Carbon flow optimization method and device for edge calculation under vehicle-road cooperation, and medium

Abstract

The invention discloses a carbon flow optimization method and device for edge calculation under vehicle-road cooperation and a medium, and relates to the technical field of traffic carbon emission. The method comprises the steps that a real-time state parameter sequence and environment information of a vehicle are acquired, and at least two candidate driving strategies are generated through a built-in carbon emission digital twinborn model; according to the candidate driving strategies, the corresponding expected carbon emission amount is calculated by inquiring the emission factor mapping table of the specific power of the vehicle and integrating; packaging the candidate driving strategy and the corresponding expected carbon emission into a bidding packet, and sending the bidding packet to an edge computing node on the roadside; based on the received bidding package, carbon right reverse auction is initiated, a combined optimization problem is solved by adopting a heuristic search algorithm, and a bid winning strategy set is determined; and generating a cooperative driving strategy according to the bid winning strategy set, and issuing the cooperative driving strategy to the corresponding vehicle end node. By means of the method, information distortion is restrained, and direct and real-time optimization control over regional total carbon emission is achieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of traffic carbon emission, and particularly relates to a carbon flow optimization method, equipment and medium for edge computing under vehicle-road cooperation. BACKGROUND

[0002] With the rapid development of intelligent transportation and vehicle-road cooperation technology, optimizing traffic flow to achieve energy saving and emission reduction has become an important research direction. The existing technical solutions mainly focus on two levels: at the macro level, traffic signal timing optimization, path induction and other means are used to improve road traffic efficiency, thereby indirectly reducing the additional emissions caused by congestion; at the micro level, the vehicle's own energy-saving driving assistance system can optimize the driving behavior of a single vehicle, but its decision is often limited to local information, lacking cooperation with surrounding vehicles and roadside systems, which may lead to local optimization but negative impact on overall traffic flow. In recent years, although some research has tried to introduce game theory or simple incentive mechanisms under the framework of vehicle-road cooperation to guide vehicles, these solutions still mainly focus on improving traffic efficiency or safety, and have not taken the total physical carbon emissions of a region as a directly quantifiable and real-time optimized control target.

[0003] Further, the existing technology has several inherent defects. First, they generally lack an effective verification mechanism for the authenticity of the information provided by vehicles, and the system cannot identify and suppress the malicious false reporting behavior of vehicles, resulting in a serious information asymmetry problem. Second, the optimization vision of these solutions is usually limited to a single intersection or road section, which is an isolated optimization, and downstream nodes cannot obtain the carbon emission characteristics of upstream vehicles, so they cannot achieve forward-looking carbon flow regulation at the road network level. Finally, existing methods treat traffic flow as a homogeneous whole and do not fully utilize the differences in vehicle type, load and energy consumption characteristics to guide in a refined and differentiated manner, resulting in one-size-fits-all control measures and the optimization potential not being fully released.

[0004] Through the above analysis, the problems and defects of the existing technology are: The vehicle-road cooperation technology in the prior art does not take the total physical carbon emissions of a region as a directly quantifiable and real-time optimized control target, and thus cannot directly, real-time, reliably and refinedly optimize the instantaneous physical carbon emissions of vehicles in the region. SUMMARY

[0005] The embodiments of the present application provide a carbon flow optimization method, equipment and medium for edge computing under vehicle-road cooperation, which can solve the problem that the vehicle-road cooperation technology does not take the total physical carbon emissions of a region as a directly quantifiable and real-time optimized control target, and thus cannot directly, real-time, reliably and refinedly optimize the instantaneous physical carbon emissions of vehicles in the region.

[0006] In a first aspect, the embodiments of the present application provide a carbon flow optimization method for vehicle-road cooperation edge computing, characterized in that the method comprises: obtaining a real-time state parameter sequence and environmental information based on a vehicle through a vehicle end node, generating at least two candidate driving strategies through a built-in carbon emission digital twin model; for the candidate driving strategies, calculating the corresponding expected carbon emissions by querying the emission factor mapping table of the specific power of the vehicle and integrating; encapsulating the candidate driving strategies and the corresponding expected carbon emissions into a bidding package, and sending it to the edge computing node of the roadside; based on the received bidding package, initiating a carbon right reverse auction, using a heuristic search algorithm to solve the combinatorial optimization problem, and determining the winning strategy set; generating a cooperative driving strategy according to the winning strategy set, and delivering it to the corresponding vehicle end node.

[0007] In an implementation manner of the present application, after the cooperative driving strategy is generated according to the winning strategy set and delivered to the corresponding vehicle end node, the method further comprises: obtaining the actual carbon emissions of the vehicle after the vehicle executes the cooperative driving strategy; comparing the actual carbon emissions with the expected carbon emissions to calculate the prediction error; based on the prediction error, updating the reputation score of the vehicle using a preset update rule, and the reputation score and the reputation weight coefficient are preset fixed mapping rules.

[0008] In an implementation manner of the present application, the real-time state parameter sequence and the environmental information of the vehicle are obtained through the vehicle end node, and at least two candidate driving strategies are generated through the built-in carbon emission digital twin model, which specifically comprises: obtaining real-time environmental information through the vehicle end node, the environmental information including rainfall, visibility, road wetness coefficient and environmental temperature; increasing the predicted safety distance according to the rainfall, visibility and road wetness coefficient, and planning a deceleration curve according to the safety distance; according to the environmental temperature, calling the emission correction factor to calibrate the output result of the carbon emission digital twin model.

[0009] In an implementation manner of the present application, the method further comprises: generating at least two candidate driving strategies based on the carbon emission digital twin model, the two candidate driving strategies including a first candidate driving strategy and a second candidate driving strategy; the first candidate driving strategy takes minimizing the impact degree during acceleration and deceleration of the vehicle as the optimization target, and obtains a smooth speed curve in combination with the deceleration curve; the second candidate driving strategy takes minimizing the driving time through the front road section as the optimization target, and obtains a minimum time speed curve.

[0010] In an implementation form of the present application, the candidate driving strategy and the corresponding expected carbon emission are packaged as a bidding package and sent to an edge computing node at the roadside, specifically comprising: based on the edge computing node including an upstream edge computing node and a downstream edge computing node; obtaining a carbon emission inertia index and an average reputation score after the upstream edge computing node completes the candidate driving strategy; packaging the carbon emission inertia index and the average reputation score into a carbon wave protocol data packet and sending it to the downstream edge computing node; the downstream edge computing node uses the carbon wave protocol data packet for feedforward calibration.

[0011] In an implementation form of the present application, the method further comprises: obtaining traffic data based on the roadside perception device, and predicting the carbon emission of each lane-level fine-grained region within a preset time in the future using a spatio-temporal graph neural network model; when it is predicted that the carbon emission of the target region will exceed a preset threshold after a preset time, triggering a carbon right reverse auction for a subset of vehicles entering the region.

[0012] In an implementation form of the present application, based on the received bidding package, a carbon right reverse auction is initiated, a heuristic search algorithm is used to solve the combinatorial optimization problem, and a winning strategy set is determined, specifically comprising: in the objective function of the optimization problem, the expected carbon emission of each vehicle is combined with a reputation weight coefficient to reduce the winning probability; the selection of each vehicle for the candidate driving strategy is encoded as a gene, and the selection of all vehicles forms a chromosome; the total expected carbon emission of all vehicles in the region is used as the fitness function; by iteratively performing selection, crossover and mutation operations, a chromosome with the minimum fitness function value is evolved, and the solution represented by the chromosome is the winning strategy set.

[0013] In an implementation form of the present application, after obtaining the traffic data based on the roadside perception device, the method further comprises: combining the actual carbon emission to calculate the carbon emission fairness index in the region, and the fairness index is used to measure the difference degree of the unit mileage carbon emission between different vehicles; according to the reputation score, the double-objective weight of the reverse auction is allocated, wherein the first weight corresponds to the minimum total expected carbon emission of the region, and the second weight corresponds to the optimization target of the carbon emission fairness index; the double-objective weight is integrated into the objective function to construct a double-objective optimization model; when generating the candidate driving strategy through the vehicle end node, the unit mileage expected carbon emission corresponding to each candidate driving strategy is calculated in parallel and packaged into the bidding package; after the edge computing node solves the double-objective optimization model, the solution that meets the total carbon emission and the unit mileage emission difference within a preset threshold is selected.

[0014] In a second aspect, the embodiments of the present application also provide a carbon flow optimization device for edge computing under vehicle-road cooperation, comprising at least one processor; and a memory in communication connection with the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the steps of any one of the carbon flow optimization methods for edge computing under vehicle-road cooperation.

[0015] In a third aspect, the embodiments of the present application also provide a nonvolatile computer storage medium for carbon flow optimization of edge computing under vehicle-road cooperation, which stores computer executable instructions configured to perform the steps of any one of the carbon flow optimization methods for edge computing under vehicle-road cooperation.

[0016] The carbon flow optimization method, device and medium for edge computing under vehicle-road cooperation provided by the embodiments of the present application can realize the generation and cost prediction of differentiated candidate driving strategies through a high-fidelity digital twin model at the vehicle end, realize global optimization through a reverse auction mechanism with the introduction of a reputation weight coefficient at a roadside edge node, and dynamically update the reputation based on actual carbon emission audit data after execution, so as to directly link the incentive mechanism and the physical emission reduction target, effectively suppress information distortion, and realize direct and real-time optimization control of regional total carbon emissions. Through model self-calibration and environment adaptive strategy generation, the accuracy of carbon emission prediction and the applicability of strategies under different working conditions are significantly improved, providing reliable input for optimization decisions; through predictive triggering based on a spatio-temporal graph neural network and carbon wave protocol feedforward across nodes, the optimization range is expanded from isolated single points to road network level cooperation, realizing forward-looking dredging of carbon emission congestion; through deep fusion of reputation points and optimization objective functions, and the innovative introduction of a double-objective optimization model considering fairness and efficiency, not only is the malicious behavior suppressed at the mathematical level, but also the fairness of all participating vehicles and the long-term stability of the system are improved, and finally sustainable fine management of regional carbon flow is realized. BRIEF DESCRIPTION OF DRAWINGS

[0017] The accompanying drawings, which are included to provide a further understanding of the present application and constitute a part of this application, illustrate certain illustrative embodiments of the present application and together with the description serve to explain the present application. In the drawings: Figure 1 A flowchart of a carbon flow optimization method for edge computing under vehicle-road cooperation provided by the embodiments of the present application; Figure 2 An internal structure schematic diagram of a carbon flow optimization device for edge computing under vehicle-road cooperation provided by the embodiments of the present application. DETAILED DESCRIPTION

[0018] In order to make the purposes, technical solutions and advantages of the present application clearer, the technical solutions of the present application will be described clearly and completely below in combination with specific embodiments of the present application and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative labor fall within the scope of protection of the present application.

[0019] The embodiments of the present application provide a carbon flow optimization method, device and medium under edge computing of vehicle-road cooperation, which solves the problem that the vehicle-road cooperation technology in the prior art does not have the total physical carbon emission of a region as a directly quantifiable and real-time optimized control target, and thus cannot directly, real-time, credibly and finely optimize the instantaneous physical carbon emission of vehicles in the region.

[0020] The technical solutions of the embodiments of the present application will be described in detail below with reference to the drawings.

[0021] Figure 1 A flow chart of a carbon flow optimization method under edge computing of vehicle-road cooperation is provided in the embodiments of the present application. As shown in Figure 1 The carbon flow optimization method under edge computing of vehicle-road cooperation provided in the embodiments of the present application specifically includes the following steps: Step 10: Obtain the real-time state parameter sequence based on the vehicle and the environmental information through the vehicle end node, and generate at least two candidate driving strategies through the built-in carbon emission digital twin model.

[0022] Firstly, it can be understood that the vehicle end node is configured on the vehicle, for example, integrated in the on-board computing unit, the edge computing node is configured in the roadside infrastructure, integrated in the roadside unit, and the edge computing node is used for wireless communication with at least one vehicle end node in the communication coverage. The vehicle end node can include a high-fidelity carbon emission digital twin model and a bid package generation module. The high-fidelity carbon emission digital twin is based on real-time state information of the vehicle obtained from the vehicle bus or the like, generates no less than two candidate driving strategies, and at the same time, the high-fidelity carbon emission digital twin also predicts the corresponding expected carbon emission cost of each candidate driving strategy by using the built-in carbon emission model. The bid package generation module is connected with the high-fidelity carbon emission digital twin, encapsulates the candidate driving strategy and the corresponding expected carbon emission cost into a bid package data structure for subsequent sending. The edge computing node includes a carbon right auction house and a dynamic reputation and market access engine. The carbon right auction house is used for receiving the bid package sent by one or more vehicle end nodes, and initiating a carbon right reverse auction according to the received bid package. In the auction process, the carbon right auction house determines a winning strategy set from the candidate driving strategies contained in all received bid packages by solving a preset optimization problem. Finally, the carbon right auction house generates a cooperative driving strategy according to the winning strategy set and sends the cooperative driving strategy to the corresponding vehicle end node.

[0023] As an optional embodiment, the real-time state parameter sequence of the vehicle and the environmental information are obtained by the vehicle end node, and at least two candidate driving strategies are generated by the built-in carbon emission digital twin model. Specifically, it can include: Step 101: obtaining real-time environmental information through the vehicle end node, the environmental information including rainfall, visibility, road wetness coefficient and environmental temperature.

[0024] In this step, a set of real-time state parameters of the vehicle are obtained in real time from the internal data bus of the vehicle or other vehicle-mounted sensors, which are used to represent the physical running state of the vehicle at the current time. The real-time state parameters can include instantaneous vehicle speed, instantaneous acceleration, engine speed, engine torque, current gear, vehicle total mass, and road slope of the vehicle driving section. Among them, the vehicle total mass can be determined according to the vehicle factory calibration parameters and the load sensor data. The road slope information can be provided by the vehicle-mounted inclination sensor, or by matching the real-time geographic position of the vehicle with the pre-stored high-precision map data.

[0025] Step 102: increasing the predicted safety distance according to the rainfall, visibility and road wetness coefficient, and planning a deceleration curve according to the safety distance; Step 103: calling the emission correction factor according to the environmental temperature to calibrate the output result of the carbon emission digital twin model.

[0026] In this step, the high-fidelity carbon emission digital twin also includes a model self-calibration unit, which inputs the prediction error As input, an online machine learning algorithm is applied to calculate the adjustment amount of the microcosmic carbon emission model parameters; during the vehicle driving process, the parameters of the microcosmic carbon emission model stored in the model parameter storage unit are iteratively updated periodically or continuously to improve the prediction accuracy of the expected carbon emission cost.

[0027] In this step, the rainfall, visibility, road wet slip coefficient, and environmental temperature are collected; the rainfall is classified as dry, light rain, moderate rain, and heavy rain; the visibility is classified as good, light haze, moderate haze, and heavy haze; the road wet slip coefficient is classified as ice and snow, rainy day, and dry road surface; and the environmental temperature covers the full working interval from severe cold to high temperature. Based on the environmental parameters, the safety distance is dynamically adjusted, and the deceleration curve conforming to the road grip limit is re-planned to balance safety and driving stability. The safety distance is composed of reaction distance and braking distance; the reaction distance is positively correlated with the current vehicle speed and negatively correlated with the visibility; the braking distance is positively correlated with the square of the current vehicle speed and negatively correlated with the road wet slip coefficient; the deceleration curve rule is based on uniform deceleration, and the maximum deceleration does not exceed the physical limit corresponding to the road wet slip coefficient to ensure that the braking process does not slip. The environmental temperature affects the engine combustion efficiency, and through the temperature-related correction factor, the expected emission output of the carbon emission digital twin model is calibrated to improve the prediction accuracy. In the temperature interval where the engine combustion efficiency is optimal, the correction factor takes the reference value; when the temperature deviates from this interval, the correction factor increases with the degree of deviation; in low-temperature and high-temperature environments, the correction factor is further increased to match the actual emission characteristics; and the expected carbon emission amount after calibration is obtained by multiplying the correction factor and the original output of the model.

[0028] Further, the state acquisition unit not only acquires real-time state parameters for representing the vehicle driving behavior, but also acquires the actual carbon emission amount of the vehicle in the previous time period from the vehicle-mounted sensors At the same time, the model self-calibration unit inputs the actual driving behavior parameters occurring in the previous time period to the expected carbon emission prediction unit, so that it calculates a predicted carbon emission amount corresponding to the actual driving behavior based on the current model parameters Subsequently, the model self-calibration unit receives and two values and compares them to calculate a prediction error, for example .

[0029] As an optional embodiment, the method can further include: step 104: generating at least two candidate driving strategies based on the carbon emission digital twin model, the two candidate driving strategies including a first candidate driving strategy and a second candidate driving strategy; In this step, after receiving the real-time state parameters of the vehicle provided by the state acquisition unit, at least two mutually distinguished candidate driving strategies are generated for a future preset time domain or space domain, and each candidate driving strategy is a data set composed of a series of control instructions or target states, such as a speed and acceleration sequence, which can be executed by the vehicle.

[0030] Step 105: The first candidate driving strategy is optimized to minimize the impact during the acceleration and deceleration of the vehicle, and a smooth speed curve is obtained in combination with the deceleration curve. In this step, for example, the first candidate driving strategy can be defined as a smooth and economic strategy, which is optimized to minimize the impact during the acceleration and deceleration of the vehicle or to minimize the output of a benchmark carbon emission model. When generating this strategy, the candidate driving strategy generation unit plans a speed curve with small absolute values of acceleration and deceleration, and the speed change process is continuous and smooth.

[0031] Step 106: The second candidate driving strategy is optimized to minimize the travel time through the front road section, and a minimum time speed curve is obtained.

[0032] In this step, the second candidate driving strategy can be defined as an efficient passing strategy, which is optimized to minimize the travel time through a specific road section, such as an intersection or a ramp merging area. When generating this strategy, the candidate driving strategy generation unit plans a speed curve with relatively large acceleration values to enable the vehicle to quickly reach the target speed or pass through the target area, while complying with the legal speed limit and vehicle physical performance constraints.

[0033] Step 20: For the candidate driving strategy, the corresponding expected carbon emissions are calculated by querying the emission factor mapping table of the vehicle specific power and integrating.

[0034] In this step, each candidate driving strategy k of the candidate driving strategy generation unit is constructed into a standardized data structure, such as a time-indexed speed sequence or an acceleration sequence where t is within a preset time interval These data structures are then transmitted to the internal processing unit of the high-fidelity carbon emission digital twin for subsequent prediction of the expected carbon emission cost. The expected carbon emission prediction unit receives each candidate driving strategy and calculates a quantitative expected carbon emission cost for each strategy, which is based on a micro carbon emission model stored in the model parameter storage unit.

[0035] In one specific implementation, the microscopic carbon emission model is a model based on vehicle specific power or a similar physical quantity. The carbon emission prediction unit is expected to first target a given candidate driving strategy, for example, based on a speed sequence. and acceleration sequence Define and combine the real-time vehicle state parameters provided by the state acquisition unit to calculate the strategy at each moment during execution. instantaneous power demand Subsequently, the expected carbon emission prediction unit will calculate the instantaneous power demand. As input, an instantaneous carbon emission rate is queried or calculated using a microscopic carbon emission model. The model maps different power demand ranges to different emission rates. Finally, it maps the entire policy execution time interval. Instantaneous carbon emission rate By performing integration, the candidate driving strategy is obtained. Total expected carbon emission cost This calculation process can be performed by... limited.

[0036] For each candidate driving strategy, the expected carbon emission prediction unit executes the above calculation process once, generating a corresponding expected carbon emission cost value for each strategy. These data pairs, consisting of candidate driving strategies and their corresponding expected carbon emission costs, are sent to the bidding package generation module. For example, an optimization method based on gradient descent is used to fine-tune the model parameters according to the magnitude and direction of the prediction error, so that the error of subsequent predictions tends to decrease. The updated model parameters are written back to the model parameter storage unit for subsequent prediction of expected carbon emission costs for candidate driving strategies. Through the continuous execution of this process, the high-fidelity carbon emission digital twin model can adapt to changes in vehicle status, such as engine aging and tire wear, thereby maintaining its high prediction accuracy.

[0037] Step 30: Package the candidate driving strategies and their corresponding expected carbon emissions into a bidding package and send it to the edge computing node on the roadside.

[0038] In this step, all candidate driving strategies and their corresponding expected carbon emission costs are received. After receiving this data, it is organized and packaged into a standardized data structure, which is the bidding package. This package is used to submit a complete bid to the edge computing node, containing multiple options. A unique vehicle identifier is used by the edge computing node to identify the source vehicle of the bid. A bid set contains at least two elements, each being a data pair represented by data from a candidate driving strategy, such as a speed sequence. ) and its corresponding expected carbon emission cost value compositions.

[0039] As an optional embodiment, the candidate driving strategy and the corresponding expected carbon emission are packaged as a bidding package and sent to the edge computing node at the roadside, which can specifically include: step 301: based on the edge computing node including upstream edge computing node and downstream edge computing node; step 302: after the upstream edge computing node completes the candidate driving strategy, the carbon emission inertia index and the average credit score are obtained; step 303: the carbon emission inertia index and the average credit score are packaged into a carbon wave protocol data packet and sent to the downstream edge computing node; step 304: the downstream edge computing node uses the carbon wave protocol data packet for feedforward calibration.

[0040] Step 40: based on the received bidding package, initiate a carbon right reverse auction, use a heuristic search algorithm to solve the combinatorial optimization problem, and determine the winning strategy set.

[0041] As an optional embodiment, based on the received bidding package, initiate a carbon right reverse auction, use a heuristic search algorithm to solve the combinatorial optimization problem, and determine the winning strategy set, which can specifically include: Step 401: in the objective function of the optimization problem, combine the credit weight coefficient with the expected carbon emission of each vehicle to reduce the winning probability; In this step, when the carbon right auction line determines the winning strategy set, the objective function of the optimization problem to be solved can be defined by the following formula: ; Wherein: is the index of the vehicle, is the index of the candidate driving strategy, is the vehicle set in the region, is the total number of candidate driving strategies of vehicle ; is a binary decision variable, indicating whether to select the strategy of vehicle ; ; is the expected carbon emission cost of vehicle when executing the strategy ; is the dynamic credit score of vehicle ; is a credit risk function based on the dynamic credit score, and the function value is a monotone decreasing function of the dynamic credit score, which is used to weight and adjust the expected carbon emission cost. The function makes the expected carbon emission cost of the vehicle with lower dynamic credit score be given higher weight in optimization solution.

[0042] Step 402: encode each vehicle's selection of candidate driving strategy as a gene, and all vehicles' selections constitute a chromosome; Step 403: take the total expected carbon emission of all vehicles in the region as the fitness function; Step 404: evolve the chromosome with the minimum fitness function value by iteratively performing selection, crossover and mutation operations, and the solution represented by the chromosome is the winning strategy set.

[0043] In this step, the objective function can be defined by the following formula: ; The optimization problem also needs to follow the constraint condition to ensure that each vehicle is finally assigned only one driving strategy.

[0044] is the unique index of the vehicle participating in this auction, is the set of all vehicles currently participating in the auction, is the vehicle 's index of a certain candidate driving strategy submitted, is the total number of candidate driving strategies submitted by the vehicle in its bid package, is the expected carbon emission cost submitted by the vehicle for its candidate driving strategy in its bid package, is the current dynamic reputation score of the vehicle , which is provided by the dynamic reputation and market access engine 22, is a binary decision variable. When its value is 1, it means that the vehicle 's candidate driving strategy wins in this auction; when its value is 0, it means that it does not win, is a reputation risk function used to risk-adjust the vehicle's bid according to its dynamic reputation score.

[0045] The reputation risk function is a monotonically decreasing function of the dynamic reputation score . For example, the function can be specifically set as where is a value normalized to the interval , and is a system parameter greater than zero, called the risk sensitivity coefficient. This function makes the value of the reputation risk function of the vehicle with a lower dynamic reputation score The larger. In the optimization solving process, the function value is used as a multiplier to increase the proportion of the expected carbon emission cost of low-reputation vehicles in their objective function, thereby reducing the possibility of winning the bid with inaccurate bids.

[0046] Step 50: According to the winning strategy set, the cooperative driving strategy is generated and delivered to the corresponding vehicle end node.

[0047] In this step, the carbon right auction house 21 uses an embedded or external optimization solver to solve the above integer programming problem to obtain a set of optimal decision variable solutions (i.e., the values of all ) that minimize the objective function value. After solving, all values of 1 form the "winning strategy set" of this auction. The carbon right auction house 21 generates the corresponding cooperative driving strategy according to the winning strategy set, and delivers the winning driving strategy to the corresponding vehicle end node 10 through the communication unit for execution. For vehicles that do not win the bid, a default or non-intervention driving instruction can be delivered.

[0048] As an optional embodiment, after generating the cooperative driving strategy according to the winning strategy set and delivering it to the corresponding vehicle end node, the method can further include: Step 60: After the vehicle executes the cooperative driving strategy, the actual carbon emission of the vehicle is obtained; Step 70: The actual carbon emission is compared with the expected carbon emission to calculate the prediction error; Step 80: Based on the prediction error, the preset update rule is used to update the reputation score of the vehicle, and the reputation score and the reputation weight coefficient are preset fixed mapping rules.

[0049] In this step, the dynamic reputation and market access engine audits the actual carbon emission generated by the vehicle after the vehicle executes the cooperative driving strategy delivered by the carbon right auction house. The engine compares the actual carbon emission value obtained with the expected carbon emission cost corresponding to the winning strategy of the vehicle, and updates the dynamic reputation score of the vehicle according to the consistency degree between the two. The updated dynamic reputation score will be used for risk adjustment when the vehicle participates in the carbon right auction next time.

[0050] Specifically, the dynamic reputation and market access engine can update the dynamic reputation score using an exponential moving average method, and the update rule of this method is defined by the following formula: ; Wherein: and are the updated and pre-updated dynamic reputation scores of the vehicle, respectively; is the performance score of the vehicle in this round of auction; is the update weight of the historical reputation, used to adjust the weight distribution of the historical reputation and the performance score in this round; actual carbon emissions of the vehicle; target strategy for the vehicle corresponding expected carbon emission cost; an evaluation function whose function value is a monotonically decreasing function of the input variable (i.e., the normalized error between the actual carbon emissions and the expected carbon emission cost), which is used to map the prediction error of the driving behavior to an instantaneous performance score.

[0051] As an optional embodiment, the method can further include: obtaining traffic flow data based on a roadside perception device, using a spatio-temporal graph neural network model to predict the carbon emissions of each lane-level fine-grained region within a preset time in the future; and triggering a carbon credit reverse auction for a subset of vehicles entering the target region when it is predicted that the carbon emissions of the target region will exceed a preset threshold after a preset time.

[0052] In this step, in addition to the carbon credit auction house and the dynamic reputation and market access engine, the edge computing node can also include a spatio-temporal carbon fluid model that aggregates driving intention information reported by all or part of the vehicle end nodes within its jurisdiction area, which can include data such as the destination of the vehicle and the driving path planned by the navigation system. Based on the aggregated driving intention information, the spatio-temporal carbon fluid model divides the road network under its jurisdiction into multiple spatio-temporal grids and predicts the traffic state of each spatio-temporal grid within a preset time period in the future. Subsequently, the model estimates the carbon emission intensity per unit time that will be generated on each spatio-temporal grid using a macroscopic traffic carbon emission model based on the predicted traffic state. This intensity is defined as the carbon potential of the spatio-temporal grid. In this way, the model generates a spatio-temporal carbon potential distribution prediction map covering the jurisdiction area and the preset future time.

[0053] Further, a trigger condition for the carbon credit reverse auction initiated by the carbon credit auction house is determined by the prediction results of the spatio-temporal carbon fluid model. The spatio-temporal carbon fluid model periodically performs the prediction function. When the prediction results show that the carbon potential at a certain road location at a certain time in the future will exceed a pre-set numerical threshold, the system determines that there is a potential risk of carbon emission congestion at this spatio-temporal point and active intervention is needed. When this trigger condition is met, the spatio-temporal carbon fluid model sends a trigger signal to the carbon credit auction house to start a carbon credit reverse auction process, thereby prospectively guiding and optimizing the carbon emission hotspots that will be formed. If the carbon potential of all spatio-temporal points within the predicted future period is lower than the threshold, the auction will not be triggered.

[0054] Further, the edge computing node comprises a carbon credit auction house connected with the dynamic reputation and market access engine, and configured to receive a bidding package sent by the communication unit of the one or more vehicle end nodes. When receiving the bidding package or a trigger signal sent by the space-time carbon flow model, the carbon credit auction house initiates a reverse auction of carbon credits, aiming to select a combination from all candidate driving strategies submitted by all participating vehicles, which minimizes the total predicted carbon emission cost in the region under the basic constraints of the traffic system operation.

[0055] As an optional embodiment, after obtaining the traffic flow data based on the roadside sensing device, the method can further comprise: calculating a carbon emission fairness index in the region in combination with the actual carbon emission, the fairness index being used to measure the difference degree of the unit mileage carbon emission between different vehicles; distributing double-target weights of the reverse auction according to the reputation score, wherein the first weight corresponds to the total expected carbon emission minimization target of the region, and the second weight corresponds to the optimization target of the carbon emission fairness index; integrating the double-target weights into the objective function in the combinatorial optimization problem to construct a double-target optimization model; in the generation of the candidate driving strategy by the vehicle end node, the unit mileage expected carbon emission corresponding to each candidate driving strategy is calculated in parallel, and is encapsulated into the bidding package for calling by the edge computing node when calculating the fairness index; after the edge computing node solves the double-target optimization model, when determining the winning strategy set, the solution with the total carbon emission meeting the standard and the unit mileage emission difference within the preset threshold is preferentially selected, and if there are multiple solutions, the optimal solution is selected in combination with the vehicle reputation score.

[0056] In this step, the edge computing node further comprises a dynamic reputation and market access engine connected with the carbon credit auction house, which is configured to audit the bidding accuracy of the participating vehicles after the auction is cleared and the strategy execution period ends, and update the dynamic reputation score of the vehicle based on the audit result, so as to exert influence on the subsequent auction process.

[0057] The dynamic reputation and market access engine can comprise an audit data acquisition unit, a reputation evaluation and update unit, and a market access control unit. The audit data acquisition unit acquires the actual carbon emission of the vehicle in the execution process after the vehicle executes the winning collaborative driving strategy . The actual carbon emission can be obtained by directly measuring through the remote sensing monitoring device deployed on the roadside, or receiving the reliable emission data reported by the vehicle end node after completing the strategy execution. At the same time, the unit acquires the expected carbon emission cost corresponding to the winning strategy of the vehicle from the carbon credit auction house .

[0058] The reputation evaluation and update unit is connected with the audit data acquisition unit, and receives and two values, and according to the consistency degree between the two values, the dynamic reputation score of the vehicle is updated, the updating process can adopt an exponential moving average method, and the updating rule is defined by the following formula: is the dynamic reputation score of the vehicle after this update, is the dynamic reputation score of the vehicle before this update, is a historical reputation update weight between 0 and 1, used to adjust the proportion of the historical reputation and the performance score in the synthesis of the new reputation score, is a momentary performance score function, which is used to map the accuracy of a bid behavior to a standardized score. The function is a monotonically decreasing function, that is, the smaller the prediction error, the higher the score.

[0059] The momentary performance score function can be specifically set as an exponential decay function: wherein is the normalized error , is the base of the natural logarithm, is a decay coefficient greater than zero, used to control the sensitivity of the score to the error.

[0060] The market access control unit is connected with the reputation evaluation and update unit, receives the updated dynamic reputation score , and adjusts the market access qualification of the vehicle according to the score value. For example, a market access reputation threshold can be preset.

[0061] When the market access control unit detects that the dynamic reputation score of a vehicle is lower than the threshold , a control instruction can be generated to temporarily suspend the participation qualification of the vehicle in the subsequent one or more auction periods, and the updated dynamic reputation score will also be provided to the carbon right auction house for risk adjustment of the bid of the vehicle in the next auction.

[0062] ​​​​​The edge computing node can also include an inter-node coordination protocol unit connected with the carbon credit auction house and the spatiotemporal carbon fluid model. The function of the inter-node coordination protocol unit is to generate and send a carbon wave protocol data packet to the upstream node after the upstream node completes a round of carbon credit auction for the vehicle flow that is about to leave its jurisdiction and enter the jurisdiction of a downstream node of a neighboring edge computing node, the carbon wave protocol data packet containing a set of data describing the overall carbon emission characteristics of the leaving vehicle flow. The set of data can include: a carbon emission inertia index representing the overall carbon emission level trend of the vehicle flow when it leaves the area of the upstream node. Specifically, the index can be calculated as the arithmetic mean of the expected carbon emission costs corresponding to the bidding strategies of all the winning vehicles in the vehicle flow. An average credit score representing the overall bid credibility of the vehicle flow. Specifically, the score can be calculated as the arithmetic mean of the dynamic credit scores of all the vehicles in the vehicle flow. After receiving the carbon wave protocol data packet, the downstream node provides the data contained in the data packet as input to its own spatiotemporal carbon fluid model. The spatiotemporal carbon fluid model of the downstream node uses the carbon emission inertia index and the average credit score in the data packet to pre-feed the prediction model to adjust the prediction of the carbon potential of the batch of traffic flow about to enter its jurisdiction.

[0063] Through the inter-node coordination protocol, information pre-feeding between adjacent edge computing nodes is achieved, enabling the downstream node to know in advance the carbon emission characteristics of the incoming traffic flow, thereby improving the accuracy of its spatiotemporal carbon fluid model prediction and providing data support for more timely auction triggering and optimization decisions.

[0064] As a further optional embodiment, the present application can be applied to urban core areas, business-intensive areas, etc. In such scenarios, there is mixed traffic of multiple vehicle types, short-distance high-frequency start-stop, and concentrated spatiotemporal carbon emissions. First, the edge computing node classifies vehicles in the jurisdiction area by fusing real-time OBD data from vehicles and road-side camera perception information: high-emission group: mainly including traditional fuel vehicles, heavy pick-ups, etc. This group of vehicles is assigned a relatively lower carbon budget base per mile and a more stringent credit risk sensitivity coefficient; medium-emission group: mainly including ordinary fuel vehicles, plug-in hybrid vehicles, etc., assigned a medium carbon budget base and a standard credit risk adjustment strategy; low-emission group: mainly including pure electric vehicles, non-motor vehicles, etc., assigned a higher carbon budget base or exempted from the mechanism. That is, the carbon budget base is not a fixed value, but is dynamically adjusted according to real-time traffic density, road saturation, and historical same-period carbon emission data. During the morning and evening peak hours, the carbon budget of each group will be tightened as a whole to cope with the expected carbon emission peak.

[0065] Further, for the problem of frequent start-stop of vehicles caused by narrow roads and dense intersections, the vehicle end node strengthens the smoothness constraint when generating the candidate driving strategy: in the candidate driving strategy generation algorithm, the penalty weight of acceleration change rate is increased, and a speed curve with gentle acceleration and deceleration is preferentially generated to reduce the instantaneous high emission caused by sudden acceleration and sudden stop; for a group of vehicles approaching a signalized intersection, the edge computing node will consider coupling the strategies of multiple vehicles when auction optimization, generating a start-stop sequence for vehicle platoon coordination, guiding the front vehicles to decelerate smoothly while suggesting the rear vehicles to enter the idle speed cruising state in advance to avoid the chain emission surge caused by wave braking. The vehicle end node combines the phase and timing information of the front intersection signal lights obtained from the edge node to pass through the green wave or predict parking, thereby reducing unnecessary parking and starting.

[0066] Further, the spatio-temporal graph neural network model is used to combine historical and real-time data to predict the carbon emission potential of each micro road segment in the future. When it is predicted that an emission hotspot will be formed in a certain area, the system will trigger the following coordinated guidance in advance: the navigation APP pushes alternative path suggestions to vehicles that are about to enter the hotspot area; for vehicles that are determined to enter the hotspot area, the carbon right auction mechanism is used to control the number of vehicles entering and the speed curve in combination to suppress the emission peak; in the objective function of the carbon right auction, higher weights or green channels are set for buses and non-motor vehicles to guide low-carbon travel modes to pass through first.

[0067] Further, in the auction optimization model, not only the total emission of the region is minimized, but also the Gini coefficient or the intra-group unit mileage emission variance is introduced as an auxiliary optimization target to limit the excessive differentiation of carbon emission opportunities among different vehicles in the same group. For high-emission group vehicles, if they long-term and stably implement the winning strategy and the prediction is accurate, they can be gradually rewarded with additional carbon budget flexibility space or participate in carbon credit trading to exchange virtual quotas with low-emission group vehicles, forming an internal incentive mechanism.

[0068] The above is a method embodiment of the present application. Based on the same inventive concept, the present application also provides a carbon flow optimization device for edge computing under vehicle-road cooperation, which has a structure as shown in Figure 2 .

[0069] Figure 2 A carbon flow optimization device for edge computing under vehicle-road cooperation provided by an embodiment of the present application has an internal structure as shown in Figure 2 . The device includes: at least one processor 201; The memory 202 stores instructions executable by the at least one processor 201, and the instructions are executed by the at least one processor 201 to enable the at least one processor 201 to: perform any one of the steps of the carbon flow optimization method for edge computing under vehicle-road synergy.

[0070] Some embodiments of the present application provide a non-volatile computer storage medium for carbon flow optimization of edge computing under vehicle-road synergy corresponding to Figure 1 The computer executable instructions are arranged to perform any one of the steps of the carbon flow optimization method for edge computing under vehicle-road synergy.

[0071] Each of the embodiments in the present application is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other. Each of the embodiments mainly describes the differences from other embodiments. In particular, the IoT device and medium embodiments are basically similar to the method embodiments, and thus are described simply. The relevant parts can be referred to the description of the method embodiments.

[0072] The system and medium provided by the embodiments of the present application are one-to-one corresponding to the method, and thus the system and medium have similar beneficial technical effects to the method. Since the beneficial technical effects of the method have been described in detail above, the beneficial technical effects of the system and medium will not be described here.

[0073] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can be in the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can be in the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0074] The present application is described with reference to flowcharts and / or block diagrams of the method, device (system), and computer program product according to the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of the flows and / or blocks in the flowcharts and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to produce a machine, so that the instructions executed by the computer or other programmable data processing devices produce a device that implements the functions specified in the flowcharts and / or block diagrams. Figure 1 The functions specified in one flow or multiple flows and / or blocks Figure 1 The functions specified in one flow or multiple flows and / or blocks

[0075] These computer program instructions can also be stored in a computer- readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instructions which implement the Figure 1 function specified in the flow or flows and / or blocks Figure 1 of the block or blocks.

[0076] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the Figure 1 function specified in the flow or flows and / or blocks Figure 1 of the block or blocks.

[0077] In a typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0078] The memory can include non-persistent memory and / or volatile memory, such as a random access memory (RAM) including a cache area for the temporary storage of data. The memory can also include non-volatile memory, such as a read only memory (ROM), EPROM, EEPROM, or flash memory. The memory can be another form of computer-readable media.

[0079] Computer-readable media includes permanent and non-permanent, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read only memory (ROM), electrically erasable programmable read only memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital versatile disc (DVD), or other optical storage, magnetic cassettes, magnetic tapes, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information accessible to a computing device. According to the definition herein, computer-readable media does not include transitory media, such as modulated data signals and carrier waves.

[0080] It should also be noted that the terms "comprising," "including," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can also include other elements not expressly listed or inherent to such process, method, article, or apparatus. An element proceeded by "comprises a... " does not, without more constraints, exclude the existence of additional identical elements in the process, method, article, or apparatus that comprises the element.

[0081] The above description is merely illustrative of the application, and not restrictive. Various modifications and changes can become apparent to those skilled in the art. Incorporating any modification, equivalent substitution, improvement, etc. within the spirit and principle of the application, shall be included in the scope of the claims of the application.

Claims

1. A carbon flow optimization method for edge computing under vehicle-road cooperative architecture, characterized in that, The method includes: By acquiring the vehicle's real-time status parameter sequence and environmental information through the vehicle's end node, at least two candidate driving strategies are generated through the built-in carbon emission digital twin model. For the candidate driving strategy, the expected carbon emissions are calculated by querying the emission factor mapping table of vehicle power ratio and integrating it. The candidate driving strategy and the corresponding expected carbon emissions are packaged into a bidding package and sent to the roadside edge computing node; Based on the received bidding package, a reverse carbon rights auction is initiated, and a heuristic search algorithm is used to solve the combinatorial optimization problem to determine the set of winning strategies. Based on the set of winning strategies, a cooperative driving strategy is generated and distributed to the corresponding vehicle-side nodes.

2. The carbon flow optimization method for edge computing under vehicle-road cooperation according to claim 1, characterized in that, After generating a cooperative driving strategy based on the winning strategy set and distributing it to the corresponding vehicle-end nodes, the method further includes: After the vehicle completes the cooperative driving strategy, the vehicle's actual carbon emissions are obtained. The actual carbon emissions are compared with the expected carbon emissions to calculate the prediction error; Based on the prediction error, the vehicle's credit score is updated using a preset update rule, and the credit score and credit weight coefficient are mapped by a preset fixed rule.

3. The carbon flow optimization method for edge computing under vehicle-road cooperation according to claim 1, characterized in that, The process involves acquiring real-time vehicle status parameter sequences and environmental information through vehicle-end nodes, and generating at least two candidate driving strategies using a built-in carbon emission digital twin model, specifically including: Real-time environmental information is obtained through vehicle-end nodes, including rainfall, visibility, road surface slippage coefficient, and ambient temperature. Based on the rainfall, visibility, and road surface slippage coefficient, the predicted safety distance is increased, and a deceleration curve is planned based on the safety distance; Based on the ambient temperature, an emission correction factor is invoked to calibrate the output of the carbon emission digital twin model.

4. The carbon flow optimization method for edge computing under vehicle-road cooperation according to claim 3, characterized in that, The method further includes: At least two candidate driving strategies are generated based on the carbon emission digital twin model, the two candidate driving strategies including a first candidate driving strategy and a second candidate driving strategy; The first candidate driving strategy aims to minimize the impact during vehicle acceleration and deceleration, and by combining the deceleration curve, a smooth speed curve is obtained. The second candidate driving strategy aims to minimize the travel time to the next road segment, resulting in a speed curve with the minimum travel time.

5. The carbon flow optimization method for edge computing under vehicle-road cooperation according to claim 1, characterized in that, The step of encapsulating the candidate driving strategies and the corresponding expected carbon emissions into a bidding package and sending it to the roadside edge computing node specifically includes: The edge computing nodes include upstream edge computing nodes and downstream edge computing nodes; After the candidate driving strategy is completed at the upstream edge computing node, the carbon emission inertia index and average credit score are obtained. The carbon emission inertia index and average reputation are encapsulated into carbon wave protocol data packets and sent to the downstream edge computing node; The downstream edge computing node uses the carbon wave protocol data packet for feedforward calibration.

6. The carbon flow optimization method for edge computing under vehicle-road cooperation according to claim 2, characterized in that, The method further includes: Based on traffic flow data acquired by roadside sensing devices, a spatiotemporal neural network model is used to predict carbon emissions in fine-grained areas at the lane level within a preset time period in the future. When it is predicted that the carbon emissions in the target area will exceed a preset threshold after a preset time, a reverse auction of carbon credits is triggered for a subset of vehicles entering the area.

7. The carbon flow optimization method for edge computing under vehicle-road cooperation according to claim 2, characterized in that, Based on the received bidding package, a reverse carbon rights auction is initiated. A heuristic search algorithm is used to solve the combinatorial optimization problem to determine the winning strategy set, specifically including: In the objective function of the optimization problem, the expected carbon emissions for each vehicle are combined with the reputation weight coefficient to reduce the probability of winning the bid; Each vehicle's choice of the candidate driving strategy is encoded as a gene, and the choices of all vehicles form a chromosome. The fitness function is the total expected carbon emissions of all vehicles in the region. By iteratively performing selection, crossover, and mutation operations, a chromosome with the smallest fitness function value is evolved, and the solution represented by this chromosome is the set of winning strategies.

8. The carbon flow optimization method for edge computing under vehicle-road cooperation according to claim 6, characterized in that, After acquiring traffic flow data based on roadside sensing devices, the method further includes: Based on the actual carbon emissions, a carbon emission fairness index is calculated for the region. This fairness index is used to measure the degree of difference in carbon emissions per unit mileage among different vehicles. Based on the credit score, the reverse auction is assigned dual-objective weights, wherein the first weight corresponds to the objective of minimizing the total expected carbon emissions in the region, and the second weight corresponds to the objective of optimizing the carbon emission fairness index. The dual-objective weights are incorporated into the objective function to construct a dual-objective optimization model; When generating the candidate driving strategy through the vehicle end node, the expected carbon emissions per unit mileage corresponding to each candidate driving strategy are calculated in parallel and packaged into the bidding package. After solving the bi-objective optimization model through the edge computing node, the solution that meets the total carbon emission standard and whose emission difference per unit mileage is within a preset threshold is selected.

9. A carbon flow optimization device for edge computing under vehicle-road cooperative architecture, characterized in that, The device includes: At least one processor; And, a memory communicatively connected to the at least one processor; The memory stores instructions that can be executed by the at least one processor, which, when executed by the at least one processor, enable the at least one processor to: perform the steps of the carbon flow optimization method for edge computing under vehicle-road cooperation as described in any one of claims 1-8.

10. A non-volatile computer storage medium for carbon flow optimization in vehicle-road cooperative edge computing, storing computer-executable instructions, characterized in that, The computer-executable instructions are configured to execute the steps of the carbon flow optimization method for edge computing under vehicle-road cooperation as described in any one of claims 1-8.

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