Intelligent vehicle speed planning method and system that balances environmental friendliness and privacy protection

Abstract

This invention discloses a speed planning method and system for intelligent vehicles that balances environmental friendliness and privacy protection, relating to the field of vehicle driving speed planning technology. The invention includes: calculating a speed-carbon emission model by fitting a nonlinear least squares method based on the generator characteristic diagram of an internal combustion engine vehicle and the motor efficiency MAP diagram of an electric vehicle, combined with carbon emission factors, thereby calculating the carbon emission values ​​of both gasoline-powered and electric vehicles at different speeds. While ensuring vehicle user privacy and driving safety, this invention provides an optimal common speed for intelligent vehicle fleets in hybrid scenarios that minimizes carbon emissions. It proposes a speed recommendation system that combines multi-vehicle carbon emission models, minimum following distance, maximum speed limit, and privacy protection, and incorporates blockchain technology for data protection, resulting in high privacy and security.

Description

Technical Field

[0001] This invention relates to the field of vehicle driving speed planning technology, specifically to an intelligent vehicle speed planning method and system that balances environmental friendliness and privacy protection. Background Technology

[0002] With the global climate change crisis becoming increasingly severe, carbon emissions from the transportation sector have become one of the key issues urgently needing to be addressed. Transportation is not only a major source of greenhouse gas emissions, but excessive fuel consumption and air pollution also have serious impacts on the environment and human health. In recent years, the rapid development of Intelligent Transportation Systems (ITS) and connected autonomous vehicles (Center for Certification, Vehicles, and Systems) technologies has made Intelligent Speed ​​Advisory Systems (ISAs), aimed at reducing emissions, an important means of reducing traffic emissions and optimizing energy use.

[0003] Current ISA (Automatic Speed ​​Advisory) systems primarily provide speed optimization suggestions for individual vehicles. However, consensus-based speed advisory systems (CSAS) can significantly reduce energy consumption and emissions caused by frequent acceleration and deceleration by providing unified speed suggestions for the entire fleet. While these systems achieve fleet speed optimization while ensuring privacy, most employ a centralized architecture, requiring vehicles to transmit private data to a central server. This introduces potential trust risks and data leakage issues. Furthermore, traditional CSAS solutions often overlook the risk of malicious vehicle interference and the unique characteristics of hybrid traffic environments. Therefore, we propose an intelligent vehicle speed planning method and system that balances environmental friendliness and privacy protection. Summary of the Invention

[0004] The purpose of this invention is to provide an intelligent vehicle speed planning method and system that takes into account both environmental friendliness and privacy protection. It can recommend a common speed with minimum carbon emissions for a mixed vehicle fleet on a road segment. It proposes a model that considers the actual speed-direct carbon emissions of internal combustion engine vehicles and the speed-indirect carbon emissions of electric vehicles. It introduces a coordination factor α to adjust the emission weights of internal combustion engine vehicles and electric vehicles according to specific road segment conditions and recent optimization objectives.

[0005] According to a first aspect of the present invention, in order to achieve the above-mentioned objective, the present invention provides the following technical solution: an intelligent vehicle speed planning method that balances environmental friendliness and privacy protection, applied to speed planning of a fleet with mixed power types, comprising the following steps:

[0006] Based on the generator characteristic diagram of internal combustion engine vehicles and the motor efficiency MAP diagram of electric vehicles, combined with the carbon emission factor, a speed-carbon emission model is calculated by fitting using the nonlinear least squares method, thereby calculating the carbon emission values ​​of fuel vehicles and electric vehicles at different speeds.

[0007] The Paillier homomorphic encryption algorithm is used to encrypt the carbon emission values ​​of the vehicle at different speeds, and the EdDSA signature algorithm is used to digitally sign the vehicle's driving information data.

[0008] Receive information data from each vehicle in the fleet after data encryption and signing, and perform weighted aggregation and decryption processing on all encrypted information data;

[0009] The common speed with minimum carbon emissions is calculated based on the aggregated driving information data. Then, based on the vehicle location and optimization objectives, and taking into account constraints and emission reduction targets, the optimized planned speed is generated.

[0010] All encrypted data uploaded by the vehicle and key data from the speed planning process are recorded using consortium blockchain technology.

[0011] Furthermore, based on the generator characteristic diagram of internal combustion engine vehicles and the motor efficiency MAP diagram of electric vehicles, combined with carbon emission factors, a speed-carbon emission model was constructed. The carbon emission values ​​of gasoline vehicles and electric vehicles at different speeds were then calculated using this model, as follows:

[0012] (21) For internal combustion engine vehicles, based on their generator universal characteristic diagram, the fuel consumption function corresponding to vehicle speed v can be obtained by fitting:

[0013]

[0014] In the formula, a0, a1, a3, and a4 are the fitting coefficients of the fuel consumption function. The fuel consumption function will have different coefficients depending on the type of vehicle. Specific parameters, as well as the root mean square error (RMSD) and R after fitting, are also important considerations. 2 The values ​​are shown in Table X; the root mean square error is less than 0.13, and R0 is... 2 The values ​​are all higher than 0.9899, ​​which further proves that the fitting accuracy is high;

[0015] For electric vehicles, based on their motor efficiency MAP, the energy consumption function corresponding to vehicle speed v is obtained through fitting. The fitted energy consumption function is:

[0016]

[0017] In the formula, b0, b1, b2, b3, and b4 are the fitting coefficients of the energy consumption function. The energy consumption function will have different coefficients depending on the type of vehicle. The specific parameters, root mean square error after fitting, and R0 are also important factors. 2 The values ​​are shown in Table X; the root mean square error is less than 0.15, R0 2 The values ​​are all higher than 0.9899, ​​which further proves that the fitting accuracy is high;

[0018] (22) Carbon emission factor per unit fuel of gasoline vehicles Given a CO2 / L value of 2.37 kg CO2 / L, calculate the CO2 emissions F of a gasoline-powered vehicle at different speeds. i for:

[0019]

[0020] In the formula, f i It refers to fuel consumption;

[0021] (23) Based on the average carbon dioxide emission factor of electricity The figure is 0.6668 (kgCO2 / kWh) and the national line loss rate. The carbon emissions E of the electric vehicle at different speeds were calculated to be 4.54%. j :

[0022]

[0023] In the formula, e j It refers to the amount of electricity consumed.

[0024] Furthermore, the Paillier homomorphic encryption algorithm is used to encrypt the vehicle's carbon emission values ​​at different speeds, and the Edwards curve digital signature algorithm is used to digitally sign the vehicle's driving information data, as detailed below:

[0025] (31) Paillier key pair generation for base station:

[0026] The base station first selects two large prime numbers p and q, and then calculates the encryption modulus n = p × q, which constitutes part of the public key;

[0027] Next, the base station calculates the Carmichael function for n:

[0028] λ=lcm(p_1,q_1)#(5)

[0029] In the formula, lcm represents the least common multiple; λ is the parameter required for decryption;

[0030] Next, define a linear function. The base station selects a generator g satisfies:

[0031] gcd(L(g λ mod n 2 ),n)=1#(6)

[0032] Then, the base station calculates μ:

[0033] μ=(L(g λ mod n 2 ))-1 modn#(7)

[0034] Finally, the public key (n, g) of the Paillier algorithm is generated for encryption, and the private key (λ, μ) is used for decryption.

[0035] (32) Encryption calculation of user vehicles:

[0036] The base station transmits its public key to all vehicles within range. For vehicles requiring speed recommendations, each user vehicle uses the base station's public key (n, g) to encrypt its private data. Specifically, each vehicle chooses a random positive integer r that is coprime to n, and then calculates the ciphertext using the following formula:

[0037] c = g m ·r n mod n 2 #(8)

[0038] Where m represents the vehicle's private data, c is the encrypted ciphertext, and g, n, r come from the public and private keys calculated above.

[0039] (33) After encrypting the private carbon emission data, the Edwards curve digital signature algorithm is selected so that the user vehicle can sign all the uploaded data.

[0040] Furthermore, the system receives encrypted and signed data from each vehicle in the fleet, and performs weighted aggregation and decryption processing on all encrypted data, as follows:

[0041] (41) The base station first verifies whether the data comes from a legitimate vehicle using the Edwards curve digital signature algorithm. If so, the encrypted carbon emission data is directly weighted and aggregated as shown in Formula 23, and the aggregated data is then decrypted using the base station's private key.

[0042]

[0043] in It is the carbon emission of internal combustion engine vehicles. i (s i ) ciphertext, It is the carbon emissions of electric vehicles E j (s j ) ciphertext;

[0044] (42) The decryption process involves calculating the plaintext of the aggregate formula based on the formula:

[0045] M = L(C λ mod n 2 )·μmod n#(10)

[0046] Where L is a linear function n, μ, λ come from the public and private keys calculated above.

[0047] Furthermore, based on the aggregated driving information data, the common speed with minimum carbon emissions is calculated. Then, according to vehicle location and optimization objectives, and considering constraints and emission reduction targets, an optimized planned speed is generated, as follows:

[0048] The objective function for speed optimization is:

[0049]

[0050] In the formula s i Indicates the speed of vehicle i;

[0051] The constraints are: minimum following distance constraint, maximum speed limit constraint, and a common speed constraint recommended for all vehicles;

[0052] St l1≤0#(12)

[0053] l2≤0#(13)

[0054]

[0055] In the formula, F i It refers to the carbon emission value of internal combustion engine vehicles, E. j α is the carbon emission value of electric vehicles, α is the coordination factor, formula (12) is the minimum following distance constraint, each vehicle needs to maintain a certain safe following distance from the vehicle in front; formula (13) requires each vehicle to comply with the current road speed limit requirements. These two constraints ensure the driving safety of the vehicles, and formula (14) represents the recommended common speed for all vehicles, where s i This represents the speed of vehicle i.

[0056] According to a second aspect of the present invention, the present invention provides an intelligent vehicle speed planning system that balances environmental friendliness and privacy protection, for implementing the above-mentioned intelligent vehicle speed planning method that balances environmental friendliness and privacy protection, comprising:

[0057] The calculation module is used to obtain the fuel consumption function and the power consumption function by fitting the generator characteristic diagram of the internal combustion engine vehicle and the motor efficiency MAP diagram of the electric vehicle, combined with the carbon emission factor, using the nonlinear least squares method, thereby calculating the carbon emission values ​​of the fuel vehicle and the electric vehicle at different speeds.

[0058] The encryption module is used to encrypt the carbon emission values ​​of the vehicle at different speeds using the Paillier homomorphic encryption algorithm, and then use the EdDSA signature algorithm to digitally sign the vehicle's driving information data.

[0059] The aggregation and decryption module is used to receive the information data from each vehicle in the fleet after data encryption and signing, and to perform weighted aggregation and decryption processing on all encrypted information data.

[0060] The planning recommendation module is used to calculate the common speed with the minimum carbon emissions based on the aggregated driving information data, and then generate the optimized planned speed according to the vehicle location and optimization objectives, taking into account the constraints and emission reduction targets.

[0061] The recording module is used to record all encrypted data uploaded by the vehicle and key data from the speed planning process using consortium blockchain technology.

[0062] According to a third aspect of the present invention, the present invention provides a terminal device, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor. The memory stores the computer program capable of running on the processor, and when the processor loads and executes the computer program, it employs the above-described intelligent vehicle speed planning method that balances environmental friendliness and privacy protection.

[0063] According to a fourth aspect of the present invention, the present invention provides a storage medium containing computer-executable instructions, which, when executed by a computer processor, are used to perform the above-described intelligent vehicle speed planning method that balances environmental friendliness and privacy protection.

[0064] The present invention has at least the following beneficial effects:

[0065] 1. This invention takes into account the actual hybrid power environment, balances environmental friendliness and privacy protection, and significantly reduces overall carbon emissions by optimizing the common speed of the hybrid vehicle fleet.

[0066] 2. This invention achieves decentralized data management, ensuring the immutability and traceability of information, thereby improving the security and credibility of the system;

[0067] 3. The dynamic coordination factor introduced in this invention enables policymakers to flexibly adjust the emission weights of internal combustion engines and electric vehicles according to actual conditions, thereby enhancing the system's adaptability and optimization effect.

[0068] 4. This invention employs Paillier homomorphic encryption and EdDSA signature algorithms to effectively protect vehicle privacy data and prevent data leakage and malicious attacks.

[0069] Of course, any product implementing this invention does not necessarily need to achieve all of the advantages described above at the same time. Attached Figure Description

[0070] Figure 1This is a flowchart illustrating the planning method described in an embodiment of the present invention. Detailed Implementation

[0071] The technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this disclosure, and not all embodiments. Based on the embodiments of this disclosure, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this disclosure.

[0072] This invention is specifically designed for intelligent vehicle fleets in hybrid environments, aiming to achieve a balance between environmental friendliness and privacy protection. First, based on engine characteristic diagrams for internal combustion engine vehicles and motor efficiency MAP diagrams for electric vehicles, and referencing published carbon emission factors, this invention fits a high-precision speed-carbon emission model. To fully consider vehicle carbon emission differences and optimization objectives, a coordination factor is introduced, and constraints such as minimum following distance and maximum safe speed are set in the model. Second, all vehicles encrypt and sign their privacy data using the Paillier homomorphic encryption algorithm and the EdDSA signature algorithm before uploading it to the base station. The base station aggregates this encrypted data, calculates the optimal speed with minimum carbon emissions, and ensures the privacy information of all vehicles and the data security of the entire recommendation process. Furthermore, consortium blockchain technology is used to record data, ensuring the traceability and immutability of data during the process, thereby further enhancing data protection and privacy security.

[0073] Example 1:

[0074] Please see Figure 1 This invention provides a technical solution: an intelligent vehicle speed planning method that balances environmental friendliness and privacy protection, applied to speed planning of mixed powertrain fleets, comprising the following steps:

[0075] S1. The vehicle registers with the certification center and obtains a valid certificate to participate in the speed consultation system. Specifically, after the vehicle provides its identity information and vehicle category to the certification center, the certification center verifies the owner's identity and vehicle status. Assuming the identity information is valid and the vehicle meets the requirements, the certification center will issue a digital certificate to the vehicle and create a public-private key pair, allowing the vehicle to sign its data with its private key. In addition, the certification center will provide the base station with a list of public keys for all vehicles so that the base station can verify the vehicle's legitimate identity in the future.

[0076] S2. Based on the generator characteristic diagram of internal combustion engine vehicles and the motor efficiency MAP diagram of electric vehicles, combined with the carbon emission factor, the speed-carbon emission model is calculated by fitting using the nonlinear least squares method, thereby calculating the carbon emission values ​​of fuel vehicles and electric vehicles at different speeds respectively.

[0077] (S21) Four types of electric vehicles and four types of internal combustion engine vehicles were selected. For internal combustion engine vehicles, based on their generator universal characteristic diagram (generator universal characteristic diagram)... Figure 1 The typical method of obtaining this information is through calibration on a motor test bench or by the OEM (Original Equipment Manufacturer). The fuel consumption function corresponding to vehicle speed v is obtained through fitting:

[0078]

[0079] In the formula, a0, a1, a3, and a4 are the fitting coefficients of the fuel consumption function. The fuel consumption function will have different coefficients depending on the type of vehicle. Specific parameters, as well as the root mean square error (RMSD) and R after fitting, are also important considerations. 2 The values ​​are shown in Table X; the root mean square error is less than 0.13, and R0 is... 2 The values ​​are all higher than 0.9899, ​​which further proves that the fitting accuracy is high;

[0080] Fuel consumption fitting coefficients and indices for four types of internal combustion engine vehicles

[0081]

[0082] For trolleybuses, according to their motor efficiency MAP (Motor Efficiency Map) Figure 1 The typical method of obtaining this information is through calibration on a motor test bench or by the OEM (Original Equipment Manufacturer). The energy consumption function corresponding to vehicle speed v is obtained through fitting. The fitted energy consumption function is as follows:

[0083]

[0084] In the formula, b0, b1, b2, b3, and b4 are the fitting coefficients of the energy consumption function. The energy consumption function will have different coefficients depending on the type of vehicle. The specific parameters, root mean square error after fitting, and R0 are also important factors. 2 The values ​​are shown in Table X; the root mean square error is less than 0.15, R0 2 The values ​​are all higher than 0.9899, ​​which further proves that the fitting accuracy is high;

[0085] Energy consumption fitting coefficients and indices of four types of electric vehicles

[0086]

[0087] The fitting methods used here are all nonlinear least squares methods, among which the trust region algorithm is employed.

[0088] (S22) Both fuel consumption and energy consumption functions have different coefficients depending on the type of vehicle. According to the carbon emission factor per unit fuel consumption of gasoline vehicles provided by the China Society of Automotive Engineers... The value is 2.37 (kgCO2 / L), from which the carbon dioxide emissions F of a gasoline-powered vehicle at different speeds can be calculated. i for:

[0089]

[0090] In the formula, f i It refers to fuel consumption;

[0091] (S23) According to the announcement of the national average carbon dioxide emission factor for electricity released on April 12, 2024, the national average carbon dioxide emission factor for electricity... The figure is 0.6668 (kg CO2 / kWh), and the national power loss rate in 2023 is... The carbon emission rate is 4.54% (%). The carbon emissions E of an electric vehicle at different speeds can be obtained using the formula. j :

[0092]

[0093] In the formula, e j It is the amount of electricity consumed;

[0094] It should be noted that formulas (3) and (4) are the speed-carbon emission model expressions for internal combustion engine vehicles and electric vehicles;

[0095] S3. The Paillier homomorphic encryption algorithm is used to encrypt the carbon emission values ​​of the vehicle at different speeds, and the EdDSA signature algorithm is used to digitally sign all the data to be uploaded by the vehicle to ensure the privacy of the vehicle driving information data throughout the transmission process. The specific steps are as follows:

[0096] Vehicle driving information data includes all speed-energy consumption values, timestamp information, vehicle location information, and identity information;

[0097] (S31) Paillier key pair generation for the base station:

[0098] The base station first selects two large prime numbers p and q, and then calculates the encryption modulus n = p × q, which constitutes part of the public key;

[0099] Next, the base station calculates the Carmichael function for n:

[0100] λ=lcm(p_1,q_1)#(5)

[0101] In the formula, lcm represents the least common multiple; λ is the parameter required for decryption;

[0102] Next, define a linear function. The base station selects a generator g satisfies:

[0103] gcd(L(g λ mod n 2 ),n)=1#(6)

[0104] Then, the base station calculates μ:

[0105] μ=(L(g λ mod n 2 ))- 1 modn#(7)

[0106] Finally, the public key (n, g) of the Paillier algorithm is generated for encryption, and the private key (λ, μ) is used for decryption.

[0107] (S32) Ciphertext calculation for user vehicles:

[0108] The base station transmits its public key to all vehicles within range. For vehicles requiring speed recommendations, each user vehicle uses the base station's public key (n, g) to encrypt its private data. Specifically, each vehicle chooses a random positive integer r that is coprime to n, and then calculates the ciphertext c using the following formula:

[0109] c = g m ·r n mod n 2 #(8)

[0110] Where m represents the vehicle's private data, c is the encrypted ciphertext, and g, n, r come from the public and private keys calculated above.

[0111] After encrypting all carbon emission data, the Edwards Curve Digital Signature Algorithm (EdDSA) is selected, enabling user vehicles to sign all uploaded data. EdDSA is based on Elliptic Curve Cryptography (ECC), which has advantages such as high efficiency, resistance to side-channel attacks, and fixed signature size. Encryption and signing ensure the privacy and authenticity of vehicle information throughout the entire transmission process.

[0112] S4. After each vehicle encrypts and signs its data, it uploads the encrypted data to the base station. The base station receives data from all vehicles in the fleet and then verifies the signatures of all data received from the vehicles. Since the base station maintains an authentication list of all registered vehicles provided by the CA, it can verify whether the sender is a legitimate vehicle by calculating a hash value based on the vehicle's public key. Afterward, the base station directly performs weighted aggregation on the encrypted carbon emission data, and then decrypts the aggregated data using the base station's private key, as follows:

[0113] (41) The base station first verifies whether the data comes from a legitimate vehicle using the Edwards curve digital signature algorithm. If so, the encrypted carbon emission data at different speeds are directly weighted and aggregated as shown in the formula, and the aggregated data is then decrypted using the base station's private key.

[0114]

[0115] in It is the carbon emission of internal combustion engine vehicles. i (s i ) ciphertext, It is the carbon emissions of electric vehicles E j (s j ) ciphertext;

[0116] (42) The decryption process involves calculating the plaintext of the aggregate formula based on the formula:

[0117] M=L(Cλmodn 2 )·μmodn#(10)

[0118] Where L is a linear function n, μ, λ come from the public and private keys calculated above;

[0119] S5. Based on the aggregated data, the base station calculates the common speed with minimum carbon emissions. Taking into account factors such as vehicle location and optimization objectives, safety constraints like minimum following distance and maximum speed limits, as well as the current emission reduction targets of road planners, it generates an optimized recommended speed. The objective function is:

[0120]

[0121] The relevant constraints are as follows:

[0122] St l1≤0#(12)

[0123] l2≤0#(13)

[0124]

[0125] In the formula, F i It refers to the carbon emission value of internal combustion engine vehicles, E. j α is the carbon emission value of electric vehicles, α is the coordination factor, formula (12) is the minimum following distance constraint, each vehicle needs to maintain a certain safe following distance from the vehicle in front; formula (13) requires each vehicle to comply with the current road speed limit requirements. These two constraints ensure the driving safety of the vehicles, and formula (14) represents the recommended common speed for all vehicles, where s i Indicates the speed of vehicle i;

[0126] S6. The base station packages all the encrypted data uploaded by the vehicle and key data from the speed recommendation process into a transaction proposal and sends it to the endorsing nodes in the blockchain; in Hyperledger Fabric, the chaincode acts as a smart contract, responsible for handling business logic;

[0127] The base station constructs a transaction proposal by calling a chaincode function, passing relevant data as parameters, and signing the proposal using its private key. The signed proposal is then sent to a designated endorsing node for endorsement. Upon receiving the transaction proposal, the endorsing node verifies the validity of the signature, confirms that the proposal originates from an authorized base station, and then executes the chaincode to generate the endorsement without actually modifying the ledger.

[0128] After approval, each approving node returns a signed "approval response". Once enough approvals are collected, the base station packages the transaction proposal with all approval responses to create the final transaction. Then, it sends the final transaction to the sorting service for sorting. The sorted transactions are batched into blocks and broadcast to peer nodes through the P2P network as persistent ledger data. After receiving a block, the peer node verifies the transactions in the block to ensure that they conform to the business logic of the chaincode.

[0129] Finally, peer nodes update their ledgers, permanently storing the transaction information on the blockchain. Due to the immutability of blockchain data, the blockchain structure ensures data integrity and reliability, making tracking and querying in HP-SAS reliable. The recommended speed is then broadcast to all user vehicles.

[0130] In summary, this invention combines the direct carbon emissions of internal combustion engine vehicles with the indirect carbon emissions of electric vehicles. It achieves decentralized data management through a consortium blockchain and privacy protection mechanisms, and introduces security measures to improve fleet safety and stability. Furthermore, considering policymakers' confidence in the indirect carbon emissions of electric vehicles, this invention introduces a coordination factor α, allowing policymakers to dynamically adjust the emission weights of internal combustion engines and electric vehicles. In practice, the indirect carbon emission coefficient of electric vehicles varies across different regions, road sections, and traffic conditions. Even on the same road section, the carbon emission coefficient may fluctuate throughout the day; for example, during peak load periods, the power grid may experience greater emissions. Relying on fossil fuels for power generation leads to an increased carbon emission coefficient; conversely, during periods of low load, the power grid may rely more on renewable energy, thereby reducing the carbon emission coefficient. If policymakers lack confidence in the carbon emissions of electric vehicles and believe that the current actual carbon emission coefficient of the power grid is higher than the benchmark, they can increase the value of α to place greater emphasis on the emissions of electric vehicles. On the other hand, when policymakers are confident that the carbon emissions of electric vehicles on a particular road segment mainly come from the power grid and that the carbon intensity is lower than the benchmark, they can decrease the value of α and focus more on optimizing the emissions of internal combustion engines. This invention aims to provide hybrid vehicle fleets with traceable and data privacy-protected speed recommendations to minimize carbon emissions.

[0131] Example 2:

[0132] This embodiment provides an intelligent vehicle speed planning system that balances environmental friendliness and privacy protection, used to implement the aforementioned intelligent vehicle speed planning method that balances environmental friendliness and privacy protection, including:

[0133] The calculation module is used to obtain the fuel consumption function and the power consumption function by fitting the generator characteristic diagram of the internal combustion engine vehicle and the motor efficiency MAP diagram of the electric vehicle, combined with the carbon emission factor, using the nonlinear least squares method, thereby calculating the carbon emission values ​​of the fuel vehicle and the electric vehicle at different speeds.

[0134] The encryption module is used to encrypt the carbon emission values ​​of the vehicle at different speeds using the Paillier homomorphic encryption algorithm, and then use the EdDSA signature algorithm to digitally sign the vehicle's driving information data.

[0135] The aggregation and decryption module is used to receive the information data from each vehicle in the fleet after data encryption and signing, and to perform weighted aggregation and decryption processing on all encrypted information data.

[0136] The planning recommendation module is used to calculate the common speed with the minimum carbon emissions based on the aggregated driving information data, and then generate the optimized planned speed according to the vehicle location and optimization objectives, taking into account the constraints and emission reduction targets.

[0137] The recording module is used to record all encrypted data uploaded by the vehicle and key data from the speed planning process using consortium blockchain technology.

[0138] Specifically, the aforementioned calculation module, encryption module, aggregation decryption module, planning recommendation module, and recording module can be embedded into a computer processing system. Based on the aforementioned intelligent vehicle speed planning method that balances environmental friendliness and privacy protection, the computer calls upon the aforementioned modules to complete the task of providing speed recommendations for the hybrid vehicle fleet with traceability and data privacy protection. The aforementioned calculation module, encryption module, aggregation decryption module, planning recommendation module, and recording module can execute operations according to the specific steps given by the aforementioned intelligent vehicle speed planning method that balances environmental friendliness and privacy protection.

[0139] It should be noted that the division of the various modules in the above system is merely a division of logical functions. In actual implementation, they can be fully or partially integrated into a single physical entity, or they can be physically separated. These modules can be implemented entirely in software through processing element calls; they can be fully implemented in hardware; or some modules can be implemented by processing element calls to software, while others are implemented in hardware. For example, the computing module can be a separate processing element, or it can be integrated into a chip in the aforementioned device. Alternatively, it can be stored as program code in the memory of the aforementioned device, and its signal processing module functions can be called and executed by a processing element of the device. The implementation of other modules is similar. Furthermore, these modules can be fully or partially integrated together, or they can be implemented independently. The processing element mentioned here can be an integrated circuit with signal processing capabilities. In the implementation process, each step of the above method or each of the above modules can be completed through the integrated logic circuits in the hardware of the processor element or through software instructions.

[0140] For example, these modules can be one or more integrated circuits configured to implement the above methods, such as one or more Application Specific Integrated Circuits (ASICs), one or more Digital Signal Processors (DSPs), or one or more Field Programmable Gate Arrays (FPGAs). As another example, when a module is implemented using processing element scheduler code, the processing element can be a general-purpose processor, such as a Central Processing Unit (CPU) or other processor capable of calling program code. Furthermore, these modules can be integrated together to form a system-on-a-chip (SOC).

[0141] Example 3:

[0142] The present invention provides a terminal device, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor. The memory stores the computer program capable of running on the processor. When the processor loads and executes the computer program, it adopts the above-mentioned intelligent vehicle speed planning method that takes into account both environmental friendliness and privacy protection.

[0143] It should be noted that the terminal device can be a computer device such as a desktop computer, a laptop computer, or a cloud server, and the terminal device includes, but is not limited to, a processor and a memory. For example, the terminal device may also include input / output devices, network access devices, and buses.

[0144] Furthermore, the processor can be a central processing unit (CPU). Of course, depending on the actual use, other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), off-the-shelf programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. can also be used. The general-purpose processor can be a microprocessor or any conventional processor, etc., and this application does not limit it in this regard.

[0145] Example 4:

[0146] The present invention provides a storage medium containing computer-executable instructions, which, when executed by a computer processor, are used to perform the above-described intelligent vehicle speed planning method that balances environmental friendliness and privacy protection.

[0147] The computer program can be stored in a computer-readable medium. The computer program includes computer program code, which can be in the form of source code, object code, executable file, or certain middleware. The computer-readable medium includes any entity or device capable of carrying computer program code, recording media, USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the computer-readable medium includes, but is not limited to, the above-mentioned components.

[0148] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0149] For those skilled in the art, the specific meaning of the above terms in this invention can be understood according to the specific circumstances. When an element is referred to as being "assembled on," "mounted on," "fixed to," or "set on" another element, it may be directly on the other element or there may be an intermediate element present. When an element is considered to be "connected to" another element, it may be directly connected to the other element or there may be an intermediate element present. The terms "vertical," "horizontal," "upper," "lower," "left," "right," and similar expressions used herein are for illustrative purposes only and do not represent the only possible embodiments.

[0150] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

[0151] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this disclosure. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

Claims

1. A smart car speed planning method that balances environmental friendliness and privacy protection, applied to speed planning of mixed powertrain fleets, characterized in that... Includes the following steps: Based on the generator characteristic diagram of internal combustion engine vehicles and the motor efficiency MAP diagram of electric vehicles, combined with the carbon emission factor, a speed-carbon emission model is calculated by fitting using the nonlinear least squares method, thereby calculating the carbon emission values ​​of fuel vehicles and electric vehicles at different speeds. The Paillier homomorphic encryption algorithm is used to encrypt the carbon emission values ​​of the vehicle at different speeds, and the EdDSA signature algorithm is used to digitally sign the vehicle's driving information data. Receive information data from each vehicle in the fleet after data encryption and signing, and perform weighted aggregation and decryption processing on all encrypted information data; The common speed with minimum carbon emissions is calculated based on the aggregated driving information data. Then, based on the vehicle location and optimization objectives, and taking into account constraints and emission reduction targets, the optimized planned speed is generated. All encrypted data uploaded by the vehicle and key data from the speed planning process are recorded using consortium blockchain technology.

2. The intelligent vehicle speed planning method that balances environmental friendliness and privacy protection according to claim 1, characterized in that, Based on the generator characteristic diagram of internal combustion engine vehicles and the motor efficiency MAP diagram of electric vehicles, and combined with carbon emission factors, a speed-carbon emission model was constructed. The carbon emission values ​​of gasoline-powered vehicles and electric vehicles at different speeds were then calculated using this model, as detailed below: (21) For internal combustion engine vehicles, based on their generator universal characteristic diagram, the fuel consumption function corresponding to vehicle speed v can be obtained by fitting: In the formula, a0, a1, a3, and a4 are the fitting coefficients of the fuel consumption function. The fuel consumption function will have different coefficients depending on the type of vehicle. Specific parameters, as well as the root mean square error (RMSD) and R after fitting, are also important considerations. 2 The values ​​are shown in Table X; the root mean square error is less than 0.13, and R0 is... 2 The values ​​are all higher than 0.9899, ​​which further proves that the fitting accuracy is high; For electric vehicles, based on their motor efficiency MAP, the energy consumption function corresponding to vehicle speed v is obtained through fitting. The fitted energy consumption function is: In the formula, b0, b1, b2, b3, and b4 are the fitting coefficients of the energy consumption function. The energy consumption function will have different coefficients depending on the type of vehicle. The specific parameters, root mean square error after fitting, and R0 are also important factors. 2 The values ​​are shown in Table X; the root mean square error is less than 0.15, R0 2 The values ​​are all higher than 0.9899, ​​which further proves that the fitting accuracy is high; (22) Carbon emission factor per unit fuel of gasoline vehicles Given a CO2 / L value of 2.37 kg CO2 / L, calculate the CO2 emissions F of a gasoline-powered vehicle at different speeds. i for: In the formula, f i It refers to fuel consumption; (23) Based on the average carbon dioxide emission factor of electricity Given a line loss rate of 0.6668 (kgCO2 / kWh) and a national line loss rate T of 4.54%, the carbon emissions E of electric vehicles at different speeds were calculated. j : In the formula, e j It refers to the amount of electricity consumed.

3. The intelligent vehicle speed planning method that balances environmental friendliness and privacy protection according to claim 2, characterized in that, The Paillier homomorphic encryption algorithm is used to encrypt the carbon emission values ​​of the vehicle at different speeds, and then the Edwards curve digital signature algorithm is used to digitally sign the vehicle's driving information data, as follows: (31) Paillier key pair generation for base station: The base station first selects two large prime numbers p and q, and then calculates the encryption modulus n = p × q, which constitutes part of the public key; Next, the base station calculates the Carmichael function for n: λ=lcm(p 1,q 1)#(5) -- In the formula, lcm represents the least common multiple; λ is the parameter required for decryption; Next, define a linear function. The base station selects a generator g satisfies: gcd(L(g λ mod n 2 ),n)=1#(6) Then, the base station calculates μ: μ=(L(g λ mode n 2 ))- 1 modern#(7) Finally, the public key (n, g) of the Paillier algorithm is generated for encryption, and the private key (λ, μ) is used for decryption. (32) Encryption calculation of user vehicles: The base station transmits its public key to all vehicles within range. For vehicles requiring speed recommendations, each user vehicle uses the base station's public key (n, g) to encrypt its private data. Specifically, each vehicle chooses a random positive integer r that is coprime to n, and then calculates the ciphertext c using the following formula: c=g m ·r n mod n 2 #(8) Where m represents the vehicle's private data, c is the encrypted ciphertext, and g, n, r come from the public and private keys calculated above. (33) After encrypting the private carbon emission data, the Edwards curve digital signature algorithm is selected so that the user vehicle can sign all the uploaded data.

4. The intelligent vehicle speed planning method that balances environmental friendliness and privacy protection according to claim 1, characterized in that, The system receives encrypted and signed data from each vehicle in the fleet, and performs weighted aggregation and decryption processing on all encrypted data, as follows: (41) The base station first verifies whether the data comes from a legitimate vehicle using the Edwards curve digital signature algorithm. If so, the encrypted carbon emission data at different speeds are directly weighted and aggregated as shown in the formula, and the aggregated data is then decrypted using the base station's private key. in It is the carbon emission of internal combustion engine vehicles. i (s i ) ciphertext, It is the carbon emissions of electric vehicles E j (s j ) ciphertext; (42) The decryption process involves calculating the plaintext of the aggregate formula based on the formula: M=L(C λ modern 2 )·μmodn#(10) Where L is a linear function n, μ, λ come from the public and private keys calculated above.

5. The intelligent vehicle speed planning method that balances environmental friendliness and privacy protection according to claim 4, characterized in that, Based on the aggregated driving information data, the common speed with minimum carbon emissions is calculated. Then, according to vehicle location and optimization objectives, and considering constraints and emission reduction targets, the optimized planned speed is generated, as follows: The objective function for speed optimization is: In the formula s i Indicates the speed of vehicle i; The constraints are: minimum following distance constraint, maximum speed limit constraint, and a common speed constraint recommended for all vehicles; Stl1≤0#(12) l2≤0#(13) In the formula, F i It refers to the carbon emission value of internal combustion engine vehicles, E. j α is the carbon emission value of electric vehicles, α is the coordination factor, formula (12) is the minimum following distance constraint, each vehicle needs to maintain a certain safe following distance from the vehicle in front; formula (13) requires each vehicle to comply with the current road speed limit requirements. These two constraints ensure the driving safety of the vehicles, and formula (14) represents the recommended common speed for all vehicles, where s i This represents the speed of vehicle i.

6. A smart car speed planning system that balances environmental friendliness and privacy protection, used to implement the smart car speed planning method that balances environmental friendliness and privacy protection as described in any one of claims 1 to 5, characterized in that, include: The calculation module is used to obtain the fuel consumption function and the power consumption function by fitting the generator characteristic diagram of internal combustion engine vehicles and the motor efficiency MAP diagram of electric vehicles, combined with the carbon emission factor, using the nonlinear least squares method, thereby calculating the carbon emission values ​​of fuel vehicles and electric vehicles at different speeds. The encryption module is used to encrypt the carbon emission values ​​of the vehicle at different speeds using the Paillier homomorphic encryption algorithm, and then use the EdDSA signature algorithm to digitally sign the vehicle's driving information data. The aggregation and decryption module is used to receive the information data from each vehicle in the fleet after data encryption and signing, and to perform weighted aggregation and decryption processing on all encrypted information data. The planning recommendation module is used to calculate the common speed with the minimum carbon emissions based on the aggregated driving information data, and then generate the optimized planned speed according to the vehicle location and optimization objectives, taking into account the constraints and emission reduction targets. The recording module is used to record all encrypted data uploaded by the vehicle and key data from the speed planning process using consortium blockchain technology.

7. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, characterized in that, The memory stores a computer program that can run on a processor. When the processor loads and executes the computer program, it employs the intelligent vehicle speed planning method that balances environmental friendliness and privacy protection, as described in any one of claims 1 to 5.

8. A storage medium containing computer-executable instructions, characterized in that, The computer-executable instructions, when executed by a computer processor, are used to perform the intelligent vehicle speed planning method that balances environmental friendliness and privacy protection as described in any one of claims 1 to 5.

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