A self-assisted system and its control method

Abstract

This invention relates to the technical field of autonomous vehicle assistance systems, providing a self-assistance system and its control method. The system includes a sensor data acquisition terminal, an assistance analysis terminal, a suspension adjustment terminal, a power adjustment terminal, and an adjustment execution terminal. The sensor data acquisition terminal acquires suspension sensor data, vehicle condition sensor data, and environmental sensor data from the autonomous vehicle. The assistance analysis terminal analyzes the assistance behavior based on the suspension sensor data, environmental data, and vehicle condition data, generating assistance analysis information. The suspension adjustment terminal analyzes the autonomous vehicle's suspension adjustment strategy based on the assistance analysis information, generating suspension adjustment strategy information. The power adjustment terminal analyzes the autonomous vehicle's power adjustment strategy based on the assistance analysis information, generating power adjustment strategy information. The adjustment execution terminal executes the adjustment strategies from the suspension adjustment strategy information and the power adjustment strategy information. This invention improves the driving stability of autonomous vehicles.

Description

Technical Field

[0001] This invention relates to the technical field of unmanned vehicle assistance systems, specifically to a self-assistance system and its control method. Background Technology

[0002] With the rapid development of autonomous driving technology, driverless vehicles have gradually entered people's lives. However, existing autonomous driving systems typically only focus on automatic navigation, obstacle avoidance, and driving control, failing to fully consider the vehicle's energy efficiency under complex road conditions. In various complex environments, such as roads with different gradients, varying loads, and even inclement weather, the vehicle's driving performance is often affected. Therefore, how to improve the adaptability of driverless vehicles under various operating conditions, especially how to optimize power output by intelligently adjusting the vehicle's power assistance system, has become an important research topic.

[0003] Many unmanned vehicle systems have been developed. Through extensive research and reference, we found existing unmanned vehicle systems disclosed in publications such as CN115107730A, CN109552216A, CN101807079A, EP2655185A1, and US20200363823A1. These systems generally include: a sensing terminal, a power terminal, the unmanned vehicle body, and a control terminal. The sensing terminal acquires operational sensing data from the unmanned vehicle body and the power terminal; the power terminal provides power to the unmanned vehicle body; and the control terminal controls the coordinated operation of the unmanned vehicle body, sensing terminal, and power terminal based on the operational sensing data. Because these unmanned vehicle systems operate in a relatively simple mode and lack power assist analysis and suspension adjustment processes, they suffer from reduced driving stability. Summary of the Invention

[0004] The purpose of this invention is to address the shortcomings of the aforementioned unmanned vehicle systems by proposing a self-assistance system and its control method.

[0005] The present invention adopts the following technical solution:

[0006] A self-assistance system includes a sensor data acquisition terminal, an assist analysis terminal, a suspension adjustment terminal, a power adjustment terminal, and an adjustment execution terminal. The sensor data acquisition terminal acquires suspension sensor data, vehicle condition sensor data, and environmental sensor data of an unmanned vehicle. The assist analysis terminal analyzes assist behavior based on the suspension sensor data, environmental data, and vehicle condition data to generate assist analysis information. The suspension adjustment terminal analyzes the unmanned vehicle's suspension adjustment strategy based on the assist analysis information to generate suspension adjustment strategy information. The power adjustment terminal analyzes the unmanned vehicle's power adjustment strategy based on the assist analysis information to generate power adjustment strategy information. The adjustment execution terminal executes the adjustment strategies from the suspension adjustment strategy information and the power adjustment strategy information.

[0007] The sensor data acquisition terminal includes a suspension sensor module, a vehicle condition sensor module, and an environment sensor module; the suspension sensor module is used to collect suspension sensor data of the unmanned vehicle's suspension system in real time; the vehicle condition sensor module is used to acquire real-time vehicle condition sensor data of the unmanned vehicle; and the environment sensor module is used to acquire environmental sensor data of the external environment.

[0008] Optionally, the assistance analysis terminal includes a data preprocessing module, a behavior recognition module, and an assistance analysis module; the data preprocessing module is used to preprocess the sensor data from the suspension sensing module, vehicle condition sensing module, and environmental sensing module to eliminate noise and redundant information; the behavior recognition module is used to identify the current driving behavior mode and changes in the driving environment of the unmanned vehicle based on the preprocessed sensor data; the assistance analysis module is used to perform assistance behavior analysis based on the driving behavior mode, changes in the driving environment, and sensor data to generate assistance analysis information.

[0009] Optionally, the suspension adjustment terminal includes a suspension adjustment strategy analysis module, a suspension command generation module, and a suspension adjustment strategy information generation module; the suspension adjustment strategy analysis module is used to perform suspension adjustment analysis based on assist analysis information; the suspension command generation module is used to generate suspension commands based on the suspension adjustment analysis results; and the suspension adjustment strategy information generation module is used to generate suspension adjustment strategy information based on the suspension commands.

[0010] Optionally, the power adjustment terminal includes a power adjustment strategy analysis module, a power command generation module, and a power adjustment strategy information generation module; the power adjustment strategy analysis module is used to perform power adjustment analysis based on assist analysis information; the power command generation module is used to generate power commands based on the power adjustment analysis results; and the power adjustment strategy information generation module is used to generate power adjustment strategy information based on the power commands.

[0011] Optionally, the adjustment execution terminal includes a suspension execution module, a power execution module, and a control feedback module; the suspension execution module is used to control the operating parameters of the unmanned vehicle's suspension system according to the suspension adjustment strategy information; the power execution module is used to adjust the power output of the unmanned vehicle's power system according to the power adjustment strategy information; and the control feedback module is used to monitor the execution effects of the suspension system and the power system in real time.

[0012] Optionally, the suspension adjustment strategy analysis module includes a suspension analysis parameter extraction submodule and a suspension stiffness adjustment index calculation submodule; the suspension analysis parameter extraction submodule is used to extract suspension analysis parameters based on assist analysis information; the suspension stiffness adjustment index calculation submodule is used to calculate the current suspension stiffness adjustment index of the unmanned vehicle based on the suspension analysis parameters.

[0013] Optionally, the power adjustment strategy analysis module includes a power analysis parameter extraction submodule and a power output adjustment index calculation submodule; the power analysis parameter extraction submodule is used to extract power analysis parameters based on assist analysis information; the power output adjustment index calculation submodule is used to calculate the current power output adjustment index of the unmanned vehicle based on the power analysis parameters.

[0014] A control method for a self-assisted system, applied to the self-assisted system described above, the control method for the self-assisted system comprising:

[0015] S1, acquire suspension sensor data, vehicle condition sensor data and environmental sensor data of unmanned vehicles;

[0016] S2 analyzes the power assist behavior based on suspension sensor data, environmental sensor data, and vehicle condition sensor data, and generates power assist analysis information.

[0017] S3, based on the assist analysis information, analyze the suspension adjustment strategy of the unmanned vehicle and generate suspension adjustment strategy information;

[0018] S4, based on the assist analysis information, analyze the power adjustment strategy of the unmanned vehicle and generate power adjustment strategy information;

[0019] S5 executes the adjustment strategies from the suspension adjustment strategy information and the power adjustment strategy information.

[0020] The beneficial effects achieved by this invention are:

[0021] 1. By configuring the sensor data acquisition terminal, real-time data collection of the unmanned vehicle's suspension system, vehicle condition, and environmental sensors can comprehensively reflect the unmanned vehicle's operating status and changes in the external environment. This configuration facilitates comprehensive and multi-layered real-time monitoring of the unmanned vehicle, providing precise data support for subsequent power assist analysis, suspension adjustment, and powertrain adjustment, thereby improving the unmanned vehicle's driving stability and intelligence level.

[0022] 2. By configuring the assist analysis terminal, especially through the collaborative work of the data preprocessing module, behavior recognition module, and assist analysis module, noise and redundant information in the sensor data can be effectively removed, driving behavior patterns and environmental changes can be accurately identified, and assist analysis information can be generated based on these analysis results. This configuration facilitates a dynamic understanding of the current driving state of the autonomous vehicle, thereby providing a scientific basis for the vehicle's suspension and power adjustment strategies, improving the system's response speed and adjustment accuracy, and enhancing the intelligent adjustment capabilities of the autonomous vehicle.

[0023] 3. By configuring the suspension adjustment terminal and coordinating the work of the suspension adjustment strategy analysis module, suspension command generation module, and suspension adjustment strategy information generation module, the suspension system of unmanned vehicles can be efficiently analyzed and adjusted based on assistance analysis information, thereby generating accurate suspension adjustment strategy information. This configuration facilitates real-time optimization of the suspension system performance according to different road conditions and driving environments, thereby improving the stability and safety of unmanned vehicles in various driving environments.

[0024] 4. Through the settings of the power adjustment terminal, especially the collaboration of the power adjustment strategy analysis module, power command generation module, and power adjustment strategy information generation module, the vehicle's power system can be optimized and adjusted based on power assist analysis information. This setting facilitates automatic adjustment of power output according to different driving needs and road condition changes, thereby providing the vehicle with a smoother and more efficient power response, ultimately improving the vehicle's acceleration performance, fuel efficiency, and the overall adaptability of the power system.

[0025] 5. By adjusting the settings of the execution terminal and coordinating the work of the suspension execution module, power execution module, and control feedback module, the suspension and power adjustment strategies can be executed precisely, and the operating status of the suspension and power systems can be monitored and adjusted in real time. This setting helps ensure that the adjustments of the suspension and power systems are carried out according to the predetermined strategies, thereby maintaining the optimal performance of the unmanned vehicle during driving, avoiding instability caused by over-adjustment, and improving the overall control accuracy of the vehicle.

[0026] 6. Through the configuration of the suspension adjustment strategy analysis module, especially the collaborative work of the suspension analysis parameter extraction submodule and the suspension stiffness adjustment index calculation submodule, key suspension parameters can be accurately extracted and the current suspension stiffness adjustment index can be calculated, thereby optimizing the suspension stiffness adjustment strategy. This configuration is beneficial for dynamically adjusting the suspension system stiffness according to real-time driving conditions and changes in external road conditions, thereby effectively improving the vehicle's driving stability, especially its adaptability to different road conditions.

[0027] 7. By configuring the power adjustment strategy analysis module, and combining it with the power analysis parameter extraction submodule and the power output adjustment index calculation submodule, the current power output adjustment index can be accurately extracted and calculated based on the power assist analysis information, thus providing a scientific basis for power system adjustment. This configuration facilitates automatic adjustment of power output according to changes in vehicle load, speed, road conditions, etc., to achieve efficient energy management and power demand matching, thereby improving the performance of the vehicle's power system and enhancing its responsiveness and range under different driving conditions.

[0028] 8. The suspension selection algorithm can take into account changes in external wind speed, direction, and force during the suspension system adjustment process of unmanned vehicles. By incorporating these wind factors into the suspension stiffness adjustment formula, the responsiveness of the suspension system is optimized, reducing the negative impact of wind on vehicle stability. Furthermore, it allows for real-time adjustment of suspension stiffness to adapt to the forces exerted on the vehicle by wind speed changes, thereby improving the driving stability of unmanned vehicles in complex weather conditions, especially in high wind speeds or unstable wind conditions, ensuring rapid response of the vehicle's suspension system and vehicle safety.

[0029] 9. By incorporating a power output selection algorithm and introducing internal vibration factors, the system can account for the impact of vibration frequencies and amplitudes at different locations on the vehicle's suspension and powertrain systems. Integrating vibration factors into the power output adjustment formula allows for real-time adjustments to the powertrain's output based on vibration conditions in different parts of the vehicle, preventing imbalances or damage caused by excessive vibration. Furthermore, during dynamic driving, optimizing power output based on vibration data enhances vehicle stability and improves the control precision of unmanned vehicles, particularly at high speeds or in complex road conditions, effectively eliminating unnecessary vibration interference.

[0030] To further understand the features and technical content of the present invention, please refer to the following detailed description and drawings of the present invention. However, the drawings provided are for reference and illustration only and are not intended to limit the present invention. Attached Figure Description

[0031] Figure 1 This is a schematic diagram of the overall structure of the present invention;

[0032] Figure 2 This is a schematic diagram of the suspension adjustment strategy analysis module in this invention;

[0033] Figure 3 This is a schematic diagram of the power adjustment strategy analysis module in this invention;

[0034] Figure 4 This is a schematic diagram of the method flow for a self-assisted system control method according to the present invention. Detailed Implementation

[0035] The following specific embodiments illustrate the implementation of the present invention. Those skilled in the art can understand the advantages and effects of the present invention from the content disclosed in this specification. The present invention can be implemented or applied through other different specific embodiments, and various details in this specification can also be modified and changed based on different viewpoints and applications without departing from the spirit of the present invention. Furthermore, the accompanying drawings of the present invention are for simple illustrative purposes only and are not depictions of actual dimensions; this is stated in advance. The following embodiments will further describe the relevant technical content of the present invention in detail, but the disclosed content is not intended to limit the scope of protection of the present invention.

[0036] Example 1: This example provides a self-assisted system. Combined with... Figure 1 As shown, a self-assistance system includes a sensor data acquisition terminal, an assist analysis terminal, a suspension adjustment terminal, a power adjustment terminal, and an adjustment execution terminal. The sensor data acquisition terminal acquires suspension sensor data, vehicle condition sensor data, and environmental sensor data of the unmanned vehicle. The assist analysis terminal analyzes the assist behavior based on the suspension sensor data, environmental data, and vehicle condition sensor data to generate assist analysis information. The suspension adjustment terminal analyzes the unmanned vehicle's suspension adjustment strategy based on the assist analysis information to generate suspension adjustment strategy information. The power adjustment terminal analyzes the unmanned vehicle's power adjustment strategy based on the assist analysis information to generate power adjustment strategy information. The adjustment execution terminal executes the adjustment strategies from the suspension adjustment strategy information and the power adjustment strategy information.

[0037] The sensor data acquisition terminal includes a suspension sensor module, a vehicle condition sensor module, and an environment sensor module; the suspension sensor module is used to collect suspension sensor data of the unmanned vehicle's suspension system in real time; the vehicle condition sensor module is used to acquire real-time vehicle condition sensor data of the unmanned vehicle; and the environment sensor module is used to acquire environmental sensor data of the external environment.

[0038] Optionally, the assistance analysis terminal includes a data preprocessing module, a behavior recognition module, and an assistance analysis module; the data preprocessing module is used to preprocess the sensor data from the suspension sensing module, vehicle condition sensing module, and environmental sensing module to eliminate noise and redundant information; the behavior recognition module is used to identify the current driving behavior mode and changes in the driving environment of the unmanned vehicle based on the preprocessed sensor data; the assistance analysis module is used to perform assistance behavior analysis based on the driving behavior mode, changes in the driving environment, and sensor data to generate assistance analysis information.

[0039] Optionally, the suspension adjustment terminal includes a suspension adjustment strategy analysis module, a suspension command generation module, and a suspension adjustment strategy information generation module; the suspension adjustment strategy analysis module is used to perform suspension adjustment analysis based on assist analysis information; the suspension command generation module is used to generate suspension commands based on the suspension adjustment analysis results; and the suspension adjustment strategy information generation module is used to generate suspension adjustment strategy information based on the suspension commands.

[0040] Optionally, the power adjustment terminal includes a power adjustment strategy analysis module, a power command generation module, and a power adjustment strategy information generation module; the power adjustment strategy analysis module is used to perform power adjustment analysis based on assist analysis information; the power command generation module is used to generate power commands based on the power adjustment analysis results; and the power adjustment strategy information generation module is used to generate power adjustment strategy information based on the power commands.

[0041] Optionally, the adjustment execution terminal includes a suspension execution module, a power execution module, and a control feedback module; the suspension execution module is used to control the operating parameters of the unmanned vehicle's suspension system according to the suspension adjustment strategy information; the power execution module is used to adjust the power output of the unmanned vehicle's power system according to the power adjustment strategy information; and the control feedback module is used to monitor the execution effects of the suspension system and the power system in real time.

[0042] Optional, combined Figure 2 As shown, the suspension adjustment strategy analysis module includes a suspension analysis parameter extraction submodule and a suspension stiffness adjustment index calculation submodule; the suspension analysis parameter extraction submodule is used to extract suspension analysis parameters based on assist analysis information; the suspension stiffness adjustment index calculation submodule is used to calculate the current suspension stiffness adjustment index of the unmanned vehicle based on the suspension analysis parameters.

[0043] Specifically, when the suspension stiffness adjustment index calculation submodule performs the calculation, it satisfies the following formula:

[0044]

[0045] f(Δθ)=cos 2(Δθ)+β·(1-cos 2 (Δθ));

[0046] Where H(t) represents the suspension stiffness adjustment index of the unmanned vehicle at time t during the driving process; Q i (t) represents the slope measurement value of the i-th road condition sensor on the autonomous vehicle; n represents the total number of road condition sensors on the autonomous vehicle; E j (t) represents the temperature measurement value of the j-th environmental sensor on the unmanned vehicle; m represents the total number of environmental sensors on the unmanned vehicle; α represents the wind force coefficient, which increases with the size of the unmanned vehicle, and the specific value is set by the administrator based on experience; W k (t) represents the wind force influence score of the k-th wind sensor on the unmanned vehicle; q represents the total number of wind sensors on the unmanned vehicle; ρ represents the air density, typically 1.225 kg / m²; v w(t) C represents the wind speed reading from the k-th wind sensor on the autonomous vehicle; d The air resistance constant of an unmanned vehicle is obtained as a known constant through wind tunnel experiments or vehicle design parameters; A c β represents the lateral area of ​​the unmanned vehicle; f(Δθ) represents the wind correction function; Δθ represents the angle difference between the current driving direction of the unmanned vehicle and the wind direction detected by the k-th wind sensor; β represents the correction coefficient. The larger the wind speed in the weather forecast information corresponding to the location of the unmanned vehicle, the larger the correction coefficient. The specific value is set by the administrator based on experience.

[0047] The suspension command generation module includes a suspension gear selection submodule and a suspension command generation submodule. The suspension gear selection submodule is used to select the suspension gear based on the suspension stiffness adjustment index. The suspension command generation submodule is used to generate corresponding suspension commands based on the suspension gear. When the suspension gear selection submodule is working, the following equation is satisfied:

[0048]

[0049] Among them, D w Indicates the gear position; h1 to h4 represent different gear selection thresholds, all set by the administrator based on experience; D w =1 indicates suspension gear 1, corresponding to a suspension stiffness parameter range of 150 to 300 N / m; D w =2 indicates suspension gear 2, corresponding to a suspension stiffness parameter range of 300 to 600 N / m; D w =2 indicates suspension gear 2, corresponding to a suspension stiffness parameter range of 600 to 800 N / m; D w =3 indicates suspension gear 3, corresponding to a suspension stiffness parameter range of 800 to 1200 N / m; D w=5 indicates suspension gear 5, and the corresponding suspension stiffness parameter range is 1200 to 2000 N / m.

[0050] The following is the program code for the above-mentioned suspension stiffness adjustment index calculation process and suspension gear selection process:

[0051]

[0052]

[0053]

[0054] Optional, combined Figure 3 As shown, the power adjustment strategy analysis module includes a power analysis parameter extraction submodule and a power output adjustment index calculation submodule; the power analysis parameter extraction submodule is used to extract power analysis parameters based on assist analysis information; the power output adjustment index calculation submodule is used to calculate the current power output adjustment index of the unmanned vehicle based on the power analysis parameters.

[0055] A control method for a self-assisted system, applied to the self-assisted system described above, combined with... Figure 4 As shown, the control method of the self-assist system includes:

[0056] S1, acquire suspension sensor data, vehicle condition sensor data and environmental sensor data of unmanned vehicles;

[0057] S2 analyzes the power assist behavior based on suspension sensor data, environmental sensor data, and vehicle condition sensor data, and generates power assist analysis information.

[0058] S3, based on the assist analysis information, analyze the suspension adjustment strategy of the unmanned vehicle and generate suspension adjustment strategy information;

[0059] S4, based on the assist analysis information, analyze the power adjustment strategy of the unmanned vehicle and generate power adjustment strategy information;

[0060] S5 executes the adjustment strategies from the suspension adjustment strategy information and the power adjustment strategy information.

[0061] In summary, by configuring the sensor data acquisition terminal to collect real-time sensor data on the unmanned vehicle's suspension system, vehicle condition, and environment, a comprehensive reflection of the unmanned vehicle's operating status and changes in the external environment can be achieved. This configuration facilitates comprehensive and multi-layered real-time monitoring of the unmanned vehicle, providing precise data support for subsequent power assist analysis, suspension adjustment, and powertrain adjustment. Through the configuration of the power assist analysis terminal, especially the collaborative work of the data preprocessing module, behavior recognition module, and power assist analysis module, noise and redundant information in the sensor data can be effectively removed, accurately identifying driving behavior patterns and environmental changes, and generating power assist analysis information based on these analysis results. This configuration facilitates a dynamic understanding of the unmanned vehicle's current driving state, providing a scientific basis for the vehicle's suspension and powertrain adjustment strategies. Through the configuration of the suspension adjustment terminal, combined with the coordinated work of the suspension adjustment strategy analysis module, suspension command generation module, and suspension adjustment strategy information generation module, efficient analysis and adjustment of the unmanned vehicle's suspension system can be performed based on the power assist analysis information, thereby generating accurate suspension adjustment strategy information. This setup facilitates real-time optimization of the suspension system's performance based on varying road conditions and driving environments. Through the power adjustment terminal, particularly the collaboration of the power adjustment strategy analysis module, power command generation module, and power adjustment strategy information generation module, the vehicle's power system can be optimized based on power assist analysis information. Furthermore, through the adjustment execution terminal, combined with the coordinated work of the suspension execution module, power execution module, and control feedback module, suspension and power adjustment strategies can be precisely executed, and the operating status of the suspension and power systems can be monitored and adjusted in real time. This setup ensures that the adjustments to the suspension and power systems are performed according to predetermined strategies, thereby maintaining optimal performance for the unmanned vehicle during operation. Through the suspension adjustment strategy analysis module, particularly the collaborative work of the suspension analysis parameter extraction submodule and the suspension stiffness adjustment index calculation submodule, key suspension parameters can be accurately extracted and the current suspension stiffness adjustment index can be calculated, thereby optimizing the suspension stiffness adjustment strategy. This setting facilitates the dynamic adjustment of the suspension system's stiffness based on real-time driving conditions and changes in external road conditions. Through the power adjustment strategy analysis module, combined with the power analysis parameter extraction submodule and the power output adjustment index calculation submodule, the current power output adjustment index can be accurately extracted and calculated based on assist analysis information, providing a scientific basis for power system adjustment. This setting also facilitates the automatic adjustment of power output based on changes in vehicle load, speed, and road conditions, achieving efficient energy management and matching power demand. The suspension gear selection algorithm considers changes in external environmental wind speed, direction, and force during suspension system adjustment in unmanned vehicles. By incorporating these wind factors into the suspension stiffness adjustment formula, the suspension system's responsiveness is optimized, reducing the negative impact of wind on vehicle stability.Furthermore, the suspension stiffness can be adjusted in real time to adapt to the forces exerted on the vehicle by changes in wind speed, thereby improving the driving stability of unmanned vehicles in complex weather conditions, especially in high wind speeds or unstable wind conditions, ensuring the rapid response of the vehicle's suspension system and the vehicle's safety. This embodiment also considers the environmental parameter of temperature, because high temperatures can cause the rubber bushings and damping elements in the suspension system to soften, reducing overall rigidity. Increasing spring stiffness can counteract this softening, maintaining the support and responsiveness of the suspension system. High temperatures may increase tire pressure or reduce road surface adhesion; increasing suspension stiffness can optimize the contact area between the tires and the ground, reducing roll and pitch, and improving steering accuracy and dynamic stability. The expansion of metal components at high temperatures may change suspension geometry parameters (such as camber and toe angles); moderately increasing stiffness can reduce the impact of deformation on vehicle dynamic performance. Prolonged high temperatures may lead to spring metal fatigue or a decline in shock absorber fluid performance; increasing stiffness can reduce compression stroke during severe bumps, preventing the chassis from colliding with the ground. Autonomous vehicles rely on high-precision sensors (such as LiDAR and cameras). Increasing stiffness can suppress high-frequency body sway, ensure stable sensor data, and avoid navigation and decision-making errors. A stiffer suspension system reduces high-frequency vibrations and reciprocating motion of components, reduces heat generated by friction, and indirectly alleviates thermal load pressure in high-temperature environments.

[0062] Example 2: This example includes all the content of Example 1 and provides a self-assist system. The power adjustment strategy analysis module includes a power analysis parameter extraction submodule and a power output adjustment index calculation submodule. The power analysis parameter extraction submodule is used to extract power analysis parameters based on assist analysis information. The power output adjustment index calculation submodule is used to calculate the current power output adjustment index of the unmanned vehicle based on the power analysis parameters.

[0063] When the power output adjustment index calculation submodule performs the calculation, the following formula is satisfied:

[0064]

[0065] R b (t)=H d ·(1+0.2·d l (t)·v(t));

[0066]

[0067] Where P represents the power output adjustment index of the unmanned vehicle at time t during the driving process; F i Let represent the noise amplitude data of the i-th noise sensor inside the unmanned vehicle at time t; n represents the total number of noise sensors inside the unmanned vehicle; R b (t) represents the road condition impact score for the b-th wheel of the unmanned vehicle; Hd This indicates the suspension stiffness corresponding to the current suspension gear of the unmanned vehicle; d l (t) represents the road surface unevenness under the b-th wheel at time t, obtained through a laser-type unevenness sensor; B represents the total number of wheels of the unmanned vehicle; v(t) represents the speed of the unmanned vehicle at time t; α represents the wind force coefficient, which increases with the size of the unmanned vehicle, and the specific value is set by the administrator based on experience; V(t) represents the vibration impact index value at time t; λ i This represents the vibration influence coefficient of the i-th vibration sensor. The closer the vibration sensor is to the center of the unmanned vehicle, the larger the vibration influence coefficient. The specific value is set by the administrator based on experience; N represents the total number of vibration sensors; A i (t) represents the vibration amplitude of the i-th vibration sensor at time t; f i (t) represents the vibration frequency of the i-th vibration sensor at time t.

[0068] The power command generation module includes a power output gear selection submodule and a power command generation submodule. The power output gear selection submodule is used to select the power output gear according to the power output adjustment index. The power command generation submodule is used to generate a corresponding power command based on the power output gear. When the power output gear selection submodule is working, the following equation is satisfied:

[0069]

[0070] Among them, D d Indicates the power output gear; P1 to P4 represent different gear selection thresholds, all set by the administrator based on experience; D d =1 indicates power output gear 1, corresponding to a power range of 10 to 30kW; D d =2 indicates power output gear 2, corresponding to a power range of 30 to 60kW; D d =3 indicates power output gear 3, corresponding to a power range of 60 to 100kW; D d =4 indicates power output gear 4, corresponding to a power range of 100 to 120kW; D d =5 indicates the power output level 5, and the corresponding power parameter range is 120 to 150kW.

[0071] The following is the program code for the above-mentioned suspension stiffness adjustment index calculation process and suspension gear selection process:

[0072]

[0073]

[0074]

[0075] In summary, by incorporating the power output selection algorithm and introducing internal vibration factors, the impact of vibration frequency and amplitude at different locations on the vehicle's suspension and powertrain systems can be considered. Integrating vibration factors into the power output adjustment formula allows for real-time adjustment of the powertrain's power output based on vibration conditions in different parts of the vehicle, preventing imbalance or damage caused by excessive vibration. Furthermore, during dynamic driving, optimizing power output based on vibration data improves vehicle stability and enhances the control precision of unmanned vehicles, particularly at high speeds or in complex road conditions, better eliminating unnecessary vibration interference.

[0076] The content disclosed above is only a preferred and feasible embodiment of the present invention, and is not intended to limit the scope of protection of the present invention. Therefore, all equivalent technical changes made based on the content of the present invention specification and drawings are included within the scope of protection of the present invention. Furthermore, the elements therein can be updated as technology develops.

Claims

1. A self-assisted system, characterized in that, The system includes a sensor data acquisition terminal, a power assist analysis terminal, a suspension adjustment terminal, a power adjustment terminal, and an adjustment execution terminal. The sensor data acquisition terminal is used to acquire suspension sensor data, vehicle condition sensor data, and environmental sensor data of the unmanned vehicle. The power assist analysis terminal is used to analyze the power assist behavior based on the suspension sensor data, environmental data, and vehicle condition sensor data to generate power assist analysis information. The suspension adjustment terminal is used to analyze the suspension adjustment strategy of the unmanned vehicle based on the power assist analysis information to generate suspension adjustment strategy information. The power adjustment terminal is used to analyze the power adjustment strategy of the unmanned vehicle based on the assist analysis information and generate power adjustment strategy information; the adjustment execution terminal is used to execute the adjustment strategy in the suspension adjustment strategy information and the power adjustment strategy information. The sensor data acquisition terminal includes a suspension sensor module, a vehicle condition sensor module, and an environment sensor module; the suspension sensor module is used to collect suspension sensor data of the unmanned vehicle's suspension system in real time; the vehicle condition sensor module is used to acquire real-time vehicle condition sensor data of the unmanned vehicle; and the environment sensor module is used to acquire environmental sensor data of the external environment. The power adjustment terminal includes a power adjustment strategy analysis module, a power command generation module, and a power adjustment strategy information generation module; The power adjustment strategy analysis module includes a power analysis parameter extraction submodule and a power output adjustment index calculation submodule; When the power output adjustment index calculation submodule performs the calculation, the following formula is satisfied: ； ； ； Where P represents the power output adjustment index of the unmanned vehicle at time t during the driving process; F i Let represent the noise amplitude data of the i-th noise sensor inside the unmanned vehicle at time t; n represents the total number of noise sensors inside the unmanned vehicle; R b (t) represents the road condition impact score for the b-th wheel of the unmanned vehicle; H d This indicates the suspension stiffness corresponding to the current suspension gear of the unmanned vehicle; d l (t) represents the road surface unevenness under the b-th wheel at time t, obtained through a laser-type unevenness sensor; B represents the total number of wheels of the unmanned vehicle; v(t) represents the speed of the unmanned vehicle at time t; α represents the wind force coefficient, which increases with the size of the unmanned vehicle, and the specific value is set by the administrator based on experience; V(t) represents the vibration impact index value at time t; λ i This represents the vibration influence coefficient of the i-th vibration sensor. The closer the vibration sensor is to the center of the unmanned vehicle, the larger the vibration influence coefficient. The specific value is set by the administrator based on experience; N represents the total number of vibration sensors; A i (t) represents the vibration amplitude of the i-th vibration sensor at time t; f i (t) represents the vibration frequency of the i-th vibration sensor at time t.

2. The self-assisted system as described in claim 1, characterized in that, The power assistance analysis terminal includes a data preprocessing module, a behavior recognition module, and a power assistance analysis module. The data preprocessing module preprocesses the sensor data from the suspension sensing module, vehicle condition sensing module, and environmental sensing module to eliminate noise and redundant information. The behavior recognition module identifies the current driving behavior pattern and changes in the driving environment of the unmanned vehicle based on the preprocessed sensor data. The power assistance analysis module performs power assistance behavior analysis based on the driving behavior pattern, changes in the driving environment, and sensor data to generate power assistance analysis information.

3. The self-assisted system as described in claim 2, characterized in that, The suspension adjustment terminal includes a suspension adjustment strategy analysis module, a suspension command generation module, and a suspension adjustment strategy information generation module. The suspension adjustment strategy analysis module is used to perform suspension adjustment analysis based on assist analysis information. The suspension command generation module is used to generate suspension commands based on the suspension adjustment analysis results. The suspension adjustment strategy information generation module is used to generate suspension adjustment strategy information based on the suspension commands.

4. A self-assisted system as described in claim 3, characterized in that, The power adjustment strategy analysis module is used to perform power adjustment analysis based on the power assist analysis information; the power command generation module is used to generate power commands based on the power adjustment analysis results; and the power adjustment strategy information generation module is used to generate power adjustment strategy information based on the power commands.

5. A self-assisted system as described in claim 4, characterized in that, The adjustment execution terminal includes a suspension execution module, a power execution module, and a control feedback module; the suspension execution module is used to control the operating parameters of the unmanned vehicle's suspension system according to the suspension adjustment strategy information; the power execution module is used to adjust the power output of the unmanned vehicle's power system according to the power adjustment strategy information; and the control feedback module is used to monitor the execution effects of the suspension system and the power system in real time.

6. A self-assisted system as described in claim 5, characterized in that, The suspension adjustment strategy analysis module includes a suspension analysis parameter extraction submodule and a suspension stiffness adjustment index calculation submodule. The suspension analysis parameter extraction submodule is used to extract suspension analysis parameters based on assist analysis information. The suspension stiffness adjustment index calculation submodule is used to calculate the current suspension stiffness adjustment index of the unmanned vehicle based on the suspension analysis parameters.

7. A self-assisted system as described in claim 6, characterized in that, The power analysis parameter extraction submodule is used to extract power analysis parameters based on the assist analysis information; the power output adjustment index calculation submodule is used to calculate the current power output adjustment index of the unmanned vehicle based on the power analysis parameters.

8. A control method for a self-assisted system, applied to a self-assisted system as described in claim 7, characterized in that, The control method for the self-assisted system includes: S1, acquire suspension sensor data, vehicle condition sensor data and environmental sensor data of unmanned vehicles; S2 analyzes the power assist behavior based on suspension sensor data, environmental sensor data, and vehicle condition sensor data, and generates power assist analysis information. S3, based on the assist analysis information, analyze the suspension adjustment strategy of the unmanned vehicle and generate suspension adjustment strategy information; S4, Analyze the power adjustment strategy of the unmanned vehicle based on the assist analysis information, and generate power adjustment strategy information; S5 executes the adjustment strategies from the suspension adjustment strategy information and the power adjustment strategy information.

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