Keyless access control and security authentication system for rentable vehicle

Abstract

The invention relates to the technical field of access control, and discloses a rentable vehicle keyless access control and security authentication system, which comprises a user identity authentication module, an access authority verification module, a dynamic risk assessment module, a vehicle access control module, an abnormal behavior monitoring module and an abnormal-risk operation feedback module, wherein identity matching verification is carried out on a user and rental information to generate an identity verification result, then access permission verification is carried out on the user and a target vehicle to obtain permission state data, then dynamic risk assessment is carried out to obtain a risk assessment level, and a dynamic authentication code is generated; and performing validity verification on the target vehicle, generating a control instruction of the target vehicle, performing abnormal behavior monitoring to generate an abnormal behavior parameter set, further identifying risk operation, and finally performing feedback to a user. According to the invention, the key-free access control precision and the security authentication continuity of the rentable vehicle can be improved.

Description

Technical Field

[0001] This invention relates to the field of access control technology, and in particular to a keyless access control and security authentication system for rentable vehicles. Background Technology

[0002] Currently, keyless vehicle rental systems mainly rely on fixed identity verification methods, such as passwords, RFID cards, or single biometric identification. These methods generally lack dynamic risk perception capabilities and cannot adjust security strategies based on users' real-time behavior patterns, historical access records, and environmental factors. This results in insufficient authentication strength in high-risk scenarios, which can easily lead to unauthorized unlocking and vehicle abuse. At the same time, existing systems mostly use static authentication codes or fixed key mechanisms, which are difficult to resist security threats such as replay attacks and man-in-the-middle attacks, posing significant security vulnerabilities.

[0003] Furthermore, most vehicle access control systems lack continuous behavior monitoring and risk prediction capabilities after a user unlocks the vehicle. Once a user gains access, the system cannot analyze in-vehicle behavior for abnormalities in real time, nor can it proactively intervene based on risk trends. This passive security strategy is not only slow to respond but also ineffective in preventing potential security incidents such as theft, vandalism, or dangerous driving. Therefore, how to combine real-time behavior monitoring and prediction with proactive feedback has become an urgent problem to be solved. Summary of the Invention

[0004] This invention provides a keyless access control and security authentication system for rentable vehicles to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides a keyless access control and security authentication system for rentable vehicles, characterized in that the system includes a user identity authentication module, an access permission verification module, a dynamic risk assessment module, a vehicle access control module, an abnormal behavior monitoring module, and an abnormal-risk operation feedback module, wherein:

[0006] The user identity authentication module is used to match and verify the pre-acquired user biometric authentication information with the rental information, and generate an identity verification result;

[0007] The access permission verification module is used to verify the access permissions of the user and the target vehicle based on the identity verification result, and obtain the user's access permission status data.

[0008] The dynamic risk assessment module is used to perform dynamic risk assessment on the user based on the permission status data, generate the user's risk assessment level, and generate the user's dynamic authentication code based on the risk assessment level.

[0009] The vehicle access control module is used to send the dynamic authentication code to the target vehicle for validity verification, and generate control instructions for the target vehicle based on the result of the validity verification.

[0010] The abnormal behavior monitoring module is used to acquire real-time in-vehicle monitoring data after the user unlocks the target vehicle using the control command, monitor the user's abnormal behavior based on the real-time in-vehicle monitoring data, and generate an abnormal behavior parameter set.

[0011] The abnormal-risk operation feedback module is used to perform risk operation prediction analysis on the risk operation data in the abnormal behavior parameter set, generate risk operation prompt instructions, and provide abnormal-risk feedback to the user based on the abnormal behavior parameter set and the risk operation prompt instructions.

[0012] In a preferred embodiment, the step of performing identity matching verification between the pre-acquired user biometric authentication information and rental information to generate an identity verification result is specifically used for:

[0013] Extract the user's biometric data from a pre-built user database to obtain the user's standard biometric set;

[0014] Extract the user's rental records from the pre-built user database to obtain the user's standard rental information set;

[0015] Extract feature vectors from the standard biometric set and the standard rental information set to construct a rental matching feature set;

[0016] The biometric data collected in real time and the rental matching feature set are used to calculate the similarity to obtain a matching similarity score;

[0017] The matching similarity score is used to determine the matching degree based on a preset matching degree threshold, and the user's identity verification result is generated.

[0018] In a preferred embodiment, the step of verifying access permissions between the user and the target vehicle based on the authentication result to obtain the user's permission status data is specifically used for:

[0019] Obtain the real-time status information of the target vehicle;

[0020] Extract the lease status information from the standard lease information set corresponding to users whose identity verification results are successful;

[0021] The lease status information is used to verify the lease validity, so as to obtain the lease verification result;

[0022] Based on the real-time status information, vehicle availability verification is performed on the target vehicle to obtain the vehicle verification result;

[0023] Based on the lease verification result and the vehicle verification result, the permission status is generated to obtain the user's permission status data.

[0024] In a preferred embodiment, the step of performing dynamic risk assessment on the user based on the permission status data and generating a risk assessment level for the user is specifically used for:

[0025] Based on the permission status data, a multidimensional anomaly analysis is performed on the user to generate the user's location anomaly, time anomaly, and standardized access frequency.

[0026] A risk score is calculated based on the location anomaly, the time anomaly, and the standardized access frequency.

[0027] The risk score is divided into levels to obtain the user's risk assessment level.

[0028] In a preferred embodiment, the step of performing multidimensional anomaly analysis on the user based on the permission status data to generate the user's location anomaly, time anomaly, and standardized access frequency is specifically used for:

[0029] Based on the permission status data, extract the user's historical location data, historical access time data, and historical access frequency data;

[0030] Perform travel location anomaly analysis on the historical location data to obtain the location anomaly degree;

[0031] Perform time anomaly analysis on the historical access time data to obtain the time anomaly degree;

[0032] The historical access frequency data is subjected to frequency standardization processing to obtain standardized access frequencies.

[0033] In a preferred embodiment, generating the user's dynamic authentication code based on the risk assessment level is specifically used for:

[0034] Select the authentication code generation parameter corresponding to the risk assessment level from the preset risk-parameter mapping table;

[0035] Based on the authentication code generation parameters, a random seed value is generated using a random number generator;

[0036] Based on the random seed value and the current timestamp, an intermediate authentication code is calculated using a dynamic authentication code generation algorithm, wherein the mathematical expression of the dynamic authentication code generation algorithm is as follows:

[0037] C mid =H(S||T)

[0038] In the formula, C mid Here, H is the intermediate authentication code, S is the hash function, S is the 128-bit random seed, T is the current timestamp, and || is the concatenation operator, where the concatenation operator means concatenating the random seed and the current timestamp into a binary string in sequence.

[0039] The intermediate authentication code is truncated to a standardized length to obtain the user's dynamic authentication code.

[0040] In a preferred embodiment, the step of sending the dynamic authentication code to the target vehicle for validity verification and generating control commands for the target vehicle based on the validity verification result is specifically used for:

[0041] The dynamic authentication code is sent to the target vehicle via wireless communication.

[0042] The expected authentication code is obtained by calculating the shared key and the current timestamp using a hash function;

[0043] After receiving the dynamic authentication code, the target vehicle compares the dynamic authentication code with the expected authentication code based on Boolean operations to obtain the validity verification result of the target vehicle.

[0044] The validity verification result is processed into an instruction conversion process to obtain the control instruction for the target vehicle.

[0045] In a preferred embodiment, the step of monitoring the user's abnormal behavior based on the real-time in-vehicle monitoring data and generating an abnormal behavior parameter set is specifically used for:

[0046] Behavioral features are extracted from the real-time in-vehicle monitoring data to obtain the behavioral feature vector of the target vehicle.

[0047] Based on the behavioral feature vector, the target vehicle is subjected to behavioral deviation anomaly detection to obtain the behavioral deviation anomaly degree;

[0048] Anomaly flags are generated based on the degree of abnormality of the described behavior;

[0049] An abnormal behavior parameter set is constructed based on the abnormal flag and the behavior feature vector.

[0050] In a preferred embodiment, the step of performing risk operation prediction analysis on the risk operation data in the abnormal behavior parameter set and generating risk operation prompt instructions is specifically used for:

[0051] Extract the abnormal behavior parameters of the abnormal behavior parameter set that are of the abnormal type of risk operation according to the time sequence to obtain time series risk data;

[0052] Based on the time-series risk data, risk prediction is performed on the user's operations to generate the probability of future risks occurring;

[0053] Risk operation prompts are generated based on the probability of future risks occurring exceeding a risk threshold.

[0054] In a preferred embodiment, the step of providing anomaly-risk feedback to the user based on the abnormal behavior parameter set and the risk operation prompt instruction is specifically used for:

[0055] Parse the risk operation prompt instructions and generate risk feedback parameters;

[0056] Based on the abnormal-risk feedback operation performed on the user, the abnormal-risk feedback operation includes: sending visual warnings through the in-vehicle display screen, outputting audio prompts through the voice system, and restricting vehicle functions when the risk is too high.

[0057] Compared with the prior art, the present invention has the following beneficial effects:

[0058] 1. This invention significantly improves the security and intelligence of vehicle access control through multi-dimensional anomaly analysis based on dynamic risk assessment and an adaptive authentication code generation mechanism. It comprehensively analyzes the user's location anomaly, time anomaly, and standardized access frequency, calculates risk scores in real time, and classifies them into low, medium, and high levels. The complexity and length of the authentication code are automatically adjusted according to different levels, achieving a precise match between authentication strength and risk level. At the same time, the system adopts a dynamic authentication code generation algorithm that combines random seeds and timestamps to ensure the uniqueness and timeliness of each access, effectively resisting security threats such as replay attacks and man-in-the-middle attacks. It not only ensures the protection strength in high-risk scenarios but also takes into account the user experience convenience in low-risk scenarios.

[0059] 2. This invention introduces a proactive feedback strategy based on real-time behavior monitoring and risk prediction, achieving continuous security authentication and proactive defense capabilities. After the user unlocks the vehicle, the system continuously collects real-time monitoring data inside the vehicle, extracts behavioral characteristics, and detects abnormal behaviors. Through time series analysis and risk prediction models, it identifies potential risky operations in advance. Once a high-risk behavior is predicted, the system immediately triggers feedback measures such as visual warnings and voice prompts, and restricts vehicle functions when necessary, preventing security incidents from occurring at the source. This mechanism not only maintains continuous monitoring and risk assessment of user behavior throughout the entire rental process, but also provides operators with real-time risk management tools, significantly reducing security risks such as vehicle abuse and theft. Attached Figure Description

[0060] Figure 1 This is a system architecture diagram of a keyless access control and security authentication system for rentable vehicles provided in an embodiment of the present invention.

[0061] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0062] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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 belong to some, but not all, embodiments of the present invention. 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.

[0063] The terminology used in the embodiments of this invention is for the purpose of describing particular embodiments only and is not intended to limit the invention. The singular forms “said” and “the” as used in the embodiments of this invention and the appended claims are also intended to include the plural forms, and “multiple” generally includes at least two unless the context clearly indicates otherwise.

[0064] Depending on the context, the word "if" or "if" as used here can be interpreted as "when," "when," "in response to determination," or "in response to detection." Similarly, depending on the context, the phrase "if determination" or "if detection (of the stated condition or event)" can be interpreted as "when determination," "in response to determination," "when detection (of the stated condition or event)," or "in response to detection (of the stated condition or event)."

[0065] Furthermore, the timing of the steps in the following method embodiments is merely an example and not a strict limitation.

[0066] In practice, the server-side equipment deployed in a keyless access control and security authentication system for rentable vehicles may consist of one or more devices. This system can be implemented as a business instance, a virtual machine, or hardware devices. For example, it can be implemented as a business instance deployed on one or more devices in a cloud node. Simply put, it can be understood as software deployed on a cloud node to provide keyless access control and security authentication for rentable vehicles to various users. Alternatively, it can be implemented as a virtual machine deployed on one or more devices in a cloud node, with application software installed to manage various users. Or, it can be implemented as a server composed of numerous identical or different types of hardware devices, with one or more devices configured to provide keyless access control and security authentication for rentable vehicles to various users.

[0067] In terms of implementation, the keyless access control and security authentication system for rentable vehicles and the user terminal are mutually compatible. That is, if the keyless access control and security authentication system for rentable vehicles is implemented as an application installed on a cloud service platform, then the user terminal is implemented as a client that establishes a communication connection with the application; or if the keyless access control and security authentication system for rentable vehicles is implemented as a website, then the user terminal is implemented as a webpage; or if the keyless access control and security authentication system for rentable vehicles is implemented as a cloud service platform, then the user terminal is implemented as a mini-program in an instant messaging application.

[0068] like Figure 1 The diagram shown is a system architecture diagram of a keyless access control and security authentication system for rentable vehicles provided in an embodiment of the present invention.

[0069] The keyless access control and security authentication system 100 for rentable vehicles described in this invention can be installed on a cloud server. In terms of implementation, it can be used as one or more service devices, or as an application installed in the cloud (e.g., a mobile service operator's server, server cluster, etc.), or it can be developed as a website. Depending on the functions implemented, the keyless access control and security authentication system 100 for rentable vehicles may include a user authentication module 101, an access permission verification module 102, a dynamic risk assessment module 103, a vehicle access control module 104, an abnormal behavior monitoring module 105, and an abnormal-risk operation feedback module 106. The modules described in this invention can also be called units, referring to a series of computer program segments that can be executed by the processor of an electronic device and can perform a fixed function, stored in the memory of the electronic device.

[0070] In this embodiment of the invention, in a keyless access control and security authentication system for rentable vehicles, each of the above-mentioned modules can be implemented independently and can call other modules. Here, "calling" can be understood as one module connecting to multiple modules of another type and providing corresponding services to those connected modules. The keyless access control and security authentication system for rentable vehicles provided by this embodiment of the invention allows for adjustment of the applicable scope of the system architecture without modifying the program code. This is achieved by adding modules and directly calling them, enabling cluster-based horizontal expansion and flexibly expanding the system. In practical applications, the above modules can be set in the same device or different devices, or in virtual devices, such as service instances in a cloud server.

[0071] The following describes, with reference to specific embodiments, each component and its specific workflow of a keyless access control and security authentication system for rentable vehicles:

[0072] The user identity authentication module is used to match and verify the pre-acquired user biometric authentication information with the rental information, and generate an identity verification result;

[0073] In this embodiment of the invention, the step of performing identity matching verification between the pre-acquired user biometric authentication information and rental information to generate an identity verification result is specifically used for:

[0074] Extract the user's biometric data from a pre-built user database to obtain the user's standard biometric set;

[0075] Extract the user's rental records from the pre-built user database to obtain the user's standard rental information set;

[0076] Extract feature vectors from the standard biometric set and the standard rental information set to construct a rental matching feature set;

[0077] The biometric data collected in real time and the rental matching feature set are used to calculate the similarity to obtain a matching similarity score;

[0078] The matching similarity score is used to determine the matching degree based on a preset matching degree threshold, and the user's identity verification result is generated.

[0079] It should be noted that the pre-built user database is a structured dataset that stores user biometric data and rental records, which is used to extract standard biometric sets and standard rental information sets during the identity matching and verification process.

[0080] It should be noted that the feature vectors of the standard biometric set and the standard rental information set are extracted through a feature extraction algorithm. This algorithm converts biometric features and rental information into numerical vector form to construct a rental matching feature set.

[0081] Furthermore, the standard biometric set includes the user's biometric data, fingerprints, and facial recognition features.

[0082] The standard rental information set includes information such as lease renewal status and rental history.

[0083] It should be noted that the similarity calculation is performed by calculating the cosine value of the angle between the biometric data and the rental matching features using cosine similarity. That is, the vector dot product and modulus operation are completed within a single clock cycle. The vector click calculation value is divided by the modulus operation value to generate a similarity score between 0 and 1. A matching similarity score of 1 indicates a perfect match, and a matching similarity score of 0 indicates no relevance.

[0084] Furthermore, the matching similarity score is a quantitative metric for similarity, used to measure the consistency of biometric characteristics.

[0085] It should be noted that the preset matching threshold is an empirical value, usually set to 0.9, and is determined through calibration experiments to balance the false recognition rate and the false rejection rate. It can be manually modified during the discrimination process.

[0086] It should be noted that the matching degree judgment is essentially a Boolean operation based on a preset matching degree threshold. The generated identity verification result is 1, which means that the verification is successful, and the identity verification result is 0, which means that the verification is unsuccessful.

[0087] The access permission verification module is used to verify the access permissions of the user and the target vehicle based on the identity verification result, and obtain the user's access permission status data.

[0088] In this embodiment of the invention, the step of verifying access permissions between the user and the target vehicle based on the authentication result to obtain the user's permission status data is specifically used for:

[0089] Obtain the real-time status information of the target vehicle;

[0090] Extract the lease status information from the standard lease information set corresponding to users whose identity verification results are successful;

[0091] The lease status information is used to verify the lease validity, so as to obtain the lease verification result;

[0092] Based on the real-time status information, vehicle availability verification is performed on the target vehicle to obtain the vehicle verification result;

[0093] Based on the lease verification result and the vehicle verification result, the permission status is generated to obtain the user's permission status data.

[0094] It should be noted that validity verification refers to the process of performing Boolean operations on the lease status information. By comparing the lease status with preset validity conditions, it is used to determine whether the lease is within its validity period, thereby ensuring the legality of user access permissions.

[0095] It should be noted that the lease verification result is a Boolean value, indicating whether the lease status is valid. A value of 1 indicates that the data is true, meaning that the lease is within its validity period, while a value of 0 indicates that the data is false, meaning that the lease is invalid.

[0096] It should be noted that availability verification is achieved by performing Boolean operations on the real-time status information of the target vehicle. Specifically, it checks whether the vehicle is idle and whether there is a malfunction to generate a vehicle verification result.

[0097] It should be noted that the vehicle verification result is a Boolean value, indicating whether the vehicle status is available. A value of 1 indicates that the data is true, meaning the vehicle is idle and available, while a value of 0 indicates that the data is false, meaning the vehicle is unavailable.

[0098] It should be noted that permission status is generated using the mathematical expression P = L valid ∧V available The process of performing Boolean operations on the lease verification result and the vehicle verification result, where P represents the permission status data, and L represents the vehicle verification result. valid For the lease verification results, V available For vehicle verification results, ∧ represents a logical operation.

[0099] Furthermore, the permission status data boolean value indicates whether the user has permission to access the target vehicle. A value of 1 indicates that the data is true, meaning the user has permission, while a value of 0 indicates that the data is false, meaning the user does not have permission.

[0100] The dynamic risk assessment module is used to perform dynamic risk assessment on the user based on the permission status data, generate the user's risk assessment level, and generate the user's dynamic authentication code based on the risk assessment level.

[0101] In this embodiment of the invention, the step of performing dynamic risk assessment on the user based on the permission status data and generating the user's risk assessment level is specifically used for:

[0102] Based on the permission status data, a multidimensional anomaly analysis is performed on the user to generate the user's location anomaly, time anomaly, and standardized access frequency.

[0103] A risk score is calculated based on the location anomaly, the time anomaly, and the standardized access frequency.

[0104] The risk score is divided into levels to obtain the user's risk assessment level.

[0105] It should be noted that the risk score is calculated using a weighted fusion risk assessment algorithm. Specifically, it is obtained by multiplying the location anomaly, the time anomaly, and the standardized access frequency by their respective weight coefficients and then summing them together. The weight coefficients for the location anomaly, the time anomaly, and the standardized access frequency are all one-third.

[0106] Furthermore, a risk score is calculated to quantify the user's dynamic risk level, providing a basis for risk assessment level classification, thereby generating a dynamic authentication code.

[0107] It should be noted that the risk classification is a process that compares the risk score with a preset risk threshold and divides the score into discrete levels based on the comparison result. The low-to-medium risk threshold is 0.3, and the medium-to-high risk threshold is 0.7.

[0108] Furthermore, when the risk score is less than or equal to the low-to-medium risk threshold, the corresponding risk assessment level is "low risk".

[0109] When the risk score is less than or equal to the medium-high risk threshold, the corresponding risk assessment level is "medium risk";

[0110] When the risk score is greater than the medium-high risk threshold, the corresponding risk assessment level is "high risk".

[0111] In this embodiment of the invention, the step of performing multi-dimensional anomaly analysis on the user based on the permission status data to generate the user's location anomaly, time anomaly, and standardized access frequency is specifically used for:

[0112] Based on the permission status data, extract the user's historical location data, historical access time data, and historical access frequency data;

[0113] Perform travel location anomaly analysis on the historical location data to obtain the location anomaly degree;

[0114] Perform time anomaly analysis on the historical access time data to obtain the time anomaly degree;

[0115] The historical access frequency data is subjected to frequency standardization processing to obtain standardized access frequencies.

[0116] It should be noted that the extracted historical location data, historical access time data, and historical access frequency data are data corresponding to users with permissions whose permission status data value is 1.

[0117] It should be noted that travel location anomaly analysis refers to the process of calculating the deviation value based on the user's current location coordinates and frequently used location coordinates using the Euclidean distance formula, and then normalizing the calculated deviation value.

[0118] It should be noted that time anomaly analysis refers to the process of processing users' historical access time data to obtain a time anomaly value. Specifically, it involves calculating the deviation between the user's current access time and the historical average access time, and using the standard deviation normalization method to quantify the degree of anomaly. The formula for calculating the time anomaly value is: Time Anomaly = |Current Time - Historical Average Time| / Historical Time Standard Deviation. The larger the time anomaly value, the higher the probability of time anomaly.

[0119] It should be noted that frequency standardization refers to the process of standardizing historical access frequency data to obtain standardized access frequency values. Specifically, the minimum-maximum scaling algorithm is used to map the original frequency values ​​to the range of [0,1] to ensure that the data scale is consistent for risk calculation. The calculation formula for frequency standardization is: Standardized access frequency = (current frequency - minimum frequency) / (maximum frequency - minimum frequency).

[0120] In this embodiment of the invention, generating the user's dynamic authentication code based on the risk assessment level is specifically used for:

[0121] Select the authentication code generation parameter corresponding to the risk assessment level from the preset risk-parameter mapping table;

[0122] Based on the authentication code generation parameters, a random seed value is generated using a random number generator;

[0123] Based on the random seed value and the current timestamp, an intermediate authentication code is calculated using a dynamic authentication code generation algorithm, wherein the mathematical expression of the dynamic authentication code generation algorithm is as follows:

[0124] C mid =H(S||T)

[0125] In the formula, C mid H is the intermediate authentication code, S is the hash function, S is the 128-bit random seed, T is the current timestamp, and || is the concatenation operator, where the concatenation operator means concatenating the random seed and the current timestamp into a binary string in sequence.

[0126] The intermediate authentication code is truncated to a standardized length to obtain the user's dynamic authentication code.

[0127] It should be noted that the risk-parameter mapping table is a pre-defined lookup table that stores the correspondence between risk assessment levels and authentication code generation parameters.

[0128] Specifically, when the risk assessment level is "low risk", a dynamic authentication code with a length of 6 bits is output through a standardized length truncation operation, corresponding to a hash function strength of "standard".

[0129] When the risk assessment level is "medium risk", a dynamic authentication code with a length of 7 bits is output through a standardized length truncation operation, which corresponds to a hash function strength of "medium".

[0130] When the risk assessment level is "high risk", a dynamic authentication code with a length of 8 bits is truncated through a standardized length truncation operation, which corresponds to a hash function strength of "high".

[0131] It should be noted that generating a random seed value through a random number generator refers to the step of using a random number generator to generate a 128-bit random seed based on the authentication code generation parameters during the dynamic authentication code generation process.

[0132] Furthermore, different hash function strengths correspond to the use of different hash functions in the dynamic authentication code generation algorithm.

[0133] The vehicle access control module is used to send the dynamic authentication code to the target vehicle for validity verification, and generate control instructions for the target vehicle based on the result of the validity verification.

[0134] In this embodiment of the invention, the step of sending the dynamic authentication code to the target vehicle for validity verification and generating control commands for the target vehicle based on the validity verification result is specifically used for:

[0135] The dynamic authentication code is sent to the target vehicle via wireless communication.

[0136] The expected authentication code is obtained by calculating the shared key and the current timestamp using a hash function;

[0137] After receiving the dynamic authentication code, the target vehicle compares the dynamic authentication code with the expected authentication code based on Boolean operations to obtain the validity verification result of the target vehicle.

[0138] The validity verification result is processed into an instruction conversion process to obtain the control instruction for the target vehicle.

[0139] It should be noted that the expected authentication code uses the same algorithm as the dynamic authentication code to ensure consistency, and the shared key is a secret value pre-stored in the vehicle control unit and the server, used for the encryption basis.

[0140] It should be noted that the validity verification result is a Boolean value, indicating whether the dynamic authentication code is valid. A value of 1 indicates that the validity verification result is true, meaning the verification passed, while a value of 0 indicates that the validity verification result is false, meaning the verification failed.

[0141] It should be noted that the instruction conversion process refers to the process of converting the validity verification result into the target vehicle control instruction. Specifically, the conversion is carried out through a predefined instruction mapping relationship. When the validity verification result is 1, an "unlock instruction" is generated to allow the vehicle to access.

[0142] When the result is 0, a "reject instruction" is generated and an alarm is triggered. This transition is performed based on hardware logic circuitry to ensure a low-latency response.

[0143] The abnormal behavior monitoring module is used to acquire real-time in-vehicle monitoring data after the user unlocks the target vehicle using the control command, monitor the user's abnormal behavior based on the real-time in-vehicle monitoring data, and generate an abnormal behavior parameter set.

[0144] In this embodiment of the invention, the step of monitoring the user's abnormal behavior based on the real-time in-vehicle monitoring data and generating an abnormal behavior parameter set is specifically used for:

[0145] Behavioral features are extracted from the real-time in-vehicle monitoring data to obtain the behavioral feature vector of the target vehicle.

[0146] Based on the behavioral feature vector, the target vehicle is subjected to behavioral deviation anomaly detection to obtain the behavioral deviation anomaly degree;

[0147] Anomaly flags are generated based on the degree of abnormality of the behavior deviation;

[0148] An abnormal behavior parameter set is constructed based on the abnormal flag and the behavior feature vector.

[0149] It should be noted that the real-time in-vehicle monitoring data includes video stream data inside and outside the vehicle, vehicle acceleration data, and in-vehicle audio data.

[0150] It should be noted that feature extraction refers to the process of performing inter-frame difference processing on video stream data to obtain motion trajectory, performing Fourier transform on acceleration data to obtain frequency features, normalizing sound data by decibels to obtain intensity features, and finally concatenating the motion trajectory, frequency features, and intensity features into a feature vector.

[0151] It should be noted that the behavioral deviation anomaly detection is calculated using an anomaly detection algorithm, the mathematical expression of which is as follows:

[0152]

[0153] In the formula, A represents the degree of behavioral deviation, F represents the behavioral feature vector, μ represents the mean vector of normal behavioral features, T represents the transpose operator, and Σ represents the mean vector of normal behavioral features. -1 The covariance matrix is ​​the inverse operation, where the covariance matrix is ​​an n×n matrix representing the degree of dispersion of normal behavioral characteristics.

[0154] It should be noted that the abnormality flag is generated by comparing the degree of abnormality of the behavior with a set abnormality deviation threshold to obtain a Boolean value indicating whether it exceeds the standard. Here, an abnormality flag of 1 indicates that abnormal behavior has been detected, and an abnormality flag of 0 indicates that the behavior is normal.

[0155] Furthermore, the abnormality of behavior deviation is a continuous numerical value that represents the degree to which user behavior deviates from the normal pattern. The value ranges from 0 to positive infinity, and the larger the value, the higher the probability of abnormality.

[0156] It should be noted that the abnormal behavior parameter set includes anomaly flags, behavior anomaly degree values, feature vector timestamps, and anomaly types, which are used as inputs for the subsequent risk prediction module.

[0157] Furthermore, the classification of anomaly types is the process of classifying the data in the set of abnormal behavior parameters according to type, wherein the anomaly types include: risky operation type, safety violation type, and behavioral deviation type.

[0158] The abnormal-risk operation feedback module is used to perform risk operation prediction analysis on the risk operation data in the abnormal behavior parameter set, generate risk operation prompt instructions, and provide abnormal-risk feedback to the user based on the abnormal behavior parameter set and the risk operation prompt instructions.

[0159] In this embodiment of the invention, the step of performing risk operation prediction analysis on the risk operation data in the abnormal behavior parameter set and generating risk operation prompt instructions is specifically used for:

[0160] Extract the abnormal behavior parameters of the abnormal behavior parameter set that are of the abnormal type of risk operation according to the time sequence to obtain time series risk data;

[0161] Based on the time-series risk data, risk prediction is performed on the user's operations to generate the probability of future risks occurring;

[0162] Risk operation prompts are generated based on the probability of future risks occurring exceeding a risk threshold.

[0163] It should be noted that the probability of future risk occurrence is a continuous value, representing the probability of a risky operation occurring within a certain period of time. The value ranges from 0 to 1, with a higher value indicating a higher risk.

[0164] It should be noted that the generation of the probability of future risk occurrence depends on the risk prediction operation. The risk prediction operation refers to the process of calculating the sum of the product of the current risk value multiplied by the current risk weight, the historical risk value sequence multiplied by the time decay weight, and then multiplied by the historical risk weight. Here, the current risk weight and the historical risk weight are each half, and the time decay weight decreases as the timestamp recording time increases, indicating that recent data has a greater impact.

[0165] Furthermore, the essence of risk prediction is to combine the current state with a weighted moving average model, and use exponential decay to give higher weight to recent data to achieve adaptive prediction, which can significantly reduce latency and meet real-time requirements.

[0166] It should be noted that the probability of future risks occurring exceeding the risk threshold will generate risk prediction flags, including risk flags and no-risk flags.

[0167] Furthermore, the generation of risk operation prompt instructions is a process of generating corresponding prompt information based on risk prediction indicators. Among them, risk indicators correspond to high-risk operation warning instructions, no-risk indicators correspond to normal operation, and no special instructions are given.

[0168] In this embodiment of the invention, the step of providing anomaly-risk feedback to the user based on the abnormal behavior parameter set and the risk operation prompt instruction is specifically used for:

[0169] Parse the risk operation prompt instructions and generate risk feedback parameters;

[0170] Based on the abnormal-risk feedback operation performed on the user, the abnormal-risk feedback operation includes: sending visual warnings through the in-vehicle display screen, outputting audio prompts through the voice system, and restricting vehicle functions when the risk is too high.

[0171] It should be noted that parsing the risk operation prompt instruction refers to the process of parsing the generated risk operation prompt instruction to obtain risk feedback parameters. Specifically, this is achieved through string parsing and logical judgment, extracting the risk level, timestamp, and operation type from the instruction and mapping them to risk feedback parameters such as risk threshold and response priority.

[0172] Compared with the prior art, the present invention has the following beneficial effects:

[0173] 1. This invention significantly improves the security and intelligence of vehicle access control through multi-dimensional anomaly analysis based on dynamic risk assessment and an adaptive authentication code generation mechanism. It comprehensively analyzes the user's location anomaly, time anomaly, and standardized access frequency, calculates risk scores in real time, and classifies them into low, medium, and high levels. The complexity and length of the authentication code are automatically adjusted according to different levels, achieving a precise match between authentication strength and risk level. At the same time, the system adopts a dynamic authentication code generation algorithm that combines random seeds and timestamps to ensure the uniqueness and timeliness of each access, effectively resisting security threats such as replay attacks and man-in-the-middle attacks. It not only ensures the protection strength in high-risk scenarios but also takes into account the user experience convenience in low-risk scenarios.

[0174] 2. This invention introduces a proactive feedback strategy based on real-time behavior monitoring and risk prediction, achieving continuous security authentication and proactive defense capabilities. After the user unlocks the vehicle, the system continuously collects real-time monitoring data inside the vehicle, extracts behavioral characteristics, and detects abnormal behaviors. Through time series analysis and risk prediction models, it identifies potential risky operations in advance. Once a high-risk behavior is predicted, the system immediately triggers feedback measures such as visual warnings and voice prompts, and restricts vehicle functions when necessary, preventing security incidents from occurring at the source. This mechanism not only maintains continuous monitoring and risk assessment of user behavior throughout the entire rental process, but also provides operators with real-time risk management tools, significantly reducing security risks such as vehicle abuse and theft.

[0175] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0176] The embodiments of this application can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence is the theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0177] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A keyless access control and security authentication system for rentable vehicles, characterized in that, The system includes a user authentication module, an access permission verification module, a dynamic risk assessment module, a vehicle access control module, an abnormal behavior monitoring module, and an abnormal-risk operation feedback module, wherein: The user identity authentication module is used to match and verify the pre-acquired user biometric authentication information with the rental information, and generate an identity verification result; The access permission verification module is used to verify the access permissions of the user and the target vehicle based on the identity verification result, and obtain the user's access permission status data. The dynamic risk assessment module is used to perform dynamic risk assessment on the user based on the permission status data, generate the user's risk assessment level, and generate the user's dynamic authentication code based on the risk assessment level. The vehicle access control module is used to send the dynamic authentication code to the target vehicle for validity verification, and generate control instructions for the target vehicle based on the result of the validity verification. The abnormal behavior monitoring module is used to acquire real-time in-vehicle monitoring data after the user unlocks the target vehicle using the control command, monitor the user's abnormal behavior based on the real-time in-vehicle monitoring data, and generate an abnormal behavior parameter set. The abnormal-risk operation feedback module is used to perform risk operation prediction analysis on the risk operation data in the abnormal behavior parameter set, generate risk operation prompt instructions, and provide abnormal-risk feedback to the user based on the abnormal behavior parameter set and the risk operation prompt instructions.

2. The keyless access control and security authentication system for rentable vehicles as described in claim 1, characterized in that, The process of matching and verifying the pre-acquired user biometric authentication information with rental information to generate an identity verification result is specifically used for: Extract the user's biometric data from a pre-built user database to obtain the user's standard biometric set; Extract the user's rental records from the pre-built user database to obtain the user's standard rental information set; Extract feature vectors from the standard biometric set and the standard rental information set to construct a rental matching feature set; The biometric data collected in real time and the rental matching feature set are used to calculate the similarity to obtain a matching similarity score; The matching similarity score is used to determine the matching degree based on a preset matching degree threshold, and the user's identity verification result is generated.

3. The keyless access control and security authentication system for rentable vehicles as described in claim 1, characterized in that, The method for verifying access permissions between the user and the target vehicle based on the authentication result, and obtaining the user's permission status data, is specifically used for: Obtain the real-time status information of the target vehicle; Extract the lease status information from the standard lease information set corresponding to users whose identity verification results are successful; The lease status information is used to verify the lease validity, so as to obtain the lease verification result; Based on the real-time status information, vehicle availability verification is performed on the target vehicle to obtain the vehicle verification result; Based on the lease verification result and the vehicle verification result, the permission status is generated to obtain the user's permission status data.

4. The keyless access control and security authentication system for rentable vehicles as described in claim 1, characterized in that, The method for performing dynamic risk assessment on the user based on the permission status data and generating the user's risk assessment level is specifically used for: Based on the permission status data, a multidimensional anomaly analysis is performed on the user to generate the user's location anomaly, time anomaly, and standardized access frequency. A risk score is calculated based on the location anomaly, the time anomaly, and the standardized access frequency. The risk score is divided into levels to obtain the user's risk assessment level.

5. The keyless access control and security authentication system for rentable vehicles as described in claim 4, characterized in that, Based on the permission status data, a multidimensional anomaly analysis is performed on the user to generate the user's location anomaly, time anomaly, and standardized access frequency, specifically for: Based on the permission status data, extract the user's historical location data, historical access time data, and historical access frequency data; Perform travel location anomaly analysis on the historical location data to obtain the location anomaly degree; Perform time anomaly analysis on the historical access time data to obtain the time anomaly degree; The historical access frequency data is subjected to frequency standardization processing to obtain standardized access frequencies.

6. The keyless access control and security authentication system for rentable vehicles as described in claim 1, characterized in that, The generation of the user's dynamic authentication code based on the risk assessment level is specifically used for: Select the authentication code generation parameter corresponding to the risk assessment level from the preset risk-parameter mapping table; Based on the authentication code generation parameters, a random seed value is generated using a random number generator; Based on the random seed value and the current timestamp, an intermediate authentication code is calculated using a dynamic authentication code generation algorithm, wherein the mathematical expression of the dynamic authentication code generation algorithm is as follows: C mid =H(S||T) In the formula, C mid H is the intermediate authentication code, S is the hash function, S is the 128-bit random seed, T is the current timestamp, and || is the concatenation operator, where the concatenation operator means concatenating the random seed and the current timestamp into a binary string in sequence. The intermediate authentication code is truncated to a standardized length to obtain the user's dynamic authentication code.

7. The keyless access control and security authentication system for rentable vehicles as described in claim 1, characterized in that, The step of sending the dynamic authentication code to the target vehicle for validity verification and generating control commands for the target vehicle based on the validity verification result is specifically used for: The dynamic authentication code is sent to the target vehicle via wireless communication. The expected authentication code is obtained by calculating the shared key and the current timestamp using a hash function; After receiving the dynamic authentication code, the target vehicle compares the dynamic authentication code with the expected authentication code based on Boolean operations to obtain the validity verification result of the target vehicle. The validity verification result is processed into an instruction conversion process to obtain the control instruction for the target vehicle.

8. The keyless access control and security authentication system for rentable vehicles as described in claim 1, characterized in that, The method of monitoring abnormal user behavior based on the real-time in-vehicle monitoring data and generating an abnormal behavior parameter set is specifically used for: Behavioral features are extracted from the real-time in-vehicle monitoring data to obtain the behavioral feature vector of the target vehicle. Based on the behavioral feature vector, the target vehicle is subjected to behavioral deviation anomaly detection to obtain the behavioral deviation anomaly degree; Anomaly flags are generated based on the degree of abnormality of the behavior deviation; An abnormal behavior parameter set is constructed based on the abnormal flag and the behavior feature vector.

9. The keyless access control and security authentication system for rentable vehicles as described in claim 1, characterized in that, The method for performing risk operation prediction and analysis on the risk operation data in the abnormal behavior parameter set and generating risk operation prompt instructions is specifically used for: Extract the abnormal behavior parameters of the abnormal behavior parameter set that are of the abnormal type of risk operation according to the time sequence to obtain time series risk data; Based on the time-series risk data, risk prediction is performed on the user's operations to generate the probability of future risks occurring; Risk operation prompts are generated based on the probability of future risks occurring exceeding a risk threshold.

10. The keyless access control and security authentication system for rentable vehicles as described in claim 1, characterized in that, The method of providing anomaly-risk feedback to the user based on the abnormal behavior parameter set and the risk operation prompt instruction is specifically used for: Parse the risk operation prompt instructions and generate risk feedback parameters; Based on the abnormal-risk feedback operation performed on the user, the abnormal-risk feedback operation includes: sending visual warnings through the in-vehicle display screen, outputting audio prompts through the voice system, and restricting vehicle functions when the risk is too high.

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