An identity authentication and data encryption method and system for a charging pile

Abstract

The application provides an identity authentication and data encryption method and device for a charging pile, relates to the field of data processing, and solves the technical problem that in the prior art, the password is verified, and the password may be maliciously stolen by others, so that the charging pile is maliciously used or user information is stolen, and user information is leaked. The method comprises the following steps: obtaining identity data of a user and basic data of a charging pile; performing two-way authentication on the user and the charging pile based on the identity data and the basic data; obtaining real-time data of the charging pile according to an authentication result; and encrypting and storing the identity data of the user and the real-time data of the charging pile. The application is used in the identity authentication and data encryption process of the charging pile.

Description

Technical Field

[0001] This application relates to the field of data processing, and in particular to a method and apparatus for authentication and data encryption of charging piles. Background Technology

[0002] Authentication of charging piles refers to the process by which the system confirms the legitimacy and permissions of a user before they use the charging pile to charge their electric vehicle. This ensures transaction security, reasonable resource allocation, and service traceability. Furthermore, charging piles contain a large amount of private data. Therefore, encrypting the data in charging piles can prevent users' private data from being maliciously leaked by others. Even if the database is illegally accessed or the hard drive is stolen, it can prevent the data from being directly used by others, which helps to protect the security of charging pile data.

[0003] Existing technologies for charging station authentication first determine whether the user's identity information is authentic and valid. If it is, they then determine whether to verify a password. The password can be entered directly on the charging station's controller or collected by a smart terminal and transmitted to the charging station's controller. If the password verification information matches the password, authentication is successful; otherwise, authentication fails. However, existing password verification technologies may be vulnerable to malicious theft of passwords, leading to unauthorized use of the charging station or the theft of user information, resulting in information leaks. Summary of the Invention

[0004] This application provides a method and apparatus for identity verification and data encryption of charging piles, which solves the technical problem that existing technologies may allow others to maliciously steal passwords, thereby maliciously using the charging pile or stealing user information, leading to the leakage of user information.

[0005] To achieve the above objectives, this application adopts the following technical solution:

[0006] Firstly, a method for authentication and data encryption of charging piles is provided, including:

[0007] Obtain user identity data and basic data of charging piles;

[0008] Two-way authentication of users and charging piles is performed based on identity data and basic data.

[0009] Obtain real-time data of the charging pile based on the certification results;

[0010] User identity data and real-time data from charging stations are stored in encrypted form.

[0011] Based on the above technical solutions, the authentication and data encryption method for charging piles provided in this application establishes a strong trust relationship between users and devices at the source of data interaction through two-way authentication, effectively resisting major threats such as identity forgery, device impersonation, and unauthorized access; by encrypting and storing sensitive information, the confidentiality and integrity of data in a persistent state are ensured, protecting user privacy and business data security, and meeting compliance requirements; it is conducive to improving the security, reliability, trustworthiness, and compliance of the charging service system.

[0012] In conjunction with the first aspect above, in one possible implementation, the two-way authentication of the user and the charging pile based on identity data and basic data includes:

[0013] Retrieve basic data of the charging pile; the basic data includes: charging pile ID and operating status; if the charging pile is in charging status, user authentication fails; otherwise, verify the identity of the charging pile.

[0014] The system retrieves the user's identity data, which includes IP address, facial image data, and fingerprint data. It then matches the user's IP address with IP addresses in a pre-defined database. If a match is found, the user's identity is authenticated; otherwise, the authentication fails.

[0015] In conjunction with the first aspect above, in one possible implementation, the authentication of the user's identity includes:

[0016] Retrieve user information corresponding to successfully matched IP addresses from a pre-set database; analyze the similarity between facial images and fingerprint data in user identity data and user information;

[0017] The similarity coefficient between identity data and user information is analyzed based on the similarity between the two. When the similarity coefficient is greater than the similarity threshold, the user's identity authentication is successful; otherwise, the user's identity authentication fails.

[0018] In conjunction with the first aspect above, in one possible implementation, the analysis of the similarity between facial images and fingerprint data in user identity data and user information includes:

[0019] The YOLOv8 algorithm is used to locate the face region in a face image and detect the feature points of the face region; a pre-trained feature extraction model is used to extract the feature information of the face image.

[0020] The similarity data between the facial image features and the user information is calculated separately; the similarity data is then weighted and summed to obtain the facial similarity; the similarity data includes: cosine similarity, Euclidean distance, and feature point matching rate;

[0021] The ridge structure in fingerprint data is enhanced by using Gabor filters. The fingerprint data is divided into several regions and the ridge direction in each region is calculated. Feature extraction algorithms are used to extract feature data from the fingerprint data. The feature data includes: coordinates and directions of endpoints and bifurcation points, feature matrix of ridge flow map, Gabor wavelet transform coefficients, and embedding vector.

[0022] The matching similarity between fingerprint data and user information is calculated separately; the fingerprint similarity between fingerprint data and user information is calculated by weighted summation; the matching similarity includes: point pattern matching similarity, ridge flow matching similarity and deep feature matching similarity.

[0023] In conjunction with the first aspect above, in one possible implementation, the step of analyzing the similarity coefficient between identity data and user information based on the similarity between the two includes:

[0024] Construct a similarity coefficient analysis function: The similarity coefficient between identity data and user information is calculated using a similarity analysis function.

[0025] in, and These are the weighting factors for facial similarity and fingerprint similarity, respectively. and These are the confidence scores for facial similarity and fingerprint similarity, respectively. and These represent facial similarity and fingerprint similarity, respectively.

[0026] In conjunction with the first aspect above, in one possible implementation, the method for obtaining the weighting factors and confidence evaluation coefficients includes:

[0027] Construct the weight factor calculation function:

[0028] ;

[0029] in, and , respectively, are the rejection rates for faces and fingerprints; k is the quality compensation coefficient; and The quality scores for the collected faces and fingerprints are respectively.

[0030] In conjunction with the first aspect above, in one possible implementation, the confidence assessment coefficient is obtained by means of:

[0031] Construct the confidence evaluation function:

[0032] ;

[0033] in, and The decision thresholds are for faces and fingerprints, respectively. It is the internal variance of the facial feature vector; The number of valid feature points to match for fingerprint recognition.

[0034] In conjunction with the first aspect above, in one possible implementation, the encrypted storage of user identity data and real-time charging pile data includes:

[0035] Retrieve user identity data and real-time data of charging piles; analyze the leakage risk coefficient of charging piles; compare the leakage risk coefficient with the preset risk threshold to obtain the risk level;

[0036] Based on the risk level, the system randomly selects the corresponding encryption algorithm from the encryption database to encrypt the identity data and real-time data, and changes the encryption algorithm in real time; the encrypted identity data and real-time data are then stored in the cloud.

[0037] In conjunction with the first aspect above, in one possible implementation, the analysis of the leakage risk coefficient of the charging pile includes:

[0038] Construct risk analysis functions: ; Calculate the leakage risk coefficient of the charging pile based on the risk analysis function;

[0039] in, This represents the weight factor for the i-th class of data; This represents the sensitivity score for the i-th type of data; This represents the maximum possible sensitivity. Network vulnerability density; This is a physical security defect; For misconfiguration rate; For attack reachability; A represents the maturity of vulnerability exploitation; A represents the attack frequency.

[0040] Secondly, an authentication and data encryption device for charging piles is provided, comprising: a communication unit and a processing unit; the communication unit is used to acquire user identity data and basic data of the charging pile; the processing unit is used to perform two-way authentication of the user and the charging pile based on the identity data and basic data; acquire real-time data of the charging pile according to the authentication result; and encrypt and store the user identity data and the real-time data of the charging pile.

[0041] Thirdly, this application provides an authentication and data encryption device for charging piles, comprising: a processor and a storage medium; the storage medium includes instructions, and the processor is configured to execute the instructions to implement the methods described in the first aspect and any possible implementation thereof. This authentication and data encryption device for charging piles can be an electronic device or a chip within an electronic device.

[0042] Fourthly, this application provides a computer-readable storage medium storing instructions that, when executed on an authentication and data encryption device for a charging pile, cause the device to perform the methods described in the first aspect and any possible implementation thereof.

[0043] Fifthly, this application provides a computer program product containing instructions that, when run on an authentication and data encryption device for a charging pile, causes the authentication and data encryption device for the charging pile to perform the methods described in the first aspect and any possible implementation thereof.

[0044] This application provides a method and apparatus for identity verification and data encryption of charging piles. The method terminates authentication directly during charging to prevent unauthorized operations when the device is maliciously occupied. It only allows trusted IPs in a preset database to initiate authentication, blocking unauthorized network access. It analyzes the characteristics of fingerprints to effectively identify forged fingerprints. It can effectively intercept others from maliciously using the charging pile, thus improving the security of charging pile identity verification.

[0045] Analyzing the leakage risk coefficient of charging piles, determining the risk level based on the leakage risk coefficient, and determining the encryption algorithm based on the risk level allows for the use of different encryption algorithms for charging piles with different risks, ensuring the security of data transmission from charging piles. Furthermore, real-time changes to the encryption algorithm can prevent others from finding encryption patterns and thus cracking the encrypted data to steal and leak it, thereby helping to reduce the risk of charging pile data leakage.

[0046] It should be understood that the descriptions of technical features, technical solutions, beneficial effects, or similar language in this application do not imply that all features and advantages can be achieved in any single embodiment. Rather, it is understood that the description of a feature or beneficial effect means that a specific technical feature, technical solution, or beneficial effect is included in at least one embodiment. Therefore, the descriptions of technical features, technical solutions, or beneficial effects in this specification do not necessarily refer to the same embodiment. Furthermore, the technical features, technical solutions, and beneficial effects described in this embodiment can be combined in any suitable manner. Those skilled in the art will understand that embodiments can be implemented without one or more specific technical features, technical solutions, or beneficial effects of a particular embodiment. In other embodiments, additional technical features and beneficial effects may be identified in specific embodiments that do not embody all embodiments. Attached Figure Description

[0047] Figure 1 A system architecture diagram of an authentication and data encryption system for charging piles is provided for embodiments of this application;

[0048] Figure 2 A flowchart illustrating an authentication and data encryption method for charging piles provided in this application embodiment;

[0049] Figure 3 A flowchart illustrating another method for authentication and data encryption of charging piles provided in this application embodiment;

[0050] Figure 4 A flowchart illustrating another method for authentication and data encryption of charging piles provided in this application embodiment;

[0051] Figure 5 A schematic diagram of an authentication and data encryption device for a charging pile provided in an embodiment of this application;

[0052] Figure 6 A schematic diagram of the hardware structure of an authentication and data encryption device for a charging pile provided in an embodiment of this application; Detailed Implementation

[0053] In the description of this application, unless otherwise stated, " / " means "or," for example, A / B can mean A or B. The "and / or" in this document is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, and B alone. Furthermore, "at least one" means one or more, and "multiple" means two or more. The terms "first," "second," etc., do not limit the quantity or order of execution, and "first," "second," etc., do not necessarily imply differences.

[0054] It should be noted that, in this application, the terms "exemplary" or "for example" are used to indicate that something is being described as an example, illustration, or illustration. Any embodiment or design described as "exemplary" or "for example" in this application should not be construed as being more preferred or advantageous than other embodiments or design solutions. Specifically, the use of terms such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner.

[0055] The authentication and data encryption method for charging piles provided in this application embodiment can be applied to, for example... Figure 1 In the server system 100 shown, such as Figure 1 As shown, the communication system includes: acquisition device 101 and processing device 102.

[0056] Among them, the data acquisition device 101 is used to acquire user identity data and basic data of charging piles.

[0057] The processing device 102 is used for two-way authentication of the user and the charging pile based on identity data and basic data; to obtain real-time data of the charging pile according to the authentication result; and to encrypt and store the user's identity data and the real-time data of the charging pile.

[0058] To address the technical problem in existing technologies where password verification may be maliciously stolen by others, leading to unauthorized use of charging stations or the theft of user information and resulting in user information leakage, this application provides an authentication and data encryption method for charging stations. The method includes: obtaining user identity data and basic data of the charging station.

[0059] Two-way authentication of users and charging piles is performed based on identity data and basic data.

[0060] Obtain real-time data of the charging pile based on the certification results;

[0061] By encrypting and storing user identity data and real-time charging pile data, a strong trust relationship is established between users and devices at the source of data interaction through two-way authentication, effectively resisting major threats such as identity forgery, device impersonation, and unauthorized access. By encrypting and storing sensitive information, the confidentiality and integrity of data in a persistent state are ensured, protecting user privacy and business data security, and meeting compliance requirements. This is conducive to improving the security, reliability, trustworthiness, and compliance of the charging service system.

[0062] like Figure 2 As shown in the embodiments of this application, the authentication and data encryption method for charging piles includes:

[0063] S201. Obtain the user's identity data and the basic data of the charging pile.

[0064] S202. Two-way authentication of users and charging piles based on identity data and basic data.

[0065] S203. Obtain real-time data of the charging pile based on the certification results.

[0066] S204. Encrypt and store the user's identity data and the charging pile's real-time data.

[0067] Based on the above technical solutions, the authentication and data encryption method for charging piles provided in this application establishes a strong trust relationship between users and devices at the source of data interaction through two-way authentication, effectively resisting major threats such as identity forgery, device impersonation, and unauthorized access; by encrypting and storing sensitive information, it ensures the confidentiality and integrity of data in a persistent state, protects user privacy and business data security, and meets compliance requirements; it is conducive to improving the security, reliability, trustworthiness, and compliance of the charging service system.

[0068] In one possible implementation of the embodiments of this application, combined with Figure 2 ,like Figure 3 As shown, the above S202 can be specifically implemented through the following S301, S302 and S303, which are explained in detail below:

[0069] S301. Retrieve basic data of the charging pile; if the charging pile is in charging mode, user authentication fails; otherwise, verify the identity of the charging pile.

[0070] The basic data includes: charging pile ID and operating status.

[0071] Example: User scenario: A user attempts to charge their phone using a charging station (charging station ID: CP001). The system needs to verify the user's identity.

[0072] Charging station status: The operating status is "idle" (not charging), so identity authentication can continue.

[0073] User identity data:

[0074] IP address: 192.168.1.100 (matches the default database).

[0075] Facial image data: Collected facial images (assuming high quality).

[0076] Fingerprint data: Collected fingerprint images (assuming medium quality).

[0077] Preset database information: IP address 192.168.1.100 corresponds to registered user User123, which stores the user's reference facial features, fingerprint features and other information.

[0078] Key parameter assumptions (based on common system settings or reasonable estimates):

[0079] Face similarity calculation weights: cosine similarity weight 0.4, Euclidean distance similarity weight 0.3, feature point matching rate weight 0.3.

[0080] Fingerprint similarity calculation weights: point pattern matching similarity weight 0.4, ridge flow matching similarity weight 0.3, and deep feature matching similarity weight 0.3.

[0081] Weighting factor formula parameters: False Rejection Rate (FRR) based on historical data; Quality compensation coefficient k = 1.0; Quality score (range 0-1, 1 is optimal).

[0082] Confidence assessment formula parameters: decision threshold is based on system settings; internal variance and number of effective feature point matches are based on feature extraction results.

[0083] Similarity threshold: 0.85 (the threshold for determining authentication success in S307).

[0084] Therefore, it can be determined that the charging pile is in an "idle" (non-charging) state; then the authentication process will continue.

[0085] S302. Retrieve the user's identity data; match the user's IP address with the IP address in the preset database; if the match is successful, authenticate the user's identity; otherwise, user identity authentication fails.

[0086] The identity data includes: IP address, facial image data, and fingerprint data.

[0087] For example: Match 192.168.1.100 with the preset database. Assuming the IP address (corresponding to User123) exists in the database, the match will be successful.

[0088] S303. Retrieve user information corresponding to the successfully matched IP address from the preset database; use the YOLOv8 algorithm to locate the face region in the face image and detect the feature points of the face region; use a pre-trained feature extraction model to extract the feature information of the face image.

[0089] Example: Use the YOLOv8 algorithm to locate the face region (assuming the location is successful).

[0090] Feature points in the face region are detected (e.g., key points such as eyes, nose, and mouth are detected).

[0091] Use a pre-trained feature extraction model (such as ResNet or a similar CNN model) to extract facial feature information and obtain a feature vector (e.g., a 128-dimensional vector).

[0092] S304. Calculate the similarity data between the facial image features and the user information respectively; use a weighted summation method to perform a weighted summation on the similarity data to obtain the facial similarity.

[0093] The similarity data includes: cosine similarity, Euclidean distance, and feature point matching rate.

[0094] Example: Similarity data calculation: Compare the collected facial features with the reference features of User123.

[0095] Cosine similarity (measures the consistency of vector direction): Assume a calculated value of 0.85 (range 0-1, 1 indicates a perfect match).

[0096] Euclidean distance (measures distance in a vector space): Assuming a calculated value of 0.25 (the smaller the distance, the more similar the vectors; it needs to be converted to a similarity score). Conversion formula: Similarity = 0.80.

[0097] Feature point matching rate (key point matching ratio): Assuming 50 feature points are detected, 45 of them match, the matching rate = 0.90.

[0098] The facial similarity is obtained by weighted summation:

[0099] Weights: Cosine similarity weight 0.4, Euclidean distance similarity weight 0.3, feature point matching rate weight 0.3 (customizable).

[0100] The facial similarity calculated using the formula is 0.85.

[0101] S305. Enhance the ridge structure in the fingerprint data using Gabor filter banks, divide the fingerprint data into several regions and calculate the ridge direction in each region; extract the feature data of the fingerprint data using a feature extraction algorithm.

[0102] Example: Fingerprint enhancement: Enhance the fingerprint ridge structure using a Gabor filter bank (assuming the process is successful).

[0103] Region segmentation and orientation calculation: Divide the fingerprint image into 16 regions and calculate the ridge orientation of each region (e.g., using the gradient method).

[0104] Feature extraction: Use feature extraction algorithms (such as MINDTCT or deep learning models) to obtain the following feature data:

[0105] Coordinates and directions of endpoints and bifurcation points: Assume 20 valid points are extracted.

[0106] Feature matrix of ridge flow direction map: Assume that a matrix is ​​obtained to represent the ridge direction pattern.

[0107] Gabor wavelet transform coefficients: Assume that a set of coefficients is extracted to represent texture features.

[0108] Embedded vector: Extract a 128-dimensional vector using a pre-trained model (such as the Siamese network).

[0109] S306. Calculate the matching similarity between fingerprint data and user information respectively; calculate the fingerprint similarity between fingerprint data and user information using a weighted summation method.

[0110] The matching similarity includes: point pattern matching similarity, ridge flow matching similarity, and deep feature matching similarity.

[0111] Example: Matching similarity calculation: Compare the collected fingerprint features with the reference features of User123.

[0112] Point pattern matching similarity (based on feature point matching): Assume a calculated value of 0.88 (using point matching algorithms, such as minutiae matching).

[0113] Ridge flow matching similarity (based on flow direction) Figure 1 Consistency: Assume the calculated value is 0.84 (using orientation histograms for comparison).

[0114] Deep feature matching similarity (based on embedding vector): Assume the cosine similarity is 0.92.

[0115] Weighted summation yields fingerprint similarity:

[0116] Weights: Point pattern matching similarity weight 0.4, ridge flow matching similarity weight 0.3, deep feature matching similarity weight 0.3 (customizable).

[0117] The fingerprint similarity calculated using the formula is 0.88.

[0118] S307. Construct a similarity coefficient analysis function, and calculate the similarity coefficient between identity data and user information based on the similarity analysis function; when the similarity coefficient is greater than the similarity threshold, the user's identity authentication is successful; otherwise, the user's identity authentication fails.

[0119] The similarity coefficient analysis function is as follows: ; and These are the weighting factors for facial similarity and fingerprint similarity, respectively. and These are the confidence scores for facial similarity and fingerprint similarity, respectively. and These represent facial similarity and fingerprint similarity, respectively.

[0120] In some implementations, the methods for obtaining weighting factors and confidence evaluation coefficients include:

[0121] Construct the weight factor calculation function:

[0122] ;

[0123] in, and , respectively, are the rejection rates for faces and fingerprints; k is the quality compensation coefficient; and These are quality scores for the collected faces and fingerprints, respectively.

[0124] Methods for obtaining confidence level evaluation coefficients include:

[0125] Construct the confidence evaluation function:

[0126] ;

[0127] in, and The decision thresholds are for faces and fingerprints, respectively. It is the internal variance of the facial feature vector; The number of valid feature points to match for fingerprint recognition.

[0128] Example: Calculating weighting factors:

[0129] Parameter assumptions (based on historical system data or default settings): Face rejection rate Ff = 0.05 (representing a 5% false rejection rate); Fingerprint rejection rate Fp = 0.03 (representing a 3% false rejection rate); Quality compensation coefficient k = 1.0; Face quality score Qf = 0.9 (high-quality image); Fingerprint quality score Qp = 0.8 (medium-quality image); The weighting factors are calculated using the following formula: ;

[0130] Calculate the confidence assessment coefficient

[0131] Parameter assumption: Face decision threshold f=0.75; fingerprint decision threshold p=0.80; the internal variance of the face feature vector σf=0.15 (indicating feature stability); the number of valid fingerprint feature point matches Np=20.

[0132] Calculated using the formula: ;

[0133] The similarity coefficient calculated by the formula is 0.8713, which is greater than the similarity threshold of 0.85; therefore, the user's identity authentication is successful.

[0134] Based on the above technical solutions, authentication is terminated directly during charging to prevent unauthorized operations when the device is maliciously occupied; authentication is only allowed to be initiated by trusted IPs in the preset database to block illegal network access; fingerprint features are analyzed to effectively identify forged fingerprints; and malicious use of the charging pile by others can be effectively intercepted, improving the security of charging pile identity verification.

[0135] In one possible implementation, combining Figure 2 ,like Figure 4 As shown, after S203, the authentication and data encryption method for charging piles provided in this application embodiment further includes the following S401 to S403:

[0136] S401. Retrieve user identity data and real-time data of charging piles; construct a risk analysis function; calculate the leakage risk coefficient of charging piles based on the risk analysis function.

[0137] Among them, the risk analysis function is: ; This represents the weight factor for the i-th class of data; This represents the sensitivity score for the i-th type of data; This represents the maximum possible sensitivity. Network vulnerability density; This is a physical security defect; For misconfiguration rate; For attack reachability; A represents the maturity of vulnerability exploitation; A represents the attack frequency.

[0138] S402. Compare the leakage risk coefficient with the preset risk threshold to obtain the risk level.

[0139] S403. Based on the risk level, randomly select the corresponding encryption algorithm from the encryption database to encrypt the identity data and real-time data, and change the encryption algorithm in real time; store the encrypted identity data and real-time data in the cloud.

[0140] Example Scenario Setting

[0141] User scenario: A user (User ID: "User_123") is using a charging station (Charging Station ID: "CP_789") to charge their phone. The system needs to retrieve the user's identity data and the charging station's real-time data to perform risk analysis and select an encryption algorithm based on the risk level.

[0142] User identity data includes name, ID number, and payment card number (assuming they have been anonymized).

[0143] Real-time charging station data includes charging station location (GPS coordinates), current battery level (kWh), charging status (e.g., "charging in"), and network connection status.

[0144] Data category definition (for risk analysis functions): Assume there are 3 categories of data (n=3), each category has a weighting factor (θ_i) and a sensitivity score (M_i). The sensitivity score ranges from 1 to 10 (10 is the highest sensitivity), and M_max = 10 (the maximum possible sensitivity value).

[0145] i=1: Personal Identification Information (PII, including name and ID number), θ_1 = 0.5, M_1 = 9 (highly sensitive).

[0146] i=2: Payment information (including payment card number and transaction records), θ_2 = 0.3, M_2 = 8 (medium to high sensitivity).

[0147] i=3: Location data (GPS coordinates of charging pile), θ_3 = 0.2, M_3 = 6 (medium sensitivity).

[0148] Security vulnerability parameters (based on system monitoring or assessment):

[0149] V_net (Network Vulnerability Density): 0.4 (indicating that 40% of network ports are vulnerable, ranging from 0 to 1).

[0150] V_phy (Physical security flaw): 0.2 (indicates that 20% of physical access points are unmonitored, ranging from 0 to 1).

[0151] V_config (Error Configuration Rate): 0.3 (Indicates 30% system configuration errors, such as the default password not being changed; range 0-1).

[0152] Attack-related parameters (based on threat intelligence):

[0153] T_acc (Attack reachability): 8 (range 0-10, 8 means that the attacker can easily access the system).

[0154] T_exp (exploitation maturity): 7 (range 0-10, 7 indicates that the exploit tool is mature).

[0155] A (Attack Frequency): 6 (range 0-10, 6 means an average of 6 attack attempts per day recently).

[0156] Risk threshold preset (system configuration):

[0157] Low risk: FX < 5.0

[0158] Medium risk: 5.0 ≤ FX < 10.0

[0159] High risk: FX ≥ 10.0

[0160] Encrypted databases (classified by risk level algorithm):

[0161] Low risk: AES-128 or Blowfish are optional.

[0162] Medium risk: AES-256 or Camellia-256 are optional.

[0163] High risk: RSA-4096, ECC-521, and ChaCha20-Poly1305 are optional.

[0164] Output objective: Calculate the leakage risk coefficient (FX), determine the risk level, and randomly select and apply an encryption algorithm.

[0165] User identity data: Name="Zhang San", ID number="110101199001011234", Payment card number="6217 1234".

[0166] Real-time charging station data: Location = 39.9042°N, 116.4074°E, Current battery level = 75.3 kWh, Charging status = Charging, Network connection = 4G

[0167] The following values ​​were calculated using the formula: Leakage risk coefficient FX = 35.38; Risk level = "High risk"; Optional algorithms: RSA-4096, ECC-521, ChaCha20-Poly1305.

[0168] Random selection: Use a random number generator to select "ECC-521"; use encryption algorithms to encrypt the user's identity data and the charging pile's real-time data before storing them in the cloud.

[0169] Based on the above technical solution, the leakage risk coefficient of charging piles is analyzed, the risk level is determined according to the leakage risk coefficient, and the encryption algorithm is determined according to the risk level. Different encryption algorithms can be used for charging piles with different risks to ensure the security of data transmission of charging piles. Furthermore, the encryption algorithm can be changed in real time to prevent others from finding the encryption pattern and cracking the encrypted data to steal and leak it. This helps to reduce the risk of leakage of charging pile data.

[0170] The above primarily describes the solutions of the embodiments of this application from the perspective of device implementation. It is understood that each device, such as an authentication and data encryption device for a charging pile, includes at least one of the hardware structures and software modules corresponding to the execution of each function in order to achieve the above-mentioned functions. Those skilled in the art should readily recognize that, based on the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein, this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed in hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0171] This application embodiment can divide the authentication and data encryption device for charging piles into functional units based on the above method example. For example, each function can be divided into separate functional units, or two or more functions can be integrated into one processing unit. The integrated unit can be implemented in hardware or as a software functional unit. It should be noted that the unit division in this application embodiment is illustrative and only represents one logical functional division; other division methods may be used in actual implementation.

[0172] When using integrated units, Figure 5 A possible structural schematic diagram of the authentication and data encryption device (denoted as 50) for charging piles involved in the above embodiments is shown. The authentication and data encryption device 50 for charging piles includes a processing unit 501 and a communication unit 502, and may also include a storage unit 503. Figure 5The schematic diagram shown can be used to illustrate the structure of the authentication and data encryption device for charging piles involved in the above embodiments.

[0173] when Figure 5 The schematic diagram shown illustrates the structure of the authentication and data encryption device for charging piles involved in the above embodiments. The processing unit 501 is used to control and manage the operation of the authentication and data encryption device for charging piles, the communication unit 502 is used for the authentication and data encryption device for charging piles to communicate with other devices, and the storage unit 503 is used to store the program code and data of the authentication and data encryption device for charging piles.

[0174] For example, communication unit 502 is used to obtain user identity data and basic data of charging pile;

[0175] The processing unit 501 is used to perform two-way authentication between the user and the charging pile based on identity data and basic data; obtain real-time data of the charging pile according to the authentication result; and encrypt and store the user's identity data and the real-time data of the charging pile.

[0176] The processing unit 501 can be a processor or a controller, and the communication unit 502 can be a communication interface, transceiver, transceiver circuit, transceiver device, etc. The term "communication interface" is a general term and may include one or more interfaces. The storage unit 503 can be a memory. When the authentication and data encryption device 50 for the charging pile is a chip, the processing unit 501 can be a processor or a controller, and the communication unit 502 can be an input interface and / or an output interface, pins, or circuits, etc. The storage unit 503 can be a storage unit within the chip (e.g., a register, cache, etc.) or a storage unit located outside the chip (e.g., read-only memory (ROM), random access memory (RAM, etc.).

[0177] The communication unit can also be called a transceiver unit. The antenna and control circuit with transceiver functions in the authentication and data encryption device 50 for the charging pile can be considered as the communication unit 502 of the authentication and data encryption device 50 for the charging pile, and the processor with processing functions can be considered as the processing unit 501 of the authentication and data encryption device 50 for the charging pile. Optionally, the device in the communication unit 502 that implements the receiving function can be considered as the communication unit, which is used to execute the receiving steps in the embodiments of this application. The communication unit can be a receiver, a receiver circuit, etc. The device in the communication unit 502 that implements the transmitting function can be considered as the transmitting unit, which is used to execute the transmitting steps in the embodiments of this application. The transmitting unit can be a transmitter, a transmitter, a transmitting circuit, etc.

[0178] Figure 5 If the integrated units in the process are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of this application, in essence, or the parts that contribute to the prior art, or all or part of the technical solutions, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute all or part of the steps of the methods described in the various embodiments of this application. Storage media for storing computer software products include various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory, random access memory, magnetic disks, or optical disks.

[0179] Figure 5 The units in the process can also be called modules; for example, a processing unit can be called a processing module.

[0180] This application embodiment also provides a hardware structure diagram of an authentication and data encryption device (denoted as 60) for a charging pile, see [link to diagram]. Figure 6 The authentication and data encryption device 60 for charging piles includes a processor 601, and optionally, a memory 602 connected to the processor 601.

[0181] In the first possible implementation, see Figure 6The authentication and data encryption device 60 for charging piles also includes a transceiver 603. The processor 601, memory 602, and transceiver 603 are connected via a bus. The transceiver 603 is used to communicate with other devices or communication networks. Optionally, the transceiver 603 may include a transmitter and a receiver. The device in the transceiver 603 that implements the receiving function can be considered as a receiver, which is used to perform the receiving steps in the embodiments of this application. The device in the transceiver 603 that implements the transmitting function can be considered as a transmitter, which is used to perform the transmitting steps in the embodiments of this application.

[0182] Based on the first possible implementation method Figure 6 The schematic diagram shown can be used to illustrate the structure of the authentication and data encryption device for charging piles involved in the above embodiments.

[0183] in, Figure 6 This can also be illustrated by the system chip in the authentication and data encryption device used for charging piles. In this case, the actions performed by the aforementioned authentication and data encryption device for charging piles can be implemented by this system chip. The specific actions performed can be found above and will not be repeated here.

[0184] In implementation, each step of the method provided in this embodiment can be completed by integrated logic circuits in the processor or by instructions in software form. The steps of the method disclosed in the embodiments of this application can be directly manifested as being executed by a hardware processor, or being executed by a combination of hardware and software modules in the processor.

[0185] The processor in this application may include, but is not limited to, at least one of the following: a central processing unit (CPU), a microprocessor, a digital signal processor (DSP), a microcontroller unit (MCU), or an artificial intelligence processor, etc., which are various computing devices that run software. Each computing device may include one or more cores for executing software instructions to perform calculations or processing. The processor may be a separate semiconductor chip or integrated with other circuits into a single semiconductor chip. For example, it may be integrated with other circuits (such as encoding / decoding circuits, hardware acceleration circuits, or various bus and interface circuits) to form a SoC (System-on-a-Chip), or it may be integrated as a built-in processor within an ASIC. The ASIC with the integrated processor may be packaged separately or together with other circuits. In addition to the cores for executing software instructions to perform calculations or processing, the processor may further include necessary hardware accelerators, such as field-programmable gate arrays (FPGAs), PLDs (programmable logic devices), or logic circuits that implement dedicated logic operations.

[0186] The memory in the embodiments of this application may include at least one of the following types: read-only memory (ROM) or other types of static storage devices capable of storing static information and instructions; random access memory (RAM) or other types of dynamic storage devices capable of storing information and instructions; or electrically erasable programmable-only memory (EEPROM). In some scenarios, the memory may also be a compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media, or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures that can be accessed by a computer, but is not limited thereto.

[0187] This application also provides a computer-readable storage medium including instructions that, when run on a computer, cause the computer to perform any of the methods described above.

[0188] This application also provides a computer program product containing instructions that, when run on a computer, cause the computer to perform any of the methods described above.

[0189] This application also provides a chip including a processor and an interface circuit. The interface circuit is coupled to the processor. The processor is used to run computer programs or instructions to implement the above-described method. The interface circuit is used to communicate with other modules outside the chip.

[0190] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented using software programs, implementation can be, in whole or in part, in the form of a computer program product. This computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium accessible to a computer or a data storage device containing one or more servers, data centers, etc., that can be integrated with the medium. The available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media (e.g., solid-state disks (SSDs)).

[0191] Although this application has been described herein in conjunction with various embodiments, those skilled in the art, by reviewing the accompanying drawings, disclosure, and appended claims, will understand and implement other variations of the disclosed embodiments in carrying out the claimed application. In the claims, the word "comprising" does not exclude other components or steps, and "a" or "an" does not exclude multiple instances. A single processor or other unit can implement several functions listed in the claims. While different dependent claims may recite certain measures, this does not mean that these measures cannot be combined to produce good results.

[0192] Although this application has been described in conjunction with specific features and embodiments, it is obvious that various modifications and combinations can be made thereto without departing from the spirit and scope of this application. Accordingly, this specification and drawings are merely exemplary illustrations of this application as defined by the appended claims, and are considered to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from the spirit and scope of this application. Thus, if such modifications and modifications of this application fall within the scope of the claims of this application and their equivalents, this application is also intended to include such modifications and modifications.

Claims

1. A method for authentication and data encryption of charging piles, characterized in that, include: Obtain user identity data and basic data of charging piles; Two-way authentication of users and charging piles is performed based on identity data and basic data. Obtain real-time data of the charging pile based on the certification results; User identity data and real-time data of charging piles are stored in encrypted form; The two-way authentication of users and charging stations based on identity data and basic data includes: Retrieve basic data of the charging pile; the basic data includes: charging pile ID and operating status; if the charging pile is in charging status, user authentication fails; otherwise, verify the identity of the charging pile. Retrieve user identity data, including IP address, facial image data, and fingerprint data; match the user's IP address with IP addresses in a preset database; if a match is successful, authenticate the user's identity; otherwise, user authentication fails. The authentication of the user's identity includes: Retrieve user information corresponding to successfully matched IP addresses from a pre-set database; analyze the similarity between facial images and fingerprint data in user identity data and user information; The similarity coefficient between the identity data and user information is analyzed based on the similarity between the two. When the similarity coefficient is greater than the similarity threshold, the user's identity authentication is successful; otherwise, the user's identity authentication fails. The method of analyzing the similarity coefficient between identity data and user information based on the similarity between the two includes: Construct a similarity coefficient analysis function: The similarity coefficient between identity data and user information is calculated using a similarity analysis function. in, and These are the weighting factors for facial similarity and fingerprint similarity, respectively. and These are the confidence scores for facial similarity and fingerprint similarity, respectively. and These represent facial similarity and fingerprint similarity, respectively.

2. The method for identity verification and data encryption for charging piles according to claim 1, characterized in that, The analysis of the similarity between facial images and fingerprint data in user identity data and user information includes: The YOLOv8 algorithm is used to locate the face region in a face image and detect the feature points of the face region; a pre-trained feature extraction model is used to extract the feature information of the face image. The similarity data between the facial image features and the user information is calculated separately; the similarity data is then weighted and summed to obtain the facial similarity; the similarity data includes: cosine similarity, Euclidean distance, and feature point matching rate; The ridge structure in fingerprint data is enhanced by using Gabor filters. The fingerprint data is divided into several regions and the ridge direction in each region is calculated. Feature extraction algorithms are used to extract feature data from the fingerprint data. The feature data includes: coordinates and directions of endpoints and bifurcation points, feature matrix of ridge flow map, Gabor wavelet transform coefficients, and embedding vector. The matching similarity between fingerprint data and user information is calculated separately; the fingerprint similarity between fingerprint data and user information is calculated by weighted summation; the matching similarity includes: point pattern matching similarity, ridge flow matching similarity and deep feature matching similarity.

3. The method for identity verification and data encryption for charging piles according to claim 1, characterized in that, The methods for obtaining the weighting factors and confidence evaluation coefficients include: Construct the weight factor calculation function: ； in, and , respectively, are the rejection rates for faces and fingerprints; k is the quality compensation coefficient; and The quality scores for the collected faces and fingerprints are respectively.

4. The method for identity verification and data encryption for charging piles according to claim 1, characterized in that, The confidence level evaluation coefficient is obtained through the following methods: Construct the confidence evaluation function: ； in, and The decision thresholds are for faces and fingerprints, respectively. It is the internal variance of the facial feature vector; The number of valid feature points to match for fingerprint recognition.

5. The authentication and data encryption method for charging piles according to claim 1, characterized in that, The process of encrypting and storing user identity data and real-time charging pile data includes: Retrieve user identity data and real-time data of charging piles; analyze the leakage risk coefficient of charging piles; compare the leakage risk coefficient with the preset risk threshold to obtain the risk level; Based on the risk level, the system randomly selects the corresponding encryption algorithm from the encryption database to encrypt the identity data and real-time data, and changes the encryption algorithm in real time; the encrypted identity data and real-time data are then stored in the cloud.

6. The authentication and data encryption method for charging piles according to claim 5, characterized in that, The analysis of the leakage risk coefficient of the charging pile includes: Construct risk analysis functions: ; Calculate the leakage risk coefficient of the charging pile based on the risk analysis function; in, This represents the weight factor for the i-th class of data; This represents the sensitivity score for the i-th type of data; This represents the maximum possible sensitivity. Network vulnerability density; This is a physical security defect; For misconfiguration rate; For attack reachability; A represents the maturity of vulnerability exploitation; A represents the attack frequency.

7. An authentication and data encryption device for charging piles, applied to the authentication and data encryption method for charging piles as described in any one of claims 1-6, characterized in that, include: Communication unit and processing unit; The communication unit is used to obtain the user's identity data and the basic data of the charging pile; The processing unit is used to perform two-way authentication between the user and the charging pile based on identity data and basic data; Obtain real-time data of the charging pile based on the certification results; User identity data and real-time data of charging piles are stored in encrypted form; The two-way authentication of users and charging stations based on identity data and basic data includes: Retrieve basic data of the charging pile; the basic data includes: charging pile ID and operating status; if the charging pile is in charging status, user authentication fails; otherwise, verify the identity of the charging pile. Retrieve user identity data, including IP address, facial image data, and fingerprint data; match the user's IP address with IP addresses in a preset database; if a match is successful, authenticate the user's identity; otherwise, user authentication fails. The authentication of the user's identity includes: Retrieve user information corresponding to successfully matched IP addresses from a pre-set database; analyze the similarity between facial images and fingerprint data in user identity data and user information; The similarity coefficient between the identity data and user information is analyzed based on the similarity between the two. When the similarity coefficient is greater than the similarity threshold, the user's identity authentication is successful; otherwise, the user's identity authentication fails. The method of analyzing the similarity coefficient between identity data and user information based on the similarity between the two includes: Construct a similarity coefficient analysis function: The similarity coefficient between identity data and user information is calculated using a similarity analysis function. in, and These are the weighting factors for facial similarity and fingerprint similarity, respectively. and These are the confidence scores for facial similarity and fingerprint similarity, respectively. and These represent facial similarity and fingerprint similarity, respectively.

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