is monitored in real time, and the dispatch plan is adjusted based on the comparison results between real-time data and predicted data.

[0015] Furthermore, the vehicle request data includes employee mobile phone number, department, starting location, vehicle use time, reason for use, vehicle use approver, and number of passengers; the vehicle status data includes vehicle location, vehicle current task, and maintenance status; the driver information data includes the driver's current task status, idle time, and past completion status; the real-time traffic condition data includes current road traffic information obtained through third-party services.

[0016] 6. Further, in step S2, the vehicle priority is evaluated using the dynamic priority model using the following dimensions:

[0017] S2-1: Get the distance d between the vehicle and the required location i ;

[0018] S2-2: If the distance d i Determine the vehicle's passenger capacity U within the set transportable distance range i ;

[0019] S2-3: Get vehicle type R i , if it is a self-owned vehicle, determine the driver's availability A i ;

[0020] S2-4: Determine the update time T of vehicle information i , vehicle information update time T iIf it exceeds the preset value, re-acquire and return to S2-1;

[0021] S2-5: Perform vehicle priority scoring for all information obtained from S2-1 to S2-4, and arrange them in descending order.

[0022] 7. Further, S2-5: Vehicle priority scoring is performed for all the information obtained from S2-1 to S2-4. The vehicle priority score P i The calculation formula is:

[0023]

[0024] in:

[0025] d i The distance between the vehicle and the required location. The closer the distance, the higher the priority. The priority of close vehicles is increased by the inverse of the distance.

[0026] U i is the vehicle's passenger capacity, 0 means an empty vehicle, 1 means a fully loaded vehicle, and empty vehicles take priority;

[0027] A i For the driver availability, use the function f(A i ) indicates the dynamic nature of the driver’s status, the closer to the end of the task, the higher the priority;

[0028] T i is the update time of vehicle information, and the weight function g(T i ) Ensure the timeliness of information;

[0029] R i Score the vehicle resource type, with the self-owned fleet scoring higher than the social transport vehicles;

[0030] α1, α2, α3, and α4 are weight factors, which are adjusted according to the needs of different business scenarios.

[0031] 8. Further, step S3 specifically includes the following sub-steps:

[0032] S3-1: Obtain basic cost data of the vehicle, including fuel consumption cost C fuel (i) Vehicle maintenance cost C maint (i) and driver labor cost C driver (i);

[0033] S3-2: Based on real-time traffic data and traffic network models, plan the optimal path for each vehicle and calculate the cost C of the total driving distance under the optimal path distance (i) When estimating the trip

[0034] Room cost Ctime (i);

[0035] S3-3: For self-owned vehicles, substitute the above cost items into the comprehensive cost function to calculate the vehicle's operating cost C:

[0036] C=C fuel (i)+C maint (i)+C driver (i)+λ1*C time (i)+λ2*C distance (i)

[0037] Among them, λ1 and λ2 are scheduling constraint adjustment coefficients, which are used to dynamically adjust the sensitivity of the model;

[0038] S3-4: For social vehicles, combined with their total travel distance cost C distance (i) Compare it with the total operating cost (C) of the owned vehicle and dynamically calculate the vehicle operating cost using the operating cost evaluation method.

[0039] Furthermore, the estimated travel time cost C time (i) The travel time of the vehicle to each node Estimate the travel time of the vehicle to each node The calculation formula is as follows:

[0040]

[0041] Introducing congestion prediction factor α j Combined with the path segment length L j , average speed v j The parameters are estimated using the following formula:

[0042]

[0043] Among them, L j is the path segment length, v j is the average speed, C j represents the waiting time, α j It is the road congestion factor, which dynamically affects the driving speed.

[0044] Furthermore, S4 calculates a comprehensive score for vehicles with higher priority scores based on the vehicle priority scores and the operating cost of the scheduling scheme, where the comprehensive score calculation formula is:

[0045] D i =P i -β*C i +γ*R i

[0046] in:

[0047] D i Comprehensive scoring is used to determine the matching priority between tasks and vehicles;

[0048] P i Score the priorities;

[0049] C i It represents the operating cost of the vehicle, which combines fuel consumption and mission cost, and ensures compliance with the cost priority strategy by adjusting the coefficient β;

[0050] R i Indicates the vehicle resource type score, with self-owned fleets having a higher priority than social transport vehicles;

[0051] β and γ are adjustment coefficients to balance economy and vehicle resource priority;

[0052] The task allocation strategy for selecting the optimal vehicle allocation task based on the comprehensive score is as follows:

[0053] First priority: On the premise of meeting the mission requirements, we will give priority to dispatching our own fleet vehicles to reduce dispatch costs and improve the utilization of our own resources;

[0054] Second priority: When the company’s own fleet resources cannot meet the mission requirements, select the highest-scoring social transport vehicles to supplement.

[0055] Furthermore, step S6 specifically includes the following steps:

[0056] S6-1: Real-time monitoring: collect vehicle location, task progress and emergencies, including real-time changes in road conditions, vehicle status changes or order changes;

[0057] S6-2: Adjust the trigger condition: When the difference between the real-time data and the predicted data exceeds the set threshold, recalculate the scheduling plan;

[0058] S6-3: Re-optimize scheduling: After the adjustment conditions are triggered, dynamically update the priority sorting and task matching scheme to ensure scheduling efficiency and on-time completion of tasks.

[0059] The present invention realizes efficient coordinated dispatch of self-owned vehicle fleets and social transportation capacity by introducing optimization algorithms and dynamic adjustment mechanisms, improves the utilization rate of self-owned vehicle fleets, reduces dependence on social transportation capacity and operating costs, and can improve vehicle utilization, reduce empty driving rate, and optimize passenger experience in a variety of enterprise vehicle use scenarios; at the same time, the microservice architecture based on Spring Cloud ensures the high scalability and stability of the system, and adapts to the vehicle dispatch needs in high concurrency and complex scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1It is a flow chart of the intelligent scheduling method based on Spring Cloud microservice architecture of the present invention. DETAILED DESCRIPTION

[0061] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0062] The embodiment of the present invention provides an intelligent scheduling method based on the Spring Cloud microservice architecture, which combines multi-dimensional data analysis, optimization algorithms and dynamic adjustment mechanisms to achieve coordinated scheduling of self-owned fleets and social transportation capacity. The scheduling algorithm calculates the optimal scheduling plan by analyzing multi-dimensional real-time data such as vehicle status, road conditions, driver availability, and demand urgency. The present invention is split into multiple microservices to handle functions such as data collection, scheduling decisions, economic evaluation, and scheduling adjustments, and achieves efficient communication and collaborative work of each service through Spring Cloud's load balancing, service discovery and message queues. The architecture can support large-scale concurrent requests and can be flexibly expanded according to demand.

[0063] like Figure 1 As shown, the method comprises the following steps:

[0064] S1: Data collection and preprocessing

[0065] Through Spring Cloud's microservices, the system integrates a data collection interface to obtain the following vehicle dispatch information in real time:

[0066] Car request data: including employee mobile phone number, department, starting location, car use time, reason for use, car use approver, and number of passengers.

[0067] Vehicle status data: vehicle location, vehicle current mission, maintenance status, etc.

[0068] Driver information data: driver’s current task status, idle time, past completion status, etc.

[0069] Real-time traffic data: Get current road traffic information through third-party services.

[0070] These vehicle dispatch information are aggregated into the central data processing service through microservices for standardization and preprocessing to ensure the consistency and reliability of the input data.

[0071] S2: Vehicle Priority Assessment

[0072] The system evaluates the priority of all vehicles (including company-owned vehicles and social transport vehicles), comprehensively considers multi-dimensional factors such as vehicle location, current status, driver availability, etc., and sorts them using a dynamic priority model. i The calculation formula is:

[0073]

[0074] in:

[0075] d i : The distance between the vehicle and the required location. The closer the distance, the higher the priority. The priority of close vehicles is increased by the inverse of the distance.

[0076] U i : The passenger capacity of the vehicle (0 means empty, 1 means fully loaded), empty vehicles are given priority;

[0077] A i : The availability of the driver, using the function f(A i ) indicates the dynamic nature of the driver’s status, the closer to the end of the task, the higher the priority;

[0078] T i : The update time of vehicle information is updated by updating the weight function g(T i ) Ensure the timeliness of information;

[0079] R i : Vehicle resource type score, the score of self-owned fleet is higher than that of social transport vehicles;

[0080] α1, α2, α3, α4: weight factors, which can be adjusted according to the needs of different business scenarios.

[0081] S3: Economic feasibility analysis

[0082] Based on the priority evaluation, the vehicle's operating cost C is further evaluated. The embodiment of the present invention uses a more complex cost function to accurately quantify various economic indicators.

[0083] 9. Step S3 specifically includes the following sub-steps:

[0084] S3-1: Obtain basic cost data of the vehicle, including fuel consumption cost C fuel (i) Vehicle maintenance cost C maint (i) and driver labor cost C driver (i);

[0085] S3-2: Based on real-time traffic data and traffic network models, plan the optimal path for each vehicle and calculate the cost C of the total driving distance under the optimal pathdistance (i) When estimating the trip

[0086] Room cost C time (i);

[0087] S3-3: For self-owned vehicles, substitute the above cost items into the comprehensive cost function to calculate the vehicle's operating cost C:

[0088] C=C fuel (i)+C maint (i)+C driver (i)+λ1*C time (i)+λ2*C distance (i)

[0089] Among them, λ1 and λ2 are scheduling constraint adjustment coefficients, which are used to dynamically adjust the sensitivity of the model;

[0090] S3-4: For social vehicles, combined with their total travel distance cost C distance (i) Compare it with the total operating cost (C) of the owned vehicle and dynamically calculate the vehicle operating cost using the operating cost evaluation method.

[0091] The estimated travel time cost C time (i) The travel time of the vehicle to each node Estimate the travel time of the vehicle to each node The calculation formula is as follows:

[0092]

[0093] Introducing congestion prediction factor α j Combined with the path segment length L j , average speed v j The parameters are estimated using the following formula:

[0094]

[0095] Among them, L j is the path segment length, v j is the average speed, C j represents the waiting time, α j It is the road congestion factor, which dynamically affects the driving speed.

[0096] This optimization process will be calculated in parallel under the Spring Cloud architecture, using the load balancing mechanism to efficiently distribute tasks and achieve low-cost low-latency computing, thereby ensuring the rapid selection of the optimal scheduling solution.

[0097] S4: Scheduling decision (generating the optimal scheduling plan)

[0098] Based on the priority evaluation, vehicles with higher priority scores are further screened for final task matching and allocation. The comprehensive scoring formula is:

[0099] D i =P i -β*C i +γ*R i

[0100] in:

[0101] D i Comprehensive scoring is used to determine the matching priority between tasks and vehicles;

[0102] P i Score the priorities;

[0103] C i It represents the operating cost of the vehicle, which combines fuel consumption and mission cost, and ensures compliance with the cost priority strategy by adjusting the coefficient β;

[0104] R i Indicates the vehicle resource type score, with self-owned fleets having a higher priority than social transport vehicles;

[0105] β and γ are adjustment coefficients to balance economy and vehicle resource priority.

[0106] The task allocation strategy for selecting the optimal vehicle allocation task based on the comprehensive score is as follows:

[0107] First priority: On the premise of meeting the mission requirements, we will give priority to dispatching our own fleet vehicles to reduce dispatch costs and improve the utilization of our own resources;

[0108] Second priority: When the company’s own fleet resources cannot meet the mission requirements, select the highest-scoring social transport vehicles to supplement.

[0109] The specific execution steps of step S4 are as follows:

[0110] Step 1: The system evaluates services by priority (Formula P i ) Real-time priority sorting of all vehicles, self-owned fleet vehicles due to R i 's weighting and have a higher score;

[0111] Step 2: Calculate the comprehensive score D based on the candidate vehicles with high priority i , select the optimal vehicle allocation task;

[0112] Step 3: Combine real-time dynamic adjustment mechanism to ensure the efficiency and accuracy of scheduling decisions.

[0113] This step has the following characteristics:

[0114] Flexibility: The system can flexibly switch between owned vehicles and social transportation capacity according to enterprise needs, supporting scheduling strategies for different scenarios; the cost factor calculation logic and weight coefficient can be adjusted to accurately reflect the differentiated characteristics of the two resources;

[0115] Real-time: Relying on the Spring Cloud microservice architecture, it realizes real-time cost calculation and scheduling optimization under high concurrency, and dynamically evaluates the supply and demand changes of its own fleet and social transportation capacity;

[0116] Accuracy: Through the collaborative analysis of multi-dimensional data (such as vehicle performance, driver performance, and road condition information), combined with the principle of priority for the company's own fleet, the accuracy and economy of dispatching decisions can be improved;

[0117] Synergy: Supports the coordinated dispatch of owned vehicles and social transportation capacity. When resources are insufficient or during peak hours, the company will give priority to its own fleet and dynamically supplement social vehicles to ensure the optimal allocation of resources.

[0118] S5: Implementing Scheduling Decisions

[0119] Generate the optimal dispatch decision based on the optimal vehicle allocation task, and push the dispatch information to each vehicle and driver through the message queue (RabbitMQ). Dispatching decisions include:

[0120] Assign vehicles and drivers;

[0121] Estimated arrival time and travel time;

[0122] Notify users and operation management systems in real time;

[0123] The system outputs the optimal scheduling decision after optimization calculation to the scheduling execution module. The strategy generation process includes:

[0124] Optimization goal: To minimize the vehicle idle rate, reduce waiting time, and improve capacity utilization, and solve the optimal solution through a linear programming model.

[0125] Algorithm framework: Based on dynamic programming and heuristic algorithms, the system will dynamically optimize vehicle resources, road conditions and order requirements in a short period of time to ensure the rapid generation of scheduling strategies.

[0126] Dynamic adjustment: During the dispatch execution process, if there are temporary order changes (such as cancellations, delays) or sudden changes in road conditions, the system can adjust the dispatch strategy in a timely manner through the feedback mechanism and perform secondary optimization dispatch.

[0127] Once the scheduling plan is generated, the system sends the plan to the vehicle and driver through the notification module, updates the task status in real time, and tracks and provides feedback on the execution of the task through the monitoring module.

[0128] S6: Real-time monitoring and dynamic adjustment services

[0129] During the dispatch execution process, the system monitors the status of each vehicle and driver in real time. Once a change occurs, the system adjusts the dispatch plan based on the comparison results of real-time data and predicted data.

[0130] The changes include:

[0131] (1) Real-time changes in road conditions: The system re-evaluates road condition data at fixed intervals and dynamically adjusts vehicle routes if traffic congestion occurs;

[0132] (2) Vehicle status change: If a self-owned vehicle is occupied due to an emergency task, the system will call on social transportation capacity (the supplier's online car-hailing service) in real time for supplementary dispatch to ensure that the task is not affected;

[0133] (3) Order changes: If a passenger changes their destination or cancels their order, the system will promptly update the vehicle task through the scheduling engine and recalculate the dispatch plan based on the actual resource situation.

[0134] The adjustment strategy is as follows:

[0135] When the difference between real-time data and predicted data exceeds the set threshold, the system re-evaluates vehicle priority and adjusts the scheduling plan based on the changes.

[0136] The dynamic adjustment formula is as follows:

[0137] ΔS=|S current -S predicted |

[0138] When ΔS exceeds the threshold, the system triggers rescheduling and notifies related services through Spring Cloud's messaging mechanism.

[0139] The present invention is implemented based on the microservice architecture of Spring Cloud, and the system architecture design supports real-time scheduling and data processing in a high-concurrency environment. Through the implementation of the following services, the efficient distribution of scheduling results and the scalability of the system are ensured. The specific services are as follows: Data acquisition service: responsible for collecting real-time data from different data sources (such as vehicle status, vehicle demand, road conditions); Scheduling algorithm service: implement core calculations such as vehicle priority calculation and economic optimization; Dynamic adjustment service: real-time monitoring of scheduling execution, handling of exceptions and emergencies; Message queue service: used for asynchronous communication between services to ensure efficient distribution of scheduling decisions; Service gateway: realize communication management between external systems and microservices through API gateway, and provide a unified entrance; Service discovery and load balancing: The system performs service discovery and registration through Spring Cloud's Eureka, and implements load balancing through Ribbon to ensure the scalability and stability of services in a high-concurrency environment.

[0140] Some code implementations are as follows:

[0141]

[0142]

[0143] Compared with the existing scheduling technology, the present invention has the following significant advantages and innovations:

[0144] (1) Multi-dimensional data fusion scheduling decision

[0145] The present invention forms the optimal dispatching decision based on comprehensive analysis of multi-dimensional data (such as vehicle status, vehicle demand, driver availability, real-time road conditions, etc.). Compared with the traditional simple dispatching algorithm that only considers vehicle distance or driver idle time, the present invention greatly improves the accuracy and flexibility of dispatching decisions and can adapt to complex enterprise vehicle use scenarios.

[0146] (2) Dynamic adjustment mechanism

[0147] The present invention has real-time dynamic adjustment capabilities. During the scheduling process, it can monitor the status of each vehicle and driver in real time, respond to emergencies (such as vehicle failures, traffic congestion, etc.) in a timely manner, and ensure the continuous optimization of the scheduling plan by recalculating priorities and making scheduling adjustments. Traditional scheduling systems are generally static decisions and cannot effectively respond to real-time changing environments. The present invention significantly enhances the flexibility and adaptability of scheduling.

[0148] (3) Economic optimization

[0149] The present invention reduces the overall cost of enterprise vehicles to the greatest extent by comprehensively evaluating vehicle operating costs (fuel, maintenance, driver costs, etc.). Compared with traditional dispatching algorithms that only focus on dispatching efficiency, the present invention not only focuses on time efficiency but also considers economic benefits, which can reduce enterprise operating expenses while ensuring service quality.

[0150] (4) Distributed microservice architecture based on Spring Cloud

[0151] The present invention adopts the microservice architecture of Spring Cloud to realize the modularization and scalability of the scheduling system. Through mechanisms such as service discovery, load balancing, and message queues, the system can support large-scale concurrent requests and has high availability. Compared with the scheduling system of the traditional monolithic architecture, the microservice architecture of the present invention can cope with more complex scheduling scenarios and has good scalability and efficient service communication capabilities.

[0152] (5) Prioritize private vehicles and combine them with social transport capacity

[0153] The present invention prioritizes the dispatch of the company's own vehicles, improves the utilization rate of the company's own vehicles, and flexibly calls on social transportation capacity, ensuring that efficient corporate car services can be provided even when vehicle resources are limited. Traditional systems often find it difficult to balance the dispatch of company-owned vehicles and external transportation capacity. The present invention solves this problem through intelligent algorithms, making resource utilization more optimized.

[0154] (6) Intelligence and Automation

[0155] The present invention realizes the full automation of the dispatching process and reduces the need for manual intervention. Through the autonomous decision-making of the intelligent algorithm, the system can quickly respond to a large number of dispatching requests and provide accurate dispatching solutions, which greatly improves work efficiency and reduces dispatching errors.

[0156] On the whole, the present invention has significant innovations in terms of scheduling accuracy, economy, real-time adjustment capabilities, and system architecture design, and can provide efficient, flexible, and economical scheduling solutions in enterprise vehicle scenarios. The present invention combines machine learning, operations optimization, geographic information system (GIS), and traffic data analysis technology to optimize vehicle scheduling decisions by analyzing multidimensional factors such as historical scheduling data, real-time traffic information, vehicle status, driver availability, and vehicle demand. The algorithm is not only applicable to corporate vehicles, but can also be promoted and applied to scenarios such as urban logistics distribution and shared travel platforms to achieve efficient resource scheduling and cost optimization. The present invention optimizes the utilization rate, passenger load factor, and economy of vehicles in corporate vehicles by constructing a real-time intelligent decision-making system to ensure that the scheduling process meets efficiency requirements and can dynamically respond to changes in actual conditions.

[0157] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. An intelligent vehicle scheduling method based on Spring Cloud microservice architecture, characterized in that: The steps include: S1. Collect vehicle dispatch information through Spring Cloud microservices. The vehicle dispatch information includes vehicle request data, vehicle status data, driver information data, and real-time traffic data. The vehicle dispatch information is aggregated to the central data processing service through microservices for standardization and preprocessing; S2: Evaluate the priority of vehicles based on the preprocessed vehicle dispatch information to obtain a vehicle priority score; S3: Evaluate the operating cost of the vehicle using the cost function; S4: Based on the vehicle priority score and the vehicle operating cost, a comprehensive score is calculated for the vehicles with the highest priority score, and the optimal vehicle allocation task is selected based on the comprehensive score; S5: Generate the optimal dispatch decision based on the optimal vehicle allocation task, and push the optimal dispatch decision to each vehicle and driver through the message queue; S6: During the dispatch execution process, the status of each vehicle and driver is monitored in real time, and the dispatch plan is adjusted based on the comparison results between real-time data and predicted data.

2. The intelligent vehicle scheduling method based on Spring Cloud microservice architecture as claimed in claim 1, characterized in that: The vehicle request data includes the employee's mobile phone number, department, starting location, vehicle usage time, reason for vehicle usage, vehicle usage approver, and number of passengers; the vehicle status data includes the vehicle location, vehicle current task, and maintenance status; the driver information data includes the driver's current task status, idle time, and past completion status; the real-time traffic condition data includes current road traffic information obtained through third-party services.

3. The intelligent vehicle scheduling method based on Spring Cloud microservice architecture as claimed in claim 1, characterized in that: In step S2, the vehicle priority is evaluated using a dynamic priority model using the following dimensions: S2-1: Get the distance d between the vehicle and the required location i ; S2-2: If the distance d i Determine the vehicle's passenger capacity U within the set transportable distance range i ; S2-3: Get vehicle type R i , if it is a self-owned vehicle, determine the driver's availability A i ; S2-4: Determine the update time T of vehicle information i , vehicle information update time T i If it exceeds the preset value, re-acquire and return to S2-1; S2-5: Perform vehicle priority scoring for all information obtained from S2-1 to S2-4, and arrange them in descending order.

4. The intelligent vehicle scheduling method based on Spring Cloud microservice architecture as claimed in claim 3 is characterized in that: S2-5: Perform vehicle priority scoring for all information obtained from S2-1 to S2-4. The vehicle priority score P i The calculation formula is: in: d i The distance between the vehicle and the required location. The closer the distance, the higher the priority. The priority of close vehicles is increased by the inverse of the distance. U i is the vehicle's passenger capacity, 0 means an empty vehicle, 1 means a fully loaded vehicle, and empty vehicles take priority; A i For the driver availability, use the function f(A i ) indicates the dynamic nature of the driver’s status, the closer to the end of the task, the higher the priority; T i is the update time of vehicle information, and the weight function g(T i ) Ensure the timeliness of information; R i Score the vehicle resource type, with the self-owned fleet scoring higher than the social transport vehicles; α1, α2, α3, and α4 are weight factors, which are adjusted according to the needs of different business scenarios.

5. The intelligent vehicle scheduling method based on Spring Cloud microservice architecture as claimed in claim 1, characterized in that: Step S3 specifically includes the following sub-steps: S3-1: Obtain basic cost data of the vehicle, including fuel consumption cost C fuel (i) Vehicle maintenance cost C maint (i) and driver labor cost C driver (i); S3-2: Based on real-time traffic data and traffic network models, plan the optimal path for each vehicle and calculate the cost C of the total driving distance under the optimal path distance (i) and the estimated travel time cost C time (i); S3-3: For self-owned vehicles, substitute the above cost items into the comprehensive cost function to calculate the vehicle's operating cost C: C=C fuel (i)+C maint (i)+C driver (i)+λ1*C time (i)+λ2*C distance (i) Among them, λ1 and λ2 are scheduling constraint adjustment coefficients, which are used to dynamically adjust the sensitivity of the model; S3-4: For social vehicles, combined with their total travel distance cost C distance (i) Compare it with the total operating cost (C) of the owned vehicle and dynamically calculate the vehicle operating cost using the operating cost evaluation method.

6. The intelligent vehicle scheduling method based on Spring Cloud microservice architecture as claimed in claim 5, characterized in that: The estimated travel time cost C time (i) The travel time of the vehicle to each node Estimate the travel time of the vehicle to each node The calculation formula is as follows: Introducing congestion prediction factor α j Combined with the path segment length L j , average speed v j The parameters are estimated using the following formula: Among them, L j is the path segment length, v j is the average speed, C j represents the waiting time, α j It is the road congestion factor, which dynamically affects the driving speed.

7. The intelligent vehicle scheduling method based on Spring Cloud microservice architecture as claimed in claim 5, characterized in that: S4 calculates a comprehensive score for vehicles with higher priority scores based on the vehicle priority score and the operating cost of the dispatch plan. The comprehensive score calculation formula is: D i =P i -β*C i +γ*R i in: D i Comprehensive scoring is used to determine the matching priority between tasks and vehicles; P i Score the priorities; C i It represents the operating cost of the vehicle, which combines fuel consumption and mission cost, and ensures compliance with the cost priority strategy by adjusting the coefficient β; R i Indicates the vehicle resource type score, with self-owned fleets having a higher priority than social transport vehicles; β and γ are adjustment coefficients to balance economy and vehicle resource priority; The task allocation strategy for selecting the optimal vehicle allocation task based on the comprehensive score is as follows: First priority: On the premise of meeting the mission requirements, we will give priority to dispatching our own fleet vehicles to reduce dispatch costs and improve the utilization of our own resources; Second priority: When the company’s own fleet resources cannot meet the mission requirements, select the highest-scoring social transport vehicles to supplement.

8. The intelligent vehicle scheduling method based on Spring Cloud microservice architecture as claimed in claim 1, characterized in that: Step S6 specifically includes: S6-1: Real-time monitoring: collect vehicle location, task progress and emergencies, including real-time changes in road conditions, vehicle status changes or order changes; S6-2: Adjust trigger conditions: When the difference between real-time data and predicted data exceeds the set threshold, recalculate the scheduling plan; S6-3: Re-optimize scheduling: After the adjustment conditions are triggered, dynamically update the priority sorting and task matching scheme to ensure scheduling efficiency and on-time completion of tasks.

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