Vehicle intelligent surface metamaterial electromagnetic imaging navigation method and system device

Abstract

The invention discloses a vehicle intelligent surface metamaterial electromagnetic imaging navigation method and system device, and relates to the technical field of metamaterial electromagnetic imaging navigation, and the vehicle intelligent surface metamaterial electromagnetic imaging navigation method comprises the steps of vehicle intelligent surface metamaterial preliminary installation, vehicle navigation data acquisition, vehicle navigation data analysis and vehicle navigation cooperative control. A highly-integrated metasurface structure is adopted on the outer surface of a vehicle, the metasurface unit transmits and receives electromagnetic waves through a microcircuit, surrounding obstacles are sensed in real time and clear electromagnetic imaging images are generated by adjusting the response characteristics of a metasurface material, and a generated environment model is combined with a vehicle-mounted navigation system, so that the vehicle-mounted navigation system can be used for navigation. According to the invention, the system is simple in structure, timely reflects the surrounding environment information, carries out path planning and obstacle avoidance operation, breaks through the limitation of a traditional sensor, achieves navigation and environment perception in various complex environments through the adjustment capability of an intelligent metasurface material, and improves the overall performance and safety of the system.

Description

Technical Field

[0001] The present invention relates to the technical field of metamaterial electromagnetic imaging navigation, and particularly relates to a method and system device for electromagnetic imaging navigation of intelligent vehicle surfaces using metamaterials. Background Art

[0002] With the rapid development of autonomous driving technology, the safety and navigation accuracy of vehicles in complex environments have become urgent problems to be solved. Currently, common autonomous driving sensors include lidar, ultrasonic sensors, cameras, and radars, etc. However, the performance of these sensors has limitations in some complex weather conditions, insufficient lighting, occlusion, and other environments. Therefore, seeking a new type of navigation method and system that can maintain high precision under various environmental conditions has become an important research direction. Due to its ability to control the propagation of electromagnetic waves, intelligent metasurface materials have shown great potential in the fields of electromagnetic imaging, sensing, and navigation. By designing a metasurface with intelligent adjustment functions on the outer surface of the vehicle, it is possible to perceive and image the vehicle's surrounding environment using electromagnetic waves without relying on traditional sensors, thereby achieving precise navigation and obstacle detection. Therefore, it is necessary to analyze a method and system device for electromagnetic imaging navigation of intelligent vehicle surfaces using metamaterials.

[0003] The prior art, such as an invention patent application with publication number CN114841260B, discloses a method for multi-sensor information fusion and autonomous obstacle avoidance navigation in a mine, including the following steps: Step 1: Real-time monitor and obtain the distance information of obstacles through ultrasonic ranging; Step 2: Construct a lower computer control system for the driverless vehicle; Step 3: Locate the destination of the unmanned vehicle through environmental feature extraction; Step 4: Perform multi-source data fusion perception to achieve obstacle perception, autonomous obstacle avoidance, and underground autonomous positioning and navigation for the unmanned vehicle in the mine. This invention uses iBeacon rough positioning and lidar vector line segment matching for fine positioning to perform positioning and navigation in the underground environment, obtains the obstacle distribution distance information based on the method of fusion perception, and reduces the distance error by fitting the curve of the relationship between the signal strength and distance of the Bluetooth base station, so as to improve the positioning and navigation accuracy and solve the deficiencies in the prior art.

[0004] In the prior art, information technology consulting can meet the basic requirements, but there are also some potential defects and challenges, which are specifically reflected in the following aspects: In the prior art, the analysis of the navigation performance index of the traditional sensors in each part carried by the vehicle during historical driving is not accurate enough, reducing the scanning frequency and sensitivity of the lidar, lacking timely judgment of whether there are sensor failures or errors, resulting in signal occlusion effects, decreased positioning accuracy, increasing the limitations of traditional sensors, and further affecting the judgment of whether the navigation performance of the traditional sensors in each part carried by the vehicle during historical driving is abnormal, resulting in insufficient analysis of the obstacle collision index of the intelligent metasurface material units installed in each part of the vehicle during driving, increasing the error rate of issuing instructions to the vehicle's driving control system, affecting path updates, and reducing the overall performance and safety of the system. Summary of the Invention

[0005] The purpose of the present invention is to provide a vehicle intelligent surface metamaterial electromagnetic imaging navigation method and system device, which solves the problems existing in the background technology.

[0006] To solve the above technical problems, the present invention adopts the following technical solutions: The present invention provides a vehicle intelligent surface metamaterial electromagnetic imaging navigation method and system device, including: preliminary installation of vehicle intelligent surface metamaterials, vehicle navigation data collection, vehicle navigation data analysis, and vehicle navigation collaborative control.

[0007] Preliminary installation of vehicle intelligent surface metamaterials: Through the historical data of the vehicle, screen the historical collision positions of the vehicle, use each historical collision position as the installation position of the metamaterial of the intelligent surface of the vehicle, and thus obtain the intelligent metasurface material units installed in each part of the vehicle.

[0008] Vehicle navigation data collection: Collect data from the traditional sensors in each part carried by the vehicle during each time period of historical driving, and thus obtain the operation data of the traditional sensors, and then analyze the navigation performance index of the traditional sensors in each part carried by the vehicle during historical driving.

[0009] Vehicle navigation data analysis: Based on the obtained navigation performance index of the traditional sensors in each part carried by the vehicle during historical driving, judge whether the traditional sensors in each part carried by the vehicle during historical driving are abnormal. If abnormal, activate the intelligent metasurface material units installed in each part of the vehicle to obtain an electromagnetic imaging module, generate an electromagnetic imaging image, and analyze the obstacle collision index of the intelligent metasurface material units installed in each part of the vehicle during driving.

[0010] Vehicle Navigation Cooperative Control: Based on the obstacle collision index of the intelligent metasurface material units installed on various parts of the vehicle during driving, an electromagnetic field model of the surrounding environment is constructed and fused with the vehicle's navigation map to perform path planning and obstacle avoidance operations.

[0011] Furthermore, the navigation performance index of the traditional sensors installed on various parts of the vehicle during historical driving is analyzed. The specific analysis method is as follows: Based on the obtained traditional sensor operation data, where the traditional sensor operation data includes the time interval, number, and output signal difference of the laser pulses of the traditional sensors installed on various parts of the vehicle at each time period, and the input light intensity difference, the time interval of the laser pulses is divided by the number of laser pulses to obtain the scanning frequency of the traditional sensors installed on various parts of the vehicle at each time period during historical driving. And the change in the output signal is divided by the change in the input light intensity to obtain the sensitivity of the traditional sensors installed on various parts of the vehicle at each time period during historical driving. Then, the reference sensitivity and reference scanning frequency of the traditional sensors installed on various parts of the vehicle during historical driving are extracted from the database, and further, the navigation performance index of the traditional sensors installed on various parts of the vehicle during historical driving is analyzed. The specific calculation formula is as follows: Among them, q i ' represents the reference scanning frequency of the traditional sensor at the i-th part of the vehicle during historical driving, q ui represents the scanning frequency of the traditional sensor at the i-th part of the vehicle during the u-th time period of historical driving, d i ' represents the reference sensitivity of the traditional sensor at the i-th part of the vehicle during historical driving, d ui ' represents the sensitivity of the traditional sensor at the i-th part of the vehicle during the u-th time period of historical driving. i represents the part number, i = 1, 2, 3,..., e, e represents the number of parts, u represents the time period number, u = 1, 2, 3,..., l, and l represents the number of time periods.

[0012] Further, to determine whether the traditional sensors of each part installed in the vehicle during historical driving are abnormal, the specific analysis method is as follows: Based on the obtained navigation performance indexes of the traditional sensors of each part installed in the vehicle during historical driving, obtain the qualified interval of the navigation performance indexes of the traditional sensors of each part installed in the vehicle during historical driving from the database, and compare the navigation performance indexes of the traditional sensors of each part installed in the vehicle during historical driving with the qualified interval of the navigation performance indexes. If the navigation performance index of the traditional sensor of a certain part installed in the vehicle during historical driving is not within the qualified interval of the navigation performance indexes, it is determined that the traditional sensor of this part installed in the vehicle during historical driving is in an abnormal state, and the intelligent metasurface material units installed in each part of the vehicle are activated, and then the electromagnetic imaging module is obtained to generate an electromagnetic imaging image.

[0013] Further, the electromagnetic imaging module includes: An electromagnetic wave transmitting unit: This unit generates electromagnetic waves through the metasurface material, propagates around the vehicle and interacts with objects.

[0014] An electromagnetic wave receiving unit: The receiving unit is used to capture the electromagnetic wave signals reflected from surrounding objects.

[0015] Based on the obtained electromagnetic wave transmitting unit and receiving unit, and performing data analysis on the electromagnetic wave transmitting unit and receiving unit, electromagnetic wave receiving data and electromagnetic wave transmitting data are obtained. The electromagnetic wave transmitting data includes the electromagnetic wave transmitting frequency, the electromagnetic intensity in each direction, and the angular range of the beam. The electromagnetic wave receiving data includes the number of electron distributions, voltage, and heat phase change value. Then, the obstacle collision index P of the intelligent metasurface material units installed in each part of the vehicle during driving is analyzed. i Based on the prior art, it is determined whether the vehicle can avoid obstacles during driving on the original path. If it cannot avoid, based on the generated electromagnetic imaging image, the path is updated to obtain updated path data, and the path safety index K of the intelligent metasurface material units installed in each part of the vehicle during driving on the updated path is analyzed. i .

[0016] Furthermore, the method for analyzing the obstacle collision index of the intelligent metasurface material units installed at various parts of the vehicle during driving is as follows: Through electromagnetic wave reflection, when the emitted electromagnetic wave encounters an obstacle and is reflected, by analyzing the characteristics of the reflected wave, and based on the electromagnetic wave emission frequency, electromagnetic intensity in each direction, and beam angle range of the intelligent metasurface material at various parts of the vehicle during driving, using the ranging principle of radar, the minimum distance between various parts of the vehicle and the obstacle during driving, the angle between the intelligent metasurface material emission beam and the obstacle surface, and the azimuth angle between the obstacle and the vehicle are obtained. And through spectrum analysis, the size, position, and shape of the obstacle are obtained by matching the signal eigenvalue in frequency, and time delay analysis and frequency analysis are carried out, and an electromagnetic field model of the surrounding environment is constructed and fused with the vehicle's navigation map to perform obstacle detection, and the obstacle collision index of the intelligent metasurface material units installed at various parts of the vehicle during driving is obtained.

[0017] Furthermore, the method for analyzing the path safety index of the intelligent metasurface material units installed at various parts of the vehicle during driving on the updated path is as follows: Based on the obtained updated path data, where the updated path data includes the electron distribution quantity, voltage, and heat phase change value of the intelligent metasurface material units installed at various parts of the vehicle during driving on the updated path, and the reference electron distribution quantity, reference voltage, and reference heat phase change value of the intelligent metasurface material units installed at various parts of the vehicle during driving on the updated path are extracted from the database. Then, the path safety index of the intelligent metasurface material units installed at various parts of the vehicle during driving on the updated path is analyzed. The specific calculation formula is: where v' i 、j' i 、f' i respectively represent the reference electron distribution quantity, reference voltage, and reference heat phase change value of the intelligent metasurface material units installed at various parts of the vehicle during driving on the updated path, and v i 、j i 、f i respectively represent the electron distribution quantity, voltage, and heat phase change value of the intelligent metasurface material units installed at various parts of the vehicle during driving on the updated path. And the updated path planning is carried out, and it is judged whether there is a safety risk in the updated driving path.

[0018] Further, to determine whether there is a safety risk in the updated driving path, the specific analysis method is as follows: Based on the path safety index of the intelligent metasurface material units installed on various parts of the vehicle during driving on the updated path, and extracting the corresponding safety level divisions from the database, obtaining the safety level, relatively safe level, warning level, and danger level. When the updated path safety index is at the warning level or danger level, an early warning signal is sent in a timely manner to remind the driver to pay attention to safety. By adjusting the optical and electrical properties through the electronic distribution reference quantity, reference voltage, and heat reference phase change value, intelligent perception and dynamic response are achieved, changing the reflectivity of the intelligent metasurface materials installed on various parts of the vehicle during driving on the updated path, adjusting the intelligent metasurface materials, and based on the generated environmental model, combining with the in-vehicle navigation system to update the path, and combining traffic signals and driving speed limits to optimize the updated path and adjust the strategy in a timely manner.

[0019] The second aspect of the present invention provides a system device for a vehicle intelligent surface metamaterial electromagnetic imaging navigation method, including: A vehicle intelligent surface metamaterial preliminary installation module: Through the historical data of the vehicle, screening the historical collision positions of the vehicle, taking each historical collision position as the installation position of the metamaterial of the intelligent surface of the vehicle, and thus obtaining the intelligent metasurface material units installed on various parts of the vehicle.

[0020] A vehicle navigation data acquisition module: Collecting data from the traditional sensors on various parts of the vehicle during each time period of historical driving, and thus obtaining the traditional sensor operation data, and then analyzing the navigation performance index of the traditional sensors on various parts of the vehicle during historical driving.

[0021] A vehicle navigation data analysis module: Based on the obtained navigation performance index of the traditional sensors on various parts of the vehicle during historical driving, determining whether the traditional sensors on various parts of the vehicle during historical driving are abnormal. If abnormal, starting the intelligent metasurface material units installed on various parts of the vehicle to obtain an electromagnetic imaging module, generating an electromagnetic imaging image, and analyzing the obstacle collision index of the intelligent metasurface material units installed on various parts of the vehicle during driving.

[0022] A vehicle navigation collaborative control module: Based on the obtained obstacle collision index of the intelligent metasurface material units installed on various parts of the vehicle during driving, constructing an electromagnetic field model of the surrounding environment, and fusing it with the navigation map of the vehicle to perform path planning and obstacle avoidance operations.

[0023] The beneficial effects of the present invention are as follows: During the implementation process, the outer surface of the vehicle adopts a highly integrated metasurface structure. These metasurface units realize the emission and reception of electromagnetic waves through microcircuits. By adjusting the response characteristics of the metasurface material, the propagation characteristics of electromagnetic waves can be optimized in different environments, the surrounding obstacles can be sensed in real time and clear electromagnetic imaging images can be generated, and the generated environmental model is combined with the vehicle-mounted navigation system to extract target information such as obstacles, pedestrians, and vehicles, construct a high-precision environmental model, timely reflect the surrounding environmental information, perform precise path planning and obstacle avoidance operations, break through the limitations of traditional sensors, utilize the adjustment ability of intelligent metasurface materials to achieve precise navigation and environmental perception in various complex environments, timely calculate the best path, obstacle avoidance strategies, traffic signal recognition, etc., send instructions to the vehicle's driving control system, and improve the overall performance and safety of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0025] Figure 1 It is a schematic flowchart of the implementation steps of the method of the present invention.

[0026] Figure 2 It is a schematic connection diagram of the system structure of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0027] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts belong to the scope of protection of the present invention.

[0028] Referring to Figure 1 As shown, the present invention provides a method for electromagnetic imaging navigation of vehicle intelligent surface metamaterials, including: preliminary installation of vehicle intelligent surface metamaterials, vehicle navigation data collection, vehicle navigation data analysis, and vehicle navigation collaborative control.

[0029] Preliminary installation of vehicle intelligent surface metamaterials: Through the historical data of the vehicle, screen the historical collision positions of the vehicle, use the historical collision positions as the installation positions of the metamaterials on the intelligent surface of the vehicle, and thus obtain the intelligent metasurface material units installed on each part of the vehicle.

[0030] Vehicle navigation data collection: Collect data from the traditional sensors on various parts of the vehicle during different time periods of its historical driving, thereby obtaining the operation data of the traditional sensors, and then analyzing the navigation performance index of the traditional sensors on various parts of the vehicle during its historical driving.

[0031] It should be noted that the traditional sensors include lidar, ultrasonic sensors, cameras, radars, etc.

[0032] Vehicle navigation data analysis: Based on the obtained navigation performance index of the traditional sensors on various parts of the vehicle during its historical driving, determine whether the traditional sensors on various parts of the vehicle are abnormal during its historical driving. If abnormal, activate the intelligent metasurface material units installed on various parts of the vehicle to obtain an electromagnetic imaging module, generate an electromagnetic imaging image, and analyze the obstacle collision index of the intelligent metasurface material units installed on various parts of the vehicle during driving.

[0033] Vehicle navigation collaborative control: Based on the obtained obstacle collision index of the intelligent metasurface material units installed on various parts of the vehicle during driving, construct an electromagnetic field model of the surrounding environment, fuse it with the vehicle's navigation map, and perform path planning and obstacle avoidance operations.

[0034] In the above embodiment, the specific analysis method for analyzing the navigation performance index of the traditional sensors on various parts of the vehicle during its historical driving is as follows: Based on the obtained operation data of the traditional sensors, where the operation data of the traditional sensors includes the time interval, number, and output signal difference of the laser pulses of the traditional sensors on various parts of the vehicle during different time periods, and the input light intensity difference. Perform a ratio process on the time interval of the laser pulses and the number of laser pulses to obtain the scanning frequency of the traditional sensors on various parts of the vehicle during different time periods of its historical driving. And perform a ratio process on the output signal change amount and the input light intensity change amount to obtain the sensitivity of the traditional sensors on various parts of the vehicle during different time periods of its historical driving. Then extract the reference sensitivity and reference scanning frequency of the traditional sensors on various parts of the vehicle during its historical driving from the database, and further analyze the navigation performance index of the traditional sensors on various parts of the vehicle during its historical driving. The specific calculation formula is as follows: where q i ' represents the reference scanning frequency of the traditional sensor on the i-th part of the vehicle during its historical driving, q ui represents the scanning frequency of the traditional sensor on the i-th part of the vehicle during the u-th time period of its historical driving, d i ' represents the reference sensitivity of the traditional sensor on the i-th part of the vehicle during its historical driving, d ui$S_{i,u}$ represents the sensitivity of the $i$-th traditional sensor mounted on the vehicle during the $u$-th time period in the historical driving. $i$ represents the part number, where $i = 1, 2, 3, \cdots, e$, and $e$ represents the number of parts. $u$ represents the time period number, where $u = 1, 2, 3, \cdots, l$, and $l$ represents the number of time periods.

[0035] It should be noted that the scanning frequency of the lidar on the vehicle in each environment is detected by a spectrum analyzer.

[0036] It should be noted that the change in the output signal and the change in the input light intensity are obtained by measuring the amplitude of the electrical signal output by the image sensor under each light condition to obtain the maximum output signal, the minimum output signal, the input maximum light intensity, and the input minimum light intensity of the vehicle in each time period in each environment, and then performing difference processing respectively to obtain the output signal difference and the input light intensity difference.

[0037] In the above embodiment, to determine whether the traditional sensors mounted on each part of the vehicle during historical driving are abnormal, the specific analysis method is as follows: Based on the obtained navigation performance index of the traditional sensors mounted on each part of the vehicle during historical driving, obtain the qualified interval of the navigation performance index of the traditional sensors mounted on each part of the vehicle during historical driving from the database, and compare the navigation performance index of the traditional sensors mounted on each part of the vehicle during historical driving with the qualified interval of the navigation performance index. If the navigation performance index of the traditional sensor mounted on a certain part of the vehicle during historical driving is not within the qualified interval of the navigation performance index, it is determined that the traditional sensor mounted on that part of the vehicle during historical driving is in an abnormal state, and the intelligent metasurface material units installed on each part of the vehicle are activated, and then the electromagnetic imaging module is obtained to generate an electromagnetic imaging image.

[0038] It should be noted that for the electromagnetic imaging image, by utilizing the reflection characteristics of electromagnetic waves, the electromagnetic imaging module can sense the surrounding environment in real time, and this process is based on the following principles:

[0039] Reflection time analysis: After the electromagnetic wave is emitted from the metasurface and encounters an obstacle, a pedestrian, or other object, it will be reflected back to the receiving unit. By analyzing the time delay of the reflected wave, the distance to the object can be estimated.

[0040] Multipath propagation: The electromagnetic wave not only reflects directly but also reaches the receiving unit through multiple paths. Through advanced signal processing algorithms, such as ground reflection and vehicle body reflection, useful information can be extracted from the complex reflected signals and a high-resolution image of the surrounding environment can be generated.

[0041] In the above embodiment, the electromagnetic imaging module includes: an electromagnetic wave emitting unit: This unit generates electromagnetic waves through the metasurface material, propagates around the vehicle, and interacts with objects.

[0042] Electromagnetic wave receiving unit: The receiving unit is used to capture the electromagnetic wave signals reflected from surrounding objects.

[0043] Based on the obtained electromagnetic wave transmitting unit and receiving unit, and performing data analysis on the electromagnetic wave transmitting unit and receiving unit, electromagnetic wave receiving data and electromagnetic wave transmitting data are obtained. The electromagnetic wave transmitting data includes the electromagnetic wave transmitting frequency, the electromagnetic intensity in each direction, and the angular range of the beam. The electromagnetic wave receiving data includes the number of electron distributions, voltage, and heat phase change value. Furthermore, the obstacle collision index P of the intelligent metasurface material units installed at various parts of the vehicle during driving is analyzed. i Based on the prior art, it is judged whether the vehicle can avoid obstacles when driving along the original path. If it cannot avoid, based on the generated electromagnetic imaging image, the path is updated to obtain updated path data, and the path safety index K of the intelligent metasurface material units installed at various parts of the vehicle during driving on the updated path is analyzed. i .

[0044] In the above embodiment, the method for specifically analyzing the obstacle collision index of the intelligent metasurface material units installed at various parts of the vehicle during driving is as follows: Through electromagnetic wave reflection, when the emitted electromagnetic wave encounters an obstacle and is reflected, by analyzing the characteristics of the reflected wave, and based on the electromagnetic wave transmitting frequency, the electromagnetic intensity in each direction, and the angular range of the beam of the intelligent metasurface material at various parts of the vehicle during driving, using the ranging principle of radar, the minimum distance between various parts of the vehicle and the obstacle, the angle between the intelligent metasurface material emission beam and the obstacle surface, and the azimuth angle between the obstacle and the vehicle are obtained. And according to spectrum analysis, the size, position, and shape of the obstacle are obtained by matching the signal eigenvalue in frequency. Time delay analysis and frequency analysis are performed, and an electromagnetic field model of the surrounding environment is constructed and fused with the vehicle's navigation map to perform obstacle detection, and the obstacle collision index of the intelligent metasurface material units installed at various parts of the vehicle during driving is obtained.

[0045] It should be noted that for obstacle detection: Electromagnetic wave imaging can be used to achieve precise identification and positioning of pedestrians, other vehicles, and static obstacles.

[0046] Dynamic object tracking: Through multiple updates of sensing data, dynamic objects such as other vehicles and pedestrians during driving can be tracked in real time.

[0047] In the above embodiment, the method for analyzing the path safety index of the intelligent metasurface material units installed in various parts of the analysis vehicle during travel on the updated path is as follows: Based on the obtained updated path data, where the updated path data includes the electron distribution quantity, voltage, and heat phase change value of the intelligent metasurface material units installed in various parts of the vehicle during travel on the updated path, and extracting from the database the reference electron distribution quantity, reference voltage, and reference heat phase change value of the intelligent metasurface material units installed in various parts of the vehicle during travel on the updated path, and then analyzing the path safety index of the intelligent metasurface material units installed in various parts of the vehicle during travel on the updated path. The specific calculation formula is as follows: where, v' i 、j' i 、f' i respectively represent the reference electron distribution quantity, reference voltage, and reference heat phase change value of the intelligent metasurface material units installed in various parts of the vehicle during travel on the updated path, v i 、j i 、f i respectively represent the reference electron distribution quantity, reference voltage, and reference heat phase change value of the intelligent metasurface material units installed in various parts of the vehicle during travel on the updated path, and perform updated path planning, and determine whether there is a safety risk in the updated travel path.

[0048] In the above embodiment, the method for determining whether there is a safety risk in the updated travel path is as follows: Based on the obtained path safety index of the intelligent metasurface material units installed in various parts of the vehicle during travel on the updated path, and extracting the corresponding safety level division from the database to obtain the safety level, relatively safe level, warning level, and danger level. When the updated path safety index is at the warning level or danger level, an early warning signal is sent in a timely manner to remind the driver to pay attention to safety. By adjusting the optical and electrical properties through the reference electron distribution quantity, reference voltage, and reference heat phase change value, intelligent perception and dynamic response are realized, the reflectivity of the intelligent metasurface materials installed in various parts of the vehicle during travel on the updated path is changed, the intelligent metasurface materials are adjusted, and based on the generated environment model, combined with the in-vehicle navigation system, the updated path is carried out, and combined with traffic signals and speed limits, the updated path is optimized, and the strategy is adjusted in a timely manner.

[0049] Referring to Figure 2 as shown, a system device for a vehicle intelligent surface metamaterial electromagnetic imaging navigation method, the technical solution includes: A vehicle intelligent surface metamaterial preliminary installation module: Through the historical data of the vehicle, screening the historical collision positions of the vehicle, taking each historical collision position as the installation position of the metamaterial on the intelligent surface of the vehicle, and thus obtaining the intelligent metasurface material units installed in various parts of the vehicle.

[0050] Vehicle Navigation Data Acquisition Module: Collect data from traditional sensors on various parts of the vehicle during different time periods of its historical driving, thereby obtaining the operating data of the traditional sensors, and then analyzing the navigation performance index of the traditional sensors on various parts of the vehicle during its historical driving.

[0051] Vehicle Navigation Data Analysis Module: Based on the obtained navigation performance index of the traditional sensors on various parts of the vehicle during its historical driving, determine whether the traditional sensors on various parts of the vehicle are abnormal during its historical driving. If abnormal, activate the intelligent metasurface material units installed on various parts of the vehicle to obtain an electromagnetic imaging module, generate an electromagnetic imaging image, and analyze the obstacle collision index of the intelligent metasurface material units installed on various parts of the vehicle during driving.

[0052] Vehicle Navigation Cooperative Control Module: Based on the obtained obstacle collision index of the intelligent metasurface material units installed on various parts of the vehicle during driving, construct an electromagnetic field model of the surrounding environment, fuse it with the vehicle's navigation map, and perform path planning and obstacle avoidance operations.

[0053] During the implementation process, the outer surface of the vehicle adopts a highly integrated metasurface structure. These metasurface units realize the emission and reception of electromagnetic waves through microcircuits. By adjusting the response characteristics of the metasurface material, the propagation characteristics of electromagnetic waves can be optimized in different environments, the surrounding obstacles can be sensed in real time and a clear electromagnetic imaging image can be generated, and the generated environmental model is combined with the in-vehicle navigation system to extract target information such as obstacles, pedestrians, and vehicles, construct a high-precision environmental model, timely reflect the surrounding environmental information, perform accurate path planning and obstacle avoidance operations, break through the limitations of traditional sensors, utilize the adjustment ability of intelligent metasurface materials to achieve precise navigation and environmental perception in various complex environments, timely calculate the best path, obstacle avoidance strategy, traffic signal recognition, etc., send instructions to the vehicle's driving control system, and improve the overall performance and safety of the system.

[0054] The above content is only an example and explanation of the concept of the present invention. Those skilled in the art of this technology can make various modifications or supplements to the described specific embodiments or use similar methods to replace them. As long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, they should all fall within the protection scope of the present invention.

Claims

1. A vehicle intelligent surface metamaterial electromagnetic imaging navigation method, characterized in that: include: Preliminary installation of vehicle intelligent surface metamaterials: Through the historical data of the vehicle, the historical collision positions of the vehicle are screened, and each historical collision position is used as the metamaterial installation position of the vehicle's intelligent surface, thereby obtaining the intelligent metasurface material units installed in various parts of the vehicle; Vehicle navigation data collection: collect data from traditional sensors installed at various parts of the vehicle at various time periods during historical driving, and then obtain traditional sensor operation data, and then analyze the navigation performance index of traditional sensors installed at various parts of the vehicle during historical driving; Vehicle navigation data analysis: Based on the obtained navigation performance index of the traditional sensors installed at various parts of the vehicle during historical driving, determine whether the traditional sensors installed at various parts of the vehicle during historical driving are abnormal. If abnormal, start the intelligent metasurface material units installed at various parts of the vehicle to obtain the electromagnetic imaging module, generate electromagnetic imaging images, and analyze the obstacle collision index of the intelligent metasurface material units installed at various parts of the vehicle during driving; Vehicle navigation collaborative control: Based on the obstacle collision index of the intelligent metasurface material units installed in various parts of the vehicle during driving, an electromagnetic field model of the surrounding environment is constructed and integrated with the vehicle's navigation map to perform path planning and obstacle avoidance operations.

2. A vehicle intelligent surface metamaterial electromagnetic imaging navigation method according to claim 1, characterized in that: The specific analysis method of analyzing the navigation performance index of the traditional sensors installed in various parts of the vehicle during historical driving is as follows: Based on the obtained traditional sensor operation data, where the traditional sensor operation data includes the time interval, number and output signal difference of the laser pulses of the traditional sensors at various parts of the vehicle in various time periods, and the input light intensity difference, the time interval of the laser pulses is processed by ratio with the number of laser pulses to obtain the scanning frequency of the traditional sensors at various parts of the vehicle in various time periods during historical driving, and the output signal change is processed by ratio with the input light intensity change to obtain the sensitivity of the traditional sensors at various parts of the vehicle in various time periods during historical driving, and the reference sensitivity and reference scanning frequency of the traditional sensors at various parts of the vehicle in historical driving are extracted from the database, and then the navigation performance index of the traditional sensors at various parts of the vehicle in historical driving is analyzed, and the specific calculation formula is: Among them, q i ' represents the reference scanning frequency of the traditional sensor at the i-th position of the vehicle during historical driving, q ui It is represented by the scanning frequency of the traditional sensor at the i-th position of the vehicle in the u-th time period during the historical driving, d i ' represents the reference sensitivity of the traditional sensor at the i-th position of the vehicle during historical driving, d ui ' represents the sensitivity of the traditional sensor installed at the i-th part of the vehicle in the u-th time period in the historical driving, i represents the number of the part, i=1,2,3,...,e, e represents the number of the parts, u represents the number of the time period, u=1,2,3,...,l, l represents the number of the time period.

3. The vehicle intelligent surface metamaterial electromagnetic imaging navigation method according to claim 1, characterized in that: The specific analysis method for judging whether the conventional sensors installed in various parts of the vehicle during historical driving are abnormal is as follows: Based on the obtained navigation performance index of the traditional sensors installed at various parts of the vehicle during the historical driving, the qualified range of the navigation performance index of the traditional sensors installed at various parts of the vehicle during the historical driving is obtained from the database, and the navigation performance index of the traditional sensors installed at various parts of the vehicle during the historical driving is compared with the qualified range of the navigation performance index; if the navigation performance index of a traditional sensor installed at a certain part of the vehicle during the historical driving is not within the qualified range of the navigation performance index, it is determined that the traditional sensor installed at this part of the vehicle during the historical driving is in an abnormal state, and the intelligent metasurface material units installed at various parts of the vehicle are started, thereby obtaining an electromagnetic imaging module and generating an electromagnetic imaging image.

4. The vehicle intelligent surface metamaterial electromagnetic imaging navigation method according to claim 3 is characterized in that: The electromagnetic imaging module comprises: Electromagnetic wave transmitting unit: This unit generates electromagnetic waves through metasurface materials, which propagate around the vehicle and interact with objects; Electromagnetic wave receiving unit: The receiving unit is used to capture electromagnetic wave signals reflected from surrounding objects; Based on the obtained electromagnetic wave transmitting unit and receiving unit, the electromagnetic wave transmitting unit and the receiving unit are subjected to data analysis to obtain electromagnetic wave receiving data and electromagnetic wave transmitting data. The electromagnetic wave transmitting data includes the electromagnetic wave transmitting frequency, the electromagnetic intensity in each direction and the angle range of the beam. The electromagnetic wave receiving data includes the number of electron distribution, voltage and heat phase change value. Then, the obstacle collision index P of the intelligent metasurface material unit installed in various parts of the vehicle during driving is analyzed. i Based on the existing technology, it is judged whether the vehicle can avoid obstacles while driving on the original path. If it cannot avoid obstacles, the path is updated based on the generated electromagnetic imaging image to obtain updated path data, and the path safety index K of the intelligent metasurface material units installed in various parts of the vehicle while driving on the updated path is analyzed. i .

5. The vehicle intelligent surface metamaterial electromagnetic imaging navigation method according to claim 4 is characterized in that: The specific analysis method for analyzing the obstacle collision index of the intelligent metasurface material units installed at various parts of the vehicle during driving is as follows: Through electromagnetic wave reflection, when the emitted electromagnetic wave encounters obstacle reflection, by analyzing the characteristics of the reflected wave, and based on the electromagnetic wave emission frequency of the intelligent metasurface material of each part of the vehicle while driving, the electromagnetic intensity in each direction and the angle range of the beam, the radar ranging principle is used to obtain the minimum distance between each part of the vehicle while driving and the obstacle, the angle between the emission beam of the intelligent metasurface material and the obstacle surface, and the azimuth between the obstacle and the vehicle. According to the spectrum analysis, the signal characteristic value matching on the frequency is used to obtain the size of the obstacle, the position and shape of the surrounding obstacles, and time delay analysis and frequency analysis are performed. The electromagnetic field model of the surrounding environment is constructed and integrated with the vehicle's navigation map to perform obstacle detection, and the obstacle collision index of the intelligent metasurface material unit at each part of the vehicle while driving is obtained.

6. The vehicle intelligent surface metamaterial electromagnetic imaging navigation method according to claim 4, characterized in that: The specific analysis method of analyzing the path safety index of the intelligent metasurface material units installed at various parts of the vehicle while traveling on the updated path is as follows: Based on the obtained update path data, where the update path data includes the electronic distribution quantity, voltage and thermal phase change value of the smart metasurface material units installed at various parts of the vehicle while traveling on the update path, the electronic distribution reference quantity, reference voltage and thermal reference phase change value of the smart metasurface material units installed at various parts of the vehicle while traveling on the update path are extracted from the database, and then the path safety index of the smart metasurface material units installed at various parts of the vehicle while traveling on the update path is analyzed, and the specific calculation formula is: Among them, v' i 、j' i 、f' i They represent the reference number of electron distribution, reference voltage and reference heat phase change value of the smart metasurface material units installed at various parts of the vehicle during the update path, v i 、j i 、f i They respectively represent the reference quantity of electron distribution, reference voltage and thermal reference phase change value of the intelligent metasurface material units installed in various parts of the vehicle while traveling on the updated path, and perform updated path planning and determine whether there is a safety risk in the updated driving path.

7. The vehicle intelligent surface metamaterial electromagnetic imaging navigation method according to claim 4, characterized in that: The specific analysis method for determining whether there is a safety risk in updating the driving path is as follows: Based on the obtained path safety index of the intelligent metasurface material units installed at various parts of the vehicle while traveling on the updated path, the corresponding safety levels are extracted from the database to obtain the safety level, relatively safe level, warning level and danger level. When the updated path safety index is at the warning level or danger level, an early warning signal is issued in time to remind the driver to pay attention to safety. The optical and electrical properties are adjusted by adjusting the electronic distribution reference quantity, reference voltage and thermal reference phase change value to realize intelligent perception and dynamic response, change the reflectivity of the intelligent metasurface materials installed at various parts of the vehicle while traveling on the updated path, adjust the intelligent metasurface materials, and based on the generated environmental model, combine with the on-board navigation system to update the path, and optimize the update path in combination with traffic signals and driving speed limits, and adjust the strategy in time.

8. A system device for executing the vehicle intelligent surface metamaterial electromagnetic imaging navigation method according to any one of claims 1 to 7, characterized in that: include: The preliminary installation module of vehicle intelligent surface metamaterials: through the historical data of the vehicle, the historical collision positions of the vehicle are screened, and each historical collision position is used as the installation position of the metamaterials of the vehicle's intelligent surface, thereby obtaining the intelligent metasurface material units installed in various parts of the vehicle; Vehicle navigation data acquisition module: collects data from traditional sensors installed at various parts of the vehicle at various time periods during historical driving, thereby obtaining traditional sensor operation data, and then analyzing the navigation performance index of traditional sensors installed at various parts of the vehicle during historical driving; Vehicle navigation data analysis module: based on the obtained navigation performance index of the traditional sensors installed at various parts of the vehicle during historical driving, it is determined whether the traditional sensors installed at various parts of the vehicle during historical driving are abnormal. If abnormal, the intelligent metasurface material units installed at various parts of the vehicle are activated to obtain the electromagnetic imaging module, generate electromagnetic imaging images, and analyze the obstacle collision index of the intelligent metasurface material units installed at various parts of the vehicle during driving; Vehicle navigation collaborative control module: Based on the obstacle collision index of the intelligent metasurface material units installed in various parts of the vehicle during driving, an electromagnetic field model of the surrounding environment is constructed and integrated with the vehicle's navigation map to perform path planning and obstacle avoidance operations.

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