New energy automobile parking space safety early warning system

Abstract

The invention relates to the technical field of automobile safety early warning, in particular to a new energy automobile parking space safety early warning system. The system comprises an environment sensing unit which collects environment data around a parking space in real time; the positioning and navigation unit obtains the relative position and dynamic information of the vehicle and the parking space based on the surrounding environment data; and the data processing and analyzing unit analyzes the information acquired by the positioning and navigation unit, and analyzes the abnormal state of the vehicle around the parking space based on the surrounding environment model. Optimization of a ramp environment and a vehicle attitude is introduced, an adaptive function for a ramp inclination angle and a vehicle pitching angle is added into an optimized kernel function, the distance between a vehicle and a parking space can be accurately calculated, attitude change caused by a ramp is considered, and therefore the adaptability of a model to a ramp parking environment is improved, and the accuracy of the model is improved. And the safety early warning capability of the system during ramp parking is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of automobile safety warning technology, and in particular to a parking space safety warning system for new energy vehicles. Background Art

[0002] With the popularization of new energy vehicles (such as electric vehicles), the demand for parking spaces not only continues to grow in quantity, but also needs to meet multiple requirements such as charging, intelligent management, and diversified usage scenarios; the new energy vehicle parking space safety warning system uses a variety of sensors (such as cameras, radars, ultrasonic sensors, etc.) to monitor the environment around the vehicle in real time, and combines positioning and navigation technologies (such as GPS, IMU inertial measurement unit), as well as data analysis and machine learning algorithms to evaluate potential risks during parking, and promptly warn the driver or take automatic measures to avoid collisions or other accidents. As part of the advanced driver assistance system (ADAS), the parking space safety warning system provides important support for achieving a higher level of automated driving, especially in the last mile low-speed driving scenario.

[0003] For users, the new energy vehicle parking safety warning system can assist car owners to park more accurately in small or complex home garages, reducing the risk of scratches and other damages; however, in complex or non-standard parking spaces, especially in parking environments with dynamic obstacles or ramps, the vehicle needs to overcome the influence of gravity and accurately control the braking and driving force, while sliding on the ramp or incorrect perception of posture angles will increase the difficulty of parking. Therefore, a new energy vehicle parking safety warning system is designed. Summary of the invention

[0004] The purpose of the present invention is to provide a new energy vehicle parking safety warning system to solve the problem raised in the above background technology that in complex or non-standard parking spaces, especially in parking environments with dynamic obstacles or ramps, the vehicle needs to overcome the influence of gravity and accurately control the braking and driving force, while the sliding on the ramp or the erroneous perception of the posture angle will increase the difficulty of parking.

[0005] To achieve the above object, the present invention provides a new energy vehicle parking space safety warning system, comprising: An environment sensing unit, which collects data about the surrounding environment of the parking space in real time; A positioning and navigation unit, wherein the positioning and navigation unit obtains the relative position and dynamic information of the vehicle and the parking space based on the surrounding environment data; A data processing and analysis unit, wherein the data processing and analysis unit analyzes the information acquired by the positioning and navigation unit, and the data processing and analysis unit analyzes the abnormal state of the vehicle around the parking space based on the surrounding environment model; A decision-making and warning unit performs risk assessment and triggers an early warning mechanism based on the analysis results.

[0006] As a further improvement of the technical solution, the parking space surrounding environment data includes the marking position of the parking space , , the relative position of the parking space , .

[0007] As a further improvement of the technical solution, the positioning and navigation unit (2) acquires the relative position and dynamic information of the vehicle and the parking space based on the surrounding environment data, comprising the following steps: S1.1. Preprocess the collected parking space surrounding environment data and collect the original coordinates of the environmental information points through sensors ; S1.2, based on the vehicle's own positioning sensors and environmental data, based on the original coordinates Determine the vehicle's current absolute position , and attitude angle , ,in, Indicates that the vehicle is The velocity component in the direction, Indicates that the vehicle is The velocity component in the direction; S1.3. Calculate the parking space marking position and the vehicle’s current absolute position Relative position : ; S1.4. Calculate the vehicle speed through the vehicle sensor data and acceleration ; S1.5, based on the absolute position of the parking space , calculate the relative position of the vehicle and the parking space : .

[0008] As a further improvement of the technical solution, the data processing and analysis unit includes a data fusion processing module and an intelligent algorithm module; The data fusion processing module integrates the relative position of the vehicle and the parking space output by the positioning and navigation unit. , the relative position of the parking space , vehicle dynamic information and environmental data to form a surrounding environment model; The intelligent algorithm module analyzes the abnormal state of the vehicle around the parking space based on the surrounding environment model.

[0009] As a further improvement of the technical solution, the data fusion processing module integrates the relative position of the vehicle and the parking space output by the positioning and navigation unit , the relative position of the parking space , vehicle dynamic information and environmental data to form a surrounding environment model, including the following steps: S2.1. Relative position of the input vehicle and parking space , the relative position of the parking space , basic processing of vehicle dynamic information and environmental data; S2.2. Convert sensor data to a unified coordinate system : ,in represents the rotation matrix, represents the translation vector; S2.3. The detection results of the same target are fused by weighted averaging: S2.4. Establish a surrounding environment model based on the fused data.

[0010] As a further improvement of the technical solution, in S2.4, establishing a surrounding environment model based on the fused data includes the following steps: S2.41. Constructing vehicle position model ; S2.42. Constructing vehicle direction model : ,in, represents the angular velocity of the vehicle; S2.43. Construct a relative position model of vehicles and parking spaces: ,in, Indicates the location of the parking space; S2.44. Integrate the above models to form a surrounding environment model.

[0011] As a further improvement of the technical solution, in S2.44, the surrounding environment model is: ; in, represents the surrounding environment abnormality assessment model; Indicates the time between the vehicle and the parking space The relative speed difference The weight coefficient representing the relative position; Represents the weight coefficient of the vehicle heading angle; The weight coefficient representing the relative position; Indicates the geometric relationship between the heading angle of the vehicle and the parking space; The surrounding environment model is optimized based on the vehicle's motion trajectory and the relative angle between the vehicle and the parking space: ; in, represents the optimized surrounding environment abnormality assessment model; represents the relative angular velocity change, , Indicates the change in relative heading angle between the parking space and the vehicle; The weight coefficient representing the change in relative angular velocity; The vehicle's future motion trajectory prediction model is introduced into the optimized surrounding environment model: ; ; in, It represents the surrounding environment abnormality assessment model after the future motion trajectory prediction model of the vehicle is introduced; Represents the vehicle motion trajectory prediction model.

[0012] As a further improvement of the technical solution, the intelligent algorithm module analyzes the abnormal state of the vehicle around the parking space through a support vector machine model based on the surrounding environment model, including the following steps: S3.1. The established surrounding environment model is used as input features, including the relative position, speed, acceleration, heading angle, etc. of the vehicle, and the input features are used as a data set; S3.2. Use the dataset to train the support vector machine model and use the kernel function to adjust the hyperparameters of the support vector machine model. S3.3, using the trained support vector machine model to analyze the real-time collected parking space surrounding environment data; S3.4. According to the output of the support vector machine, an abnormal state evaluation value of the vehicle and the surrounding environment of the parking space is obtained.

[0013] As a further improvement of the technical solution, in S3.2, the kernel function is: ; in, Indicates the distance between the vehicle and the parking space; A state feature vector representing the vehicle; The state feature vector representing the parking space; Represents the scale parameter in the kernel function; According to the influence of ramp parking environment on vehicle posture, the kernel function is optimized: ; ; ; in, Indicates the distance between the vehicle and the parking space after optimization; represents the terrain adaptability function, taking into account the influence of the slope angle of the ramp on the relative position of the vehicle and the parking space; Represents the vehicle attitude function, taking into account the impact of the vehicle's pitch angle on the relative position of the vehicle and the parking space; Indicates the slope angle; Indicates the pitch angle of the vehicle; Represents the weight coefficient that adjusts the intensity of the terrain's influence on the kernel function; Represents the weight coefficient that controls the influence of the pitch angle on the kernel function.

[0014] As a further improvement of the technical solution, the decision-making and warning unit performs risk assessment and triggers an early warning mechanism according to the analysis results, including the following steps: S4.1. Receive the abnormality assessment result from the data processing and analysis unit; S4.2, set medium risk and high risk levels to determine whether there are low risks of safety hazards in the surrounding environment of the parking space; S4.3. A warning reminder is triggered when there is a medium risk in the environment around the parking space, and an emergency warning is triggered when there is a high risk.

[0015] Compared with the prior art, the present invention has the following beneficial effects: 1. In the new energy vehicle parking safety warning system, the optimization of ramp environment and vehicle posture is introduced into the traditional support vector machine model. In order to cope with the ramp environment, the optimized kernel function adds the adaptability function of the ramp inclination angle and the vehicle pitch angle, which can more accurately calculate the distance between the vehicle and the parking space, taking into account the posture changes caused by the ramp, thereby improving the adaptability of the model to the ramp parking environment and ensuring the system's safety warning capability when parking on the ramp.

[0016] 2. The new energy vehicle parking safety warning system introduces a vehicle future motion trajectory prediction model, which can predict the possible position and state of the vehicle in the future based on the vehicle's current position, speed, acceleration and other information; for ramp parking, the system can predict the vehicle's deviation due to the change of ramp terrain, and identify potential risks in time, such as predicting whether the distance between the vehicle and the parking space boundary is too close, so as to issue a warning in advance or trigger automatic braking; The prediction of future motion trajectory helps the system to accurately predict the movement of the vehicle in a ramp environment when the vehicle is about to enter the parking space, avoiding accidental collisions or parking failures caused by changes in the ramp angle. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is the overall flow chart of the present invention; The meaning of each number in the figure is: 1. Environmental perception unit; 2. Positioning and navigation unit; 3. Data processing and analysis unit; 31. Data fusion processing module; 32. Intelligent algorithm module; 4. Decision-making and warning unit. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] Example: See Figure 1 As shown, a parking space safety warning system for new energy vehicles is provided, including an environment sensing unit 1, which collects parking space surrounding environment data in real time; Among them, the parking space surrounding environment data includes the parking space marking position (i.e. the boundary or center of the parking space), , the relative position of the parking space (i.e. the specific location of the parking spaces in or around the parking spaces), .

[0020] In this embodiment, the new energy vehicle parking space safety warning system further includes a positioning and navigation unit 2, which obtains the relative position and dynamic information of the vehicle and the parking space based on the surrounding environment data; The positioning and navigation unit 2 obtains the relative position and dynamic information (posture, speed, acceleration) of the vehicle and the parking space based on the surrounding environment data, including the following steps: S1.1. Preprocess the collected parking space surrounding environment data and collect the original coordinates of the environmental information points through sensors , original coordinates It is the location coordinates of the environmental information points collected by the sensor, representing the raw data in the sensor's local coordinate system; S1.2, based on the vehicle's own positioning sensors (such as GPS, IMU) and environmental data, based on the original coordinates Determine the vehicle's current absolute position , and attitude angle , ,in, Indicates that the vehicle is The velocity component in the direction, Indicates that the vehicle is The velocity component in the direction; S1.3. Calculate the parking space marking position and the vehicle’s current absolute position Relative position : ; S1.4. Calculate the vehicle speed through the vehicle sensor data and acceleration ; S1.5, based on the absolute position of the parking space , calculate the relative position of the vehicle and the parking space : .

[0021] The new energy vehicle parking space safety warning system also includes a data processing and analysis unit 3, which analyzes the information obtained by the positioning and navigation unit 2, and the data processing and analysis unit 3 analyzes the abnormal state of the vehicle around the parking space based on the surrounding environment model; The data processing and analysis unit 3 includes a data fusion processing module 31 and an intelligent algorithm module 32; The data fusion processing module 31 integrates the relative position of the vehicle and the parking space output by the positioning and navigation unit 2. , the relative position of the parking space , vehicle dynamic information and environmental data to form a surrounding environment model. Vehicle dynamic information includes posture, speed, and acceleration; The intelligent algorithm module 32 analyzes the abnormal state of the vehicle around the parking space based on the surrounding environment model.

[0022] Furthermore, the data fusion processing module 31 integrates the relative position of the vehicle and the parking space output by the positioning and navigation unit 2 , the relative position of the parking space , vehicle dynamic information (posture, speed, acceleration) and environmental data to form a surrounding environment model, including the following steps: S2.1. Relative position of the input vehicle and parking space , the relative position of the parking space , basic processing of vehicle dynamic information (posture, speed, acceleration) and environmental data, including time synchronization (synchronizing the data collected by the environmental perception unit, positioning and navigation unit according to timestamps to ensure data consistency), unit and format unification (converting data from different sources into a unified coordinate system, unit (such as metric) and data format), anomaly detection and elimination (checking the data for outliers (such as sensor noise or invalid data) and eliminating or repairing them); S2.2. Convert sensor data to a unified coordinate system : ,in represents the rotation matrix, represents the translation vector; S2.3. The detection results of the same target are fused by weighted averaging: S2.4. A surrounding environment model is established based on the fused data. The surrounding environment model is used to reflect the dynamic and static information around the vehicle.

[0023] In S2.4 of this embodiment, establishing a surrounding environment model based on the fused data includes the following steps: S2.41. Constructing vehicle position model ; S2.42. Constructing vehicle direction model : ,in, represents the angular velocity of the vehicle; S2.43. Construct a relative position model of vehicles and parking spaces: ,in, Indicates the location of the parking space; S2.44. Integrate the above models to form a surrounding environment model, which is used to perceive, analyze and predict static and dynamic information in the parking space and the surrounding environment in real time, and provide data support for safety warning and auxiliary decision-making.

[0024] In S2.44 of this embodiment, the surrounding environment model is: ; in, represents the surrounding environment abnormality assessment model; Indicates the time between the vehicle and the parking space The relative speed difference The weight coefficient representing the relative position; Represents the weight coefficient of the vehicle heading angle; The weight coefficient representing the relative position; Indicates the geometric relationship between the heading angle of the vehicle and the parking space. It calculates the angle between the vehicle's heading and the parking space marking. If the angle deviation between the vehicle and the parking space is large (i.e. the vehicle may deviate from the parking space), the risk will be greater. The surrounding environment model is optimized based on the vehicle's motion trajectory and the relative angle between the vehicle and the parking space: By analyzing the actual driving path of the vehicle, we can more accurately understand how the vehicle moved to its current position, which is crucial for identifying abnormal driving behaviors (such as sudden turns or sudden braking), which may indicate impending risks; the relative angle between the vehicle and the parking space reflects the way the vehicle enters the parking space. If the angle is not appropriate, it may cause parking difficulties or increase the risk of collision. Therefore, accurately capturing this angle can help the system better evaluate whether the current parking operation is safe; by combining the actual motion trajectory and relative angle of the vehicle, the system can build a more detailed model of the surrounding environment, which includes not only static information (such as parking space boundaries) but also dynamic changes (such as how the vehicle approaches the parking space). Such a model can provide richer contextual information for risk assessment and make the decision-making process more intelligent; different drivers have different driving habits, and different parking spaces have different geometric characteristics. By considering the actual motion trajectory and relative angle, the system can better adapt to various parking scenarios and maintain high performance and reliability even in complex or non-standard parking spaces; ; in, represents the optimized surrounding environment abnormality assessment model; represents the relative angular velocity change, , Indicates the change in relative heading angle between the parking space and the vehicle; The weight coefficient representing the change in relative angular velocity; The vehicle's future motion trajectory prediction model is introduced into the optimized surrounding environment model: ; ; in, It represents the surrounding environment abnormality assessment model after the future motion trajectory prediction model of the vehicle is introduced; Represents the vehicle motion trajectory prediction model.

[0025] By predicting the possible position and state of the vehicle in the future, the system can identify potential risks before an accident occurs. The system can predict the possible position and state of the vehicle in the future based on the vehicle's current position, speed, acceleration and other information. For ramp parking, the system can predict the vehicle's deviation due to the change of ramp terrain and identify potential risks in time, such as predicting whether the distance between the vehicle and the parking space boundary is too close, so as to issue a warning in advance or trigger automatic braking; for example, if it is predicted that the vehicle will collide with an obstacle or deviate from the parking space, the system can issue a warning in advance to give the driver enough time to take corrective measures; the prediction model provides important information input for the system. Based on these predicted data, the system can make more intelligent decisions, such as adjusting the vehicle's speed, direction or initiating emergency braking to ensure safe parking; the introduction of future motion trajectory prediction helps the system better adapt to the dynamically changing parking environment. Even if there are moving objects or other uncertain factors around, the system can adjust the strategy in time according to the latest prediction results to maintain a high degree of safety and reliability; The intelligent algorithm module 32 analyzes the abnormal state of the vehicle around the parking space based on the surrounding environment model through the support vector machine model, including the following steps: S3.1. The established surrounding environment model is used as input features, including the relative position, speed, acceleration, heading angle, etc. of the vehicle, and the input features are used as a data set; S3.2. Use the dataset to train a support vector machine (SVM) model. The support vector machine can handle nonlinear problems (e.g., complex relationships between the angle and position of the vehicle and the parking space) by mapping the data into a high-dimensional feature space. The kernel function (RBF) is used to adjust the hyperparameters of the support vector machine (SVM) model (e.g., penalty coefficient and kernel function parameters) to improve classification accuracy. S3.3, use the trained support vector machine model to analyze the real-time collected parking space surrounding environment data, and the SVM model will output the prediction result (normal or abnormal) based on the input surrounding environment data; S3.4. According to the output of the support vector machine, an abnormal state evaluation value of the vehicle and the surrounding environment of the parking space is obtained.

[0026] Among them, the kernel function in S3.2 is: ; in, Indicates the distance between the vehicle and the parking space; Represents the vehicle's state feature vector (position, speed, acceleration, attitude, etc.); The state feature vector representing the parking space (the position of the parking space’s markings, the relative position of the parking space, the position of obstacles near the parking space, etc.); Represents the scale parameter in the kernel function, which controls the sensitivity of the similarity calculation between vehicles and parking spaces; According to the influence of ramp parking environment on vehicle posture, the kernel function is optimized: In a ramp parking environment, the vehicle's posture (such as pitch angle) will change due to the tilt of the ground, which directly affects the relative position and angle relationship between the vehicle and the parking space; the vehicle's pitch angle will affect the sensing range and accuracy of its sensors. For example, when going uphill or downhill, the distance sensing in front of and behind the vehicle may be affected by posture changes; in a ramp environment, traditional kernel functions may not fully consider the impact of vehicle posture and terrain, resulting in inaccurate risk assessment. By optimizing the kernel function, the impact of these factors on vehicle behavior can be captured more finely, thereby making more reasonable risk predictions; in some extreme cases, such as parking on a steep ramp, the vehicle's posture may change significantly. The optimized kernel function can ensure that the system can still maintain high accuracy and response speed in this case, avoiding potential safety hazards; ; ; ; in, Indicates the distance between the vehicle and the parking space after optimization; It represents the terrain adaptability function, taking into account the influence of the slope angle on the relative position of the vehicle and the parking space, and correcting the relative position error caused by the terrain tilt; Represents the vehicle attitude function, taking into account the impact of the vehicle's pitch angle on the relative position of the vehicle and the parking space; Indicates the slope angle; Indicates the pitch angle of the vehicle; Represents the weight coefficient that adjusts the intensity of the terrain's influence on the kernel function; Represents the weight coefficient that controls the influence of the pitch angle on the kernel function.

[0027] The new energy vehicle parking space safety warning system also includes a decision-making and warning unit 4, which performs risk assessment and triggers a warning mechanism based on the analysis results; In this embodiment, the decision-making and warning unit 4 performs risk assessment and triggers the early warning mechanism according to the analysis results, including the following steps: S4.1, receiving the abnormality assessment result from the data processing and analysis unit 3; S4.2, set the risk level to determine whether there is a low risk of safety hazard in the environment around the parking space; determine whether it is within the normal range based on the relative position and dynamic information (such as speed, acceleration, posture, etc.) between the vehicle and the parking space; Among them, medium risk (the vehicle's dynamic information shows some unusual trends, such as the vehicle deviating from the parking space, excessive speed or abnormal acceleration); High risk (the relative position of the vehicle and the parking space is significantly deviated, there is a risk of collision or the obstacles around the parking space are too close, and the speed and angle of the vehicle change abnormally); S4.3. When there is a medium risk in the environment around the parking space, a warning reminder is triggered (prompting the driver: reminding the driver to pay attention to parking safety through the vehicle display, audio system or mobile phone application; displaying a warning message: such as "the parking space is slightly off, please operate with caution"); when there is a high risk, an emergency warning is triggered (an emergency alarm sounds; the automatic parking assistance system is activated, automatically adjusting the vehicle speed, lane deviation, etc. to avoid a collision; sending a notification: notifying the owner, parking management personnel or the automation system for timely processing).

[0028] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments, and the above embodiments and descriptions are only preferred examples of the present invention, and are not intended to limit the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, and these changes and improvements all fall within the scope of the present invention to be protected.

Claims

1. A new energy vehicle parking space safety warning system, characterized in that: include: An environment sensing unit (1), the environment sensing unit (1) collects parking space surrounding environment data in real time; A positioning and navigation unit (2), wherein the positioning and navigation unit (2) acquires the relative position and dynamic information of the vehicle and the parking space based on surrounding environment data; A data processing and analysis unit (3), wherein the data processing and analysis unit (3) analyzes the information acquired by the positioning and navigation unit (2), and the data processing and analysis unit (3) analyzes the abnormal state of the vehicle around the parking space based on a surrounding environment model; A decision-making and warning unit (4), wherein the decision-making and warning unit (4) performs risk assessment according to the analysis results and triggers a warning mechanism.

2. The parking space safety warning system for new energy vehicles according to claim 1 is characterized by: The parking space surrounding environment data includes the parking space marking position , , the relative position of the parking space , .

3. The parking space safety warning system for new energy vehicles according to claim 1 is characterized by: The positioning and navigation unit (2) acquires the relative position and dynamic information of the vehicle and the parking space based on the surrounding environment data, comprising the following steps: S1.

1. Preprocess the collected parking space surrounding environment data and collect the original coordinates of the environmental information points through sensors ; S1.2, based on the vehicle's own positioning sensors and environmental data, based on the original coordinates Determine the vehicle's current absolute position , and attitude angle , ,in, Indicates that the vehicle is The velocity component in the direction, Indicates that the vehicle is The velocity component in the direction; S1.

3. Calculate the parking space marking position and the vehicle’s current absolute position Relative position : ; S1.

4. Calculate the vehicle speed through the vehicle sensor data and acceleration ; S1.5, based on the absolute position of the parking space , calculate the relative position of the vehicle and the parking space : .

4. The parking space safety warning system for new energy vehicles according to claim 1 is characterized by: The data processing and analysis unit (3) comprises a data fusion processing module (31) and an intelligent algorithm module (32); The data fusion processing module (31) integrates the relative position of the vehicle and the parking space output by the positioning and navigation unit (2). , the relative position of the parking space , vehicle dynamic information and environmental data to form a surrounding environment model; The intelligent algorithm module (32) analyses the abnormal state of the vehicle around the parking space based on the surrounding environment model.

5. The parking space safety warning system for new energy vehicles according to claim 1 is characterized by: The data fusion processing module (31) integrates the relative position of the vehicle and the parking space output by the positioning and navigation unit (2) , the relative position of the parking space , vehicle dynamic information and environmental data to form a surrounding environment model, including the following steps: S2.

1. Relative position of the input vehicle and parking space , the relative position of the parking space , basic processing of vehicle dynamic information and environmental data; S2.

2. Convert sensor data to a unified coordinate system : ,in represents the rotation matrix, represents the translation vector; S2.

3. The detection results of the same target are fused by weighted averaging: S2.

4. Establish a surrounding environment model based on the fused data.

6. The parking space safety warning system for new energy vehicles according to claim 1 is characterized by: In S2.4, establishing a surrounding environment model based on the fused data includes the following steps: S2.

41. Constructing vehicle position model ; S2.

42. Constructing vehicle direction model : ,in, represents the angular velocity of the vehicle; S2.

43. Construct a relative position model of vehicles and parking spaces: ,in, Indicates the location of the parking space; S2.

44. Integrate the above models to form a surrounding environment model.

7. The parking space safety warning system for new energy vehicles according to claim 1 is characterized by: In S2.44, the surrounding environment model is: ； in, represents the surrounding environment abnormality assessment model; Indicates the time between the vehicle and the parking space The relative speed difference The weight coefficient representing the relative position; Represents the weight coefficient of the vehicle heading angle; The weight coefficient representing the relative position; Indicates the geometric relationship between the heading angle of the vehicle and the parking space; The surrounding environment model is optimized based on the vehicle's motion trajectory and the relative angle between the vehicle and the parking space: ； in, represents the optimized surrounding environment abnormality assessment model; Represents the relative angular velocity change; The weight coefficient representing the change in relative angular velocity; The vehicle's future motion trajectory prediction model is introduced into the optimized surrounding environment model: ； in, It represents the surrounding environment abnormality assessment model after the future motion trajectory prediction model of the vehicle is introduced; Represents the vehicle motion trajectory prediction model.

8. The parking space safety warning system for new energy vehicles according to claim 1 is characterized by: The intelligent algorithm module (32) analyzes the abnormal state of the vehicle around the parking space through a support vector machine model based on the surrounding environment model, including the following steps: S3.1, taking the established surrounding environment model as input features and taking the input features as a data set; S3.

2. Use the dataset to train the support vector machine model and use the kernel function to adjust the hyperparameters of the support vector machine model. S3.3, using the trained support vector machine model to analyze the real-time collected parking space surrounding environment data; S3.

4. According to the output of the support vector machine, an abnormal state evaluation value of the vehicle and the surrounding environment of the parking space is obtained.

9. The parking space safety warning system for new energy vehicles according to claim 1 is characterized by: In S3.2, the kernel function is: ； in, Indicates the distance between the vehicle and the parking space; A state feature vector representing the vehicle; The state feature vector representing the parking space; Represents the scale parameter in the kernel function; According to the influence of ramp parking environment on vehicle posture, the kernel function is optimized: ； in, Indicates the distance between the vehicle and the parking space after optimization; represents the terrain adaptability function; represents the vehicle posture function; Indicates the slope angle; Indicates the pitch angle of the vehicle.

10. The parking space safety warning system for new energy vehicles according to claim 1 is characterized by: The decision-making and warning unit (4) performs risk assessment and triggers a warning mechanism based on the analysis results, including the following steps: S4.1, receiving the abnormality assessment result from the data processing and analysis unit (3); S4.2, set medium risk and high risk levels to determine whether there are low risks of safety hazards in the surrounding environment of the parking space; S4.

3. A warning reminder is triggered when there is a medium risk in the environment around the parking space, and an emergency warning is triggered when there is a high risk.

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