A method, device and medium for dynamic optimization of fleet speed and carbon emissions

Abstract

The application discloses a kind of transport fleet speed and carbon emission dynamic optimization method, equipment and medium, comprising: constructing the transport fleet including fuel car and electric car, based on transport data, calculate the rolling resistance, slope resistance, air resistance and calculate resistance in the process of vehicle driving, construct the fuel consumption model of fuel car, the power consumption rate model of electric car, construct the objective function of vehicle carbon emission rate optimization, set the optimization rule of dynamic weight factor in objective function, determine whether congestion appears at unloading point in prediction time domain or according to real-time carbon emission weight correction coefficient, optimize dynamic weight factor in objective function, output optimal speed sequence, based on optimal speed sequence adjust the speed of each vehicle in transport fleet in real time.The equipment includes one or more processors, memory, and the medium has a computer program stored for executing the method.The application has the advantages of significant emission reduction effect, system collaborative optimization, intelligent self-adaptation, high practicality and universality, and improved comprehensive benefits.

Description

Technical Field

[0001] This invention relates to the field of green transportation and intelligent scheduling technology, specifically to a method, equipment, and medium for dynamically optimizing the speed and carbon emissions of a transportation fleet. Background Technology

[0002] Carbon emissions from transportation have long accounted for over 20% of global emissions, and this proportion is even higher in many developed countries. Road transport is the dominant contributor, accounting for the vast majority of transportation carbon emissions. Canals are major national infrastructure projects characterized by massive earthwork, long construction periods, and concentrated resource consumption. The earthwork transportation phase is a major contributor to energy consumption and carbon emissions, with numerous heavy-duty dump trucks and other transport equipment continuously operating both inside and outside the construction site.

[0003] Currently, carbon emission management in earthmoving transportation is inadequate and faces several challenges: vehicle speed is largely determined by drivers' subjective experience, leading to uneconomical driving behaviors such as rapid acceleration and deceleration, resulting in energy waste and increased carbon emissions, and overall speed management is inefficient; modern construction sites typically employ mixed fleets of gasoline and electric vehicles, whose carbon emission mechanisms are completely different, making traditional optimization strategies difficult to apply and hindering overall fleet emission reduction; construction sites have complex road conditions, with constantly changing temporary road gradients and road surfaces, frequent changes in vehicle load, and frequent congestion at loading and unloading points; the difficulty in coordinating vehicle operations at the system level can lead to congestion at critical points.

[0004] In summary, existing technologies lack a solution that can respond in real time to complex and dynamic working conditions, systematically and collaboratively optimize mixed vehicle fleets at the speed of minimizing carbon emissions, and achieve a low-carbon and efficient earthmoving transportation method. Summary of the Invention

[0005] To address the aforementioned shortcomings of existing technologies, this invention provides a method, equipment, and medium for dynamically optimizing the speed and carbon emissions of a transportation fleet.

[0006] To achieve the above-mentioned objectives, the technical solution adopted by this invention is as follows: A method for dynamically optimizing the speed and carbon emissions of a transport fleet is provided, comprising the following steps: S1: Construct a transportation fleet that includes both fuel-powered and electric vehicles, acquire real-time transportation data from vehicles using onboard IoT terminals, and calculate the rolling resistance of the vehicles during operation based on this data. Slope resistance air resistance and calculate resistance ; S2: Based on rolling resistance Slope resistance air resistance and calculate resistance Construct fuel consumption models for gasoline vehicles and electricity consumption rate models for electric vehicles, and calculate the real-time carbon emission rate of gasoline vehicles and the indirect carbon emission rate of electric vehicles. S3: Construct an objective function for optimizing vehicle carbon emission rates using real-time carbon emission rates of gasoline vehicles and indirect carbon emission rates of electric vehicles, and set the discrete step length for speed optimization. and time step and constraints; S4: Based on regional power grid carbon intensity factor Real-time queue length at unloading points Set the optimization rules for the dynamic weighting factors within the objective function; S5: Determine whether congestion occurs at the unloading point within the prediction time domain, or optimize the dynamic weighting factor within the objective function based on the real-time carbon emission weighting correction coefficient, and optimize the discrete step size based on the objective function and the set velocity. and time step Optimize vehicle speeds, output the optimal speed sequence, and adjust the real-time speed of each vehicle in the transport fleet based on the optimal speed sequence.

[0007] Further, step S1 includes: S11: Construct a transportation fleet that includes fuel-powered vehicles and electric vehicles. Each vehicle in the fleet is equipped with an onboard IoT terminal to obtain real-time transportation data. S12: Instantaneous speed based on vehicle movement Acceleration a Total vehicle mass m The road gradient ahead of the driving section i Calculate the rolling resistance during vehicle movement Slope resistance air resistance and calculate resistance ; Rolling resistance The calculation method is as follows: ;in, g It is the acceleration due to gravity. This refers to the tire rolling resistance coefficient. Gradient resistance during driving The calculation method is as follows: ; Air resistance during driving The calculation method is as follows: ;in, air density, This is the drag coefficient. For windward area; Calculate resistance The calculation method is as follows: ; This is the rotational mass conversion factor.

[0008] Further, step S2 includes: S21: Based on rolling resistance Slope resistance air resistance and calculate resistance Construct a fuel consumption model for gasoline-powered vehicles and calculate their real-time fuel consumption. ; ; in, This is the engine thermal efficiency correction factor. It is a fuel with a low calorific value; S22: Based on the real-time fuel consumption of gasoline vehicles Calculate the real-time carbon emission rate of gasoline vehicles ; ; in, Carbon emission factors of fuel used in gasoline-powered vehicles; S23: Discharge efficiency of electric vehicle batteries at different states of charge (SOC) and the energy recovery efficiency of the braking system. Construct an energy consumption rate model for electric vehicles to calculate their real-time energy consumption. ; ; in, For electric vehicles during braking acceleration a Braking correction coefficient under certain conditions; when the electric vehicle accelerates... hour, This indicates the energy recovery rate, as acceleration... hour, Indicates energy consumption; S24: Based on the real-time power consumption of the electric vehicle Calculate the indirect carbon emission rate of electric vehicles ; ; in, This represents the carbon intensity factor of the regional power grid.

[0009] Further, step S3 includes: S31: Utilizing the real-time carbon emission rate of gasoline vehicles and the indirect carbon emission rate of electric vehicles Built in the prediction time domain T The objective function for optimizing the carbon emission rates of all vehicles within the internal transport fleet ; ; in, i This refers to the number of the fuel-powered vehicles in the transport fleet. The number of fuel-powered vehicles in the transport fleet. For the first i The carbon emission rate of a fuel-powered vehicle As a dynamic weighting factor for carbon emissions from gasoline vehicles, As a dynamic weighting factor for carbon emissions from electric vehicles, j This refers to the serial numbers of the electric vehicles in the transport fleet. For the first i The indirect carbon emission rate of an electric vehicle For congestion penalty dynamic weighting factor, This refers to the length of the vehicle queue at the unloading point. for t The unloading point number at any given time. express t The unloading point is constantly changing due to the length of the vehicle queue. The penalty items, Gather at the unloading point; S32: The constraint is that the vehicle reaches the destination within the time window required by the mission, and the limits of the vehicle's speed and acceleration are used as physical limit constraints; and the distance step length for speed optimization is adaptively determined based on the current vehicle state and the characteristics of the road conditions ahead. and time step Specifically: Extract the sequence of road curve curvature radii within a set distance in front of the vehicle. and slope sequence , M The number of road curves. U This refers to the slope quantity; like or If the road conditions ahead are deemed complex, the optimal walking distance for speed optimization will be set. Time step Otherwise, if the road conditions ahead are deemed good, the optimal walking distance for speed is set. Time step .

[0010] Furthermore, the optimization rules for the dynamic weighting factors within the objective function specifically include: Dynamic weighting factors for optimizing electric vehicle carbon emissions based on vehicle carbon emissions. : when If the real-time grid carbon intensity is at a low point, the carbon emission weighting correction factor is calculated. , The average carbon intensity of the regional power grid is adjusted using carbon emission weighting factors. Update dynamic weighting factors ; ; in, Updated dynamic weighting factors for carbon emissions from electric vehicles The dynamic weighting factor for the carbon emissions of electric vehicles before the update; when When the carbon intensity of the regional power grid is at a low point (such as during the peak period of photovoltaic power generation), the gradient direction of the carbon emission weight is calculated to reduce the dynamic weight factor of electric vehicle carbon emissions and guide the algorithm to find a speed curve in the solution space that is conducive to electric vehicles carrying more loads and running faster.

[0011] Optimize the dynamic weighting factor of congestion penalty based on unloading efficiency. ; Based on the vehicle arrival rate at the unloading point during historical time periods and unloading processing rate Calculate traffic intensity ; ; Vehicle arrival rate This represents the number of vehicles arriving at the unloading point per unit time within a historical period, and the unloading processing rate. This indicates the number of vehicles that completed unloading per unit of time within a historical period; When traffic intensity Or real-time queue length When this is the case, an amplification factor is introduced. Amplify the dynamic weighting factor of congestion penalty ; ; in, The amplified dynamic weighting factor for congestion penalties Dynamic weighting factor for congestion penalty before amplification This is the set queue length threshold.

[0012] Further, step S5 includes: The method for determining whether congestion occurs at the unloading point within the prediction time domain and optimizing the dynamic weighting factor in the objective function includes the following steps: In the prediction time domainT Inside, based on the vehicle's current speed and location Calculate the set of times when each vehicle arrives at the unloading point. , K This refers to the number of vehicles in the transport fleet. For the first K The time it takes for the vehicle to arrive at the unloading point; If time set There exists and If congestion occurs, proceed to step S11; otherwise, no congestion has occurred. Perform step S4 to introduce the amplification factor. Amplify the dynamic weighting factor of congestion penalty Then proceed to step S6; The method for optimizing the dynamic weighting factor within the objective function based on the real-time carbon emission weighting correction coefficient is as follows: Real-time monitoring of the regional power grid carbon intensity and calculation of the real-time carbon emission weighting correction coefficient. ,like Then proceed to step S4, based on the carbon emission weighting correction coefficient. Update dynamic weighting factors Then proceed to step S6; otherwise, maintain the dynamic weighting factor. constant.

[0013] Step S6: Objective function based on updated dynamic weight factors J And the set speed optimization distance from the walking length and time step Iteratively optimize the real-time speed of each vehicle. The objective function is calculated in each iteration of optimization. until satisfied If convergence is achieved, the optimal velocity sequence after convergence will be output. , For prediction of the time domain T Internally optimized vehicle speed e To optimize the number of iterations; If iterative optimization has been performed E If it still does not converge after this, then take E The velocity sequence corresponding to the minimum value of each objective function is taken as the optimal velocity sequence. ; Based on the optimal velocity sequence Adjust the real-time speed of each vehicle in the transport fleet.

[0014] An electronic device is provided, comprising: one or more processors; and a memory for storing one or more programs, which, when executed by the one or more processors, cause the one or more processors to implement the above-described method for dynamically optimizing the speed and carbon emissions of a transport fleet.

[0015] A computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the above-described method for dynamically optimizing the speed and carbon emissions of a transport fleet.

[0016] The beneficial effects of this invention are as follows: Significant emission reduction effect: Through refined and dynamic speed management, combined with fleet collaborative scheduling, the overall carbon emissions of mixed fleets can be effectively reduced by 10%-20%, resulting in outstanding environmental benefits.

[0017] System collaborative optimization: It breaks through the limitations of single-vehicle optimization, starts from the system as a whole, and actively manages traffic flow through fine-tuning of speed, avoiding huge carbon emissions caused by congestion, improving construction efficiency, and achieving an emission reduction effect of 1+1>2.

[0018] Strong intelligent self-adaptation: It adopts model predictive control algorithm, which can perform rolling optimization and respond to dynamic changes such as road conditions and load in real time. It has strong robustness and is superior to static or fixed rule-based speed recommendation strategies.

[0019] High practicality and versatility: The model is compatible with both fuel-powered and electric vehicles and can respond to changes in the cleanliness of the power grid. It is perfectly suited for the mixed fleet scenarios commonly seen in modern green construction sites and has high promotional value.

[0020] Enhancing overall benefits: While reducing carbon emissions, smooth traffic flow also reduces vehicle wear and tear and accident risks, improves overall transportation efficiency, and achieves a win-win situation for both environmental and economic benefits. Attached Figure Description

[0021] Figure 1 A flowchart for a method to dynamically optimize the speed and carbon emissions of a transport fleet. Detailed Implementation

[0022] The specific embodiments of the present invention are described below to enable those skilled in the art to understand the present invention. However, it should be understood that the present invention is not limited to the scope of the specific embodiments. For those skilled in the art, various changes are obvious as long as they are within the spirit and scope of the present invention as defined and determined by the appended claims. All inventions utilizing the concept of the present invention are protected.

[0023] like Figure 1 As shown, a method for dynamically optimizing the speed and carbon emissions of a transport fleet includes the following steps: S1: Construct a transportation fleet that includes both fuel-powered and electric vehicles, acquire real-time transportation data from vehicles using onboard IoT terminals, and calculate the rolling resistance of the vehicles during operation based on this data. Slope resistance air resistance and calculate resistance .

[0024] Step S1 specifically includes the following steps: S11: Construct a transportation fleet including fuel-powered vehicles and electric vehicles. Each vehicle in the fleet is equipped with an onboard IoT terminal to obtain real-time transportation data. In this embodiment, the transportation data includes the vehicle's ID and location. Instantaneous speed, engine speed, motor power, real-time fuel consumption, electrical energy consumption, load status, acceleration, load mass, etc. This embodiment takes a construction section of a large canal project as an example. Under the traditional operation mode, after vehicles load soil in the excavation area, they drive to the unloading area (unloading point), which easily leads to severe congestion at the entrance of the unloading area. The average waiting time for vehicles is as long as 20 minutes, and idling emissions are high. This invention is applied to manage a soil transportation fleet consisting of 15 diesel dump trucks and 5 electric dump trucks.

[0025] Data collection: All 20 vehicles were equipped with onboard IoT terminals (OBD interface + GPS) to report speed, location, engine speed, fuel consumption, power consumption, and load status (estimated by airbag pressure sensors) in real time. 5G network coverage was established in the construction area.

[0026] System integration: The cloud-based decision-making center is connected to a high-precision map (including temporary road slope information) and the project department's fleet dispatch management system (to obtain the real-time number of vehicles queuing in the unloading area).

[0027] Suppose that at 2 PM on a certain weekday afternoon, the system begins running a control cycle (predictive time domain). T The cycle is 60 seconds.

[0028] The system detected 12 vehicles currently in operation: Vehicles V1-V3 (diesel) and V4 (electric) are fully loaded and only 500-800 meters from the unloading area; Vehicles V5-V12 (hybrid) are 1.5-3 kilometers away and are approaching. According to the fleet dispatch management system, two vehicles are currently unloading at the unloading area, and their unloading operation is expected to be completed in another 5 minutes. The high-precision map shows that the temporary road leading to the unloading area has a 200-meter-long uphill section with a 5% gradient.

[0029] S12: Instantaneous speed based on vehicle movement Acceleration aTotal vehicle mass m The road gradient ahead of the driving section i Calculate the rolling resistance during vehicle movement Slope resistance air resistance and calculate resistance ; Rolling resistance The calculation method is as follows: ;in, g It is the acceleration due to gravity. This refers to the tire rolling resistance coefficient. Gradient resistance during driving The calculation method is as follows: ; Air resistance during driving The calculation method is as follows: ;in, air density, This is the drag coefficient. For windward area; Calculate resistance The calculation method is as follows: ; This is the rotational mass conversion factor.

[0030] S2: Based on rolling resistance Slope resistance air resistance and calculate resistance Construct fuel consumption models for gasoline-powered vehicles and energy consumption rate models for electric vehicles. Step S2 specifically includes the following steps: S21: Based on rolling resistance Slope resistance air resistance and calculate resistance Construct a fuel consumption model for gasoline-powered vehicles and calculate their real-time fuel consumption. ; ; in, This is the engine thermal efficiency correction factor. It is a fuel with a low calorific value; S22: Based on the real-time fuel consumption of gasoline vehicles Calculate the real-time carbon emission rate of gasoline vehicles ; ; in, Carbon emission factors of fuel used in gasoline-powered vehicles; S23: Discharge efficiency of electric vehicle batteries at different states of charge (SOC) and the energy recovery efficiency of the braking system. Construct an energy consumption rate model for electric vehicles to calculate their real-time energy consumption. ; ; in, For electric vehicles during braking acceleration a Braking correction coefficient under certain conditions; when the electric vehicle accelerates... hour, This indicates the energy recovery rate, as acceleration... hour, Indicates energy consumption; S24: Based on the real-time power consumption of the electric vehicle Calculate the indirect carbon emission rate of electric vehicles ; ; in, This represents the carbon intensity factor of the regional power grid.

[0031] S3: Construct an objective function for optimizing vehicle carbon emission rates using real-time carbon emission rates of gasoline vehicles and indirect carbon emission rates of electric vehicles, and set the discrete step length for speed optimization. and time step and constraints. Step S3 specifically includes the following steps: S31: Utilizing the real-time carbon emission rate of gasoline vehicles and the indirect carbon emission rate of electric vehicles Built in the prediction time domain T The objective function for optimizing the carbon emission rates of all vehicles within the internal transport fleet ; ; in, i This refers to the number of the fuel-powered vehicles in the transport fleet. The number of fuel-powered vehicles in the transport fleet. For the first i The carbon emission rate of a fuel-powered vehicle As a dynamic weighting factor for carbon emissions from gasoline vehicles, As a dynamic weighting factor for carbon emissions from electric vehicles, j This refers to the serial numbers of the electric vehicles in the transport fleet. For the first i The indirect carbon emission rate of an electric vehicle For congestion penalty dynamic weighting factor, This refers to the length of the vehicle queue at the unloading point. for t The unloading point number at any given time. express t The unloading point is constantly changing due to the length of the vehicle queue. The penalty items, Gather at the unloading point; S32: The constraint is that the vehicle reaches the destination within the time window required by the mission, and the limits of the vehicle's speed and acceleration are used as physical limit constraints; and the distance step length for speed optimization is adaptively determined based on the current vehicle state and the characteristics of the road conditions ahead. and time step Specifically: Extract the sequence of road curve curvature radii within a set distance in front of the vehicle. and slope sequence , M The number of road curves. U This refers to the slope quantity; like or If the road conditions ahead are deemed complex, the optimal walking distance for speed optimization will be set. Time step Otherwise, if the road conditions ahead are deemed good, the optimal walking distance for speed is set. Time step .

[0032] S4: Based on regional power grid carbon intensity factor Real-time queue length at unloading points The optimization rules for the dynamic weighting factors within the objective function are set, specifically including: Dynamic weighting factors for optimizing electric vehicle carbon emissions based on vehicle carbon emissions. : when If the real-time grid carbon intensity is at a low point, the carbon emission weighting correction factor is calculated. , The average carbon intensity of the regional power grid is adjusted using carbon emission weighting factors. Update dynamic weighting factors ; ; in, Updated dynamic weighting factors for carbon emissions from electric vehicles The dynamic weighting factor for the carbon emissions of electric vehicles before the update; when When the carbon intensity of the regional power grid is at a low point (such as during the peak period of photovoltaic power generation), the gradient direction of the carbon emission weight is calculated to reduce the dynamic weight factor of electric vehicle carbon emissions and guide the algorithm to find a speed curve in the solution space that is conducive to electric vehicles carrying more loads and running faster.

[0033] Optimize the dynamic weighting factor of congestion penalty based on unloading efficiency. ; Based on the vehicle arrival rate at the unloading point during historical time periods and unloading processing rate Calculate traffic intensity ; ; Vehicle arrival rate This represents the number of vehicles arriving at the unloading point per unit time within a historical period, and the unloading processing rate. This indicates the number of vehicles that completed unloading per unit of time within a historical period; In this embodiment, based on the current positions and speeds of the 12 vehicles, it is predicted that 15 vehicles will arrive at the unloading area within the next 15 minutes. Therefore, the vehicle arrival rate is... =15 vehicles / 15 minutes = 1 vehicle / minute. Based on the most recent 10 unloading records, the system calculates the average service time to be 6 minutes per vehicle; therefore, the unloading processing rate is... Vehicles per minute. Calculate traffic intensity: ; When traffic intensity Or real-time queue length When this is the case, an amplification factor is introduced. Amplify the dynamic weighting factor of congestion penalty ; ; in, The amplified dynamic weighting factor for congestion penalties Dynamic weighting factor for congestion penalty before amplification This is the set queue length threshold.

[0034] In this embodiment, it is necessary to adjust the dynamic weighting factor of congestion penalty. Set to 100 times the baseline value, that is At the same time, the carbon intensity of the power grid was checked. (Normal), therefore the dynamic weighting factor for carbon emissions from fuel vehicles Dynamic weighting factors for carbon emissions from electric vehicles Maintain the baseline value.

[0035] At this point, the algorithm will prioritize traffic management as the highest priority and amplify the factor. For a large value, such as 10 or 100, this operation makes the objective function... In this context, any potential solution that could exacerbate congestion will incur a huge penalty value.

[0036] S5: Determine whether congestion occurs at the unloading point within the prediction time domain, or optimize the dynamic weighting factor within the objective function based on the real-time carbon emission weighting correction coefficient, and optimize the discrete step size based on the objective function and the set velocity. and time step Optimize vehicle speeds, output the optimal speed sequence, and adjust the real-time speed of each vehicle in the transport fleet based on the optimal speed sequence.

[0037] Step S5 specifically includes the following steps: The method for determining whether congestion occurs at the unloading point within the prediction time domain and optimizing the dynamic weighting factor in the objective function includes the following steps: In the prediction time domain T Inside, based on the vehicle's current speed and location Calculate the set of times when each vehicle arrives at the unloading point. , K This refers to the number of vehicles in the transport fleet. For the first K The time it takes for the vehicle to arrive at the unloading point; If time set There exists and If congestion occurs, proceed to step S11; otherwise, no congestion has occurred. Perform step S4 to introduce the amplification factor. Amplify the dynamic weighting factor of congestion penalty Then proceed to step S6; The method for optimizing the dynamic weighting factor within the objective function based on the real-time carbon emission weighting correction coefficient is as follows: Real-time monitoring of the regional power grid carbon intensity and calculation of the real-time carbon emission weighting correction coefficient. ,like Then proceed to step S4, based on the carbon emission weighting correction coefficient. Update dynamic weighting factors Then proceed to step S6; otherwise, maintain the dynamic weighting factor. constant.

[0038] Step S6: Objective function based on updated dynamic weight factors J And the set speed optimization distance from the walking length and time step Iteratively optimize the real-time speed of each vehicle. The objective function is calculated in each iteration of optimization. until satisfied If convergence is achieved, the optimal velocity sequence after convergence will be output. , For prediction of the time domain T Internally optimized vehicle speed e To optimize the number of iterations; This embodiment detects a 5% uphill slope 200 meters ahead of the V7 electric vehicle, classifying it as a complex operating condition. The speed search step size is set. Predict the sampling time interval in the time domain This ensures that speed planning is smooth enough on uphill sections.

[0039] If iterative optimization has been performed E If it still does not converge after this, then take E The velocity sequence corresponding to the minimum value of each objective function is taken as the optimal velocity sequence. ; Based on the optimal velocity sequence Adjust the real-time speed of each vehicle in the transport fleet.

[0040] This embodiment calculates an acceleration strategy for near-end vehicles V1 and V2, suggesting an increase in speed to 35 km / h to reduce their arrival time. Arrive 2 minutes early. For the mid-range electric vehicle V7: The calculated adjustment strategy suggests accelerating to 40km / h on flat roads to conserve kinetic energy, and controlling the speed at 28km / h when going uphill. Data processing logic shows that this strategy delays the arrival time of electric vehicle V7 by 45 seconds, thus filling the gap after V1 and V2 leave.

[0041] Based on the aforementioned speed optimization strategy, the information was sent to the in-vehicle tablets in each vehicle via the 5G network. The interface displayed: "Dear V7 driver, we suggest you accelerate to 40 km / h on the flat section ahead (300 meters), then use inertia to climb the hill, and reduce your speed to 28 km / h at the top. Reason: To smooth traffic and save energy. This mission is expected to reduce carbon dioxide emissions by 2.1 kg."

[0042] In this embodiment, the real-time grid carbon intensity rate optimization strategy is as follows: The cloud-based decision center has been additionally connected to the API interface of the provincial power grid real-time carbon intensity monitoring platform, which can obtain the real-time carbon intensity factor of the power grid once per minute. For example, during the peak photovoltaic power generation period from 11:30 to 13:30 on a certain day, the system monitored that the power grid carbon intensity dropped from the usual 0.55 kg CO2 / kWh to 0.12 kg CO2 / kWh.

[0043] Calculate the carbon emission weighting correction factor at this time. ;because The analysis indicates that the real-time grid carbon intensity is underestimated. The carbon emission weighting of electric vehicles is also considered. Updated to 0.22 times the baseline value, i.e. To reflect the energy-saving bias, the weighting of gasoline-powered vehicles remains unchanged. This means that, from the perspective of the optimization algorithm, the cost of emitting 1kg of CO2 from an electric vehicle is only 22% of the original cost, so the algorithm will significantly relax the restrictions on the energy consumption of electric vehicles.

[0044] The system calculates the current total capacity demand of the fleet. This is based on the carbon emission weighting of electric vehicles. The priority coefficient of electric vehicle transportation tasks is extremely low in the task allocation matrix. Set it to the highest level (e.g., 1.0), and prioritize gasoline vehicles. Set it to 0.6.

[0045] Electric vehicle V is currently on a flat road with 80% load. Due to its extremely low carbon emission weight, the objective function... J The main limitations are time window constraints and power consumption. Under the premise of ensuring safety, the electric vehicle V can travel at 45km / h (close to the economic speed limit), instead of the usual 35km / h.

[0046] The estimated transport time for this trip is The transportation time is longer than the original plan. It shortened the journey by 5 minutes. Although the electricity consumption per vehicle increased by about 5%, the total carbon emissions were reduced by 40% due to the extremely low carbon intensity of the power grid, and transportation efficiency was improved by 20%.

Claims

1. A method for dynamically optimizing the speed and carbon emissions of a transport fleet, characterized in that, Includes the following steps: S1: Construct a transportation fleet that includes both fuel-powered and electric vehicles, acquire real-time transportation data from vehicles using onboard IoT terminals, and calculate the rolling resistance of the vehicles during operation based on this data. Slope resistance air resistance and calculate resistance ; S2: Based on rolling resistance Slope resistance air resistance and calculate resistance Construct fuel consumption models for gasoline vehicles and electricity consumption rate models for electric vehicles, and calculate the real-time carbon emission rate of gasoline vehicles and the indirect carbon emission rate of electric vehicles. S3: Construct an objective function for optimizing vehicle carbon emission rates using real-time carbon emission rates of gasoline vehicles and indirect carbon emission rates of electric vehicles, and set the discrete step length for speed optimization. and time step and constraints; S4: Based on regional power grid carbon intensity factor Real-time queue length at unloading points Set the optimization rules for the dynamic weighting factors within the objective function; S5: Determine whether congestion occurs at the unloading point within the prediction time domain, or optimize the dynamic weighting factor within the objective function based on the real-time carbon emission weighting correction coefficient, and optimize the discrete step size based on the objective function and the set velocity. and time step Optimize vehicle speeds, output the optimal speed sequence, and adjust the real-time speed of each vehicle in the transport fleet based on the optimal speed sequence.

2. The method for dynamic optimization of transport fleet speed and carbon emissions according to claim 1, characterized in that, Step S1 includes: S11: Construct a transportation fleet that includes fuel-powered vehicles and electric vehicles. Each vehicle in the fleet is equipped with an onboard IoT terminal to obtain real-time transportation data. S12: Instantaneous speed based on vehicle movement Acceleration a Total vehicle mass m The road gradient ahead of the driving section i Calculate the rolling resistance during vehicle movement Slope resistance air resistance and calculate resistance ; Rolling resistance The calculation method is as follows: ;in, g It is the acceleration due to gravity. This refers to the tire rolling resistance coefficient. Gradient resistance during driving The calculation method is as follows: ； Air resistance during driving The calculation method is as follows: ;in, air density, This is the drag coefficient. For windward area; Calculate resistance The calculation method is as follows: ; This is the rotational mass conversion factor.

3. The method for dynamic optimization of transport fleet speed and carbon emissions according to claim 2, characterized in that, Step S2 includes: S21: Based on rolling resistance Slope resistance air resistance and calculate resistance Construct a fuel consumption model for gasoline-powered vehicles and calculate their real-time fuel consumption. ; ； in, This is the engine thermal efficiency correction factor. It is a fuel with a low calorific value; S22: Based on the real-time fuel consumption of gasoline vehicles Calculate the real-time carbon emission rate of gasoline vehicles ; ； in, Carbon emission factors of fuel used in gasoline-powered vehicles; S23: Discharge efficiency of electric vehicle batteries at different states of charge (SOC) and the energy recovery efficiency of the braking system. Construct an energy consumption rate model for electric vehicles to calculate their real-time energy consumption. ; ； in, For electric vehicles during braking acceleration a Braking correction coefficient under certain conditions; when the electric vehicle accelerates... hour, This indicates the energy recovery rate, as acceleration... hour, Indicates energy consumption; S24: Based on the real-time power consumption of the electric vehicle Calculate the indirect carbon emission rate of electric vehicles ; ； in, This represents the carbon intensity factor of the regional power grid.

4. The method for dynamic optimization of transport fleet speed and carbon emissions according to claim 3, characterized in that, Step S3 includes: S31: Utilizing the real-time carbon emission rate of gasoline vehicles and the indirect carbon emission rate of electric vehicles Built in the prediction time domain T The objective function for optimizing the carbon emission rates of all vehicles within the internal transport fleet ; ； in, i This refers to the number of the fuel-powered vehicles in the transport fleet. The number of fuel-powered vehicles in the transport fleet. For the first i The carbon emission rate of a fuel-powered vehicle As a dynamic weighting factor for carbon emissions from gasoline vehicles, As a dynamic weighting factor for carbon emissions from electric vehicles, j This refers to the serial numbers of the electric vehicles in the transport fleet. For the first i The indirect carbon emission rate of an electric vehicle For congestion penalty dynamic weighting factor, This refers to the length of the vehicle queue at the unloading point. for t The unloading point number at any given time. express t The unloading point is constantly changing due to the length of the vehicle queue. The penalty items, Gather at the unloading point; S32: The constraint is that the vehicle reaches the destination within the time window required by the mission, and the limits of the vehicle's speed and acceleration are used as physical limit constraints; and the distance step length for speed optimization is adaptively determined based on the current vehicle state and the characteristics of the road conditions ahead. and time step Specifically: Extract the sequence of road curve curvature radii within a set distance in front of the vehicle. and slope sequence , M The number of road curves. U This refers to the slope quantity; like or If the road conditions ahead are deemed complex, the optimal walking distance for speed optimization will be set. Time step Otherwise, if the road conditions ahead are deemed good, the optimal walking distance for speed is set. Time step .

5. The method for dynamic optimization of transport fleet speed and carbon emissions according to claim 4, characterized in that, The optimization rules for the dynamic weighting factors within the objective function specifically include: Dynamic weighting factors for optimizing electric vehicle carbon emissions based on vehicle carbon emissions. : when If the real-time grid carbon intensity is at a low point, the carbon emission weighting correction factor is calculated. , The average carbon intensity of the regional power grid is adjusted using carbon emission weighting factors. Update dynamic weighting factors ; ； in, Updated dynamic weighting factors for carbon emissions from electric vehicles The dynamic weighting factor for the carbon emissions of electric vehicles before the update; when When the carbon intensity of the regional power grid is at a low point (such as during the peak period of photovoltaic power generation), the gradient direction of the carbon emission weight is calculated to reduce the dynamic weight factor of electric vehicle carbon emissions and guide the algorithm to find a speed curve in the solution space that is conducive to electric vehicles carrying more loads and running faster. Optimize the dynamic weighting factor of congestion penalty based on unloading efficiency. ; Based on the vehicle arrival rate at the unloading point during historical time periods and unloading processing rate Calculate traffic intensity ; ； Vehicle arrival rate This represents the number of vehicles arriving at the unloading point per unit time within a historical period, and the unloading processing rate. This indicates the number of vehicles that completed unloading per unit of time within a historical period; When traffic intensity Or real-time queue length When this is the case, an amplification factor is introduced. Amplify the dynamic weighting factor of congestion penalty ; ； in, The amplified dynamic weighting factor for congestion penalties Dynamic weighting factor for congestion penalty before amplification This is the set queue length threshold.

6. The method for dynamic optimization of transport fleet speed and carbon emissions according to claim 5, characterized in that, Step S5 includes: The method for determining whether congestion occurs at the unloading point within the prediction time domain and optimizing the dynamic weighting factor in the objective function includes the following steps: In the prediction time domain T Inside, based on the vehicle's current speed and location Calculate the set of times when each vehicle arrives at the unloading point. , K This refers to the number of vehicles in the transport fleet. For the first K The time it takes for the vehicle to arrive at the unloading point; If time set There exists and If congestion occurs, proceed to step S11; otherwise, no congestion has occurred. Perform step S4 to introduce the amplification factor. Amplify the dynamic weighting factor of congestion penalty Then proceed to step S6; The method for optimizing the dynamic weighting factor within the objective function based on the real-time carbon emission weighting correction coefficient is as follows: Real-time monitoring of the regional power grid carbon intensity and calculation of the real-time carbon emission weighting correction coefficient. ,like Then proceed to step S4, based on the carbon emission weighting correction coefficient. Update dynamic weighting factors Then proceed to step S6; otherwise, maintain the dynamic weighting factor. constant. Step S6: Objective function based on updated dynamic weight factors J And the set speed optimization distance from the walking length and time step Iteratively optimize the real-time speed of each vehicle. The objective function is calculated in each iteration of optimization. until satisfied If convergence is achieved, the optimal velocity sequence after convergence will be output. , For prediction of the time domain T Internally optimized vehicle speed e To optimize the number of iterations; If iterative optimization has been performed E If it still does not converge after this, then take E The velocity sequence corresponding to the minimum value of each objective function is taken as the optimal velocity sequence. ; Based on the optimal velocity sequence Adjust the real-time speed of each vehicle in the transport fleet.

7. An electronic device, characterized in that, include: One or more processors; A memory for storing one or more programs that, when executed by one or more processors, enable the one or more processors to implement the method for dynamic optimization of transport fleet speed and carbon emissions as described in any one of claims 1-6.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by the processor, the program implements the method for dynamic optimization of transport fleet speed and carbon emissions as described in any one of claims 1-6.

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