Method, device and equipment for determining ordered charging station based on post-quantum homomorphic encryption and medium

Abstract

The application discloses an ordered charging station determination method and device based on post-quantum homomorphic encryption, equipment and medium, relates to the field of information security technology, obtains the ciphertext feature vector sent by the vehicle terminal; any coordinate in the edge node coverage area is taken as the vehicle position to construct the candidate charging station set; the candidate charging station in the candidate charging station set is screened to obtain the target candidate charging station set; the target candidate charging station set and the ciphertext feature vector are sent to the cloud end, so that the cloud end calculates the comprehensive score of each candidate charging station; the target candidate charging station set and the comprehensive score vector returned by the cloud end are forwarded to the vehicle terminal, so that the vehicle terminal decrypts the comprehensive score vector to obtain the target comprehensive score, to determine the ordered charging station, solve the technical problem that privacy protection and operation efficiency are difficult to consider and cannot resist quantum attack in the ordered charging scene, reduce the calculation delay, and meet the individual needs of the ordered charging station.

Description

Technical Field

[0001] This invention relates to the field of information security technology, and in particular to a method, apparatus, equipment and medium for determining ordered charging stations based on post-quantum homomorphic encryption. Background Technology

[0002] With the continuous increase in the number of electric vehicles, vehicle-to-grid (V2G) interaction is becoming increasingly frequent. Orderly charging technology, by coordinating the charging behavior of a large number of electric vehicles, transforms them from simple "energy consumers" into "grid coordinators." However, the communication process of orderly charging involves the exchange of a large amount of private data, including identity information, financial information, spatiotemporal trajectories, and vehicle energy status. The leakage of this data could lead to the theft of payment information, the discovery of user behavior patterns, and even provide a basis for large-scale attacks against the power grid.

[0003] Currently, research on privacy protection for orderly charging still faces many challenges. First, existing privacy protection schemes sacrifice system efficiency, making it difficult to meet the real-time and large-scale data processing requirements of orderly charging services. Second, existing methods often focus on single technologies or approaches, such as using only differential privacy or relying solely on traditional encryption algorithms, resulting in a trade-off between security and efficiency. Furthermore, to achieve accurate recommendations, the system needs to aggregate users' multimodal data, but a complete solution that can efficiently and accurately accomplish this task while protecting privacy is currently lacking. More critically, traditional public-key cryptography faces the potential threat of future quantum computing attacks, failing to provide long-term, quantum-resistant security for orderly charging services.

[0004] As can be seen from the above, how to solve the technical problems of balancing privacy protection and operational efficiency in orderly charging scenarios, as well as the inability to resist quantum attacks, reduce computational latency, and meet the personalized needs of orderly charging stations are problems that need to be solved in this field. Summary of the Invention

[0005] In view of this, the purpose of this invention is to provide a method, apparatus, device, and medium for determining an ordered charging station based on post-quantum homomorphic encryption. This solution addresses the technical problems of balancing privacy protection and operational efficiency in ordered charging scenarios, as well as the inability to resist quantum attacks, thereby reducing computational latency and meeting the personalized needs of ordered charging stations. The specific solution is as follows: In a first aspect, this application discloses a method for determining ordered charging stations based on post-quantum homomorphic encryption, applied to edge nodes, including: Obtain the ciphertext feature vector sent by the vehicle terminal; the ciphertext feature vector is the feature vector obtained by the vehicle terminal encrypting the user sensitive feature vector using the CKKS homomorphic encryption scheme and based on the public key. Determine the coverage area of ​​the edge nodes, take any coordinate in the coverage area of ​​the edge nodes as the vehicle position, and construct a set of candidate charging stations based on the vehicle position and a preset distance threshold; Based on preset constraints, candidate charging stations in the candidate charging station set are filtered to obtain a target candidate charging station set; The target candidate charging station set and the encrypted feature vector are sent to the cloud so that the cloud can calculate the comprehensive score of each candidate charging station in the target candidate charging station set based on the encrypted feature vector; the comprehensive score is an evaluation index used to characterize the quality of the candidate charging stations. The set of target candidate charging stations and the comprehensive score vector returned from the cloud, including the comprehensive score, are forwarded to the vehicle terminal so that the vehicle terminal can decrypt the comprehensive score vector to obtain the target comprehensive score and determine the ordered charging stations from the set of target candidate charging stations based on the target comprehensive score.

[0006] Optionally, obtaining the encrypted feature vector sent by the vehicle terminal includes: A one-time session key is generated between the edge node and the vehicle terminal using a quantum key encapsulation mechanism; Based on the one-time session key, an encrypted secure channel is established between the edge node and the vehicle terminal; Using the encrypted secure channel, the encrypted feature vector sent by the vehicle terminal is obtained.

[0007] Optionally, the process involves filtering candidate charging stations in the candidate charging station set based on preset constraints to obtain a target candidate charging station set; then sending the target candidate charging station set and the encrypted feature vector to the cloud, so that the cloud can calculate a comprehensive score for each candidate charging station in the target candidate charging station set based on the encrypted feature vector, including: The power limit of the area where the candidate charging station is located is used as a preset constraint. Based on the preset constraints, the candidate charging stations in the candidate charging station set are filtered to obtain the target candidate charging station set; Calculate the power risk score and congestion score of the candidate charging stations in the target candidate charging station set; The target candidate charging station set, encrypted feature vector, power risk score, and congestion score are sent to the cloud so that the cloud can calculate the comprehensive score of each candidate charging station in the target candidate charging station set based on the encrypted feature vector, power risk score, and congestion score.

[0008] Optionally, the formula for calculating the power risk score of candidate charging stations in the target candidate charging station set is as follows: ; in, Based on power risk, As weight, Predicting charging power demand for vehicles This represents the average power margin of the site. The formula for calculating the overall score of each candidate charging station in the target candidate charging station set is as follows: ; in, For comprehensive scoring, For the ciphertext feature vector, For weight parameters, For the feature vector of the charging station, For congestion.

[0009] Optionally, forwarding the set of target candidate charging stations and the comprehensive score vector returned from the cloud, including the comprehensive score, to the vehicle terminal, so that the vehicle terminal can decrypt the comprehensive score vector to obtain the target comprehensive score, and determine the ordered charging stations from the set of target candidate charging stations based on the target comprehensive score, includes: The set of target candidate charging stations and the comprehensive score vector returned from the cloud, including the comprehensive score, are forwarded to the vehicle terminal. The vehicle terminal then uses its private key to decrypt the comprehensive score vector to obtain the target comprehensive score. The target comprehensive scores are then sorted, and the candidate charging station with the highest target comprehensive score is selected as the ordered charging station.

[0010] Optionally, the method for determining ordered charging stations based on post-quantum homomorphic encryption further includes: Determine the communication overhead and end-to-end latency between edge nodes and vehicle terminals, as well as between edge nodes and the cloud. A multi-objective optimization function is constructed based on the interactive communication overhead and the end-to-end delay. Using system parameters as control variables and a preset security level as a constraint, the multi-objective optimization function is minimized to obtain the minimization result; the system parameters include a preset distance threshold, an upper limit on the number of candidate sites, and the number of sites for CKKS encrypted packaging; Configure the system parameters based on the minimized result.

[0011] Optionally, the method for determining ordered charging stations based on post-quantum homomorphic encryption further includes: The power grid status and congestion information of the charging station's location are updated periodically. The preset distance threshold in the system parameters is adaptively adjusted based on the updated power grid status, site congestion information, and system service quality.

[0012] Secondly, this application discloses an ordered charging station determination device based on post-quantum homomorphic encryption, applied to edge nodes, comprising: The ciphertext feature vector acquisition module is used to acquire the ciphertext feature vector sent by the vehicle terminal; the ciphertext feature vector is the feature vector obtained by the vehicle terminal encrypting the user sensitive feature vector using the CKKS homomorphic encryption scheme and based on the public key. The set construction module is used to determine the coverage area of ​​the edge node, take any coordinate in the coverage area of ​​the edge node as the vehicle position, and construct a set of candidate charging stations based on the vehicle position and a preset distance threshold. The filtering module is used to filter the candidate charging stations in the candidate charging station set based on preset constraints to obtain the target candidate charging station set. The collection sending module is used to send the target candidate charging station set and the encrypted feature vector to the cloud, so that the cloud can calculate the comprehensive score of each candidate charging station in the target candidate charging station set based on the encrypted feature vector; the comprehensive score is an evaluation index used to characterize the quality of the candidate charging stations. The forwarding module is used to forward the target candidate charging station set and the comprehensive score vector returned by the cloud, including the comprehensive score, to the vehicle terminal, so that the vehicle terminal can decrypt the comprehensive score vector to obtain the target comprehensive score, and determine the ordered charging station from the target candidate charging station set according to the target comprehensive score.

[0013] Thirdly, this application discloses an electronic device, comprising: Memory, used to store computer programs; A processor is used to execute the computer program to implement the aforementioned method for determining ordered charging stations based on post-quantum homomorphic encryption.

[0014] Fourthly, this application discloses a computer storage medium for storing a computer program; wherein, when the computer program is executed by a processor, it implements the steps of the aforementioned disclosed method for determining an ordered charging station based on post-quantum homomorphic encryption.

[0015] As can be seen, this application provides an ordered charging station determination method based on post-quantum homomorphic encryption, applied to edge nodes. The method includes: obtaining the ciphertext feature vector sent by the vehicle terminal; the ciphertext feature vector is obtained by encrypting user-sensitive feature vectors using the CKKS homomorphic encryption scheme and based on a public key, thus protecting user privacy from the source and resisting future quantum computing attacks; determining the edge node coverage area, using any coordinate within the edge node coverage area as the vehicle location to achieve location ambiguity and further protect user location privacy; constructing a candidate charging station set based on the vehicle location and a preset distance threshold to reduce ciphertext computation and communication overhead; and filtering candidate charging stations in the candidate charging station set based on preset constraints to obtain a target candidate charging station set, eliminating infeasible or high-risk options. The system reduces the cloud computing burden on charging stations and enables coordinated management of grid load through orderly charging. It sends a set of target candidate charging stations and encrypted feature vectors to the cloud, allowing the cloud to calculate a comprehensive score for each candidate charging station based on the encrypted feature vectors. This comprehensive score serves as an evaluation indicator characterizing the quality of candidate charging stations, maintaining data availability without visibility, and outputting an accurate comprehensive score, balancing security and decision-making precision. The system then forwards the set of target candidate charging stations and the comprehensive score vector returned from the cloud to the vehicle terminal, allowing the vehicle terminal to decrypt the comprehensive score vector to obtain the target comprehensive score. Based on this target comprehensive score, the system determines the orderly charging stations from the target candidate charging station set, achieving end-to-end privacy protection and meeting users' personalized needs for orderly charging stations. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0017] Figure 1 This is a flowchart of a method for determining an ordered charging station based on post-quantum homomorphic encryption disclosed in this application; Figure 2 This is a flowchart of an adaptive adjustment and closed-loop feedback method disclosed in this application; Figure 3 This application discloses an overall flowchart for determining an ordered charging station based on post-quantum homomorphic encryption. Figure 4 This is a comparison chart of quantum security levels disclosed in this application; Figure 5 This is a graph showing the time prediction result of a quantum computing solution disclosed in this application; Figure 6 This is a graph showing the variation of quantum security with key length as disclosed in this application; Figure 7 This is a diagram showing the assessment results of one dimension of quantum security disclosed in this application; Figure 8 This is a trend forecast diagram of the security of quantum computing development and encryption schemes disclosed in this application; Figure 9 This application discloses a comprehensive performance radar chart; Figure 10 This is a comparison chart of communication delay and privacy protection disclosed in this application; Figure 11 This is a security analysis diagram disclosed in this application; Figure 12 This is an expense analysis diagram disclosed in this application; Figure 13 This is a comparison result diagram of quantum security disclosed in this application; Figure 14 This application discloses a comprehensive scoring and ranking chart; Figure 15 This is a schematic diagram of the structure of an ordered charging station determination device based on post-quantum homomorphic encryption disclosed in this application; Figure 16 This is a schematic diagram of an electronic device structure provided in this application. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] See Figure 1 As shown, this embodiment of the invention discloses a method for determining ordered charging stations based on post-quantum homomorphic encryption, applied to edge nodes, and specifically includes: Step S11: Obtain the ciphertext feature vector sent by the vehicle terminal; the ciphertext feature vector is the feature vector obtained by the vehicle terminal encrypting the user sensitive feature vector using the CKKS homomorphic encryption scheme and based on the public key.

[0020] In this embodiment, a one-time session key is generated between the edge node and the vehicle terminal using a quantum key encapsulation mechanism; an encrypted secure channel is established between the edge node and the vehicle terminal based on the one-time session key; and the encrypted secure channel is used to obtain the ciphertext feature vector sent by the vehicle terminal.

[0021] In this step, before data interaction, to ensure the confidentiality and integrity of communication, the edge node and the vehicle terminal first establish an encrypted secure channel. The edge node and the vehicle terminal then negotiate a one-time session key using a post-quantum key encapsulation mechanism. The session key negotiation can be represented as: ; ; ; One-time session key K The two parties will establish an encrypted secure channel with authentication for the transmission of all subsequent interactive data, ensuring the confidentiality, integrity, and resistance to replay attacks.

[0022] In this step, the encrypted feature vector sent by the vehicle terminal is obtained. The encrypted feature vector is generated by the vehicle terminal encrypting the user-sensitive feature vector using the homomorphic encryption algorithm based on RLWE (Ring Learning With Errors) in the CKKS homomorphic encryption scheme, thus preventing privacy leakage from the source. User-sensitive feature vectors include, but are not limited to, vehicle SOC (State of Charge), estimated time of arrival, and charging preferences, and can be represented as: ; in, In a charged state, For the estimated arrival time, For price sensitivity, Based on user charging preferences; The vehicle terminal uses its public key to encrypt the user-sensitive feature vector based on the CKKS homomorphic encryption scheme, obtaining a ciphertext feature vector, which is represented as: .

[0023] Step S12: Determine the coverage area of ​​the edge node, take any coordinate in the coverage area of ​​the edge node as the vehicle position, and construct a set of candidate charging stations based on the vehicle position and a preset distance threshold.

[0024] In this embodiment, to reduce the risk of location exposure, any coordinate within the coverage area of ​​the edge node accessed by the vehicle is used. Approximate as the vehicle position, and based on a preset distance threshold. R Construct a candidate charging station set to reduce communication overhead and cloud computing burden while ensuring privacy is not compromised: ; in, K This represents the maximum number of candidate sites.

[0025] Step S13: Based on preset constraints, filter the candidate charging stations in the candidate charging station set to obtain the target candidate charging station set.

[0026] In this embodiment, the power limit of the area where the candidate charging station is located is used as a preset constraint; based on the preset constraint, the candidate charging stations in the candidate charging station set are filtered to obtain the target candidate charging station set.

[0027] In this step, the upper limit of the power of the area where the candidate charging station is located is used as a preset constraint condition. Candidate charging stations that do not meet the power feasibility condition are eliminated to obtain the target candidate charging station set.

[0028] Step S14: Send the target candidate charging station set and the encrypted feature vector to the cloud so that the cloud can calculate the comprehensive score of each candidate charging station in the target candidate charging station set based on the encrypted feature vector; the comprehensive score is an evaluation index used to characterize the quality of the candidate charging stations.

[0029] In this embodiment, the power risk score and congestion score of the candidate charging stations in the target candidate charging station set are calculated; the target candidate charging station set, the encrypted feature vector, the power risk score and the congestion score are sent to the cloud, so that the cloud can calculate the comprehensive score of each candidate charging station in the target candidate charging station set based on the encrypted feature vector, the power risk score and the congestion score.

[0030] The formula for calculating the power risk score of candidate charging stations in the target candidate charging station set is as follows: ; in, Based on power risk, As weight, Predicting charging power demand for vehicles This represents the average power margin of the site.

[0031] Using a linear scoring model, the comprehensive score of each candidate charging station in the target candidate charging station set is calculated. The linear scoring model has the following form: ; in, For comprehensive scoring, For the ciphertext feature vector, For weight parameters, For the feature vector of the charging station, For congestion, This indicates element-wise multiplication. This indicates ciphertext subtraction.

[0032] Charging station feature vector This includes, but is not limited to, price, number of available guns, and estimated waiting time.

[0033] For candidate charging stations in the target candidate charging station set, a power risk score is introduced. and congestion This approach aims to balance grid security and user experience during recommendations. The calculation process involves only ciphertext-to-plaintext multiplication and addition, with a homomorphic multiplication depth of 1, resulting in high computational efficiency and eliminating the need for bootstrapping. The final comprehensive score for candidate sites is then obtained. .

[0034] Without decryption, the cloud performs multi-objective optimization calculations on the ciphertext feature vectors reported by edge nodes to obtain a comprehensive score. The comprehensive score vector is generated and returned to the edge nodes.

[0035] Step S15: Forward the target candidate charging station set and the comprehensive score vector returned from the cloud, including the comprehensive score, to the vehicle terminal so that the vehicle terminal can decrypt the comprehensive score vector to obtain the target comprehensive score, and determine the ordered charging station from the target candidate charging station set according to the target comprehensive score.

[0036] In this embodiment, the set of target candidate charging stations and the comprehensive score vector returned from the cloud, including the comprehensive score, are forwarded to the vehicle terminal. The vehicle terminal then uses its private key to decrypt the comprehensive score vector to obtain the target comprehensive score. The target comprehensive scores are then sorted, and the candidate charging station corresponding to the target comprehensive score with the highest sorted score is selected as the ordered charging station.

[0037] In this step, the set of target candidate charging stations and the comprehensive score vector returned from the cloud are forwarded to the vehicle terminal. After receiving the comprehensive score vector, the vehicle terminal decrypts it using its local private key to obtain the target comprehensive score for each candidate station. ; Then, the vehicle terminal sorts the target comprehensive scores and selects the candidate charging station corresponding to the target comprehensive score with the highest score as the final recommended orderly charging station: ; The system will then display the final recommended charging stations to users.

[0038] In this embodiment, the method for determining ordered charging stations based on post-quantum homomorphic encryption further includes: determining the interaction communication overhead and end-to-end latency between edge nodes and vehicle terminals, and between edge nodes and the cloud; constructing a multi-objective optimization function based on the interaction communication overhead and the end-to-end latency; minimizing the multi-objective optimization function with system parameters as control variables and a preset security level as a constraint to obtain a minimization result; the system parameters include, but are not limited to, a preset distance threshold, an upper limit on the number of candidate stations, and the number of stations packaged in CKKS ciphertext; and configuring the system parameters based on the minimization result.

[0039] To achieve a quantifiable balance between security and performance, this invention addresses the communication overhead between edge nodes and vehicle terminals, as well as between edge nodes and the cloud. and end-to-end delay Explicitly indexed. End-to-end latency is defined as: ; in, and With the upper limit of the number of candidate sites K Number of sites packaged with CKKS encrypted data S Strong correlation. By constructing a system based on... R , K , S The multi-objective optimization problem with variables such as λ can minimize latency and communication overhead while satisfying the preset security level λ, thus providing a basis for system parameter configuration.

[0040] In addition, the method for determining ordered charging stations based on post-quantum homomorphic encryption also includes: periodically updating the power grid status and station congestion information of the area where the charging station is located; and adaptively adjusting the preset distance threshold in the system parameters based on the updated power grid status, station congestion information, and system service quality.

[0041] In this step, the edge nodes periodically update the power grid status and site congestion information of the area where the charging station is located, and based on the system service quality... QoS The system adaptively adjusts preset distance thresholds or privacy radii based on site congestion information to achieve dynamic optimization of the recommendation strategy. The adaptive adjustment and closed-loop feedback process is as follows: Figure 2 As shown. The adaptive strategy can be expressed as: ; in, For adjustment function, The step size.

[0042] The present invention proposes an overall process for determining an ordered charging station based on post-quantum homomorphic encryption, as follows: Figure 3As shown, the specific steps include: (1) The vehicle terminal performs data encryption and upload: The vehicle terminal encrypts the user's sensitive feature vector and sends it to the edge node; (2) Edge nodes perform encrypted preprocessing and recommendation calculation: edge nodes determine the coverage area of ​​edge nodes and screen candidate charging stations.

[0043] (3) Cloud-based collaborative optimization and result feedback: The aggregator server completes multi-objective optimization and comprehensive score calculation based on the linear scoring model in encrypted state, and feeds back the comprehensive score vector including the comprehensive score.

[0044] (4) Vehicle terminal decryption and recommendation display: The vehicle terminal decrypts the comprehensive score vector to obtain the target comprehensive score, so as to determine the final recommended orderly charging station.

[0045] This invention aims to address the challenges of easily leaked user privacy data, the difficulty in balancing security and efficiency in existing solutions, and the inability to withstand future quantum computing attacks in the orderly charging scenario for electric vehicles. Based on RLWE (Real-Time Array Module), a quantum-resistant privacy-preserving recommendation framework is constructed. The vehicle terminal employs the CKKS homomorphic encryption scheme to encrypt sensitive user feature vectors containing location, state of charge, and charging preferences, protecting user privacy data from the source. Edge nodes determine their coverage areas and screen candidate charging stations, achieving "usable but invisible" data, effectively reducing communication overhead and computational latency, lowering end-to-end latency to millisecond levels, and meeting real-time recommendation requirements. In the cloud, the aggregator server performs multi-objective optimization and comprehensive scoring calculation based on a linear scoring model in encrypted form, ensuring the accuracy of scheduling decisions. The entire process deeply integrates post-quantum cryptography and homomorphic encryption, achieving "usable but invisible" data throughout its entire lifecycle. While ensuring quantum attack resistance, it meets the real-time, large-scale data processing, and recommendation accuracy requirements of the orderly charging service.

[0046] This application compares the RLWE-based homomorphic encryption algorithm with traditional algorithms in several aspects, including RSA (Rivest-Shamir-Adleman), ECC (Elliptic Curve Cryptography), and PQC (Post-Quantum Cryptography). The comparison results regarding quantum security levels are as follows: Figure 4 As shown, the quantum computing time prediction results are as follows: Figure 5 As shown, the quantum security variation with key length is as follows: Figure 6 As shown, the assessment results for the quantum security dimension are as follows: Figure 7As shown, the development trends of quantum computing and the security trends of encryption schemes are predicted as follows: Figure 8 As shown, the RLWE-based homomorphic encryption algorithm of this invention achieves a quantum security level of 9.5 (normalized index) in terms of quantum attack resistance, which is significantly higher than the traditional RSA algorithm. Furthermore, it maintains high security across different key lengths, demonstrating long-term security in a quantum computing environment.

[0047] The algorithm in this application, which combines HE (Homomorphic Encryption) and PQC, is compared with both the PQC algorithm and the HE algorithm. The overall performance radar chart is shown below. Figure 9 As shown, the comparison results of communication latency and privacy protection are as follows: Figure 10 As shown, the security analysis is as follows: Figure 11 As shown, the cost analysis is as follows: Figure 12 As shown, the comparison results for quantum security are as follows: Figure 13 As shown, the overall score ranking is as follows: Figure 14 As shown, in the comprehensive performance evaluation, the present invention, which combines post-quantum cryptography and homomorphic encryption, demonstrates a balanced performance across multiple dimensions, including privacy protection, quantum security, and real-time performance, achieving the highest overall score.

[0048] Based on the orderly charging scenario of electric vehicles, this invention sets specific technical goals around the requirements of "privacy protection, quantum security, and real-time efficient recommendation": First, by introducing the CKKS homomorphic encryption scheme, it ensures that sensitive user data remains encrypted throughout the entire transmission and processing process, resisting quantum attacks and internal snooping; Second, it keeps computationally intensive tasks in the cloud while offloading lightweight encrypted aggregation to edge nodes to reduce communication latency and improve the real-time performance of recommendations; Finally, through a balance modeling of security and efficiency, it provides a quantitative basis for configuring system parameters, thereby achieving optimal performance while ensuring security.

[0049] This application provides a method for determining ordered charging stations based on post-quantum homomorphic encryption, applied to edge nodes. The method includes: acquiring ciphertext feature vectors sent by vehicle terminals; these ciphertext feature vectors are obtained by encrypting user-sensitive feature vectors using the CKKS homomorphic encryption scheme and based on a public key, thus protecting user privacy from the source and resisting future quantum computing attacks; determining the coverage area of ​​the edge nodes, using any coordinate within the coverage area as the vehicle's location to achieve location ambiguity and further protect user location privacy; constructing a candidate charging station set based on the vehicle's location and a preset distance threshold to reduce ciphertext computation and communication overhead; and filtering candidate charging stations in the candidate charging station set based on preset constraints to obtain a target candidate charging station set, eliminating infeasible or high-risk charging stations. The system reduces the cloud computing burden and enables coordinated management of grid load through orderly charging. It sends a set of target candidate charging stations and encrypted feature vectors to the cloud, allowing the cloud to calculate a comprehensive score for each candidate charging station based on the encrypted feature vectors. This comprehensive score serves as an evaluation indicator characterizing the quality of candidate charging stations, maintaining data availability without visibility, and outputting an accurate comprehensive score, balancing security and decision-making accuracy. The system then forwards the set of target candidate charging stations and the comprehensive score vector returned from the cloud to the vehicle terminal. The vehicle terminal decrypts the comprehensive score vector to obtain the target comprehensive score and determines the orderly charging station from the target candidate charging station set based on the target comprehensive score. This achieves end-to-end privacy protection and meets users' personalized needs for orderly charging stations.

[0050] See Figure 15 As shown, this embodiment of the invention discloses an ordered charging station determination device based on post-quantum homomorphic encryption, applied to edge nodes, which may specifically include: The ciphertext feature vector acquisition module 11 is used to acquire the ciphertext feature vector sent by the vehicle terminal; the ciphertext feature vector is the feature vector obtained by the vehicle terminal encrypting the user sensitive feature vector using the CKKS homomorphic encryption scheme and based on the public key. The set construction module 12 is used to determine the coverage area of ​​the edge node, take any coordinate in the coverage area of ​​the edge node as the vehicle position, and construct a set of candidate charging stations based on the vehicle position and a preset distance threshold. The filtering module 13 is used to filter the candidate charging stations in the candidate charging station set based on preset constraints to obtain a target candidate charging station set. The set of target candidate charging stations 14 is used to send the set of target candidate charging stations and the encrypted feature vector to the cloud, so that the cloud can calculate the comprehensive score of each candidate charging station in the set of target candidate charging stations based on the encrypted feature vector; the comprehensive score is an evaluation index used to characterize the quality of candidate charging stations. The forwarding module 15 is used to forward the target candidate charging station set and the comprehensive score vector returned by the cloud, including the comprehensive score, to the vehicle terminal, so that the vehicle terminal can decrypt the comprehensive score vector to obtain the target comprehensive score, and determine the ordered charging station from the target candidate charging station set according to the target comprehensive score.

[0051] In some specific embodiments, the ciphertext feature vector acquisition module 11 may specifically include: A one-time session key generation module is used to generate a one-time session key between the edge node and the vehicle terminal using a quantum key encapsulation mechanism; The encrypted secure channel establishment module is used to establish an encrypted secure channel between the edge node and the vehicle terminal based on the one-time session key; The vector acquisition module is used to acquire the encrypted feature vector sent by the vehicle terminal using the encrypted secure channel.

[0052] In some specific embodiments, the filtering module 13 may specifically include: The preset constraint determination module is used to use the power upper limit of the area where the candidate charging station is located as the preset constraint. The target candidate charging station set determination module is used to filter the candidate charging stations in the candidate charging station set based on the preset constraints to obtain the target candidate charging station set.

[0053] In some specific embodiments, the collection sending module 14 may specifically include: The power risk score and congestion score calculation module is used to calculate the power risk score and congestion score of the candidate charging stations in the target candidate charging station set. The comprehensive scoring calculation module is used to send the target candidate charging station set, the encrypted feature vector, the power risk score, and the congestion score to the cloud, so that the cloud can calculate the comprehensive score of each candidate charging station in the target candidate charging station set based on the encrypted feature vector, the power risk score, and the congestion score.

[0054] In some specific embodiments, the formula for calculating the power risk score of candidate charging stations in the target candidate charging station set is as follows: ; in, Based on power risk, As weight, Predicting charging power demand for vehicles This represents the average power margin of the site. The formula for calculating the overall score of each candidate charging station in the target candidate charging station set is as follows: ; in, For comprehensive scoring, For the ciphertext feature vector, For weight parameters, For the feature vector of the charging station, For congestion, This indicates element-wise multiplication. This indicates ciphertext subtraction.

[0055] In some specific embodiments, the forwarding module 15 may specifically include: The decryption and sorting module is used to forward the set of target candidate charging stations and the comprehensive score vector returned from the cloud, including the comprehensive score, to the vehicle terminal. The vehicle terminal uses its private key to decrypt the comprehensive score vector to obtain the target comprehensive score, and then sorts the target comprehensive scores. The candidate charging station corresponding to the target comprehensive score with the highest score value after sorting is selected as the ordered charging station.

[0056] In some specific embodiments, the ordered charging station determination device based on post-quantum homomorphic encryption may further include: The interactive communication overhead and end-to-end latency determination module is used to determine the interactive communication overhead and end-to-end latency between the edge node and the vehicle terminal, as well as between the edge node and the cloud. A multi-objective optimization function construction module is used to construct a multi-objective optimization function based on the interactive communication overhead and the end-to-end delay; The minimization module is used to minimize the multi-objective optimization function with system parameters as control variables and a preset security level as constraints to obtain the minimization result; the system parameters include a preset distance threshold, an upper limit on the number of candidate sites, and the number of CKKS encrypted packaging sites; A configuration module is used to configure the system parameters based on the minimized result.

[0057] In some specific embodiments, the ordered charging station determination device based on post-quantum homomorphic encryption may further include: The periodic update module is used to periodically update the power grid status and site congestion information of the area where the charging station is located. The adaptive adjustment module is used to adaptively adjust the preset distance threshold in the system parameters based on the updated power grid status, site congestion information, and system service quality.

[0058] Figure 16 This is a schematic diagram of an electronic device provided in an embodiment of this application. The electronic device includes: one or more processors; a memory for storing one or more programs; and when the one or more programs are executed by the one or more processors, the one or more processors implement the ordered charging station determination method based on post-quantum homomorphic encryption as described above. Figure 16 The diagram shown is a hardware structure schematic of an electronic device provided in an embodiment of the present invention, except... Figure 16 In addition to the processor, memory, and network interface shown, the device in this embodiment may include other hardware depending on its actual functions, which will not be described in detail here.

[0059] Accordingly, this application also provides a computer-readable storage medium storing computer instructions thereon, which, when executed by a processor, implement the ordered charging station determination method based on post-quantum homomorphic encryption as described above. The computer-readable storage medium can be an internal storage unit of any device with data processing capabilities, such as a hard disk or memory; it can also be an external storage device, such as a plug-in hard disk, smart memory card, SD card, flash memory card, etc.

[0060] Furthermore, the computer-readable storage medium may include both internal storage units and external storage devices for storing programs, data, and temporary input / output data.

[0061] Finally, 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 a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0062] The foregoing has provided a detailed description of the method, apparatus, device, and storage medium for determining an ordered charging station based on post-quantum homomorphic encryption provided by the present invention. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for determining ordered charging stations based on post-quantum homomorphic encryption, characterized in that, Applied to edge nodes, including: Obtain the ciphertext feature vector sent by the vehicle terminal; the ciphertext feature vector is the feature vector obtained by the vehicle terminal encrypting the user sensitive feature vector using the CKKS homomorphic encryption scheme and based on the public key. Determine the coverage area of ​​the edge nodes, take any coordinate in the coverage area of ​​the edge nodes as the vehicle position, and construct a set of candidate charging stations based on the vehicle position and a preset distance threshold; Based on preset constraints, candidate charging stations in the candidate charging station set are filtered to obtain a target candidate charging station set; The target candidate charging station set and the encrypted feature vector are sent to the cloud so that the cloud can calculate the comprehensive score of each candidate charging station in the target candidate charging station set based on the encrypted feature vector; the comprehensive score is an evaluation index used to characterize the quality of the candidate charging stations. The set of target candidate charging stations and the comprehensive score vector returned from the cloud, including the comprehensive score, are forwarded to the vehicle terminal so that the vehicle terminal can decrypt the comprehensive score vector to obtain the target comprehensive score and determine the ordered charging stations from the set of target candidate charging stations based on the target comprehensive score.

2. The method for determining an ordered charging station based on post-quantum homomorphic encryption according to claim 1, characterized in that, The acquisition of the encrypted feature vector sent by the vehicle terminal includes: A one-time session key is generated between the edge node and the vehicle terminal using a quantum key encapsulation mechanism; Based on the one-time session key, an encrypted secure channel is established between the edge node and the vehicle terminal; Using the encrypted secure channel, the encrypted feature vector sent by the vehicle terminal is obtained.

3. The method for determining an ordered charging station based on post-quantum homomorphic encryption according to claim 1, characterized in that, The candidate charging stations in the candidate charging station set are filtered based on preset constraints to obtain the target candidate charging station set; The target candidate charging station set and the encrypted feature vector are sent to the cloud, so that the cloud can calculate the comprehensive score of each candidate charging station in the target candidate charging station set based on the encrypted feature vector, including: The power limit of the area where the candidate charging station is located is used as a preset constraint. Based on the preset constraints, the candidate charging stations in the candidate charging station set are filtered to obtain the target candidate charging station set; Calculate the power risk score and congestion score of the candidate charging stations in the target candidate charging station set; The target candidate charging station set, encrypted feature vector, power risk score, and congestion score are sent to the cloud so that the cloud can calculate the comprehensive score of each candidate charging station in the target candidate charging station set based on the encrypted feature vector, power risk score, and congestion score.

4. The method for determining an ordered charging station based on post-quantum homomorphic encryption according to claim 3, characterized in that, The formula for calculating the power risk score of candidate charging stations in the target candidate charging station set is as follows: ； in, Based on power risk, As weight, Predicting charging power demand for vehicles This represents the average power margin of the site. The formula for calculating the overall score of each candidate charging station in the target candidate charging station set is as follows: ； in, For comprehensive scoring, For ciphertext feature vectors, For weight parameters, For the feature vector of the charging station, For congestion, This indicates element-wise multiplication. This indicates ciphertext subtraction.

5. The method for determining an ordered charging station based on post-quantum homomorphic encryption according to claim 1, characterized in that, The step of forwarding the set of target candidate charging stations and the comprehensive score vector returned from the cloud, including the comprehensive score, to the vehicle terminal, so that the vehicle terminal can decrypt the comprehensive score vector to obtain the target comprehensive score, and determine the ordered charging stations from the set of target candidate charging stations based on the target comprehensive score, includes: The set of target candidate charging stations and the comprehensive score vector returned from the cloud, including the comprehensive score, are forwarded to the vehicle terminal. The vehicle terminal then uses its private key to decrypt the comprehensive score vector to obtain the target comprehensive score. The target comprehensive scores are then sorted, and the candidate charging station with the highest target comprehensive score is selected as the ordered charging station.

6. The method for determining an ordered charging station based on post-quantum homomorphic encryption according to claim 1, characterized in that, Also includes: Determine the communication overhead and end-to-end latency between edge nodes and vehicle terminals, as well as between edge nodes and the cloud. A multi-objective optimization function is constructed based on the interactive communication overhead and the end-to-end delay. Using system parameters as control variables and a preset security level as a constraint, the multi-objective optimization function is minimized to obtain the minimization result; the system parameters include a preset distance threshold, an upper limit on the number of candidate sites, and the number of sites for CKKS encrypted packaging; Configure the system parameters based on the minimized result.

7. The method for determining an ordered charging station based on post-quantum homomorphic encryption according to any one of claims 1 to 6, characterized in that, Also includes: The power grid status and congestion information of the charging station's location are updated periodically. The preset distance threshold in the system parameters is adaptively adjusted based on the updated power grid status, site congestion information, and system service quality.

8. An ordered charging station determination device based on post-quantum homomorphic encryption, characterized in that, Applied to edge nodes, including: The ciphertext feature vector acquisition module is used to acquire the ciphertext feature vector sent by the vehicle terminal; the ciphertext feature vector is the feature vector obtained by the vehicle terminal encrypting the user sensitive feature vector using the CKKS homomorphic encryption scheme and based on the public key. The set construction module is used to determine the coverage area of ​​the edge node, take any coordinate in the coverage area of ​​the edge node as the vehicle position, and construct a set of candidate charging stations based on the vehicle position and a preset distance threshold. The filtering module is used to filter the candidate charging stations in the candidate charging station set based on preset constraints to obtain the target candidate charging station set. The collection sending module is used to send the target candidate charging station set and the encrypted feature vector to the cloud, so that the cloud can calculate the comprehensive score of each candidate charging station in the target candidate charging station set based on the encrypted feature vector; the comprehensive score is an evaluation index used to characterize the quality of the candidate charging stations. The forwarding module is used to forward the target candidate charging station set and the comprehensive score vector returned by the cloud, including the comprehensive score, to the vehicle terminal, so that the vehicle terminal can decrypt the comprehensive score vector to obtain the target comprehensive score, and determine the ordered charging station from the target candidate charging station set according to the target comprehensive score.

9. An electronic device, characterized in that, include: Memory, used to store computer programs; A processor for executing the computer program to implement the ordered charging station determination method based on post-quantum homomorphic encryption as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, Used to store a computer program; wherein, when the computer program is executed by a processor, it implements the ordered charging station determination method based on post-quantum homomorphic encryption as described in any one of claims 1 to 7.

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