Network connection vehicle protection system based on multi-mode perception and quantum key fusion and control method
Through the networked vehicle protection system that integrates multimodal perception and quantum key, the problems of sensor signal instability, distortion of fusion result and rapid energy consumption are solved, and high-reliability data fusion and secure communication are achieved, ensuring the stable operation and intelligent protection of the system in complex environments.
Patent Information
- Application Number
- CN202510624800.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-05-15
- Publication Date
- 2025-08-15
AI Technical Summary
In the existing connected vehicle protection system, multimodal sensors have dramatic increase in signal noise, unstable feature extraction due to environmental changes, inconsistent synchronization between sensors, traditional fusion methods have distorted results in high conflicts, secure communication lacks flexible switching mechanisms, and protection decisions ignore energy consumption, resulting in rapid exhaustion.
A system that integrates multimodal perception and quantum key is adopted to calculate sensor reliability and fusion conflict through filtering, denoising and normalizing the sensor signal. Combining Denster-Schafer evidence theory and multi-layer perceptron model, adaptive fusion is carried out, and dynamic key management and energy feedback mechanism are introduced to generate optimal protection actions.
It improves the system's perception robustness, decision-making intelligence and communication security in complex environments, realizes high-reliability convergence of multi-source data and controllable energy consumption protection, ensures data packaging consistency and cross-platform communication compatibility, and enhances the stable operation of the system under high concurrent big data.
Smart Images
Figure CN120498665A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of automobile protection technology, and specifically to a connected vehicle protection system and control method based on multimodal perception and quantum key fusion. Background Art
[0002] The connected vehicle protection system, based on the fusion of multimodal sensing and quantum key technology, is an intelligent protection system that integrates advanced sensing technology and quantum secure communication. The system uses multimodal sensors (such as visual cameras, millimeter-wave radar, and LiDAR) to collect real-time information about the vehicle's surrounding environment and utilizes quantum key distribution (QKD) technology to ensure the security of the communication link. The system can pre-process sensor data, assess environmental reliability, calculate conflict levels, and dynamically select fusion strategies (such as statistical fusion or neural network fusion) based on the conflict situation to generate accurate situational awareness results. Combining dynamic threat assessment and reinforcement learning algorithms, the system generates optimal protective actions and transmits them to the vehicle's execution unit via a quantum encryption channel. It also manages energy consumption to ensure the efficient execution of protective actions. This system can effectively improve the safety and reliability of connected vehicles in complex environments, providing strong support for autonomous driving and intelligent transportation. In the existing connected vehicle protection system, multimodal sensors often experience sharp increases in signal noise and unstable feature extraction due to changes in ambient lighting, rain, snow, fog, and haze. At the same time, inconsistencies are prone to occur between sensors in data acquisition and timing synchronization. Traditional evidence statistical fusion methods distort the fusion results when the degree of conflict is high, and a single data-driven model is difficult to take into account the perception accuracy under different degrees of conflict. In addition, the current system lacks a flexible switching mechanism between quantum keys and post-quantum algorithms in terms of secure communication, which can easily lead to waste of key resources or decreased security. What's more, protection decisions ignore energy consumption constraints, and there is a risk of rapid energy depletion during execution. Therefore, to address the above problems, a connected vehicle protection system and control method based on multimodal perception and quantum key fusion are proposed. Summary of the Invention
[0003] The purpose of the present invention is to provide a connected vehicle protection system and control method based on the fusion of multimodal perception and quantum keys, so as to solve the problem that in the existing connected vehicle protection system, multimodal sensors often cause a sharp increase in signal noise and unstable feature extraction due to changes in ambient lighting, rain, snow, fog and haze; at the same time, inconsistencies are prone to occur between sensors in data acquisition and timing synchronization, and the traditional evidence statistical fusion method distorts the fusion results when the conflict degree is high, while a single data-driven model is difficult to take into account the perception accuracy under different conflict degrees; in addition, the current system lacks a flexible switching mechanism for quantum keys and post-quantum algorithms in terms of secure communication, which can easily lead to waste of key resources or reduced security; what's more, protection decisions ignore energy consumption constraints, and there is a risk of rapid energy depletion during execution.
[0004] To achieve the above object, the present invention provides the following technical solutions: A connected vehicle protection system and control method based on multimodal perception and quantum key fusion includes the following steps: S1: Get the time domain signal of the i-th sensor , and the time domain signal of the i-th sensor Perform filtering, denoising and normalization to extract feature vectors: , ; S2: Calculate the reliability of the i-th sensor based on the ambient visibility V and dust concentration D , reliability of the i-th sensor The calculation formula is: , ; S3: Based on the Danster-Schafer evidence theory, the belief quality function of the j-th sensor is used Calculating multi-sensor fusion conflict , multi-sensor fusion conflict degree The calculation formula is: ; S4: Perform adaptive multimodal fusion: when When weighted evidence statistics fusion is used to generate the situation vector ; when When the multi-layer perceptron data is driven to fusion, the situation vector is generated. ; S5: Based on the degree of conflict Calculating smoothing weights , and synthesize the final situation vector , the smoothing weight The calculation formula is:
[0005] Synthesize the final situation vector The calculation formula is: ; S6: and historical situation characteristics The current threat level is obtained through S-shaped function mapping , current threat level ; S7: State vector and immediate reward function , using the Q-learning algorithm to update and , obtain the optimal protection action.
[0006] As a further optimized content of the present invention, the following steps are also included: S8: Based on the number of available bits in the local key buffer One-time key requirements Compare and choose one-time pad or post-quantum encryption algorithm; S9: Move The selected key k is used for encryption and encapsulation to generate a secure message P that complies with the C-V2X protocol; S10: According to the energy consumption coefficient of each action , enforcement strength and real-time charging power , update the remaining energy , the remaining energy The calculation formula is:
[0007] and will Feedback to the next decision step.
[0008] As a further optimization of the present invention, in S1, the filter is a bandpass filter with a bandwidth of 0.1-50 Hz; the denoising adopts a wavelet threshold denoising algorithm; and the normalization processing includes scaling the eigenvector to the range of [0,1] according to the maximum and minimum values.
[0009] As a further optimized content of the present invention, in S2, the mathematical expression of the reliability function is:
[0010] Where, and is a constant obtained through calibration.
[0011] As a further optimization of the present invention, in S3, the belief quality function Based on sensor output and noise model construction, and satisfying .
[0012] As a further optimization of the present invention, in S4, the weighted evidence statistical fusion is calculated by the following formula: .
[0013] As a further optimized content of the present invention, in which: in S4, the multilayer perceptron includes three fully connected layers, the number of nodes in each layer is 128, 64, and 32 respectively, and the activation function is ReLU.
[0014] As a further optimized content of the present invention, wherein: in S5, the smoothing factor Updated in real time via online gradient descent algorithm.
[0015] As a further optimized content of the present invention, it includes: a multimodal sensor data acquisition and preprocessing module for acquiring and preprocessing the original signals of each sensor; Environmental reliability assessment module, used to calculate the reliability of each sensor based on environmental visibility and dust concentration; Conflict measurement module, used to calculate the multi-sensor fusion conflict degree based on the Danster-Schafer evidence theory; Adaptive fusion module, which performs statistical fusion in low-conflict situations and neural network fusion in high-conflict situations; Cascade smooth fusion module, used to smoothly synthesize the final situation vector according to the conflict degree; Dynamic threat assessment module, used to map the fusion situation and historical characteristics into the current threat level; Decision module, used to generate optimal protection actions based on reinforcement learning algorithm; A quantum key management module, which monitors the key buffer status and switches between one-time pad and post-quantum algorithms; An encryption and encapsulation module, used for encrypting and protocol encapsulating the protection action; The energy management module is used to calculate the action energy consumption and real-time charging power and update the remaining energy of the system.
[0016] As a further optimized content of the present invention, a computer program is stored thereon, and when the computer program is executed by a processor, the steps in the method as described in any one of claims 1 to 8 are executed.
[0017] Compared with the prior art, the present invention has the following beneficial effects: 1. This invention constructs a control method for a connected vehicle protection system based on multimodal perception and quantum key fusion. To address issues such as sensor data distortion under environmental interference, severe fusion conflicts, inflexible use of secure communication keys, and neglect of energy consumption in protection decisions, it proposes a situational awareness process including an adaptive fusion mechanism, an optimal protection decision-making strategy based on reinforcement learning, and a dynamic key management and energy feedback mechanism. This effectively improves the system's perception robustness, decision-making intelligence, and communication security in complex traffic environments, achieving highly reliable fusion of multi-source perception data, controllable energy consumption in action execution, and dynamic encryption protection for information transmission. 2. This invention introduces a dynamic key selection mechanism, enabling flexible switching between one-time pad and post-quantum encryption algorithms based on the key buffer state, thereby improving the information security of connected vehicles in highly dynamic communication environments. Simultaneously, by integrating the secure encryption encapsulation process, the resulting protection actions can be directly adapted to the C-V2X protocol specification, ensuring data encapsulation consistency and cross-platform communication compatibility. Furthermore, the constructed fusion weight and situation synthesis method dynamically adjusts the fusion strategy based on environmental factors and the degree of conflict between multi-source data, improving the accuracy and anti-interference capability of the final situation vector. 3. In the present invention, a multi-layer perceptron neural network model is embedded in the multimodal fusion framework, which realizes the deep modeling of data in high-conflict scenarios and enhances the system's ability to extract nonlinear and complex features. At the same time, the online gradient update mechanism of the smoothing factor ensures the real-time and continuity of the fusion output, avoiding strategy jumps. In terms of system structure design, the modular division of various functional units, including situational awareness, key management, energy management, encryption encapsulation, etc., improves the integration and scalability of the entire system in the actual deployment process, and further ensures the stable operation and reliable protection effect of the system in high concurrency, big data, and multiple interference scenarios. BRIEF DESCRIPTION OF THE DRAWINGS
[0018] Figure 1 This is a flow chart of the control method of the connected vehicle protection system based on multimodal perception and quantum key fusion of the present invention. DETAILED DESCRIPTION
[0019] See also Figure 1 , the present invention provides a technical solution: A connected vehicle protection system and control method based on multimodal perception and quantum key fusion includes the following steps: S1: Get the time domain signal of the i-th sensor , and the time domain signal of the i-th sensor Perform filtering, denoising and normalization to extract feature vectors: , ; S2: Calculate the reliability of the i-th sensor based on the ambient visibility V and dust concentration D , reliability of the i-th sensor The calculation formula is: , ; S3: Based on the Danster-Schafer evidence theory, the belief quality function of the j-th sensor is used Calculating multi-sensor fusion conflict , multi-sensor fusion conflict degree The calculation formula is: ; S4: Perform adaptive multimodal fusion: when When weighted evidence statistics fusion is used to generate the situation vector ; when When the multi-layer perceptron data is driven to fusion, the situation vector is generated. ; S5: Based on the degree of conflict Calculating smoothing weights , and synthesize the final situation vector , the smoothing weight The calculation formula is:
[0020] Synthesize the final situation vector The calculation formula is: ; S6: and historical situation characteristics The current threat level is obtained through S-shaped function mapping , current threat level ; S7: State vector and immediate reward function , using the Q-learning algorithm to update and , obtain the optimal protection action, through multimodal data preprocessing and dynamic reliability evaluation, combined with adaptive fusion and reinforcement learning, to achieve accurate perception of threats in complex environments and real-time protection decision-making.
[0021] As a technical solution for further implementation of this solution, the following steps are also included: S8: Based on the number of available bits in the local key buffer One-time key requirements Compare and choose one-time pad or post-quantum encryption algorithm; S9: Move The selected key k is used for encryption and encapsulation to generate a secure message P that complies with the C-V2X protocol; S10: According to the energy consumption coefficient of each action , enforcement strength and real-time charging power , update the remaining energy , the remaining energy The calculation formula is:
[0022] and will Feedback to the next decision step, introducing a dynamic switching mechanism between quantum and post-quantum keys and energy closed-loop management to ensure communication security and continuous system operation; As a technical solution for further implementing this solution, in S1, the filter is a bandpass filter with a bandwidth of 0.1–50 Hz; the denoising adopts a wavelet threshold denoising algorithm; the normalization processing includes scaling the feature vector to the range of [0, 1] according to the maximum and minimum values, accurately filtering out noise and abnormal components, and improving the quality of feature extraction and the robustness of the subsequent fusion algorithm; As a technical solution for further implementing this solution, in S2, the mathematical expression of the reliability function is:
[0023] Where, and The constants obtained through calibration are combined with the calibration models of visibility and dust concentration to achieve accurate quantification of reliability under environmental influences; As a technical solution for further implementation of this solution, in S3, the belief quality function Based on sensor output and noise model construction, and satisfying ,construct a complete and self-consistent belief distribution to ensure the accuracy and reliability of conflict measurement; As a technical solution for further implementing this solution, in S4, the weighted evidence statistical fusion is calculated using the following formula: , Maintain statistical fusion accuracy in low-conflict environments and effectively utilize multi-source information for situation assessment; As a technical solution for further implementing this solution, in S4, the multi-layer perceptron includes three fully connected layers, with the number of nodes in each layer being 128, 64, and 32 respectively, and the activation function being ReLU, which improves the data-driven fusion capability in high-conflict scenarios through a deep neural network structure; As a further technical solution of this solution, in S5, the smoothing factor Through the online gradient descent algorithm, the fusion weight transition curve is updated in real time and adaptively adjusted to achieve smooth transition and dynamic response; As a further technical solution for the implementation of this solution, it includes: a multimodal sensor data acquisition and preprocessing module for acquiring and preprocessing the original signals of each sensor; Environmental reliability assessment module, used to calculate the reliability of each sensor based on environmental visibility and dust concentration; Conflict measurement module, used to calculate the multi-sensor fusion conflict degree based on the Danster-Schafer evidence theory; Adaptive fusion module, which performs statistical fusion in low-conflict situations and neural network fusion in high-conflict situations; Cascade smooth fusion module, used to smoothly synthesize the final situation vector according to the conflict degree; Dynamic threat assessment module, used to map the fusion situation and historical characteristics into the current threat level; Decision module, used to generate optimal protection actions based on reinforcement learning algorithm; A quantum key management module, which monitors the key buffer status and switches between one-time pad and post-quantum algorithms; An encryption and encapsulation module, used for encrypting and protocol encapsulating the protection action; Energy management module, used to calculate motion energy consumption and real-time charging power and update the system's remaining energy. The modular design facilitates system integration and function expansion, improving maintainability. As a technical solution for further implementing this solution, a computer program is stored thereon. When the computer program is executed by a processor, the steps in the method described in any one of claims 1 to 8 are executed. The software implementation is flexible and can be quickly deployed on a hardware platform, thereby realizing the reusability and upgradability of the patented method.
[0024] This article uses specific examples to illustrate the principles and implementation methods of the present invention. The above examples are only used to help understand the method of the present invention and its core ideas. The above is only a preferred implementation method of the present invention. It should be pointed out that due to the limitations of textual expression, there are objectively infinite specific structures. For ordinary technicians in this technical field, without departing from the principles of the present invention, they can make several improvements, modifications or changes, and can also combine the above technical features in an appropriate manner; these improvements, modifications, changes or combinations, or the direct application of the inventive concept and technical solution to other occasions without improvement, should be regarded as the scope of protection of the present invention.
Claims
1. A control method for a connected vehicle protection system based on multimodal perception and quantum key fusion, characterized in that: The following steps are involved: S1: Get the time domain signal of the i-th sensor , and the time domain signal of the i-th sensor Perform filtering, denoising and normalization to extract feature vectors: ， ； S2: Calculate the reliability of the i-th sensor based on the ambient visibility V and dust concentration D , reliability of the i-th sensor The calculation formula is: ， ； S3: Based on the Danster-Schafer evidence theory, the belief quality function of the j-th sensor is used Calculating multi-sensor fusion conflict , multi-sensor fusion conflict degree The calculation formula is: ； S4: Perform adaptive multimodal fusion: when When weighted evidence statistics fusion is used to generate the situation vector ; when When the multi-layer perceptron data is driven to fusion, the situation vector is generated. ; S5: Based on the degree of conflict Calculating smoothing weights , and synthesize the final situation vector , the smoothing weight The calculation formula is: ； Synthesize the final situation vector The calculation formula is: ； S6: and historical situation characteristics The current threat level is obtained through S-shaped function mapping , current threat level ; S7: State vector and immediate reward function , using the Q-learning algorithm to update and , obtain the optimal protection action.
2. The control method of the connected vehicle protection system based on multimodal perception and quantum key fusion according to claim 1 is characterized by: The following steps are also included: S8: Based on the number of available bits in the local key buffer One-time key requirements Compare and choose one-time pad or post-quantum encryption algorithm; S9: Move The selected key k is used for encryption and encapsulation to generate a secure message P that complies with the C-V2X protocol; S10: According to the energy consumption coefficient of each action , enforcement strength and real-time charging power , update the remaining energy , the remaining energy The calculation formula is: ； and will Feedback to the next decision step.
3. The control method of the connected vehicle protection system based on multimodal perception and quantum key fusion according to claim 1 is characterized by: In S1, the filter is a bandpass filter with a bandwidth of 0.1–50 Hz; the denoising adopts a wavelet threshold denoising algorithm; and the normalization processing includes scaling the eigenvector to the range of [0, 1] according to the maximum and minimum values.
4. The control method of the connected vehicle protection system based on multimodal perception and quantum key fusion according to claim 1 is characterized in that: In S2, the mathematical expression of the reliability function is: ； Where, and is a constant obtained through calibration.
5. The control method of the connected vehicle protection system based on multimodal perception and quantum key fusion according to claim 1 is characterized in that: In S3, the belief quality function Based on sensor output and noise model construction, and satisfying .
6. The control method of the connected vehicle protection system based on multimodal perception and quantum key fusion according to claim 1 is characterized by: In S4, the weighted evidence statistical fusion is calculated by the following formula: 。 7. The control method of the connected vehicle protection system based on multimodal perception and quantum key fusion according to claim 1 is characterized by: In S4, the multilayer perceptron includes three fully connected layers, the number of nodes in each layer is 128, 64, and 32 respectively, and the activation function is ReLU.
8. The control method of the connected vehicle protection system based on multimodal perception and quantum key fusion according to claim 1 is characterized in that: In S5, the smoothing factor Updated in real time via online gradient descent algorithm.
9. The connected vehicle protection system based on multimodal perception and quantum key fusion according to claim 1 is characterized by: It includes a multimodal sensor data acquisition and preprocessing module, which is used to acquire and preprocess the original signals of each sensor; Environmental reliability assessment module, used to calculate the reliability of each sensor based on environmental visibility and dust concentration; Conflict measurement module, used to calculate the multi-sensor fusion conflict degree based on the Danster-Schafer evidence theory; Adaptive fusion module, which performs statistical fusion in low-conflict situations and neural network fusion in high-conflict situations; Cascade smooth fusion module, used to smoothly synthesize the final situation vector according to the conflict degree; Dynamic threat assessment module, used to map the fusion situation and historical characteristics into the current threat level; Decision module, used to generate optimal protection actions based on reinforcement learning algorithm; A quantum key management module, which monitors the key buffer status and switches between one-time pad and post-quantum algorithms; An encryption and encapsulation module, used for encrypting and protocol encapsulating the protection action; The energy management module is used to calculate the action energy consumption and real-time charging power and update the remaining energy of the system.
10. A connected vehicle protection system based on multimodal perception and quantum key fusion, wherein a computer program is stored, characterized in that When the computer program is executed by a processor, the steps in the method according to any one of claims 1 to 8 are executed.