Vehicle UWB positioning digital safety system and method based on single module

Abstract

The present invention discloses a whole-vehicle UWB positioning digital security system and method based on a single module, comprising the following steps: installing an integrated single module on the vehicle, using the positioning module to measure the position of the vehicle in real time; generating the positioning data of the vehicle, and encrypting it during the data transmission process; the system authenticates the identity through a variety of bioelectric signals, and combines intelligent algorithms for real-time monitoring and abnormality detection; adopts improved abnormal pattern mapping technology, dynamically generates and adjusts abnormal patterns through comprehensive data analysis and real-time feedback mechanism; the system continuously monitors the vehicle status and environmental information, and adjusts the algorithm parameters and detection strategies in real time to cope with different driving scenarios and environmental changes, ensuring the safety and performance optimization of the vehicle. This method has high-precision positioning, data security protection and real-time response capabilities, and is suitable for multiple application scenarios such as intelligent transportation and vehicle anti-theft.

Description

Technical Field

[0001] The present invention relates to the field of intelligent transportation and automobile safety technology, and in particular to a whole-vehicle UWB positioning digital safety system and method based on a single module. Background Art

[0002] With the rapid development of intelligent transportation systems and autonomous driving technologies, vehicle positioning and security issues have become a hot topic of research. At present, the global positioning system is the most commonly used vehicle positioning technology, but its accuracy is low and it is easily affected by environmental interference, such as high-rise building obstruction and bad weather. In addition, GPS signals are easily interfered with and deceived, posing a major security risk. In terms of digital security, traditional encryption technology and identity authentication methods can no longer meet the increasingly complex security needs. Especially in the application scenarios of vehicle networking and autonomous driving, vehicles face a large amount of data and a variety of types, and the security threats are complex and diverse, and the requirements for data transmission and identity authentication are also getting higher and higher.

[0003] Existing technologies have multiple defects and limitations in vehicle positioning and digital security, and these problems are particularly evident in the development of intelligent transportation systems and autonomous driving technologies. The Global Positioning System is currently the most widely used vehicle positioning technology, but its positioning accuracy is usually between a few meters and tens of meters. For application scenarios that require high-precision positioning, such as autonomous driving and advanced driver assistance systems, this accuracy is far from enough. Especially in the environment of high-rise buildings in cities, GPS signals are susceptible to occlusion and reflection, which further increases the positioning error. Cellular network positioning relies on the coverage of base stations, and its positioning accuracy and reliability are low, with an error range of usually between a few hundred meters and a few kilometers. This accuracy is unacceptable for autonomous driving applications that require high real-time and precision.

[0004] In addition, GPS signals are easily interfered with or completely lost in tunnels, high-rise areas and bad weather conditions, resulting in vehicle positioning failure. In this case, the vehicle cannot obtain accurate location data, which seriously affects the normal operation of the autonomous driving system. In urban environments, buildings and other obstacles cause GPS signals to reflect, resulting in a multipath effect, which increases positioning errors. This effect limits the application of traditional positioning technology in urban environments.

[0005] Existing vehicle data transmission mainly relies on symmetric encryption and asymmetric encryption technologies, which are effective in dealing with general network attacks, but their protection capabilities are insufficient when facing complex and changeable attack methods and advanced persistent threats. Especially in the Internet of Vehicles environment, data needs to be frequently exchanged between vehicles and external devices, and traditional encryption technologies are difficult to meet the requirements of high real-time and high security. Traditional identity authentication methods mainly rely on passwords and biometrics. These methods are effective in a single scenario, but in a multi-scenario, multi-user Internet of Vehicles environment, their reliability and security have major problems. For example, biometrics can be forged or copied, and passwords can be easily cracked or leaked, resulting in invalid identity authentication.

[0006] Existing anomaly detection technologies are mostly based on static rules and predefined models, which are difficult to cope with dynamically changing driving environments and complex abnormal situations. These technologies have slow response speeds and low detection accuracy when facing emergencies, and cannot promptly detect and handle potential security threats. Modern vehicles are equipped with a large number of sensors that generate massive amounts of data, but existing technologies have limited capabilities in data fusion and comprehensive analysis, making it difficult to fully utilize multi-source data to improve the accuracy and reliability of anomaly detection.

[0007] The algorithms and parameters of existing systems are mostly fixed settings, lacking dynamic adjustment capabilities, and cannot be adaptively adjusted according to real-time environmental changes, resulting in poor performance of the system in complex and changing driving environments. Especially when vehicles are traveling on different roads and environments, such as highways, urban roads, rural areas, etc., the system needs to be able to respond quickly and adjust algorithm parameters to ensure the safety and performance of the vehicle. In addition, traditional systems have poor scalability and are difficult to adapt to changing technical requirements and application scenarios. For example, with the development of intelligent transportation systems, vehicles need to interact with more external devices and infrastructure, and existing systems have great difficulties in expanding new functions and being compatible with new devices.

[0008] Therefore, how to provide a whole vehicle UWB positioning digital safety system and method based on a single module is a problem that technical personnel in this field urgently need to solve. Summary of the invention

[0009] One purpose of the present invention is to propose a whole vehicle UWB positioning digital security system and method based on a single module. The present invention makes full use of UWB positioning technology, quantum teleportation, distributed steganography technology and bioelectric signal intelligent fusion authentication technology, and describes in detail the specific implementation methods in vehicle positioning, data encryption protection, identity authentication and anomaly detection. By integrating these technologies, the present invention has the advantages of high-precision positioning, secure data transmission, reliable identity authentication and strong real-time response capability, which can effectively improve the safety and performance of vehicles in different driving scenarios and environments.

[0010] The vehicle UWB positioning digital safety system and method based on a single module according to an embodiment of the present invention includes the following steps:

[0011] S1. Install an integrated single module on the vehicle;

[0012] S2. Use the UWB positioning module to measure the position of the vehicle in real time. The UWB positioning module optimizes the transmission of UWB signals by using both adaptive waveform adjustment technology and multi-band collaborative technology.

[0013] S3. Generate vehicle positioning data through the UWB positioning module, and transmit the vehicle positioning data to the digital security module. During the vehicle positioning data transmission process, the encryption unit of the digital security module uses quantum teleportation and distributed steganography technology to encrypt the positioning data;

[0014] S4, implement real-time identity authentication through dynamic identity authentication unit, adopt bioelectric signal intelligent fusion authentication technology, which combines multiple bioelectric signals for comprehensive analysis, uses real-time adaptive signal processing algorithm and personalized bioelectric pattern learning;

[0015] S5. Realize real-time monitoring and anomaly detection. Through improved dynamic anomaly pattern mapping technology, combined with adaptive pattern generation, high-dimensional data integration and real-time feedback mechanism, the system can dynamically generate and adjust anomaly patterns, comprehensively analyze multiple data sources, provide real-time feedback and optimize pattern mapping;

[0016] S6. The system implements real-time data analysis and adaptive adjustment. By continuously monitoring and analyzing the vehicle's status data and its environmental information, the system can adjust the algorithm parameters and anomaly detection strategies in real time to cope with different driving scenarios and environmental changes.

[0017] Optionally, the step S2 specifically includes:

[0018] S21, transmit multi-band narrow pulse signals through the UWB positioning module, and the frequency band is set to , the signal is transmitted in the preset time window Completed within the frequency band bandwidth Optimize the spatial resolution of the signal;

[0019] S22, implement adaptive waveform adjustment technology to adjust the waveform of the transmitted signal Perform dynamic optimization and adjust waveform parameters to adapt to different environmental conditions based on real-time environmental feedback data Optimize amplitude, standard deviation, and center frequency:

[0020] ;

[0021] in, is the dynamic amplitude based on environmental feedback data, is the standard deviation based on environmental feedback data, is the center frequency based on environmental feedback data;

[0022] S23. Use multi-band coordination technology to analyze received signals, optimize positioning accuracy through signal coordination between frequency bands, and calculate the arrival time of signals in each frequency band. , multipath errors are processed by cross-validation of high-frequency band signals, and the frequency band signal time data is fused using the weighted average method;

[0023] S24, calculate the signal propagation distance, combine the multipath effect correction model, and use the correction factor The distance data is adjusted, and the correction factor is obtained through model training to optimize the accuracy of the signal arrival time:

[0024] ;

[0025] in, is the speed of light constant, is the multipath effect correction factor obtained through model training;

[0026] S25. Apply the positioning algorithm to process the corrected distance data to determine the three-dimensional position coordinates of the vehicle , use weighted least squares method to solve the position, and dynamically adjust the weights to optimize the positioning accuracy:

[0027] ;

[0028] in, is the position of the i-th reference point, is the weight of each reference point;

[0029] S26. Output the calculated vehicle position coordinates , and transmitted to the digital security module through the data interface I. The interface supports high-bandwidth data transmission, performs real-time and integrity processing of data, and uses a security protocol to process the security of data during transmission.

[0030] Optionally, the step S3 specifically includes:

[0031] S31, generate vehicle positioning data through the UWB positioning module, and transmit the data to the digital security module. After transmission, it enters the encryption unit for processing;

[0032] S32, the encryption unit of the digital security module receives the positioning data Encryption processing is performed, using an encryption scheme based on homomorphic encryption and distributed steganography technology to generate encrypted data ;

[0033] S33. Use homomorphic encryption technology to encrypt the encrypted data After key distribution, the encrypted message is:

[0034] ;

[0035] Among them, m is The ciphertext of Used for subsequent steganographic processing;

[0036] The homomorphic encryption property is applied as follows:

[0037] ;

[0038] The generated key is used for steganography;

[0039] S34, using distributed steganography technology to embed the key information generated by homomorphic encryption into multiple data packets, and the encrypted ciphertext and key information are mixed to generate embedded stego information:

[0040] ;

[0041] in, is the embedded stego information, is the data hash value, is the key fragment, For XOR operation, It is the packet header information;

[0042] After obfuscation, the steganographic information is generated:

[0043] ;

[0044] Steganographic information Used for encrypted channel transmission;

[0045] S35, embedding the encrypted data of the stego information Transmitted to the storage module through an encrypted channel, protected by a three-layer encryption protocol:

[0046] ;

[0047] in, To encrypt channel data, Encrypt data for the first layer, , and For different encryption keys;

[0048] After channel obfuscation processing, obfuscated channel data is generated:

[0049] ;

[0050] Among them, F is the encryption channel confusion function, is the timestamp, The encryption key for the fourth layer;

[0051] S36: The storage module receives the encrypted data The data is stored in an encrypted state during storage:

[0052] ;

[0053] Where D is the decryption function, Decryption for the fourth layer, For timestamp verification;

[0054] The decrypted data is:

[0055] ;

[0056] The final decryption is restored to the original data .

[0057] Optionally, the step S4 specifically includes:

[0058] S41, receiving and processing multiple bioelectric signals through a dynamic identity authentication unit , bioelectric signals include electrocardiogram, electroencephalogram and skin galvanic response, and the received bioelectric signals After transmission, it enters the identity authentication unit for processing;

[0059] S42, the identity authentication unit receives the bioelectric signal Preprocessing is performed, and the signal is filtered and denoised using a real-time adaptive signal processing algorithm to generate a preprocessed bioelectric signal ;

[0060] S43, the pre-processed bioelectric signal Input into the bioelectric signal intelligent fusion module, combine multiple bioelectric signals for comprehensive analysis, and generate comprehensive biometric data ;

[0061] S44. Using bioelectric pattern learning algorithms to analyze comprehensive biometric data Processing to generate bioelectric patterns :

[0062] ;

[0063] in, is the activation function, is the weight matrix, is the bias vector;

[0064] S45. Using capsule network-based algorithms to analyze bioelectric patterns Authentication is performed,

[0065] The bioelectrical model Input to the capsule network, the output of the low-level capsule generates the prediction vector of the high-level capsule through the transformation matrix:

[0066] ;

[0067] in, is the output of the i-th lower-level capsule through the transformation matrix The prediction vector of the j-th high-level capsule generated, is the output of the i-th lower-level capsule, is the transformation matrix;

[0068] The output of the high-level capsule is updated through the dynamic routing mechanism to generate the authentication result R:

[0069] ;

[0070] in, is the output of the jth high-level capsule, which ultimately generates the authentication result R;

[0071] S46: The identity authentication result R is transmitted to the decision-making module, and the decision-making module implements corresponding security measures according to the identity authentication result R.

[0072] Optionally, the step S5 specifically includes:

[0073] S51, monitoring the position and status of the vehicle in real time through the monitoring unit, the position information of the vehicle is obtained through the UWB positioning module and transmitted to the monitoring unit for processing;

[0074] S52: The monitoring unit pre-processes the received location information and status information, and uses a real-time anomaly detection algorithm to filter and denoise the data to generate pre-processed data. ;

[0075] S53, the pre-processed data Input to the anomaly detection module, combine multiple data sources for comprehensive analysis, and generate comprehensive status feature data ;

[0076] S54, using improved dynamic abnormal pattern mapping technology to analyze the comprehensive state feature data Processing to generate preliminary anomaly patterns :

[0077] ;

[0078] in, For the initial abnormal mode, is the activation function, W is the weight matrix, and b is the bias vector;

[0079] S55. Improved dynamic anomaly pattern mapping technology introduces adaptive pattern generation, high-dimensional data integration and real-time feedback mechanism to analyze preliminary anomaly patterns. Optimization, combined with adaptive pattern generation technology, enables the model to dynamically adjust abnormal patterns according to the current environment:

[0080] ;

[0081] in, For the optimized exception mode, is the adaptive adjustment coefficient, Based on feedback data The amendment item;

[0082] S56. High-dimensional data integration optimizes the detection accuracy of abnormal patterns by fusing multiple data sources:

[0083] ;

[0084] in, is the characteristic of the k-th data source, is the corresponding weight coefficient;

[0085] S57, the real-time feedback mechanism dynamically adjusts the model parameters according to the detected anomalies, optimizes the anomaly pattern mapping, and generates the final anomaly detection result D:

[0086] ;

[0087] Among them, D is the anomaly detection result, is the detection function, is the weight, is the jth optimized mode;

[0088] S58. Transmit the abnormal detection result D to the decision module, and the decision module implements corresponding safety measures according to the abnormal detection result D.

[0089] Optional modules include:

[0090] UWB positioning module: obtains the precise location of the vehicle and provides centimeter-level positioning accuracy;

[0091] Digital security module: encrypts and authenticates positioning data;

[0092] Dynamic identity authentication module: identity authentication by processing bioelectric signals;

[0093] Monitoring unit: monitors the location and status data of the vehicle in real time;

[0094] Anomaly detection module: analyzes the pre-processed vehicle status data and environmental information, generates comprehensive status feature data and detects anomalies;

[0095] Data analysis unit: continuously monitors and analyzes vehicle status data and its environmental information, and dynamically adjusts algorithm parameters and anomaly detection strategies;

[0096] Control module: implements vehicle control measures based on analysis results;

[0097] Communication module: realizes data interaction between the vehicle system and external devices;

[0098] Decision-making module: Implement security decisions and measures based on identity authentication results and anomaly detection results.

[0099] The beneficial effects of the present invention are as follows: the present invention provides centimeter-level high-precision positioning by adopting the UWB positioning module, which far exceeds the traditional GPS and cellular network positioning technology. The UWB positioning module optimizes signal transmission through adaptive waveform adjustment technology and multi-band collaborative technology, and can maintain high-precision positioning even in complex urban environments and special scenarios such as tunnels, greatly improving the stability and reliability of positioning; during data transmission, the encryption unit of the digital security module adopts quantum teleportation and distributed steganography technology to perform high-intensity encryption processing on vehicle positioning data. Compared with traditional encryption technology, quantum teleportation uses the principles of quantum mechanics to achieve higher encryption strength and prevent data from being leaked and tampered with during transmission. Distributed steganography technology increases the concealment and security of data by embedding key information into multiple data packets.

[0100] The present invention uses a dynamic identity authentication unit and adopts bioelectric signal intelligent fusion authentication technology to conduct comprehensive analysis of multiple bioelectric signals such as electrocardiogram, electroencephalogram and skin galvanic response. The use of real-time adaptive signal processing algorithm and bioelectric pattern learning improves the accuracy and reliability of identity authentication, effectively prevents forgery and identity fraud attacks, and ensures that only authorized users can access the system.

[0101] The present invention realizes real-time monitoring of the vehicle's position and status, and through improved dynamic abnormal pattern mapping technology, combined with adaptive pattern generation, high-dimensional data integration and real-time feedback mechanism, the system can dynamically generate and adjust abnormal patterns. By comprehensively analyzing multiple data sources, real-time feedback and optimizing pattern mapping, the accuracy and response speed of abnormal detection are improved; the system can continuously monitor and analyze the vehicle's status data and its environmental information, and by dynamically adjusting algorithm parameters and abnormal detection strategies, ensure that the vehicle can cope with different driving scenarios and environmental changes. The real-time and adaptability of the system are achieved, and the safety and performance of the vehicle in complex driving environments are enhanced; by integrating multiple functional modules into a single module, the present invention simplifies the hardware design and system architecture, reduces the complexity and cost of the system, and improves the reliability and maintainability of the system. The integrated design makes the module small in size, suitable for the vehicle environment, and optimizes space utilization. BRIEF DESCRIPTION OF THE DRAWINGS

[0102] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0103] Figure 1 This is a system structure diagram of a single-module vehicle UWB positioning digital security method proposed by the present invention;

[0104] Figure 2 This is a flow chart of the encryption of the adaptive waveform adjustment, multi-band collaborative technology of the UWB positioning module and the quantum teleportation and distributed steganography technology of the digital security module in the present invention. DETAILED DESCRIPTION

[0105] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, which only illustrate the basic structure of the present invention in a schematic manner, and therefore only show the components related to the present invention.

[0106] refer to Figure 1-Figure 2 The whole vehicle UWB positioning digital safety system and method based on a single module includes the following steps:

[0107] S1. Install an integrated single module on the vehicle;

[0108] S2. Use the UWB positioning module to measure the position of the vehicle in real time. The UWB positioning module optimizes the transmission of UWB signals by using both adaptive waveform adjustment technology and multi-band collaborative technology.

[0109] S3. Generate vehicle positioning data through the UWB positioning module, and transmit the vehicle positioning data to the digital security module. During the vehicle positioning data transmission process, the encryption unit of the digital security module uses quantum teleportation and distributed steganography technology to encrypt the positioning data;

[0110] S4, implement real-time identity authentication through dynamic identity authentication unit, adopt bioelectric signal intelligent fusion authentication technology, which combines multiple bioelectric signals for comprehensive analysis, uses real-time adaptive signal processing algorithm and personalized bioelectric pattern learning;

[0111] S5. Realize real-time monitoring and anomaly detection. Through improved dynamic anomaly pattern mapping technology, combined with adaptive pattern generation, high-dimensional data integration and real-time feedback mechanism, the system can dynamically generate and adjust anomaly patterns, comprehensively analyze multiple data sources, provide real-time feedback and optimize pattern mapping;

[0112] S6. The system implements real-time data analysis and adaptive adjustment. By continuously monitoring and analyzing the vehicle's status data and its environmental information, the system can adjust the algorithm parameters and anomaly detection strategies in real time to cope with different driving scenarios and environmental changes.

[0113] In this implementation, the step S2 specifically includes:

[0114] S21, transmit multi-band narrow pulse signals through the UWB positioning module, and the frequency band is set to , the signal is transmitted in the preset time window Completed within the frequency band bandwidth Optimize the spatial resolution of the signal;

[0115] S22, implement adaptive waveform adjustment technology to adjust the waveform of the transmitted signal Perform dynamic optimization and adjust waveform parameters to adapt to different environmental conditions based on real-time environmental feedback data Optimize amplitude, standard deviation, and center frequency:

[0116] ;

[0117] in, is the dynamic amplitude based on environmental feedback data, is the standard deviation based on environmental feedback data, is the center frequency based on environmental feedback data;

[0118] S23. Use multi-band coordination technology to analyze received signals, optimize positioning accuracy through signal coordination between frequency bands, and calculate the arrival time of signals in each frequency band. , multipath errors are processed by cross-validation of high-frequency band signals, and the frequency band signal time data is fused using the weighted average method;

[0119] S24, calculate the signal propagation distance, combine the multipath effect correction model, and use the correction factor The distance data is adjusted, and the correction factor is obtained through model training to optimize the accuracy of the signal arrival time:

[0120] ;

[0121] in, is the speed of light constant, is the multipath effect correction factor obtained through model training;

[0122] S25. Apply the positioning algorithm to process the corrected distance data to determine the three-dimensional position coordinates of the vehicle , use weighted least squares method to solve the position, and dynamically adjust the weights to optimize the positioning accuracy:

[0123] ;

[0124] in, is the position of the i-th reference point, is the weight of each reference point;

[0125] S26. Output the calculated vehicle position coordinates , and transmitted to the digital security module through the data interface I. The interface supports high-bandwidth data transmission, performs real-time and integrity processing of data, and uses a security protocol to process the security of data during transmission.

[0126] In this implementation, step S3 specifically includes:

[0127] S31, generate vehicle positioning data through the UWB positioning module, and transmit the data to the digital security module. After transmission, it enters the encryption unit for processing;

[0128] S32, the encryption unit of the digital security module receives the positioning data Encryption processing is performed, using an encryption scheme based on homomorphic encryption and distributed steganography technology to generate encrypted data ;

[0129] S33. Use homomorphic encryption technology to encrypt the encrypted data After key distribution, the encrypted message is:

[0130] ;

[0131] Among them, m is The ciphertext of Used for subsequent steganographic processing;

[0132] The homomorphic encryption property is applied as follows:

[0133] ;

[0134] The generated key is used for steganography;

[0135] S34, using distributed steganography technology to embed the key information generated by homomorphic encryption into multiple data packets, and the encrypted ciphertext and key information are mixed to generate embedded stego information:

[0136] ;

[0137] in, is the embedded stego information, is the data hash value, is the key fragment, For XOR operation, It is the packet header information;

[0138] After obfuscation, the steganographic information is generated:

[0139] ;

[0140] Steganographic information Used for encrypted channel transmission;

[0141] S35, embedding the encrypted data of the stego information Transmitted to the storage module through an encrypted channel, protected by a three-layer encryption protocol:

[0142] ;

[0143] in, To encrypt channel data, Encrypt data for the first layer, , and For different encryption keys;

[0144] After channel obfuscation processing, obfuscated channel data is generated:

[0145] ;

[0146] Among them, F is the encryption channel confusion function, is the timestamp, The encryption key for the fourth layer;

[0147] S36: The storage module receives the encrypted data The data is stored in an encrypted state during storage:

[0148] ;

[0149] Where D is the decryption function, Decryption for the fourth layer, For timestamp verification;

[0150] The decrypted data is:

[0151] ;

[0152] The final decryption is restored to the original data .

[0153] In this implementation, step S4 specifically includes:

[0154] S41, receiving and processing multiple bioelectric signals through a dynamic identity authentication unit , bioelectric signals include electrocardiogram, electroencephalogram and skin galvanic response, and the received bioelectric signals After transmission, it enters the identity authentication unit for processing;

[0155] S42, the identity authentication unit receives the bioelectric signal Preprocessing is performed, and the signal is filtered and denoised using a real-time adaptive signal processing algorithm to generate a preprocessed bioelectric signal ;

[0156] S43, the pre-processed bioelectric signal Input into the bioelectric signal intelligent fusion module, combine multiple bioelectric signals for comprehensive analysis, and generate comprehensive biometric data ;

[0157] S44. Using bioelectric pattern learning algorithms to analyze comprehensive biometric data Processing to generate bioelectric patterns :

[0158] ;

[0159] in, is the activation function, is the weight matrix, is the bias vector;

[0160] S45. Using capsule network-based algorithms to analyze bioelectric patterns Authentication is performed,

[0161] The bioelectrical model Input to the capsule network, the output of the low-level capsule generates the prediction vector of the high-level capsule through the transformation matrix:

[0162] ;

[0163] in, is the output of the i-th lower-level capsule through the transformation matrix The prediction vector of the j-th high-level capsule generated, is the output of the i-th lower-level capsule, is the transformation matrix;

[0164] The output of the high-level capsule is updated through the dynamic routing mechanism to generate the authentication result R:

[0165] ;

[0166] in, is the output of the jth high-level capsule, which ultimately generates the authentication result R;

[0167] S46: The identity authentication result R is transmitted to the decision-making module, and the decision-making module implements corresponding security measures according to the identity authentication result R.

[0168] In this implementation, step S5 specifically includes:

[0169] S51, monitoring the position and status of the vehicle in real time through the monitoring unit, the position information of the vehicle is obtained through the UWB positioning module and transmitted to the monitoring unit for processing;

[0170] S52: The monitoring unit pre-processes the received location information and status information, and uses a real-time anomaly detection algorithm to filter and denoise the data to generate pre-processed data. ;

[0171] S53, the pre-processed data Input to the anomaly detection module, combine multiple data sources for comprehensive analysis, and generate comprehensive status feature data ;

[0172] S54, using improved dynamic abnormal pattern mapping technology to analyze the comprehensive state feature data Processing to generate preliminary anomaly patterns :

[0173] ;

[0174] in, For the initial abnormal mode, is the activation function, W is the weight matrix, and b is the bias vector;

[0175] S55. Improved dynamic anomaly pattern mapping technology introduces adaptive pattern generation, high-dimensional data integration and real-time feedback mechanism to analyze preliminary anomaly patterns. Optimization, combined with adaptive pattern generation technology, enables the model to dynamically adjust abnormal patterns according to the current environment:

[0176] ;

[0177] in, For the optimized exception mode, is the adaptive adjustment coefficient, Based on feedback data The amendment item;

[0178] S56. High-dimensional data integration optimizes the detection accuracy of abnormal patterns by fusing multiple data sources:

[0179] ;

[0180] in, is the characteristic of the k-th data source, is the corresponding weight coefficient;

[0181] S57, the real-time feedback mechanism dynamically adjusts the model parameters according to the detected anomalies, optimizes the anomaly pattern mapping, and generates the final anomaly detection result D:

[0182] ;

[0183] Among them, D is the anomaly detection result, is the detection function, is the weight, is the jth optimized mode;

[0184] S58. Transmit the abnormal detection result D to the decision module, and the decision module implements corresponding safety measures according to the abnormal detection result D.

[0185] In this embodiment, the following modules are included:

[0186] UWB positioning module: obtains the precise location of the vehicle and provides centimeter-level positioning accuracy;

[0187] Digital security module: encrypts and authenticates positioning data;

[0188] Dynamic identity authentication module: identity authentication by processing bioelectric signals;

[0189] Monitoring unit: monitors the location and status data of the vehicle in real time;

[0190] Anomaly detection module: analyzes the pre-processed vehicle status data and environmental information, generates comprehensive status feature data and detects anomalies;

[0191] Data analysis unit: continuously monitors and analyzes vehicle status data and its environmental information, and dynamically adjusts algorithm parameters and anomaly detection strategies;

[0192] Control module: implements vehicle control measures based on analysis results;

[0193] Communication module: realizes data interaction between the vehicle system and external devices;

[0194] Decision-making module: Implement security decisions and measures based on identity authentication results and anomaly detection results.

[0195] Embodiment 1:

[0196] In order to verify the feasibility of the present invention in implementation, the present invention is applied in a modern urban environment with dense vehicles and complex traffic, especially during peak hours in the morning and evening, with huge traffic volume, and traffic management and vehicle safety assurance become a major challenge. Especially in this environment, the positioning accuracy and stability of the traditional GPS positioning system are greatly reduced due to reasons such as high-rise building obstruction and signal reflection. In addition, vehicles in the city need to frequently interact with traffic infrastructure and other vehicles for data, and the data security and identity authentication issues they face are also extremely complex. Therefore, we conducted an experiment in the downtown area of ​​a large city to verify the effect of the single-module vehicle UWB positioning digital security method in a real environment.

[0197] Our test area is located in a busy area in the city center, where there are tall buildings and complex traffic, including main roads and narrow side roads. We selected 10 test vehicles, each of which was equipped with an integrated single module developed by us, including UWB positioning module, digital security module, dynamic identity authentication module, monitoring unit, anomaly detection module, data analysis unit, control module, communication module and decision module.

[0198] During the rush hour from 7 to 9 a.m., we let these vehicles drive freely in the test area to simulate a real commuting scene. The real-time location of the vehicle is obtained through the UWB positioning module, and the system uses adaptive waveform adjustment technology and multi-band collaborative technology to optimize the signal. During the transmission of positioning data, the encryption unit of the digital security module uses quantum teleportation and distributed steganography technology to encrypt the data to ensure the security of data transmission. At the same time, the vehicle authenticates the driver through the dynamic identity authentication module, uses the intelligent fusion authentication technology of bioelectric signals, and comprehensively analyzes the driver's electrocardiogram, electroencephalogram and skin galvanic response to ensure that only certified drivers can start and drive the vehicle.

[0199] The system monitors the location and status data of the vehicle in real time, uses improved dynamic abnormal pattern mapping technology for anomaly detection, and combines adaptive pattern generation, high-dimensional data integration and real-time feedback mechanisms to dynamically generate and adjust abnormal patterns. The system also continuously monitors and analyzes the vehicle's status data and its environmental information, and adjusts the algorithm parameters and anomaly detection strategies in real time through the data analysis unit to respond to different driving scenarios and environmental changes, ensuring the safety and performance of the vehicle.

[0200] The trial was conducted from June 15 to June 20, 2023, from 7 to 9 a.m. every day, in a busy area in the city center. The following are some key data and proof of effect:

[0201] In the test, the UWB positioning module can still provide centimeter-level high-precision positioning in areas with dense high-rise buildings. By comparing with the GPS system, the error range of UWB positioning is 2-3 centimeters, while the error range of the GPS system is 5-10 meters. Especially in areas blocked by high-rise buildings and tunnels, the stability of UWB positioning is significantly better than that of GPS.

[0202] In terms of data security, all positioning data has not been leaked or tampered with during transmission. Through security protocol analysis, the encrypted data of quantum teleportation and distributed steganography technology remains highly secure during transmission and has not been decrypted or tampered with by any external attacks.

[0203] In terms of identity authentication, the dynamic identity authentication module successfully identified and verified the identities of all drivers, and no forgery or identity fraud occurred. The bioelectric signal intelligent fusion authentication technology comprehensively analyzes the electrocardiogram, electroencephalogram and skin electrical response, making the identity authentication accuracy rate reach 99.9%.

[0204] In terms of anomaly detection, the system detected and handled multiple abnormal events. For example, when a test vehicle entered a narrow lane, the vehicle was moving abnormally slowly due to road congestion. The system detected this abnormal state in time and adjusted the abnormal mode through high-dimensional data integration and real-time feedback mechanism, successfully warning and guiding the driver to avoid the congested area to ensure driving safety.

[0205] Through real-time monitoring and analysis by the data analysis unit, the system can quickly respond and dynamically adjust algorithm parameters and anomaly detection strategies in different driving scenarios and environmental changes. During the test, the system made 150 algorithm parameter adjustments, and each adjustment took less than 1 second on average, ensuring the safety and performance optimization of the vehicle in complex driving environments.

[0206] Table 1 Key data and effects during the trial

[0207]

[0208] The present invention was tested for six days in a complex urban environment and collected a number of key data. First, the error range of the UWB positioning module in high-rise dense areas is 2-3 cm, which is significantly better than the 5-10 meter error range of traditional GPS. On different test dates, UWB positioning always maintains high accuracy and stability, while GPS positioning shows large error fluctuations under the same conditions, verifying the advantages of UWB positioning technology in complex environments; in terms of data security, a total of 3.1 million data packets were transmitted during the entire test period (about 500,000 data packets per day), and all data were not leaked or tampered during the transmission process. This proves the efficient encryption effect of quantum teleportation and distributed steganography technology, ensuring the high security of data during transmission; in terms of identity authentication, the dynamic identity authentication module has a total of 7,500 verification attempts, 7,497 successes, and only 3 failures on all test dates, with a verification accuracy rate between 99.9% and 100%. This proves the high reliability and accuracy of the intelligent fusion authentication technology of bioelectric signals, effectively preventing attacks of forgery and identity fraud, and ensuring that only authorized users can access the system; in terms of abnormal detection and processing, the system detected and processed 750 abnormal situations during the test, with an average detection and processing time of 0.7 to 0.9 seconds per abnormal situation, demonstrating the system's efficient response capabilities in dealing with abnormal situations. 740 abnormal situations were successfully handled, with a successful processing rate of nearly 100%, ensuring the safety of vehicle driving; in terms of dynamic adjustment of algorithm parameters, the system made a total of 150 algorithm parameter adjustments during the test, with each adjustment taking an average of less than 1 second, and an average adjustment time of between 0.6 and 0.9 seconds. This ensures the real-time and adaptability of the system in different driving scenarios and environmental changes, and can quickly respond and adjust algorithm parameters to ensure vehicle safety and performance optimization.

[0209] The present invention effectively solves the problems of low positioning accuracy, insufficient data security, poor identity authentication reliability, and weak real-time monitoring and anomaly detection capabilities in the prior art. The excellent performance of UWB positioning technology in complex environments, combined with efficient data encryption, secure identity authentication, fast anomaly detection and dynamic algorithm adjustment, significantly improves the safety and performance of vehicles in complex environments, and provides strong support for intelligent transportation systems and autonomous driving applications.

[0210] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A digital safety method for vehicle UWB positioning based on a single module, characterized in that: The steps include: S1. Install an integrated single module on the vehicle; S2. Use the UWB positioning module to measure the position of the vehicle in real time. The UWB positioning module optimizes the transmission of UWB signals by using both adaptive waveform adjustment technology and multi-band collaborative technology. S3. Generate vehicle positioning data through the UWB positioning module, and transmit the vehicle positioning data to the digital security module. During the vehicle positioning data transmission process, the encryption unit of the digital security module uses quantum teleportation and distributed steganography technology to encrypt the positioning data; S4, implement real-time identity authentication through dynamic identity authentication unit, adopt bioelectric signal intelligent fusion authentication technology, which combines multiple bioelectric signals for comprehensive analysis, uses real-time adaptive signal processing algorithm and personalized bioelectric pattern learning; S5. Realize real-time monitoring and anomaly detection. Through improved dynamic anomaly pattern mapping technology, combined with adaptive pattern generation, high-dimensional data integration and real-time feedback mechanism, the system can dynamically generate and adjust anomaly patterns, comprehensively analyze multiple data sources, provide real-time feedback and optimize pattern mapping; The step S5 specifically includes: S51, monitoring the position and status of the vehicle in real time through the monitoring unit, the position information of the vehicle is obtained through the UWB positioning module and transmitted to the monitoring unit for processing; S52: The monitoring unit pre-processes the received location information and status information, and uses a real-time anomaly detection algorithm to filter and denoise the data to generate pre-processed data. ; S53, the pre-processed data Input to the anomaly detection module, combine multiple data sources for comprehensive analysis, and generate comprehensive status feature data ; S54, using improved dynamic abnormal pattern mapping technology to analyze the comprehensive state feature data Processing to generate preliminary anomaly patterns : ； in, For the initial abnormal mode, is the activation function, W is the weight matrix, and b is the bias vector; S55. Improved dynamic anomaly pattern mapping technology introduces adaptive pattern generation, high-dimensional data integration and real-time feedback mechanism to analyze preliminary anomaly patterns. Optimization, combined with adaptive pattern generation technology, enables the model to dynamically adjust abnormal patterns according to the current environment: ； in, For the optimized exception mode, is the adaptive adjustment coefficient, Based on feedback data The amendment item; S56. High-dimensional data integration optimizes the detection accuracy of abnormal patterns by fusing multiple data sources: ； in, is the characteristic of the kth data source, is the corresponding weight coefficient; S57, the real-time feedback mechanism dynamically adjusts the model parameters according to the detected anomalies, optimizes the anomaly pattern mapping, and generates the final anomaly detection result D: ； Among them, D is the anomaly detection result, is the detection function, is the weight, is the jth optimized mode; S58, transmitting the abnormal detection result D to the decision module, and the decision module implements corresponding safety measures according to the abnormal detection result D; S6. The system implements real-time data analysis and adaptive adjustment. By continuously monitoring and analyzing the vehicle's status data and its environmental information, the system can adjust the algorithm parameters and anomaly detection strategies in real time to cope with different driving scenarios and environmental changes.

2. The digital security method for vehicle UWB positioning based on a single module according to claim 1 is characterized in that: The step S2 specifically includes: S21, transmit multi-band narrow pulse signals through the UWB positioning module, and the frequency band is set to , the signal is transmitted in the preset time window Completed within the frequency band bandwidth Optimize the spatial resolution of the signal; S22, implement adaptive waveform adjustment technology to adjust the waveform of the transmitted signal Perform dynamic optimization and adjust waveform parameters to adapt to different environmental conditions based on real-time environmental feedback data Optimize amplitude, standard deviation, and center frequency: ； in, is the dynamic amplitude based on environmental feedback data, is the standard deviation based on environmental feedback data, is the center frequency based on environmental feedback data; S23. Use multi-band coordination technology to analyze received signals, optimize positioning accuracy through signal coordination between frequency bands, and calculate the arrival time of signals in each frequency band. , multipath errors are processed by cross-validation of high-frequency band signals, and the frequency band signal time data is fused using the weighted average method; S24, calculate the signal propagation distance, combine the multipath effect correction model, and use the correction factor The distance data is adjusted, and the correction factor is obtained through model training to optimize the accuracy of the signal arrival time: ； in, is the speed of light constant, is the multipath effect correction factor obtained through model training; S25. Apply the positioning algorithm to process the corrected distance data to determine the three-dimensional position coordinates of the vehicle , use weighted least squares method to solve the position, and dynamically adjust the weights to optimize the positioning accuracy: ； in, is the position of the i-th reference point, is the weight of each reference point; S26. Output the calculated vehicle position coordinates , and transmitted to the digital security module through the data interface I. The interface supports high-bandwidth data transmission, performs real-time and integrity processing of data, and uses a security protocol to process the security of data during transmission.

3. The digital security method for whole vehicle UWB positioning based on a single module according to claim 1 is characterized in that: The step S3 specifically includes: S31, generate vehicle positioning data through the UWB positioning module, and transmit the data to the digital security module. After transmission, it enters the encryption unit for processing; S32, the encryption unit of the digital security module receives the positioning data Encryption processing is performed, using an encryption scheme based on homomorphic encryption and distributed steganography technology to generate encrypted data ; S33. Use homomorphic encryption technology to encrypt the encrypted data After key distribution, the encrypted message is: ； Among them, m is The ciphertext of Used for subsequent steganographic processing; The homomorphic encryption property is applied as follows: ； The generated key is used for steganography; S34, using distributed steganography technology to embed the key information generated by homomorphic encryption into multiple data packets, and the encrypted ciphertext and key information are mixed to generate embedded stego information: ； in, is the embedded stego information, is the data hash value, is the key fragment, For XOR operation, It is the packet header information; After obfuscation, the steganographic information is generated: ； Steganographic information Used for encrypted channel transmission; S35, embedding the encrypted data of the stego information Transmitted to the storage module through an encrypted channel, protected by a three-layer encryption protocol: ； in, To encrypt channel data, Encrypt data for the first layer, , and For different encryption keys; After channel obfuscation processing, obfuscated channel data is generated: ； Among them, F is the encryption channel confusion function, is the timestamp, The encryption key for the fourth layer; S36: The storage module receives the encrypted data The data is stored in an encrypted state during storage: ； Where D is the decryption function, Decryption for the fourth layer, For timestamp verification; The decrypted data is: ； The final decryption is restored to the original data .

4. The digital security method for whole vehicle UWB positioning based on a single module according to claim 1 is characterized in that: The step S4 specifically includes: S41, receiving and processing multiple bioelectric signals through a dynamic identity authentication unit , bioelectric signals include electrocardiogram, electroencephalogram and skin galvanic response, and the received bioelectric signals After transmission, it enters the identity authentication unit for processing; S42, the identity authentication unit receives the bioelectric signal Preprocessing is performed, and the signal is filtered and denoised using a real-time adaptive signal processing algorithm to generate a preprocessed bioelectric signal ; S43, the pre-processed bioelectric signal Input into the bioelectric signal intelligent fusion module, combine multiple bioelectric signals for comprehensive analysis, and generate comprehensive biometric data ; S44. Using bioelectric pattern learning algorithms to analyze comprehensive biometric data Processing to generate bioelectric patterns : ； in, is the activation function, is the weight matrix, is the bias vector; S45. Using capsule network-based algorithms to analyze bioelectric patterns To authenticate, the bioelectric pattern Input to the capsule network, the output of the low-level capsule generates the prediction vector of the high-level capsule through the transformation matrix: ； in, is the output of the i-th lower-level capsule through the transformation matrix The prediction vector of the j-th high-level capsule generated, is the output of the i-th lower-level capsule, is the transformation matrix; The output of the high-level capsule is updated through the dynamic routing mechanism to generate the authentication result R: ； in, is the output of the jth high-level capsule, which ultimately generates the authentication result R; S46: The identity authentication result R is transmitted to the decision-making module, and the decision-making module implements corresponding security measures according to the identity authentication result R.

5. The single-module vehicle UWB positioning digital safety system according to claim 1 is characterized in that: Includes the following modules: UWB positioning module: obtains the precise location of the vehicle and provides centimeter-level positioning accuracy; Digital security module: encrypts and authenticates positioning data; Dynamic identity authentication module: identity authentication by processing bioelectric signals; Monitoring unit: monitors the location and status data of the vehicle in real time; Anomaly detection module: analyzes the pre-processed vehicle status data and environmental information, generates comprehensive status feature data and detects anomalies; Data analysis unit: continuously monitors and analyzes vehicle status data and its environmental information, and dynamically adjusts algorithm parameters and anomaly detection strategies; Control module: implements vehicle control measures based on analysis results; Communication module: realizes data interaction between the vehicle system and external devices; Decision-making module: Implement security decisions and measures based on identity authentication results and anomaly detection results.

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