Intelligent automobile speed planning method and system giving consideration to environment friendliness and privacy protection

Abstract

The invention discloses an intelligent automobile speed planning method and system considering environment friendliness and privacy protection, and relates to the technical field of automobile driving speed planning. The method comprises the steps that according to a generator characteristic diagram of an internal combustion engine automobile and a motor efficiency MAP diagram of an electric automobile, carbon emission factors are combined, a speed-carbon emission model is obtained through calculation after non-linear least square fitting, and therefore carbon emission values of the fuel automobile and the electric automobile at different speeds are obtained through calculation. On the basis of ensuring vehicle user privacy and driving safety, an optimal common speed with minimum carbon emission is provided for an intelligent vehicle fleet in a hybrid power scene, and a speed recommendation system combining a multi-class vehicle carbon emission model, a minimum vehicle following distance, a maximum speed limit and privacy protection is provided. And a block chain technology is introduced into data protection, so that relatively high privacy and security are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of automobile driving speed planning, and in particular to an intelligent automobile speed planning method and system that takes into account both environmental friendliness and privacy protection. Background Art

[0002] As global climate change becomes increasingly serious, carbon emissions from the transportation industry have become one of the key issues that need to be addressed. Transportation is not only the main source of greenhouse gas emissions, but also has a serious impact on the environment and human health due to excessive fuel consumption and air pollution. In recent years, the rapid development of intelligent transportation systems (ITS) and connected autonomous vehicles (CAVs) technologies has made the Intelligent Speed ​​Advisory System (ISA) with emission reduction as its goal an important means to reduce transportation emissions and optimize energy use.

[0003] The current ISA system mainly provides speed optimization suggestions for individual vehicles. However, the consensus-based speed advisory system (CSAS) can significantly reduce energy consumption and emissions caused by frequent acceleration and deceleration by providing unified speed suggestions for fleets. Such systems achieve fleet speed optimization while ensuring privacy, but most of them adopt a centralized architecture, requiring vehicles to transmit private data to a central server, which brings potential trust risks and data leakage issues. In addition, traditional CSAS solutions usually ignore the risk of malicious vehicle interference and the particularity of hybrid traffic environments. Therefore, we propose an intelligent vehicle speed planning method and system that takes into account both environmental friendliness and privacy protection. Summary of the invention

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

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

[0006] According to 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 using the nonlinear least squares method to calculate 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 then the EdDSA signature algorithm is used to digitally sign the vehicle's driving information data;

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

[0009] The common speed with the lowest carbon emissions is calculated based on the aggregated driving information data, and then the optimized planned speed is generated based on the vehicle position and optimization target, taking into account the constraints and emission reduction targets;

[0010] All encrypted data uploaded by the vehicle and key data of the speed planning process are recorded through alliance blockchain technology.

[0011] Furthermore, according to 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, the speed-carbon emission model is fitted and constructed, and the carbon emission values ​​of the fuel vehicle and the electric vehicle at different speeds are calculated according to the speed-carbon emission model, as follows:

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

[0013]

[0014] Where a0, a1, a3, and a4 are the fitting coefficients of the fuel consumption function. The fuel consumption function will have different coefficients according to different types of vehicles. The specific parameters and the root mean square error (RMSD) and R after fitting are 2 The values ​​are shown in Table X. The root mean square error is less than 0.13, R 2 The values ​​are all higher than 0.9899, ​​which further proves that the fitting accuracy is high;

[0015] For electric vehicles, according to the motor efficiency MAP diagram, the energy consumption function corresponding to the vehicle speed v is obtained by fitting. The energy consumption function after fitting is:

[0016]

[0017] Where b0, b1, b2, b3, b4 are the fitting coefficients of the energy consumption function. The energy consumption function will have different coefficients according to different types of vehicles. The specific parameters and the root mean square error and R after fitting are 2 The values ​​are shown in Table X. The root mean square error is less than 0.15, R 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 fuel vehicles =2.37kgCO2 / L, calculate the carbon dioxide emissions of fuel vehicles at different speeds F i for:

[0019]

[0020] In the formula, f i is the fuel consumption;

[0021] (23) Based on the average CO2 emission factor of electricity 0.6668 (kgCO2 / kWh) and the national line loss rate is 4.54(%), and the carbon emissions E corresponding to different speeds of electric vehicles are calculated. j :

[0022]

[0023] In the formula, e j is the amount of electrical energy consumed.

[0024] Furthermore, 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:

[0025] (31) Generation of Paillier key pair of 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] The base station then calculates the Carmichael function of n:

[0028] λ=lcm(p_1,q_1)#(5)

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

[0030] Next, define the 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, generate the public key (n, g) of the Paillier algorithm for encryption and the private key (λ, μ) for decryption;

[0035] (32) Ciphertext calculation of user vehicle:

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

[0037] c=g m ·r n mod n 2 #(8)

[0038] Among them, m represents the private data of the vehicle, c is the encrypted ciphertext, and g, n, and r come from the public key and private key calculated above;

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

[0040] Furthermore, the information data of each vehicle in the fleet is received after data encryption and signing, and all the encrypted information data are weighted aggregated and decrypted, as follows:

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

[0042]

[0043] in Is the carbon emission of internal combustion engine vehicles F i (s i ), The carbon emissions of electric vehicles are j (s j )’s ciphertext;

[0044] (42) The decryption process is to calculate the aggregated plaintext according to 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, the common speed with the lowest carbon emission is calculated based on the aggregated driving information data, and then the optimized planning speed is generated according to the vehicle position and optimization target, and considering the constraints and emission reduction targets, as follows:

[0048] The objective function for building speed optimization is:

[0049]

[0050] Where s i represents the speed of vehicle i;

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

[0052] St l1≤0#(12)

[0053] l2≤0#(13)

[0054]

[0055] In the formula, F i is the carbon emission value of internal combustion engine vehicles, E j 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 with the vehicle in front; formula (13) requires each vehicle to follow the maximum speed limit of the current road. These two constraints ensure the driving safety of the vehicle. Formula (14) indicates that a common speed is recommended for all vehicles, where s i 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 takes into account both environmental friendliness and privacy protection, which is used to implement the above-mentioned intelligent vehicle speed planning method that takes into account both environmental friendliness and privacy protection, comprising:

[0057] A calculation module is used to obtain the fuel consumption function and the electric energy consumption function by using the nonlinear least square method according to 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, so as to calculate the carbon emission values ​​of the fuel vehicle and the electric vehicle at different speeds respectively;

[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 of each vehicle in the fleet after data encryption and signing, and 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 lowest carbon emissions based on the aggregated driving information data, and then generate the optimized planning speed according to the vehicle position and optimization target, 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 of the speed planning process through the alliance blockchain technology.

[0062] According to a third aspect of the present invention, the present invention provides a terminal device, comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, wherein the memory stores a computer program capable of running on the processor, and when the processor loads and executes the computer program, the above-mentioned intelligent car speed planning method that takes into account both environmental friendliness and privacy protection is adopted.

[0063] According to a fourth aspect of the present invention, the present invention provides a storage medium comprising computer executable instructions, which, when executed by a computer processor, are used to execute the above-mentioned intelligent car speed planning method that takes into account both environmental friendliness and privacy protection.

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

[0065] 1. The present invention takes into account the actual hybrid environment, takes into account both environmental friendliness and privacy protection, and significantly reduces the overall carbon emissions by optimizing the common speed of the hybrid fleet;

[0066] 2. The present invention realizes the decentralized management of data, ensures the immutability and traceability of information, and thus improves the security and credibility of the system;

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

[0068] 4. The present invention adopts Paillier homomorphic encryption and EdDSA signature algorithm to effectively protect the privacy data of the vehicle and prevent data leakage and malicious attacks.

[0069] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] Figure 1It is a flowchart of the planning method described in an embodiment of the present invention. DETAILED DESCRIPTION

[0071] The following will be combined with the drawings in the embodiments of the present disclosure to clearly and completely describe the technical solutions in the embodiments of the present disclosure. Obviously, the described embodiments are only part of the embodiments of the present disclosure, rather than all the embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present disclosure.

[0072] The present invention is designed for smart car fleets in a hybrid environment, aiming to achieve a balance between environmental friendliness and privacy protection; first, based on the engine characteristic diagram of internal combustion engine vehicles and the motor efficiency MAP diagram of electric vehicles, and referring to the published carbon emission factors, the present invention fits a high-precision speed-carbon emission model. In order to fully consider the differences in vehicle carbon emissions and the optimization goals, a coordination factor is introduced, and constraints such as the minimum following distance and the maximum safe speed are set in the model. Secondly, all vehicles encrypt and sign the privacy data through the Paillier homomorphic encryption algorithm and the EdDSA signature algorithm and upload them to the base station. The base station aggregates these encrypted data, calculates the optimal speed with the lowest carbon emissions, and ensures the privacy information of all vehicles and the data security of the entire recommendation process. In addition, the alliance blockchain technology is used to record the data to ensure that the data in the process is traceable and cannot be tampered with, thereby further enhancing data protection and privacy security.

[0073] Embodiment 1:

[0074] See also Figure 1 The present invention provides a technical solution: an intelligent vehicle speed planning method that takes into account both environmental friendliness and privacy protection, which is applied to the speed planning of a fleet with mixed power types, and includes the following steps:

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

[0076] S2. Based on 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, the speed-carbon emission model is calculated by using the nonlinear least squares method to fit, thereby calculating the carbon emission values ​​of the fuel vehicle and the electric vehicle at different speeds respectively;

[0077] (S21) Four electric vehicles and four internal combustion engine vehicles are selected. For the internal combustion engine vehicles, according to their generator universal characteristic diagram (Generator Universal Characteristic Figure 1 The general way to obtain it is through the calibration of the motor test bench or provided by the OEM). The fuel consumption function corresponding to the vehicle speed v is obtained by fitting:

[0078]

[0079] Where a0, a1, a3, and a4 are the fitting coefficients of the fuel consumption function. The fuel consumption function will have different coefficients according to different types of vehicles. The specific parameters and the root mean square error (RMSD) and R after fitting are 2 The values ​​are shown in Table X. The root mean square error is less than 0.13, R 2 The values ​​are all higher than 0.9899, ​​which further proves that the fitting accuracy is high;

[0080] Fuel consumption fitting coefficients and indicators of four internal combustion engine vehicles

[0081]

[0082] For electric vehicles, according to their motor efficiency MAP diagram (Motor Efficiency MAP Figure 1 The general acquisition method is to obtain it through the calibration of the motor test bench or provided by the OEM), and the energy consumption function corresponding to the vehicle speed v is obtained by fitting. The energy consumption function after fitting is:

[0083]

[0084] Where b0, b1, b2, b3, b4 are the fitting coefficients of the energy consumption function. The energy consumption function will have different coefficients according to different types of vehicles. The specific parameters and the root mean square error and R after fitting are 2 The values ​​are shown in Table X. The root mean square error is less than 0.15, R 2 The values ​​are all higher than 0.9899, ​​which further proves that the fitting accuracy is high;

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

[0086]

[0087] The fitting methods used here are all nonlinear least squares methods, in which the Trust Region algorithm is used;

[0088] (S22) The fuel consumption function and energy consumption function have different coefficients for different types of vehicles. According to the unit fuel carbon emission factor of fuel vehicles given by the China Association of Automotive Engineers, =2.37 (kgCO2 / L), the carbon dioxide emissions of fuel vehicles at different speeds can be calculated i for:

[0089]

[0090] In the formula, f i is the fuel consumption;

[0091] (S23) According to the announcement of the national average carbon dioxide emission factor for electricity issued on April 12, 2024, the national average carbon dioxide emission factor for electricity 0.6668 (kgCO2 / kWh), and the national power loss rate in 2023 is 4.54 (%), and the carbon emissions of electric vehicles at different speeds can be obtained according to the formula E j :

[0092]

[0093] In the formula, e j is the amount of electrical energy consumed;

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

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

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

[0097] (S31) Generation of Paillier key pair of 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] The base station then calculates the Carmichael function of n:

[0100] λ=lcm(p_1,q_1)#(5)

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

[0102] Next, define the 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, generate the public key (n, g) of the Paillier algorithm for encryption and the private key (λ, μ) for decryption;

[0107] (S32) Calculation of the ciphertext of the user's vehicle:

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

[0109] c=g m ·r n mod n 2 #(8)

[0110] Among them, m represents the private data of the vehicle, c is the encrypted ciphertext, and g, n, and r come from the public key and private key calculated above;

[0111] After encrypting all carbon emission data, the Edwards Curve Digital Signature Algorithm (EdDSA) is selected to enable the user vehicle to sign all uploaded data; EdDSA is based on elliptic curve cryptography (ECC) and has the advantages of high efficiency, resistance to side channel attacks, and fixed signature size. Encryption and signing ensure the privacy and authenticity of vehicle information during the entire transmission process;

[0112] S4. After encrypting and signing the data, each vehicle uploads the encrypted data to the base station. The base station receives the data from all vehicles in the fleet and then verifies the signatures of all data received from the vehicles. Since the base station maintains the authentication list of all registered vehicles provided by the CA, it can verify whether the sender is a legitimate vehicle by calculating the hash value based on the vehicle public key. The base station will then directly perform weighted aggregation on the encrypted carbon emission data and decrypt 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 based on the Edwards curve digital signature algorithm. If so, the encrypted carbon emission data at different speeds are directly weighted aggregated, as shown in the formula, and the aggregated data is then decrypted using the base station's private key;

[0114]

[0115] in Is the carbon emission of internal combustion engine vehicles F i (s i ), The carbon emissions of electric vehicles are j (s j )’s ciphertext;

[0116] (42) The decryption process is to calculate the aggregated plaintext according to 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. The base station calculates the common speed with the lowest carbon emissions based on the aggregated data, and generates the optimized recommended speed based on factors such as vehicle location and optimization objectives, taking into account safety constraints such as the minimum following distance and maximum speed limit, as well as the current emission reduction goals of the road planner. 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 is the carbon emission value of internal combustion engine vehicles, E j 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 with the vehicle in front; formula (13) requires each vehicle to follow the maximum speed limit of the current road. These two constraints ensure the driving safety of the vehicle. Formula (14) indicates that a common speed is recommended for all vehicles, where s i represents the speed of vehicle i;

[0126] S6. The base station packages all the encrypted data uploaded by the vehicle and the key data of the speed recommendation process into a transaction proposal and sends it to the endorsement node in the blockchain; in Hyperledger Fabric, the chain code acts as a smart contract and is responsible for processing business logic;

[0127] The base station builds a transaction proposal by calling the chain code function, passing the relevant data as parameters, and signing the proposal with its private key. The signed proposal is then sent to the designated endorsement node for endorsement. After receiving the transaction proposal, the endorsement node verifies the validity of the signature and confirms that the proposal comes from the authorized base station. It then executes the chain code and generates an endorsement without actually modifying the ledger.

[0128] After obtaining approval, each approval 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, which is then sent 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 a peer node receives a block, it verifies the transactions in the block to ensure that they comply with the business logic of the chain code.

[0129] Finally, the peer node will update its ledger and store the transaction content permanently on the blockchain. Due to the immutability of blockchain data, the blockchain structure ensures the integrity and reliability of the data, making tracking and querying in HP-SAS reliable. Subsequently, the recommended speed will be broadcast to all user vehicles.

[0130] In summary, the present invention combines the direct carbon emissions of fuel-powered vehicles and the indirect carbon emissions of electric vehicles, realizes decentralized data management through alliance chains and privacy protection mechanisms, and introduces security protection measures to improve the driving safety and stability of the fleet. In addition, considering the confidence of policymakers in the indirect carbon emissions of electric vehicles, the present invention introduces a coordination factor α, which allows 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 in different regions, road sections and traffic conditions. Even on the same road section, the carbon emission coefficient may fluctuate depending on the time of day. For example, during peak load periods, the power grid may be more Reliance on fossil fuels for power generation leads to an increase in the carbon emission coefficient; conversely, during low-load periods, 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 actual carbon emission coefficient of the current power grid is higher than the benchmark, the value of α can be increased to place more emphasis on the emissions of electric vehicles; on the other hand, when policymakers are confident that the carbon emissions of electric vehicles on a specific road section mainly come from the power grid and the carbon intensity is lower than the benchmark, the value of α can be reduced to focus more on optimizing the emissions of internal combustion engines; the present invention aims to provide speed recommendations with traceability and data privacy protection for hybrid vehicle fleets to minimize carbon emissions.

[0131] Embodiment 2:

[0132] This embodiment provides an intelligent vehicle speed planning system that takes into account both environmental friendliness and privacy protection, which is used to implement the above-mentioned intelligent vehicle speed planning method that takes into account both environmental friendliness and privacy protection, including:

[0133] A calculation module is used to obtain the fuel consumption function and the electric energy consumption function by using the nonlinear least square method according to 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, so as to calculate the carbon emission values ​​of the fuel vehicle and the electric vehicle at different speeds respectively;

[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 of each vehicle in the fleet after data encryption and signing, and 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 lowest carbon emissions based on the aggregated driving information data, and then generate the optimized planning speed according to the vehicle position and optimization target, 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 of the speed planning process through the alliance blockchain technology.

[0138] Specifically, the above-mentioned calculation module, encryption module, aggregation and decryption module, planning recommendation module and recording module can be embedded in a computer processing system. The computer calls the above-mentioned modules to complete the task of providing speed recommendations with traceability and data privacy protection to the hybrid vehicle fleet based on the above-mentioned intelligent vehicle speed planning method that takes into account both environmental friendliness and privacy protection; the above-mentioned calculation module, encryption module, aggregation and decryption module, planning recommendation module and recording module can perform operations according to the specific steps given in the intelligent vehicle speed planning method that takes into account both environmental friendliness and privacy protection.

[0139] It should be noted that it should be understood that the division of the various modules of the above system is only the division of logical functions. In actual implementation, they can be fully or partially integrated into one physical entity, or they can be physically separated, and these modules can all be implemented in the form of software calling through processing elements; they can also be all implemented in the form of hardware; some modules can also be implemented in the form of software calling through processing elements, and some modules can be implemented in the form of hardware. For example, the calculation module can be a separately established processing element, or it can be integrated in a chip of the above-mentioned device. In addition, it can also be stored in the memory of the above-mentioned device in the form of program code, and called and executed by a processing element of the above-mentioned device. The functions of the above signal processing module, and the implementation of other modules is similar. In addition, these modules can be fully or partially integrated together, or they can be implemented independently. The processing element described 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 by the hardware integrated logic circuit in the processor element or the instructions in the form of software.

[0140] For example, the above modules may be one or more integrated circuits configured to implement the above methods, such as one or more application specific integrated circuits (ASIC), or one or more digital singnal processors (DSP), or one or more field programmable gate arrays (FPGA). For another example, when a module is implemented in the form of a processing element scheduling program code, the processing element may be a general-purpose processor, such as a central processing unit (CPU) or other processors that can call program code. For another example, these modules may be integrated together and implemented in the form of a system-on-a-chip (SOC).

[0141] Embodiment three:

[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 a computer program capable of running on the processor. When the processor loads and executes the computer program, the above-mentioned intelligent car speed planning method that takes into account both environmental friendliness and privacy protection is adopted.

[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 can also include input and output devices, network access devices and buses.

[0144] Furthermore, the processor may adopt a central processing unit (CPU). Of course, according to actual usage, other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGAs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. may also be adopted. The general-purpose processor may adopt a microprocessor or any conventional processor, etc., and the present application does not impose any restrictions on this.

[0145] Embodiment 4:

[0146] The present invention provides a storage medium containing computer executable instructions, which are used to execute the above-mentioned intelligent car speed planning method that takes into account both environmental friendliness and privacy protection when executed by a computer processor.

[0147] Among them, the computer program can be stored in a computer-readable medium, the computer program includes computer program code, the computer program code can be in the form of source code, object code, executable file or certain middleware, etc. The computer-readable medium includes any entity or device that can carry the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal and software distribution medium, 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 article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0149] For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to the specific circumstances. When an element is referred to as being "assembled on", "installed on", "fixed on" or "set on" another element, it can be directly on the other element or there can also be a centered element. When an element is considered to be "connected" to another element, it can be directly connected to the other element or there may be a centered element at the same time. The terms "vertical", "horizontal", "up", "down", "left", "right" and similar expressions used herein are only for illustrative purposes and are not intended to be the only implementation method.

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

[0151] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present disclosure. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

Claims

1. An intelligent vehicle speed planning method that takes into account both environmental friendliness and privacy protection is applied to the speed planning of a fleet of mixed power types, and is characterized by: The following steps are involved: According to 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 using the nonlinear least squares method to calculate 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 then the EdDSA signature algorithm is used to digitally sign the vehicle's driving information data; Receive encrypted and signed information data from each vehicle in the fleet, and perform weighted aggregation and decryption processing on all encrypted information data; The common speed with the lowest carbon emissions is calculated based on the aggregated driving information data, and then the optimized planned speed is generated based on the vehicle position and optimization target, taking into account the constraints and emission reduction targets; All encrypted data uploaded by the vehicle and key data of the speed planning process are recorded through alliance blockchain technology.

2. The intelligent vehicle speed planning method that takes into account both environmental friendliness and privacy protection according to claim 1 is characterized in that: According to 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 constructed by fitting, and the carbon emission values ​​of fuel vehicles and electric vehicles at different speeds are calculated according to the speed-carbon emission model, as follows: (21) For internal combustion engine vehicles, based on the universal characteristic diagram of its generator, the fuel consumption function corresponding to the vehicle speed v is obtained by fitting: Where a0, a1, a3, and a4 are the fitting coefficients of the fuel consumption function. The fuel consumption function will have different coefficients for different types of vehicles. The specific parameters and the root mean square error (RMSD) and R after fitting are 2 The values ​​are shown in Table X. The root mean square error is less than 0.13, R 2 The values ​​are all higher than 0.9899, ​​which further proves that the fitting accuracy is high; For electric vehicles, according to the motor efficiency MAP diagram, the energy consumption function corresponding to the vehicle speed v is obtained by fitting. The energy consumption function after fitting is: Where b0, b1, b2, b3, b4 are the fitting coefficients of the energy consumption function. The energy consumption function will have different coefficients according to different types of vehicles. The specific parameters and the root mean square error and R after fitting are 2 The values ​​are shown in Table X. The root mean square error is less than 0.15, R 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 fuel vehicles =2.37kgCO2 / L, calculate the carbon dioxide emissions of fuel vehicles at different speeds F i for: In the formula, f i is the fuel consumption; (23) Based on the average CO2 emission factor of electricity =0.6668 (kgCO2 / kWh) and the national line loss rate T is 4.54 (%), and the carbon emissions E corresponding to different speeds of electric vehicles are calculated. j : In the formula, e j is the amount of electrical energy consumed.

3. The intelligent vehicle speed planning method that takes into account both environmental friendliness and privacy protection according to claim 2 is 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) Generation of Paillier key pair of 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; The base station then calculates the Carmichael function of n: λ=lcm(p 1,q 1)#(5) -- Where lcm represents the least common multiple; λ is the parameter required for decryption; Next, define the 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, generate the public key (n, g) of the Paillier algorithm for encryption and the private key (λ, μ) for decryption; (32) Ciphertext calculation of user vehicle: The base station transmits its public key to all vehicles within the communication range. For vehicles that need speed recommendations, each user vehicle uses the base station's public key (n, g) to encrypt its private data. Specifically, each vehicle selects a random positive integer r that is coprime with n, and then calculates the ciphertext c using the following formula: c=g m ·r n mod n 2 #(8) Among them, m represents the private data of the vehicle, c is the encrypted ciphertext, and g, n, and r come from the public key and private key calculated above. (33) After encrypting the private carbon emission data, the Edwards curve digital signature algorithm is selected to enable the user vehicle to sign all uploaded data.

4. The intelligent vehicle speed planning method that takes into account both environmental friendliness and privacy protection according to claim 1 is characterized in that: Receive the encrypted and signed information data from each vehicle in the fleet, and perform weighted aggregation and decryption on all encrypted information data, as follows: (41) The base station first verifies whether the data comes from a legitimate vehicle based on the Edwards curve digital signature algorithm. If so, the encrypted carbon emission data at different speeds are directly weighted aggregated, as shown in the formula, and the aggregated data is then decrypted using the base station's private key; in Is the carbon emission of internal combustion engine vehicles F i (s i ), The carbon emissions of electric vehicles are j (s j )’s ciphertext; (42) The decryption process is to calculate the aggregated plaintext according to 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 takes into account both environmental friendliness and privacy protection according to claim 4 is characterized in that: Based on the aggregated driving information data, the common speed with the lowest carbon emissions is calculated. Then, based on the vehicle position and optimization target, and taking into account the constraints and emission reduction targets, the optimized planning speed is generated as follows: The objective function for building speed optimization is: Where s i represents the speed of vehicle i; The constraints are: minimum following distance constraint, maximum speed limit constraint, and recommending a common speed constraint for all vehicles; Stl1≤0#(12) l2≤0#(13) In the formula, F i is the carbon emission value of internal combustion engine vehicles, E j 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 with the vehicle in front; formula (13) requires each vehicle to follow the maximum speed limit of the current road. These two constraints ensure the driving safety of the vehicle. Formula (14) indicates that a common speed is recommended for all vehicles, where s i represents the speed of vehicle i.

6. An intelligent vehicle speed planning system that takes into account both environmental friendliness and privacy protection, used to implement the intelligent vehicle speed planning method that takes into account both environmental friendliness and privacy protection as described in any one of claims 1 to 5, characterized in that: include: A calculation module is used to obtain the fuel consumption function and the electric energy consumption function by using the nonlinear least square method according to 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, so as to calculate the carbon emission values ​​of the fuel vehicle and the electric vehicle at different speeds respectively; 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 of each vehicle in the fleet after data encryption and signing, and perform weighted aggregation and decryption processing on all encrypted information data; The planning recommendation module is used to calculate the common speed with the lowest carbon emissions based on the aggregated driving information data, and then generate the optimized planning speed according to the vehicle position and optimization target, 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 of the speed planning process through the alliance 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 be run on the processor. When the processor loads and executes the computer program, the intelligent car speed planning method that takes into account both environmental friendliness and privacy protection as described in any one of claims 1 to 5 is adopted.

8. A storage medium containing computer executable instructions, characterized in that: The computer executable instructions are used to execute the intelligent vehicle speed planning method that takes into account both environmental friendliness and privacy protection when executed by a computer processor as claimed in any one of claims 1 to 5.

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