Intelligent scheduling optimization system based on bus real-time data and control method thereof

Abstract

The invention relates to the technical field of intelligent scheduling of bus real-time data, in particular to an intelligent scheduling optimization system based on bus real-time data and a control method thereof, comprising a data acquisition module, a regional scheduling module, a charging strategy module, an intelligent optimization module, a communication module and a display and control module; the data acquisition module is used for acquiring operation data of the electric bus in real time, including position, speed, battery remaining capacity, current passenger number and environment information; according to the system, the running route and the charging plan of the buses can be dynamically adjusted, the operation efficiency of the electric bus fleet is improved, the charging strategy module combines the battery electric quantity, the endurance mileage prediction and the charging station load, the charging process is optimized, the charging efficiency is improved, and the charging efficiency of the electric bus fleet is improved. And the intelligent optimization module considers a battery life optimization strategy, and the service life of the battery is prolonged by reasonably scheduling the charging time and power.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent dispatching of real-time public transportation data, and in particular to an intelligent dispatching optimization system based on real-time public transportation data and a control method thereof. Background Art

[0002] The intelligent dispatch optimization system based on real-time bus data is an advanced public transportation management system that uses real-time data collection, analysis and optimization algorithms to improve the operational efficiency, passenger satisfaction and service quality of bus fleets;

[0003] The goal of this system is to reduce the idle rate of buses, improve vehicle utilization, reduce passenger waiting time, and optimize the use of charging facilities through intelligent scheduling and management, thereby improving the operating efficiency and service quality of the entire public transportation system. At the same time, it also helps to reduce operating costs and environmental impact and achieve sustainable development of public transportation.

[0004] The scheduling of traditional buses relies on fixed routes and schedules, while charging management lacks intelligent optimization. With the popularization of electric buses, the dependence on charging facilities increases, and an intelligent scheduling system that can respond to operational needs and grid conditions in real time is needed. The existing scheduling system is often unable to dynamically adjust the bus's route and charging plan, resulting in low operating efficiency and waste of charging resources. Therefore, an intelligent scheduling optimization system based on real-time bus data and its control method are proposed to address the above problems. Summary of the invention

[0005] The purpose of the present invention is to provide an intelligent dispatching optimization system based on real-time bus data and a control method thereof, so as to solve the problem that the dispatching of traditional buses depends on fixed routes and schedules, while the charging management lacks intelligent optimization. With the popularization of electric buses, the dependence on charging facilities increases, and an intelligent dispatching system that can respond to operational needs and power grid conditions in real time is needed. The existing dispatching systems are often unable to dynamically adjust the bus's operating routes and charging plans, resulting in low operating efficiency and waste of charging resources.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] The intelligent dispatching optimization system based on real-time bus data and its control method include a data acquisition module, a regional dispatching module, a charging strategy module, an intelligent optimization module, a communication module and a display and control module; the data acquisition module is used to collect the operation data of electric buses in real time, including position, speed, remaining battery power, current number of passengers and environmental information; the regional dispatching module dynamically dispatches electric bus fleets according to real-time operation data and regional needs, and optimizes the distribution and operation routes of buses; the charging strategy module optimizes the charging process of electric buses in combination with battery power, mileage prediction, charging station location and charging load, and balances charging demand and grid load; the intelligent optimization module forms an optimization model by comprehensively considering strategies such as charging priority setting, charging time window optimization, charging power optimization, multi-vehicle charging scheduling and battery life optimization, so as to maximize the fleet operation efficiency, reduce charging time and extend battery life; the communication module is used to interact with the bus dispatching center, charging station and grid management system for data to ensure real-time information transmission and decision execution; the display and control module provides a visual interface, displays the operation status, dispatching plan and charging arrangement of the bus fleet in real time, and supports manual intervention and control.

[0008] As a further optimized content of the present invention, the charging strategy module includes a charging priority setting strategy, which specifically calculates the charging priority of the vehicle by the following formula:

[0009]

[0010] Where: P i is the charging priority of vehicle i, C max is the upper limit of battery capacity, C i is the current battery power, R rep is the required cruising range, R i is the remaining cruising range of vehicle i, U i For vehicle mission urgency, U max is the maximum value of task urgency, w1, w2 and w3 are weight coefficients.

[0011] As a further optimized content of the present invention, the charging strategy module includes a charging time window optimization strategy, and the optimal charging time window is selected specifically by the following formula:

[0012]

[0013] Where, T opt is the start time of the optimal charging time window, T s and eare the earliest and latest time that the vehicle can be charged, respectively; λ(t) is the load of the power grid during time period t; and L(t) is the vehicle charging demand during time period t.

[0014] As a further optimized content of the present invention, the charging strategy module includes a charging power optimization strategy, which dynamically adjusts the charging power through the following formula:

[0015]

[0016] Where P(t) is the charging power of the vehicle at time t, P max is the maximum power of the charging station, C i (t) is the battery power of the vehicle at time t, T i is the expected charging time of vehicle i, λ(t) is the grid load during time period t, and Λ is the upper limit of the grid load.

[0017] As a further optimized content of the present invention, the charging strategy module includes a multi-vehicle charging scheduling strategy, which specifically optimizes the multi-vehicle charging scheduling of the charging station through the following formula:

[0018]

[0019] In the formula, S j For charging station j in time window ΔT j The total charge capacity in j is the set of vehicles charging simultaneously at charging station j, P station is the maximum charging capacity of the charging station, T i is the charging time of vehicle i.

[0020] As a further optimized content of the present invention, the charging strategy module includes a battery life optimization strategy, which specifically optimizes the battery life through the following formula:

[0021]

[0022] Where, L b is the battery life, L0 is the initial battery life, k and n are the battery attenuation coefficients, which are related to the charging power, and P(t1) is the charging power.

[0023] As a further optimization of the present invention, the intelligent optimization module constructs a multi-objective optimization model based on the above-mentioned charging priority setting, charging time window optimization, charging power optimization, multi-vehicle charging scheduling and battery life optimization strategy to comprehensively optimize the overall operating efficiency and battery life of the fleet.

[0024] As further optimized content of the present invention, the objective function of the multi-objective optimization model includes minimizing charging waiting time, minimizing impact on the power grid, extending battery life, minimizing charging cost and maximizing operational efficiency. The multi-objective optimization model is solved by a multi-objective optimization algorithm, including particle swarm optimization, genetic algorithm or simulated annealing algorithm.

[0025] As further optimized content of the present invention, the intelligent optimization module can dynamically adjust the weight coefficient of each strategy through a machine learning algorithm according to real-time data and historical data to adapt to the ever-changing operating environment.

[0026] As further optimized content of the present invention, the following steps are included: Step 1: Data acquisition: through the data acquisition module, the operation data of the electric bus is obtained in real time, including the position, speed, remaining battery power, current number of passengers, and environmental information;

[0027] Step 2: Regional demand analysis: Based on historical passenger demand data and real-time collected passenger data, analyze the passenger flow change trend in each region, predict future passenger flow demand, and adjust the regional dispatch plan of buses;

[0028] Step 3: Range prediction: Based on the battery power, real-time operating speed, terrain conditions, and weather conditions of the electric bus, a range prediction algorithm is used to calculate the vehicle's range to optimize the charging strategy.

[0029] Step 4: Charging priority setting: Set charging priorities for electric buses based on battery power, driving range, operational urgency and charging facility availability to ensure that key vehicles are charged first;

[0030] Step 5: Charging time window optimization: Through the intelligent optimization module, combined with the grid load conditions, the charging time window is optimized to avoid the peak period of the grid and reduce the impact of charging on the grid;

[0031] Step 6: Real-time adjustment of dispatch plan: According to the real-time operation status of buses and emergencies, the dispatch plan is adjusted dynamically to ensure the continuity and stability of the operation of the electric bus fleet;

[0032] Step 7: Charging load management: Collaborate with the grid management system to dynamically adjust the charging power and time according to the real-time charging demand and grid load conditions to ensure smooth operation of the grid and avoid overload;

[0033] Step 8: Dispatch and charging strategy optimization: Analyze historical and real-time data through machine learning algorithms to continuously optimize dispatch and charging strategies, improve the overall operational efficiency of the electric bus fleet, and extend battery life;

[0034] Step 9: Emergency handling: When traffic jams or emergency repairs are detected, the system automatically triggers emergency dispatch measures, reallocates vehicle resources and adjusts charging plans to ensure smooth operations;

[0035] Step 10: Manual intervention and control: Through the display and control module, dispatchers are allowed to monitor the operating status and charging arrangements of the bus fleet in real time, and conduct manual intervention and adjustments based on actual conditions to ensure the flexibility and safety of system operation.

[0036] Compared with the prior art, the present invention has the following beneficial effects:

[0037] In the present invention, through real-time data analysis and intelligent optimization, the system can dynamically adjust the bus's route and charging plan to improve the fleet's operational efficiency. The charging strategy module combines battery power, range prediction and charging station load to optimize the charging process and reduce charging time and cost. The intelligent optimization module considers the battery life optimization strategy and extends the battery life by reasonably scheduling charging time and power. The system can adapt to different operational needs and grid load changes to ensure the efficient operation of electric buses. At the same time, the display and control module provides a visual interface to support manual intervention and control, increasing the flexibility of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 The present invention is a flow chart of an intelligent dispatching optimization system based on real-time public transportation data and a control method thereof. DETAILED DESCRIPTION

[0039] See also Figure 1 , the present invention provides a technical solution:

[0040] The intelligent dispatching optimization system based on real-time bus data and its control method include data acquisition module, regional dispatching module, charging strategy module, intelligent optimization module, communication module and display and control module; the data acquisition module is used to collect the operation data of electric buses in real time, including position, speed, remaining battery power, current number of passengers and environmental information; the regional dispatching module dynamically dispatches the electric bus fleet according to real-time operation data and regional demand, and optimizes the distribution and operation route of the buses; the charging strategy module optimizes the charging process of electric buses by combining battery power, mileage prediction, charging station location and charging load, and balances charging demand and grid load; the intelligent optimization module sets the charging priority by comprehensively considering the charging priority. The system uses strategies such as scheduling, charging time window optimization, charging power optimization, multi-vehicle charging scheduling and battery life optimization to form an optimization model to maximize the fleet operation efficiency, reduce charging time and extend battery life. The communication module is used to interact with the bus dispatch center, charging station and power grid management system to ensure real-time information transmission and decision execution. The display and control module provides a visual interface to display the operation status, scheduling plan and charging arrangement of the bus fleet in real time, and supports manual intervention and control. Through real-time data collection and intelligent optimization, it can dynamically adjust the bus's operation route and charging plan, thereby improving the operation efficiency and the intelligent level of charging management. At the same time, it supports manual intervention and increases the flexibility and adaptability of the system.

[0041] As a technical solution for further implementation of this solution, the charging strategy module includes a charging priority setting strategy, which specifically calculates the charging priority of the vehicle through the following formula:

[0042]

[0043] Where: P i is the charging priority of vehicle i, C max is the upper limit of battery capacity, C i is the current battery power, R rep is the required cruising range, R i is the remaining cruising range of vehicle i, U i For vehicle mission urgency, U max is the maximum value of the task urgency, w1, w2 and w3 are weight coefficients. Through the charging priority setting strategy, the system can intelligently determine the charging order according to the vehicle's power, range requirements and task urgency, ensuring that vehicles with high priority can be charged in time, thereby improving the charging efficiency and the overall operational efficiency of the fleet;

[0044] As a technical solution for further implementation of this solution, the charging strategy module includes a charging time window optimization strategy, and the optimal charging time window is selected by the following formula:

[0045]

[0046] Where, T opt is the start time of the optimal charging time window, T s and e are the earliest and latest time that the vehicle can be charged, respectively. λ(t) is the load of the power grid during time period t. L(t) is the demand for vehicle charging during time period t. The charging time window optimization strategy enables the system to charge vehicles during periods of low power grid load, thereby reducing the impact on the power grid and achieving a balanced power grid load. At the same time, it ensures that vehicles can be charged at the right time and improves the utilization rate of charging facilities.

[0047] As a technical solution for further implementation of this solution, the charging strategy module includes a charging power optimization strategy, which dynamically adjusts the charging power through the following formula:

[0048]

[0049] Where P(t) is the charging power of the vehicle at time t, P max is the maximum power of the charging station, C i (t) is the battery power of the vehicle at time t, T i is the estimated charging time of vehicle i, λ(t) is the grid load during time period t, Λ is the load upper limit of the grid, and the charging power optimization strategy allows the system to dynamically adjust the charging power according to the real-time grid load and the charging demand of the vehicle, ensuring that the charging process is both efficient and does not exceed the load upper limit of the grid, thereby improving charging safety and grid stability;

[0050] As a technical solution for further implementation of this solution, the charging strategy module includes a multi-vehicle charging scheduling strategy, which specifically optimizes the multi-vehicle charging scheduling of the charging station through the following formula:

[0051]

[0052] In the formula, S j For charging station j in time window ΔT j The total charge capacity in j is the set of vehicles charging simultaneously at charging station j, P station is the maximum charging capacity of the charging station, T i The multi-vehicle charging scheduling strategy enables the system to schedule charging for multiple electric buses at the same charging station, optimize the allocation of charging resources, reduce the charging waiting time, and improve the service efficiency of the charging station and the operation efficiency of the vehicle;

[0053] As a technical solution for further implementation of this solution, the charging strategy module includes a battery life optimization strategy, which specifically optimizes the battery life through the following formula:

[0054]

[0055] Where, L b is the battery life, L0 is the initial battery life, k and n are the battery attenuation coefficients, which are related to the charging power, and P(t1) is the charging power, which can be used to calculate the battery life;

[0056] As a technical solution for further implementation of this plan, the intelligent optimization module builds a multi-objective optimization model based on the above-mentioned charging priority setting, charging time window optimization, charging power optimization, multi-vehicle charging scheduling and battery life optimization strategy to comprehensively optimize the overall operational efficiency and battery life of the fleet. By building a multi-objective optimization model, this module can achieve comprehensive optimization of the fleet's operational efficiency and battery life, ensuring that electric buses can extend battery life and reduce maintenance costs while meeting operational needs;

[0057] As a technical solution for further implementation of this solution, the objective function of the multi-objective optimization model includes minimizing charging waiting time, minimizing impact on the power grid, extending battery life, minimizing charging cost and maximizing operational efficiency. The multi-objective optimization model is solved by a multi-objective optimization algorithm, including particle swarm optimization, genetic algorithm or simulated annealing algorithm. The multi-objective optimization model comprehensively considers multiple optimization objectives, so that the system can reduce the burden on the power grid, reduce charging costs, and extend battery life while ensuring operational efficiency, thereby achieving a dual improvement in economic and environmental benefits;

[0058] As a technical solution for further implementation of this plan, the intelligent optimization module can dynamically adjust the weight coefficients of each strategy through machine learning algorithms based on real-time data and historical data to adapt to the ever-changing operating environment. The intelligent optimization module uses machine learning algorithms to dynamically adjust the strategy weights, so that the system can adaptively respond to changes in the operating environment, improve the flexibility and accuracy of scheduling decisions, and ensure that the system can maintain optimal performance under various conditions;

[0059] As a technical solution for further implementation of this plan, the following steps are included: Step 1: Data acquisition: Through the data acquisition module, the operation data of the electric bus is obtained in real time, including the position, speed, remaining battery power, current number of passengers, and environmental information;

[0060] Step 2: Regional demand analysis: Based on historical passenger demand data and real-time collected passenger data, analyze the passenger flow change trend in each region, predict future passenger flow demand, and adjust the regional dispatch plan of buses;

[0061] Step 3: Range prediction: Based on the battery power, real-time operating speed, terrain conditions, and weather conditions of the electric bus, a range prediction algorithm is used to calculate the vehicle's range to optimize the charging strategy.

[0062] Step 4: Charging priority setting: Set charging priorities for electric buses based on battery power, driving range, operational urgency and charging facility availability to ensure that key vehicles are charged first;

[0063] Step 5: Charging time window optimization: Through the intelligent optimization module, combined with the grid load conditions, the charging time window is optimized to avoid the peak period of the grid and reduce the impact of charging on the grid;

[0064] Step 6: Real-time adjustment of dispatch plan: According to the real-time operation status of buses and emergencies, the dispatch plan is adjusted dynamically to ensure the continuity and stability of the operation of the electric bus fleet;

[0065] Step 7: Charging load management: Collaborate with the grid management system to dynamically adjust the charging power and time according to the real-time charging demand and grid load conditions to ensure smooth operation of the grid and avoid overload;

[0066] Step 8: Dispatch and charging strategy optimization: Analyze historical and real-time data through machine learning algorithms to continuously optimize dispatch and charging strategies, improve the overall operational efficiency of the electric bus fleet, and extend battery life;

[0067] Step 9: Emergency handling: When traffic jams or emergency repairs are detected, the system automatically triggers emergency dispatch measures, reallocates vehicle resources and adjusts charging plans to ensure smooth operations;

[0068] Step 10: Manual intervention and control: Through the display and control module, dispatchers are allowed to monitor the operating status and charging arrangements of the bus fleet in real time, and conduct manual intervention and adjustments based on actual conditions to ensure the flexibility and safety of system operation.

[0069] This article uses specific examples to illustrate the principles and implementation methods of the present invention. The above examples are only used to help understand the method and core ideas of the present invention. The above is only a preferred implementation of the present invention. It should be pointed out that due to the limitations of textual expression and the objective existence of infinite specific structures, ordinary technicians in this technical field can make several improvements, modifications or changes without departing from the principles of the present invention, and can also combine the above technical features in an appropriate manner; these improvements, modifications, changes or combinations, or the direct application of the inventive concept and technical solution to other occasions without improvement, should be regarded as the protection scope of the present invention.

Claims

1. Intelligent dispatch optimization system based on real-time bus data, characterized by: It includes data acquisition module, regional scheduling module, charging strategy module, intelligent optimization module, communication module and display and control module; The data acquisition module is used to collect the operation data of the electric bus in real time, including the position, speed, remaining battery power, current number of passengers, and environmental information; The regional dispatch module dynamically dispatches the electric bus fleet according to real-time operation data and regional demand, and optimizes the distribution and operation routes of the buses; The charging strategy module optimizes the charging process of the electric bus by combining battery power, range prediction, charging station location and charging load, balancing charging demand and grid load; The intelligent optimization module forms an optimization model by comprehensively considering strategies such as charging priority setting, charging time window optimization, charging power optimization, multi-vehicle charging scheduling and battery life optimization to maximize fleet operation efficiency, reduce charging time and extend battery life; The communication module is used to exchange data with the bus dispatch center, charging stations and power grid management system to ensure real-time information transmission and decision execution; The display and control module provides a visual interface, displays the operation status, dispatch plan and charging arrangement of the bus fleet in real time, and supports manual intervention and control.

2. The intelligent dispatching optimization system based on real-time public transportation data according to claim 1 is characterized in that: The charging strategy module includes a charging priority setting strategy, which specifically calculates the charging priority of the vehicle through the following formula: Where: P i is the charging priority of vehicle i, C max is the upper limit of battery capacity, C i is the current battery power, R rep is the required cruising range, R i is the remaining cruising range of vehicle i, U i For vehicle mission urgency, U max is the maximum value of task urgency, w1, w2 and w3 are weight coefficients.

3. The intelligent dispatching optimization system based on real-time public transportation data according to claim 1 is characterized in that: The charging strategy module includes a charging time window optimization strategy, and specifically selects the optimal charging time window through the following formula: Where, T opt is the start time of the optimal charging time window, T s and e are the earliest and latest time that the vehicle can be charged, respectively; λ(t) is the load of the power grid during time period t; and L(t) is the vehicle charging demand during time period t.

4. The intelligent dispatching optimization system based on real-time public transportation data according to claim 1 is characterized in that: The charging strategy module includes a charging power optimization strategy, which dynamically adjusts the charging power through the following formula: Where P(t) is the charging power of the vehicle at time t, P max is the maximum power of the charging station, C i (t) is the battery power of the vehicle at time t, T i is the expected charging time of vehicle i, λ(t) is the grid load during time period t, and Λ is the upper limit of the grid load.

5. The intelligent dispatching optimization system based on real-time public transportation data according to claim 1 is characterized in that: The charging strategy module includes a multi-vehicle charging scheduling strategy, which specifically optimizes the multi-vehicle charging scheduling of the charging station through the following formula: In the formula, S j For charging station j in time window ΔT j The total charge capacity in j is the set of vehicles charging simultaneously at charging station j, P station is the maximum charging capacity of the charging station, T i is the charging time of vehicle i.

6. The intelligent dispatching optimization system based on real-time public transportation data according to claim 1 is characterized in that: The charging strategy module includes a battery life optimization strategy, which specifically optimizes the battery life through the following formula: Where, L b is the battery life, L0 is the initial battery life, k and n are the battery attenuation coefficients, which are related to the charging power, and P(t1) is the charging power.

7. The intelligent dispatching optimization system based on real-time public transportation data according to claim 1 is characterized in that: The intelligent optimization module constructs a multi-objective optimization model based on the above-mentioned charging priority setting, charging time window optimization, charging power optimization, multi-vehicle charging scheduling and battery life optimization strategy to comprehensively optimize the overall operating efficiency and battery life of the fleet.

8. The intelligent dispatching optimization system based on real-time public transportation data according to claim 1 is characterized in that: The objective functions of the multi-objective optimization model include minimizing charging waiting time, minimizing impact on the power grid, extending battery life, minimizing charging costs and maximizing operational efficiency. The multi-objective optimization model is solved by a multi-objective optimization algorithm, including particle swarm optimization, genetic algorithm or simulated annealing algorithm.

9. The intelligent dispatching optimization system based on real-time public transportation data according to claim 1 is characterized in that: The intelligent optimization module can dynamically adjust the weight coefficient of each strategy through machine learning algorithms based on real-time data and historical data to adapt to the ever-changing operating environment.

10. The control method of the intelligent dispatching optimization system based on real-time public transportation data according to any one of claims 1 to 9, characterized in that: The method comprises the following steps: Step 1: Data acquisition: through the data acquisition module, the operation data of the electric bus is obtained in real time, including the position, speed, remaining battery power, current number of passengers, and environmental information; Step 2: Regional demand analysis: Based on historical passenger demand data and real-time collected passenger data, analyze the passenger flow change trend in each region, predict future passenger flow demand, and adjust the regional dispatch plan of buses; Step 3: Range prediction: Based on the battery power, real-time operating speed, terrain conditions, and weather conditions of the electric bus, a range prediction algorithm is used to calculate the vehicle's range to optimize the charging strategy. Step 4: Charging priority setting: Set charging priorities for electric buses based on battery power, driving range, operational urgency and charging facility availability to ensure that key vehicles are charged first; Step 5: Charging time window optimization: Through the intelligent optimization module, combined with the grid load conditions, the charging time window is optimized to avoid the peak period of the grid and reduce the impact of charging on the grid; Step 6: Real-time adjustment of dispatch plan: Dynamically adjust the dispatch plan according to the real-time operation status of the bus and emergencies to ensure the continuity and stability of the electric bus fleet operation; Step 7: Charging load management: Collaborate with the grid management system to dynamically adjust the charging power and time according to the real-time charging demand and grid load conditions to ensure smooth operation of the grid and avoid overload; Step 8: Dispatch and charging strategy optimization: Analyze historical and real-time data through machine learning algorithms to continuously optimize dispatch and charging strategies, improve the overall operational efficiency of the electric bus fleet, and extend battery life; Step 9: Emergency handling: When traffic jams or emergency repairs are detected, the system automatically triggers emergency dispatch measures, reallocates vehicle resources and adjusts charging plans to ensure smooth operations; Step 10: Manual intervention and control: Through the display and control module, dispatchers are allowed to monitor the operating status and charging arrangements of the bus fleet in real time, and conduct manual intervention and adjustments based on actual conditions to ensure the flexibility and safety of system operation.

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