Novel compact brake force sensor design method and system

Abstract

The invention relates to the field of vehicle engineering, and discloses a novel compact brake force sensor design method and system.The method comprises the steps that brake system information of a simulated vehicle is obtained, brake engineering data is determined, brake force is monitored in real time to obtain brake monitoring data, a brake optimization value is calculated, and then corresponding brake mode requirements are mined; the method comprises the following steps: constructing a brake adjustment system by combining a driving state, determining a brake adjustment module, analyzing an adjustment and improvement trend of the brake adjustment module, constructing a brake trend map, identifying core parameter points of the map, querying a force optimization direction, determining a sensor structure, querying an adaptive process and material attributes, analyzing a brake demand target, and combining a real-time brake condition. And generating a braking force sensor design scheme. The overall performance of the vehicle brake system can be improved.

Description

Technical Field

[0001] The present invention relates to a design method and system for a new type of compact brake force sensor, belonging to the field of vehicle engineering. Background Art

[0002] In the modern automotive industry and transportation field, the performance of the braking system plays a crucial role in ensuring the driving safety of vehicles. As a key component in the braking system, the performance of the brake force sensor directly affects the precise control and overall safety of the braking system.

[0003] Currently, traditional brake force sensors generally have the problem of large volume. This not only occupies too much space in the limited space layout of vehicles, increasing the difficulty of vehicle design and assembly, but also is not conducive to the overall lightweight design of vehicles, resulting in increased vehicle energy consumption. For example, some large brake force sensors require specific large spaces for installation, restricting the flexibility of component layout in compact spaces within vehicles; from a functional perspective, the measurement accuracy of existing sensors is also difficult to meet the growing high-precision braking requirements. Under complex road conditions and driving conditions, they cannot accurately sense the subtle changes in brake force, thus affecting the precise regulation of braking force by the braking system and reducing the stability and safety of vehicle braking. Therefore, a new type of compact brake force sensor design method is needed to improve the overall performance of the vehicle braking system. Summary of the Invention

[0004] The present invention provides a design method and system for a new type of compact brake force sensor, and its main purpose is to improve the overall performance of the vehicle braking system.

[0005] To achieve the above object, a design method for a new type of compact brake force sensor provided by the present invention includes:

[0006] Obtain the braking system information corresponding to the simulated vehicle, based on the braking system information, determine the braking engineering data corresponding to the simulated vehicle, based on the braking engineering data, monitor the brake force of the simulated vehicle in real time to obtain braking monitoring data, and based on the braking monitoring data, calculate the braking optimization value corresponding to the simulated vehicle;

[0007] Explore the braking mode requirements corresponding to the braking optimization value, analyze the demand quantitative relationship corresponding to the braking mode requirements, and combine the demand quantitative relationship with the driving state of the simulated vehicle to construct the braking adjustment system corresponding to the simulated vehicle;

[0008] Based on the braking adjustment system, determine the brake adjustment module corresponding to the simulated vehicle, identify the key adjustment features in the brake adjustment module, analyze the adjustment improvement trend corresponding to the brake adjustment module based on the key adjustment features, and construct a braking trend map corresponding to the simulated vehicle based on the adjustment improvement trend;

[0009] Identify the core parameter points in the braking trend map, query the force optimization direction corresponding to the braking trend map based on the core parameter points, extract the optimized force points in the force optimization direction, calculate the force balance value corresponding to the optimized force points, and determine the sensor structure that the simulated vehicle conforms to based on the force balance value;

[0010] Query the structural design process adapted to the sensor structure, determine the material structure attributes corresponding to the structural design process, analyze the braking demand target corresponding to the simulated vehicle based on the material structure attributes, and generate a sensor design scheme for the braking force of the simulated vehicle based on the braking demand target in combination with the real-time braking situation of the simulated vehicle.

[0011] Optionally, based on the braking engineering data, perform real-time monitoring on the braking force of the simulated vehicle to obtain braking monitoring data, including:

[0012] Analyze the braking influencing factors in the braking engineering data;

[0013] Based on the braking influencing factors, identify the pressure fluctuation characteristics of the simulated vehicle during the braking process;

[0014] Conduct characteristic analysis on the pressure fluctuation characteristics to obtain braking response characteristics;

[0015] Based on the braking response characteristics, extract the resonance frequency points of the simulated vehicle during the braking process;

[0016] Based on the resonance frequency points, perform real-time monitoring on the braking force of the simulated vehicle to obtain braking monitoring data.

[0017] Optionally, the calculation of the braking optimization value corresponding to the simulated vehicle based on the braking monitoring data includes:

[0018] Use the following formula to calculate the braking optimization value corresponding to the simulated vehicle:

[0019]

[0020] Where Qy represents the braking optimization value corresponding to the simulated vehicle, N represents the number of divided segments of the braking process corresponding to the simulated vehicle, k represents the quantity index corresponding to the divided segments, T kDenote the braking time corresponding to the k-th segmentation section, and α denotes the braking force change coefficient. Denote the instantaneous change rate corresponding to the k-th segmentation section, β denotes the braking force deviation coefficient, and F k (t) denotes the braking force change function corresponding to the k-th segmentation section. Denote the average braking force corresponding to all segmentation sections.

[0021] Optionally, constructing the braking adjustment system corresponding to the simulated vehicle by combining the demand quantitative relationship with the driving state of the simulated vehicle includes:

[0022] Analyze the association rules corresponding to the demand quantitative relationship and the driving state;

[0023] Based on the association rules, quantitatively evaluate the braking demand of the simulated vehicle under the current driving state to obtain a quantitative evaluation result;

[0024] Select potential braking strategies that are suitable for the current braking demand from the quantitative evaluation results;

[0025] Conduct dynamic simulation on the potential braking strategies to obtain a dynamic simulation effect;

[0026] Construct the braking adjustment system corresponding to the simulated vehicle according to the dynamic simulation effect.

[0027] Optionally, identifying the key adjustment features in the brake adjustment module includes:

[0028] Analyze the corresponding operation process links of the brake adjustment module;

[0029] Clarify the detailed process parameters corresponding to the operation process links;

[0030] Calculate the parameter sensitivity of the detailed process parameters under different braking conditions;

[0031] Based on the parameter sensitivity, screen the key parameter factors in the detailed process parameters;

[0032] Query the cooperative adjustment relationship between the key parameter factors;

[0033] Based on the cooperative adjustment relationship, identify the key adjustment features in the brake adjustment module.

[0034] Optionally, constructing the braking trend map corresponding to the simulated vehicle based on the adjustment improvement trend includes:

[0035] Analyze the trend change nodes in the adjustment improvement trend;

[0036] Based on the trend change nodes, perform feature annotation on the simulated vehicle under different braking conditions to obtain the annotated condition features;

[0037] Perform curve fitting on the annotated engineering data to obtain the annotated engineering curve;

[0038] Extract the curve fluctuation points from the annotated engineering curve;

[0039] Based on the curve fluctuation points, construct the braking trend map corresponding to the simulated vehicle.

[0040] Optionally, the identification of the core parameter points in the braking trend map includes:

[0041] Query the multi-dimensional feature distribution in the braking trend map;

[0042] Based on the multi-dimensional feature distribution, locate the mutation region in the braking trend map that exceeds the preset threshold;

[0043] Perform clustering analysis on the mutation region to obtain the regional analysis result;

[0044] Extract the center points of the sub-regions from the regional analysis result;

[0045] Identify the core parameter points in the center points of the sub-regions.

[0046] Optionally, the calculation of the force balance value corresponding to the optimized force point includes:

[0047] Calculate the force balance value corresponding to the optimized force point using the following formula:

[0048]

[0049] Among them, EZ represents the force balance value corresponding to the optimized force point, M represents the total number corresponding to the optimized force point, j represents the quantity index corresponding to the optimized force point, F j represents the detailed force value corresponding to the j-th force point, W j represents the weight coefficient corresponding to the j-th force point, θ j represents the direction angle corresponding to the direction of the j-th force point and the reference direction, I jv represents the interaction coefficient corresponding to between the j-th force point and the v-th force point, λ represents the reference adjustment factor, and B represents the balance reference value.

[0050] Optionally, the query of the structural design process adapted to the sensor structure includes:

[0051] Analyze the connection relationship between the components corresponding to the sensor structure;

[0052] Construct the structural topology diagram corresponding to the connection relationship;

[0053] Query the operating conditions of each component in the structural topology diagram;

[0054] Comprehensively scoring the operating conditions to obtain an operating condition scoring index;

[0055] Based on the operating condition scoring index, a structural design process suitable for the sensor structure is queried.

[0056] In order to solve the above problems, the present invention also provides a novel compact brake force sensor design system, the system comprising:

[0057] An optimization value calculation module is used to obtain brake system information corresponding to the simulated vehicle, determine brake engineering data corresponding to the simulated vehicle based on the brake system information, monitor the brake force of the simulated vehicle in real time based on the brake engineering data, obtain brake monitoring data, and calculate the brake optimization value corresponding to the simulated vehicle based on the brake monitoring data;

[0058] A system construction module, used to mine the braking mode requirements corresponding to the braking optimization value, analyze the quantitative relationship of requirements corresponding to the braking mode requirements, combine the quantitative relationship of requirements with the driving state of the simulated vehicle, and construct a braking adjustment system corresponding to the simulated vehicle;

[0059] A map construction module is used to determine the brake adjustment module corresponding to the simulated vehicle based on the brake adjustment system, identify key adjustment features in the brake adjustment module, analyze the adjustment improvement trend corresponding to the brake adjustment module based on the key adjustment features, and construct a brake trend map corresponding to the simulated vehicle based on the adjustment improvement trend;

[0060] a structure determination module, used for identifying core parameter points in the braking trend map, querying the force optimization direction corresponding to the braking trend map based on the core parameter points, extracting the optimized force points in the force optimization direction, calculating the force balance value corresponding to the optimized force point, and determining the sensor structure that the simulated vehicle complies with based on the force balance value;

[0061] A solution generation module is used to query the structural design process that the sensor structure is adapted to, determine the material structure properties corresponding to the structural design process, analyze the braking demand target corresponding to the simulated vehicle based on the material structure properties, and generate a sensor design solution for the braking force of the simulated vehicle based on the braking demand target combined with the real-time braking situation of the simulated vehicle.

[0062] Compared with the problems described in the background technology, the present invention can accurately determine the braking engineering data by obtaining the braking system information corresponding to the simulated vehicle, provide a basis for real-time monitoring of the braking force, help obtain accurate braking monitoring data, and can be used to explore the braking mode requirements, and then build a braking adjustment system to lay the foundation for improving the performance of the vehicle's braking system. The present invention mines the braking mode requirements corresponding to the braking optimization value, and analyzes the quantitative relationship of the requirements corresponding to the braking mode requirements, mines the braking mode requirements corresponding to the braking optimization value, and analyzes the quantitative relationship of the requirements corresponding to the braking mode requirements. Furthermore, based on the braking adjustment system, the present invention determines the braking adjustment module corresponding to the simulated vehicle, which can accurately adapt to the complex and diverse driving conditions of the vehicle. , through targeted optimization of each link of the brake system, it can greatly improve the braking performance and ensure the safety and reliability of vehicle braking in different scenarios. Furthermore, the present invention helps to accurately diagnose potential problems of the brake system, optimize the system design, and avoid blind adjustments by identifying the core parameter points in the brake trend map. At the same time, when the vehicle is actually running, according to these core parameter points, braking abnormalities can be detected in time, driving safety can be improved, and stable and reliable operation of the vehicle can be ensured. Finally, the present invention can improve the accuracy and stability of sensor installation by querying the structural design process of the sensor structure adaptation, and ensure its normal operation under complex working conditions; on the other hand, the appropriate process helps to optimize sensor performance and improve the accuracy and reliability of data acquisition. Therefore, the new compact brake force sensor design method and system provided by the embodiment of the present invention can improve the overall performance of the vehicle brake system. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 A schematic flow chart of a novel compact brake force sensor design method provided by an embodiment of the present invention;

[0064] Figure 2 A schematic diagram of modules for implementing the novel compact brake force sensor design system provided in one embodiment of the present invention.

[0065] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings in conjunction with the embodiments. DETAILED DESCRIPTION

[0066] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0067] An embodiment of the present application provides a design method for a new type of compact brake force sensor. The execution subject of the design method for the new type of compact brake force sensor includes, but is not limited to, at least one of electronic devices such as a server, a terminal, etc. that can be configured to execute the method provided by the embodiment of the present application. In other words, the design method for the new type of compact brake force sensor can be executed by software or hardware installed on a terminal device or a server device. The server includes, but is not limited to: a single server, a server cluster, a cloud server, or a cloud server cluster, etc.

[0068] Embodiment 1:

[0069] Referring to Figure 1 As shown, it is a schematic flowchart of the design method for a new type of compact brake force sensor provided by an embodiment of the present invention. In this embodiment, the design method for the new type of compact brake force sensor includes:

[0070] S1. Obtain the brake system information corresponding to the simulated vehicle. Based on the brake system information, determine the braking engineering data corresponding to the simulated vehicle. Based on the braking engineering data, monitor the brake force of the simulated vehicle in real time to obtain braking monitoring data. Based on the braking monitoring data, calculate the braking optimization value corresponding to the simulated vehicle.

[0071] By obtaining the brake system information corresponding to the simulated vehicle, the present invention can accurately determine the braking engineering data, provide a basis for real-time monitoring of the brake force, help obtain accurate braking monitoring data, and can be used to explore the braking mode requirements, and then build a braking adjustment system, laying a foundation for improving the performance of the vehicle brake system.

[0072] Among them, the simulated vehicle refers to a simulation vehicle model used to simulate the running state of a real vehicle in a virtual environment or a specific test scenario for research, testing, and optimization of the brake system, etc.; the brake system information refers to covering many key aspects of the brake system, including but not limited to the structural parameters of brake components (such as the size of brake pads, the material and specifications of brake discs, etc.), the pressure characteristics of the brake oil circuit, the working parameters of the brake assist system, and the original signal data fed back by various sensors, etc. These information comprehensively reflect the working state and performance basis of the brake system. Optionally, the obtaining of the brake system information corresponding to the simulated vehicle can be achieved by the data-driven method. For example: collect a large amount of running data of the brake systems of real vehicles, including data under different road conditions and driving behaviors, and use data mining techniques in machine learning to analyze and extract the features and rules related to the brake system information for obtaining the brake system information of the simulated vehicle.

[0073] Furthermore, based on the brake system information, the present invention determines the braking engineering data corresponding to the simulated vehicle, which can accurately reflect the performance of the brake system under different working conditions, and optimize the brake system design, ensure braking safety and stability, and improve the overall vehicle handling performance, thereby effectively reducing design risks and costs.

[0074] Among them, the braking engineering data refers to a series of key quantitative information that comprehensively reflects the braking performance and process of the vehicle. It covers mechanical data during braking, such as the magnitude, distribution of braking force and its change curve over time, which are used to measure the intensity and dynamic characteristics of the force exerted by the brake system; kinematic data, such as braking deceleration, braking distance, braking time, etc., which intuitively show the speed reduction of the vehicle during braking and the space and duration required for stopping; and thermal data, including the temperature change of brake components during braking, because heat generated by brake friction will affect the system performance and safety; in addition, it also involves oil pressure data in the hydraulic braking system, etc. Optionally, the determination of the braking engineering data corresponding to the simulated vehicle can be achieved through a braking test bench. For example, by installing various sensors on the simulated vehicle, such as force sensors, acceleration sensors, displacement sensors, etc., and conducting actual braking tests on the braking test bench to directly measure and obtain braking engineering data such as braking force, braking deceleration, and braking distance.

[0075] Furthermore, based on the braking engineering data, the present invention monitors the braking force of the simulated vehicle in real time to obtain braking monitoring data, which can intuitively present the actual operation state of the brake system under different working conditions, help detect abnormal braking force in a timely manner, ensure vehicle braking safety, and also provide reliable first-hand data support for the subsequent fine-tuning and optimization of the braking system, greatly improving the vehicle braking performance.

[0076] Among them, the braking monitoring data refers to a series of data obtained by real-time monitoring of the braking force of the simulated vehicle. These data comprehensively reflect various information during braking, including the change curve of the magnitude of braking force over time, which can intuitively show the intensity of the braking force exerted by the brake system at different times; braking response time, which records the delay from the start of braking to the actual effect; specific values and characteristics of pressure fluctuations, such as the fluctuation amplitude, frequency and trend mentioned above; and relevant information about the resonance frequency point, etc.

[0077] As an embodiment of the present invention, based on the braking engineering data, the braking force of the simulated vehicle is monitored in real time to obtain braking monitoring data, including: analyzing the braking influencing factors in the braking engineering data; identifying the pressure fluctuation characteristics of the simulated vehicle during the braking process based on the braking influencing factors; analyzing the characteristics of the pressure fluctuation characteristics to obtain braking response characteristics; extracting the resonance frequency points of the simulated vehicle during the braking process based on the braking response characteristics; and monitoring the braking force of the simulated vehicle in real time based on the resonance frequency points to obtain braking monitoring data.

[0078] Among them, the braking influencing factors refer to various factors that can affect the braking effect of the vehicle, including the component characteristics of the braking system itself, such as the friction coefficient of the brake pads, the material and structure of the brake discs; the driving state of the vehicle, such as vehicle speed and load; and external environmental conditions, such as the friction coefficient and slope of the road surface, etc.; the pressure fluctuation characteristics refer to the fact that during the braking process, the pressure in the braking system is not constant but fluctuates, and the pressure fluctuation characteristics describe the law of this pressure change, including the amplitude of the pressure fluctuation, that is, the difference between the maximum and minimum values of the pressure; the fluctuation frequency, that is, the number of pressure fluctuations per unit time; and the trend of pressure change, whether it gradually rises, falls, or changes periodically, etc.; the braking response characteristics refer to the performance description of the vehicle braking system in responding after receiving a braking signal, which includes the braking response time, that is, the time elapsed from when the driver steps on the brake pedal to when the braking system starts to generate effective braking force; the rate of increase in braking force, which reflects how much braking force the braking system can apply in a short period of time; and the stability during the braking process, such as whether the braking force can be output smoothly, and whether there are obvious jitters or mutations, etc.; the resonance frequency point refers to that during the vehicle braking process, each component of the braking system will vibrate due to the force, and when the vibration frequency of some components is close to or equal to the external excitation frequency (such as the pressure change frequency during braking, the vibration frequency during vehicle driving, etc.), resonance will occur, and the resonance frequency point refers to the specific frequency value that causes the components of the braking system to resonate during the braking process.

[0079] Furthermore, the braking influencing factors in the analyzed braking engineering data can be realized through a correlation analysis algorithm, such as: Pearson correlation coefficient algorithm, to calculate the correlation between each parameter in the braking engineering data and the braking force, and determine which parameters are the main braking influencing factors according to the magnitude of the correlation coefficient; the identification of the pressure fluctuation characteristics during the braking process of the simulated vehicle can be realized through frequency domain analysis, such as: converting the pressure signal from the time domain to the frequency domain, and finding the main frequency components of the pressure fluctuation through spectrum analysis, so as to determine the frequency characteristics of the pressure fluctuation; the characteristic analysis of the pressure fluctuation characteristics can be realized through a comparative analysis method, such as: comparing the current pressure fluctuation characteristics with the known standard braking response characteristics, finding the differences and similarities, and thus inferring the braking response characteristics; the extraction of the resonance frequency points during the braking process of the simulated vehicle can be realized through experimental testing, such as: installing vibration sensors on the simulated vehicle, collecting vibration signals during braking, and finding the resonance frequency points through the analysis of the vibration signals; the real-time monitoring of the braking force of the simulated vehicle can be realized through sensor measurement, such as: installing force sensors, pressure sensors, etc. at key parts of the braking system, measuring the braking force and related parameters in real time, and transmitting the measurement data to a computer for processing through a data acquisition system, so as to obtain braking monitoring data.

[0080] Based on the braking monitoring data, the present invention calculates the braking optimization value corresponding to the simulated vehicle, can identify the efficiency bottleneck during the braking process, specifically improve the braking response speed and energy recovery efficiency. At the same time, the dynamic calculation of the optimization value helps to achieve the adaptive adjustment of the braking system, and enhance the braking stability and safety of the vehicle under different road conditions.

[0081] Among them, the braking optimization value is a quantitative index comprehensively evaluating the quality of the braking performance of the simulated vehicle. And the lower the braking optimization value, generally the more stable the change of the braking force during the braking process, the smaller the deviation from the average braking force, which means the better the braking performance of the vehicle; the braking time refers to the time length experienced from the start of applying the braking force to the end of each segment (the k-th divided segment) of the braking process of the simulated vehicle.

[0082] As an embodiment of the present invention, the calculation of the braking optimization value corresponding to the simulated vehicle based on the braking monitoring data includes:

[0083] Use the following formula to calculate the braking optimization value corresponding to the simulated vehicle:

[0084]

[0085] Wherein, Qy represents the braking optimization value corresponding to the simulated vehicle, N represents the number of segments into which the braking process of the simulated vehicle is divided, k represents the quantity index corresponding to the number of segments, T k represents the braking time corresponding to the k-th division segment, α represents the braking force change coefficient, represents the instantaneous change rate corresponding to the k-th division segment, β represents the braking force deviation coefficient, F k (t) represents the braking force change function corresponding to the k-th division segment, represents the average braking force corresponding to all division segments.

[0086] Specifically, the braking force change coefficient is used to measure the importance of the instantaneous change rate of the braking force in the calculation of the braking optimization value. For example, in some scenarios with high requirements for braking smoothness, this coefficient can be appropriately increased to make the influence of the braking force change rate on the braking optimization value more significant; the instantaneous change rate represents the instantaneous change rate of the braking force corresponding to the k-th division segment with respect to time, that is, the change speed of the braking force at a certain moment, which reflects the dynamic change of the braking force during the braking process. If the instantaneous change rate is large, it means that the braking force has a large change in a short time, which may cause a jerky feeling when the vehicle brakes; the braking force deviation coefficient is used to measure the importance of the deviation between the braking force and the average braking force in the calculation of the braking optimization value. This coefficient can be increased to make the influence of the braking force deviation on the braking optimization value greater, thereby highlighting the influence of the stability of the braking force on the braking performance; the braking force change function represents the functional relationship between the braking force corresponding to the k-th division segment and time, which describes how the braking force changes over time during this braking time period; the average braking force is the average value of the braking forces corresponding to all division segments of the simulated vehicle, which comprehensively reflects the average level of the braking force during the entire braking process and serves as a reference standard for comparing with the braking forces of each segment, so as to evaluate whether the magnitude of the braking force in each braking process is reasonable and the degree of difference from the overall average level.

[0087] S2. Explore the braking mode requirements corresponding to the braking optimization value, analyze the demand quantitative relationship corresponding to the braking mode requirements, and combine the demand quantitative relationship with the driving state of the simulated vehicle to construct the braking adjustment system corresponding to the simulated vehicle.

[0088] The present invention explores the braking mode requirements corresponding to the braking optimization value, analyzes the demand quantitative relationship corresponding to the braking mode requirements, explores the braking mode requirements corresponding to the braking optimization value, and analyzes the demand quantitative relationship corresponding to the braking mode requirements.

[0089] Among them, the braking mode requirement refers to the specific requirements for the braking force magnitude, the speed and duration of braking force application, the smoothness of braking, etc. of the braking system to ensure safe and stable braking under different driving conditions (such as high-speed driving, low-speed turning, emergency avoidance, etc.), different road conditions (dry road surface, slippery road surface, ice and snow road surface, etc.) and different load conditions; the demand quantitative relationship refers to the mathematical or logical association existing between the various factors in the braking mode requirement after quantification. For example, the numerical relationship between the braking force magnitude and the vehicle speed, road surface friction coefficient, and vehicle load; the corresponding relationship between the braking response time and the risk level. Optionally, the excavation of the braking mode requirement corresponding to the braking optimization value can be achieved through the working condition analysis method. For example, under the high-speed driving working condition, braking needs to ensure that the vehicle decelerates stably and does not skid. According to the braking optimization value, it is judged whether the current braking force meets the requirements of rapid and smooth deceleration, so as to determine the braking mode requirement; the analysis of the demand quantitative relationship corresponding to the braking mode requirement can be achieved through the regression analysis algorithm. For example, the relationship between the braking force and the vehicle load is analyzed by linear regression to obtain a linear equation to describe their quantitative relationship.

[0090] Furthermore, the present invention combines the demand quantitative relationship with the driving state of the simulated vehicle to construct a braking adjustment system corresponding to the simulated vehicle, which can accurately adapt to the real-time state of the vehicle, enabling the simulated vehicle to adjust braking as needed under different road conditions and vehicle speeds. This not only enhances braking safety and reduces accident risks, but also improves driving comfort and controllability, optimizing the overall driving experience.

[0091] Among them, the driving state refers to a comprehensive description of various aspects of a simulated vehicle during operation, which covers the vehicle's speed information, including instantaneous speed, average speed, and speed change trend, which directly affects the intensity and urgency of braking demand; the vehicle's acceleration, which reflects the speed of speed change and is related to the smoothness and response speed during braking; the driving direction, under different directions such as turning and straight driving, the center of gravity transfer and tire force of the vehicle are different, and the braking requirements also vary; there is also the load condition of the vehicle, the greater the load, the greater the inertia, and the corresponding force and distance required for braking also increase. In addition, the driving state also involves the road conditions where the vehicle is located, such as flat, bumpy, uphill and downhill, as well as the road surface friction coefficient, dry, wet, ice and snow road surfaces, etc.; the braking adjustment system refers to a comprehensive system that integrates various braking adjustment strategies and methods to ensure that the simulated vehicle can achieve safe and efficient braking under various driving states. This system dynamically adjusts parameters such as the magnitude, direction, and application time of the braking force according to the real-time driving state of the vehicle through the coordinated control of various components of the braking system. It includes sensors for real-time monitoring of the vehicle's driving state, a controller for making decisions and executing corresponding braking strategies based on association rules and quantitative evaluation results, and an actuator (such as a brake pump, brake motor, etc.) responsible for specifically implementing the braking operation.

[0092] As an embodiment of the present invention, the construction of the braking adjustment system corresponding to the simulated vehicle by combining the demand quantitative relationship with the driving state of the simulated vehicle includes: analyzing the association rules corresponding to the demand quantitative relationship and the driving state; based on the association rules, quantitatively evaluating the braking demand of the simulated vehicle under the current driving state to obtain a quantitative evaluation result; screening out potential braking strategies that are suitable for the current braking demand from the quantitative evaluation result; dynamically simulating the potential braking strategies to obtain a dynamic simulation effect; and constructing the braking adjustment system corresponding to the simulated vehicle according to the dynamic simulation effect.

[0093] Among them, the association rule refers to a logical description that reveals the internal relationship between the quantitative relationship of requirements and the simulated vehicle driving state, which clarifies how the parameters of the braking system (such as the magnitude of braking force, braking response time, braking pressure, etc.) should be adjusted according to the quantitative relationship of requirements under different driving states (such as vehicle speed, road surface condition, vehicle load, etc.); the quantitative evaluation result refers to a numerical evaluation of the braking requirements of the simulated vehicle in the current driving state, and the result can include quantitative data such as the exact value of the braking force required currently, the response speed that the braking system should achieve, and the maximum allowable pressure fluctuation range during braking; the potential braking strategy refers to a series of braking operation plans formulated according to the quantitative evaluation result, which can be used to meet the current braking requirements, and it covers adjustment strategies in different aspects of the braking system, such as changing the application method of braking force (whether it is gradually increasing or instantaneously increasing), adjusting the working intensity of the braking assist system, and changing the contact method between the brake pads and the brake discs; the dynamic simulation effect refers to the result presented after running and testing the potential braking strategy in the simulation environment, and the dynamic simulation effect includes the speed change curve, displacement change situation, force conditions of various components of the braking system, and whether phenomena such as wheel lock-up or vehicle side-slip occur during braking.

[0094] Furthermore, the parsing of the association rule corresponding to the quantitative relationship of requirements and the driving state can be achieved through the Apriori algorithm. For example, by analyzing the braking data of the vehicle under different vehicle speeds, loads, and road surface conditions, the Apriori algorithm can discover association rules such as "when the vehicle speed is greater than 60 km / h and the road surface is wet, the braking distance will increase by 20% - 30%"; the quantitative evaluation of the braking requirements of the simulated vehicle in the current driving state can be achieved through a quantitative evaluation tool. For example, tools such as MATLAB; screening out the potential braking strategy that adapts to the current braking requirements from the quantitative evaluation results can be achieved through a multi-objective optimization method. For example, by comprehensively considering multiple objectives such as braking distance, braking smoothness, and comfort, screening out the final potential braking strategy from numerous possible braking strategies; the dynamic simulation of the potential braking strategy can be achieved through a numerical integration algorithm. For example, in Adams / Car, the Runge-Kutta algorithm is used to numerically solve the dynamic equation of the vehicle during braking, and the speed change curve, displacement change situation, etc. of the vehicle when applying a certain potential braking strategy are simulated; the construction of the braking adjustment system corresponding to the simulated vehicle can be achieved through system integration and control methods. For example, combining hardware devices such as sensors, controllers, and actuators with control algorithms to construct a complete braking adjustment system.

[0095] S3. Based on the braking adjustment system, determine the brake adjustment module corresponding to the simulated vehicle, identify the key adjustment features in the brake adjustment module, analyze the adjustment improvement trend corresponding to the brake adjustment module based on the key adjustment features, and construct a braking trend map corresponding to the simulated vehicle based on the adjustment improvement trend.

[0096] Based on the braking adjustment system, the present invention determines the brake adjustment module corresponding to the simulated vehicle, which can accurately adapt to the complex and diverse driving conditions of the vehicle. Through targeted optimization of each link of the braking system, it can greatly improve the braking performance and ensure the safety and reliability of vehicle braking in different scenarios.

[0097] Among them, the brake adjustment module refers to the key execution unit in the braking adjustment system, which covers a series of components and control logics for adjusting the performance of the braking system; from the hardware level, it includes a brake pressure regulating valve that can adjust the braking force and an actuator that can change the contact state between the brake pads and the brake disc; in terms of software, it has an algorithm program that can accurately control the hardware actions according to the real-time driving state of the vehicle and the instructions of the braking adjustment system. By integrating these hardware and software components, the brake adjustment module can dynamically optimize the braking force, response time and stability, ensuring that the simulated vehicle can achieve efficient and safe braking operations under various driving conditions. Optionally, the determination of the brake adjustment module corresponding to the simulated vehicle can be achieved through the function decomposition method. For example, the braking adjustment system is required to have the functions of quickly increasing the braking force during emergency braking and smoothly braking during normal driving. Through function decomposition, it can be determined that the brake adjustment module needs to have a rapid pressure increase sub-module and a progressive braking control sub-module.

[0098] Furthermore, by identifying the key adjustment features in the brake adjustment module, the present invention can accurately locate the core elements affecting the braking performance, which helps to focus resources on the key parts during research and development and optimization, greatly improving the research and development efficiency and optimization effect of the braking system. At the same time, it is also convenient to quickly diagnose and solve braking-related problems during the actual use of the vehicle, ensuring vehicle braking safety.

[0099] Among them, the key adjustment feature refers to a characteristic that can significantly affect the performance of the brake adjustment module and can be optimized by adjusting the key parameter factors and their coordination relationships. For example, the dynamic adjustment characteristic of the braking force, that is, adjusting the braking force size in real time and accurately according to different braking conditions, this characteristic depends on key parameter factors such as vehicle speed and road surface friction coefficient and their coordinated adjustment relationships.

[0100] As an embodiment of the present invention, the identification of the key adjustment features in the brake adjustment module includes: analyzing the brake adjustment module for the corresponding operation process link; clarifying the detailed process parameters corresponding to the operation process link; calculating the parameter sensitivity of the detailed process parameters under different braking conditions; screening the key parameter factors in the detailed process parameters based on the parameter sensitivity; querying the collaborative adjustment relationship between the key parameter factors; and identifying the key adjustment features in the brake adjustment module based on the collaborative adjustment relationship.

[0101] Among them, the operation process link refers to a series of continuous operation steps from receiving a braking instruction to completing a braking adjustment action, which covers multiple sub-processes such as signal reception and processing, braking force calculation, and actuator movement control. For example, when the driver steps on the brake pedal, the sensor transmits the pedal travel signal to the electronic control unit (ECU), which is an operation process link; the ECU calculates the required braking force according to the signal and sends a control signal to the brake pressure regulator, which is another operation process link; the detailed process parameters refer to the specific data indicators describing the state and characteristics of each operation process link. In the signal reception and processing link, the detailed process parameters can include the accuracy of the sensor, the signal transmission delay time, etc.; in the braking force calculation link, parameters such as vehicle mass, current vehicle speed, and road surface friction coefficient involved in calculating the braking force are involved; in the actuator movement control link, parameters such as the pressure adjustment range of the brake pressure regulator and the gap between the brake pads and the brake discs are available; the parameter sensitivity refers to an index that measures the degree of influence of the numerical change of the detailed process parameters on the final braking performance of the brake adjustment module under different braking conditions. For example, in the case of high-speed driving and emergency braking, a small change in the vehicle speed parameter can cause a large change in the braking force requirement, indicating that the vehicle speed parameter has a high sensitivity to braking performance under this condition; the key parameter factor refers to the parameter selected from numerous detailed process parameters that has the most significant impact on the braking performance of the brake adjustment module. For example, under various braking conditions, the magnitude of the braking force is always a key factor affecting the braking distance and braking stability. Therefore, parameters such as vehicle speed and road surface friction coefficient involved in calculating the braking force can become key parameter factors; the collaborative adjustment relationship refers to the relationship in which key parameter factors affect and cooperate with each other to achieve the best braking performance. For example, when the vehicle speed increases, to ensure safe braking, not only the braking force needs to be increased (parameters related to braking force calculation), but also the response speed of the brake pressure regulator may need to be adjusted (parameters related to actuator movement control). There is a collaborative adjustment relationship between these two key parameter factors.

[0102] Furthermore, the analysis of the corresponding operation process links of the brake adjustment module can be achieved through process analysis methods. For example, starting from when the brake pedal is depressed, analyze how the signal is transmitted to the electronic control unit (ECU), how the ECU processes the signal and calculates the braking force, and then how the braking force is transmitted to the brake device through the hydraulic or pneumatic system to finally achieve the braking effect, so as to determine each operation process link; the clarification of the detailed process parameters corresponding to the operation process link can be achieved through experimental measurement methods. For example, during braking, use a pressure sensor to measure the pressure in the brake pipeline and a displacement sensor to measure the gap between the brake pads and the brake disc to clarify the values of these parameters; the calculation of the parameter sensitivity of the detailed process parameters under different braking conditions can be achieved through numerical simulation methods. For example, use MATLAB / Simulink to build a brake system model. Under different braking conditions such as different vehicle speeds and road surface conditions, change the parameter of the braking force distribution coefficient and observe the changes in the braking response and stability of the vehicle to calculate its sensitivity; the screening of the key parameter factors in the detailed process parameters can be achieved through the sensitivity ranking method. For example, rank the sensitivities of all parameters and select the top 30% of the parameters with the highest sensitivity as the key parameter factors because these parameters have the most significant impact on the performance of the brake adjustment module; the query of the collaborative adjustment relationship between the key parameter factors can be achieved through the Bayesian network algorithm. For example, in a Bayesian network of the brake adjustment module, node A and node B respectively represent two key parameter factors, and the way of their collaborative effect can be judged through the network structure and the conditional probability table; the identification of the key adjustment features in the brake adjustment module can be achieved through the analytic hierarchy process. For example, take performance goals such as braking safety and braking comfort as the target layer, key parameter factors and collaborative adjustment relationships as the criterion layer, calculate the weights of each criterion for the target through AHP, and find out the key adjustment features corresponding to the factors with larger weights.

[0103] Based on the key adjustment features, the present invention analyzes the corresponding adjustment and improvement trends of the brake adjustment module, which can intuitively present the current performance short - board of the module. Along the improvement trend, improvement strategies can be specifically formulated, such as adjusting the structural design or replacing the matching materials, which not only improves the performance stability and reliability of the brake adjustment module.

[0104] Among them, the adjustment improvement trend refers to the prediction and analysis results of the performance optimization direction of the brake adjustment module based on key adjustment features, which cover multiple dimensions. For example, in terms of mechanical structure, it may show a development trend towards a more compact, lightweight and durable design to improve space utilization efficiency and extend service life; in terms of braking performance, with problems such as slow braking response reflected by key features, the trend points to a faster braking response speed and a stronger braking force output adjustment ability to ensure accurate braking under different working conditions; in terms of control logic, it may evolve towards intelligence and adaptability. Optionally, the analysis of the adjustment improvement trend corresponding to the brake adjustment module can be achieved through data analysis tools, such as Excel, SPSS, etc.

[0105] Furthermore, based on the adjustment improvement trend, the present invention constructs a braking trend map corresponding to the simulated vehicle, which can visually present the change trend of braking performance over time or working conditions, helping to quickly understand the state of the braking system at different stages and accurately locate potential problems.

[0106] Among them, the braking trend map refers to a set of charts or graphs that visually present the change trend of the braking performance of the simulated vehicle with different factors (such as time, working conditions, etc.). And the braking trend map can adopt various forms. For example, a line chart shows the change trend of braking force over time, a scatter plot combines different colors or shapes to represent the relationship between braking distance and vehicle speed under different working conditions, and a heat map shows the response time of the braking system under different combinations of road surface conditions and vehicle speeds.

[0107] As an embodiment of the present invention, constructing the braking trend map corresponding to the simulated vehicle based on the adjustment improvement trend includes: analyzing the trend change nodes in the adjustment improvement trend; based on the trend change nodes, performing feature marking on the simulated vehicle under different braking conditions to obtain marked condition features; performing curve fitting on the marked engineering data to obtain a marked engineering curve; extracting the curve fluctuation points from the marked engineering curve; and constructing the braking trend map corresponding to the simulated vehicle based on the curve fluctuation points.

[0108] Among them, the trend change node refers to a time point or operating condition point in the adjusted and improved trend data that represents a significant change in braking performance or related parameters. For example, when a simulated vehicle is undergoing a braking test, the instant when the braking force suddenly increases or decreases is a trend change node; or at a certain moment, when the braking distance significantly shortens or lengthens, the corresponding time point also belongs to the trend change node. The marked operating condition characteristics refer to the detailed description and marking of various characteristics of the simulated vehicle under different braking conditions based on the trend change node. For example, at the moment corresponding to the trend change node, if the vehicle is in the operating condition of high-speed driving and emergency braking, the marked operating condition characteristics will record information such as the vehicle speed at this time (e.g., 100 km / h), braking mode (emergency braking), road surface condition (dry), etc. The marked engineering curve refers to the curve obtained by curve fitting of the braking performance data corresponding to the marked operating condition characteristics (such as the data of braking force changing with time, the relationship data between braking distance and vehicle speed, etc.) through mathematical methods. For example, with time as the abscissa and braking force as the ordinate, the braking force data measured at a series of different time points are curve-fitted to obtain a curve that can roughly reflect the changing trend of braking force with time, which is the marked engineering curve. The curve fluctuation point refers to the point in the marked engineering curve that deviates from the overall changing trend of the curve, resulting in obvious undulations or turning points in the curve. For example, in the marked engineering curve of braking force changing with time, if there is a point where the braking force suddenly drops significantly and then quickly rebounds, this point is the curve fluctuation point.

[0109] Furthermore, the analysis of the trend change node in the adjusted and improved trend can be achieved through the sliding window method. For example: The sliding window method can be adopted, setting a window with a fixed size to slide on the data sequence, calculating the statistical characteristics of the data within the window such as mean, variance, etc. When these characteristics change significantly, it is considered a trend change node. The feature marking of the simulated vehicle under different braking conditions can be achieved through the decision tree algorithm. For example: Classification marking can be performed according to the input characteristics of braking condition data to obtain the marked operating condition characteristics. The curve fitting of the marked engineering data can be achieved through the polynomial fitting algorithm. For example: Selecting an appropriate polynomial order to fit the data to obtain the marked engineering curve. The extraction of the curve fluctuation point in the marked engineering curve can be achieved through the wavelet transform algorithm. For example: By transforming the curve data through the wavelet function, the fluctuation of the data can be analyzed at different scales to accurately extract the curve fluctuation point. The construction of the braking trend map corresponding to the simulated vehicle can be achieved through the Dijkstra algorithm. For example: It can be used to find key information such as the shortest path in the constructed graph, helping to analyze the key path and changes of the braking trend, and finally obtaining the braking trend map.

[0110] S4. Identify the core parameter points in the braking trend map. Based on the core parameter points, query the force optimization direction corresponding to the braking trend map, extract the optimized force points in the force optimization direction, calculate the force balance value corresponding to the optimized force points, and determine the sensor structure that the simulated vehicle conforms to based on the force balance value.

[0111] By identifying the core parameter points in the braking trend map, the present invention helps to accurately diagnose potential problems in the braking system, optimize the system design, avoid blind adjustment. At the same time, during the actual operation of the vehicle, based on these core parameter points, braking anomalies can be detected in a timely manner, improving driving safety and ensuring the stable and reliable operation of the vehicle.

[0112] Among them, the core parameter points refer to the points further selected from the center points of the sub-regions, which play a key role in understanding the braking trend, diagnosing problems in the braking system, and optimizing braking performance. For example, in the braking trend map, under the braking condition corresponding to the center point of a certain sub-region, the relationship between the braking force and the braking distance happens to be in the critical state of the braking system design. Once deviating from this point, the braking performance will drop sharply, then the center point of this sub-region will be identified as a core parameter point.

[0113] As an embodiment of the present invention, the identification of the core parameter points in the braking trend map includes: querying the multi-dimensional feature distribution in the braking trend map; positioning the mutation regions in the braking trend map that exceed the preset threshold based on the multi-dimensional feature distribution; performing cluster analysis on the mutation regions to obtain a regional analysis result; extracting the center points of the sub-regions in the regional analysis result; and identifying the core parameter points in the center points of the sub-regions.

[0114] Among them, the multi-dimensional feature distribution means that the braking trend map contains multiple parameter dimensions related to braking, such as braking force magnitude, braking time, braking distance, vehicle speed, acceleration, etc. For example, under different braking conditions, the joint distribution state presented by the curve of braking force changing with time and the curve of braking distance changing with vehicle speed, as well as the frequencies of different parameters appearing in different value ranges; the preset threshold refers to a set reference standard value used to judge whether the parameter changes in the braking trend map are significant. For different braking parameter dimensions, corresponding thresholds are set according to factors such as the design requirements of the braking system, safety standards, and past experience, etc.; the mutation region refers to the map region corresponding to when the changes of one or more parameters in the braking trend map exceed the preset threshold range. For example, in the braking trend map composed of the braking force-time curve and the braking distance-vehicle speed scatter plot, if within a certain vehicle speed range, the braking distance suddenly increases significantly and the braking force also shows abnormal fluctuations, exceeding their respective set preset thresholds, then the map region formed by this vehicle speed range and the value ranges of the corresponding braking force and braking distance is the mutation region; the regional analysis result refers to the result obtained after clustering analysis of the mutation region. Clustering analysis divides the parameter points with similar characteristics in the mutation region into the same category, so as to discover the internal laws and grouping situations among these parameter points. For example, through clustering analysis, the parameter points in the mutation region may be divided into two categories. One category is the parameter mutation caused by the vehicle's emergency braking, and the other category is the parameter abnormality caused by a fault in a certain component of the braking system. These classifications and related feature descriptions constitute the regional analysis result; the sub-region center point refers to a point representing the central position of each sub-region corresponding to each cluster in the regional analysis result, that is, the sub-region center point. For example, in a two-dimensional braking parameter sub-region (such as the braking force-braking time sub-region), the coordinate value of the sub-region center point is the coordinate corresponding to the average value of all braking force and braking time data points in this sub-region.

[0115] Furthermore, the query of the multi-dimensional feature distribution in the braking trend map can be realized by the t-SNE algorithm. For example, using the t-SNE algorithm, high-dimensional data can be mapped to a two-dimensional or three-dimensional space. By calculating the similarity between data points, similar data points are mapped to nearby positions to visually present the multi-dimensional feature distribution. The positioning of the mutation region exceeding the preset threshold in the braking trend map can be realized by the wavelet transform method. For example, it can decompose the signal into components of different frequencies, detect the mutation points in the data by analyzing the changes in wavelet coefficients, and then determine the mutation region. The clustering analysis of the mutation region can be realized by the clustering algorithm. For example, using the K-Means clustering algorithm, it randomly selects K initial clustering centers, then divides the data points into different clusters according to the distance between the data points and the clustering centers, and continuously iteratively updates the clustering centers until the clustering result converges, so as to obtain the regional analysis result. The extraction of the sub-region center points in the regional analysis result can be realized by the convex hull algorithm. For example, for the data points of each clustering cluster, its convex hull is constructed, and then the geometric center of the convex hull is calculated as the sub-region center point. The identification of the core parameter points in the sub-region center points can be realized by the analytic hierarchy process. For example, a hierarchical structure model is constructed, and the attributes of the sub-region center points are used as indicators. The weights of the indicators are determined by pairwise comparison, and the parameter points in the sub-region center points with higher comprehensive weights are the core parameter points.

[0116] Based on the core parameter points, the present invention queries the force optimization direction corresponding to the braking trend map and extracts the optimized force points in the force optimization direction, which helps to focus resources on the core links when optimizing the braking performance, avoid blind attempts, and greatly improve the optimization efficiency.

[0117] Among them, the force optimization direction refers to the direction or path that the force adjustment should follow during the braking process reflected in the braking trend map in order to achieve better braking effects, improve braking system performance and other goals. For example, whether to adjust in the direction of increasing braking force, reducing braking response time, or optimizing the force distribution, etc. This needs to be determined in combination with the core parameter points and the actual needs and goals of the braking system; the optimized force point refers to the specific point of action or numerical point of the force that plays a key role and is of great significance in the force optimization direction. It is the specific implementation point of the force optimization direction. It can be that the force at a certain specific position needs to be adjusted to a specific value, or the force at a certain moment needs to reach a certain specific value, etc. Optionally, querying the force optimization direction corresponding to the braking trend map can be achieved through a genetic algorithm. For example: through continuous iteration and optimization of the genetic algorithm, find the force adjustment strategy that makes the fitness function optimal, and its corresponding direction is the force optimization direction; extracting the optimized force points in the force optimization direction can be achieved through extreme value search. For example: in the optimization direction of increasing braking force, gradually increase the braking force and test the braking distance to find the braking force value when the braking distance is the shortest, which is an optimized force point.

[0118] Furthermore, by calculating the force balance value corresponding to the optimized force point, the present invention helps to ensure that the force distribution among the braking components is more reasonable under different braking conditions, and avoid excessive wear of components or deviation of braking effects caused by uneven local force.

[0119] Among them, the force balance value is a comprehensive measurement index, which is obtained by calculating and integrating various factors related to the optimized force point, and is used to evaluate the degree of balance of the force distribution of the entire system under the action of these optimized force points. The higher the force balance value, the more reasonable and balanced the force distribution among the force points, and the better the stability and performance of the system.

[0120] As an embodiment of the present invention, calculating the force balance value corresponding to the optimized force point includes:

[0121] Calculating the force balance value corresponding to the optimized force point by using the following formula:

[0122]

[0123] Among them, EZ represents the force balance value corresponding to the optimized force point, M represents the total number corresponding to the optimized force point, j represents the quantity index corresponding to the optimized force point, F j represents the detailed force value corresponding to the j-th force point, W j represents the weight coefficient corresponding to the j-th force point, θ j represents the direction angle corresponding to the direction of the j-th force point and the reference direction, I jvIt represents the corresponding interaction coefficient between the j-th force point and the v-th force point, λ represents the reference adjustment factor, and B represents the balance reference value.

[0124] Specifically, the detailed force value refers to the magnitude value of the specific force on the j-th optimized force point, which reflects the intensity of the force actually applied or existing at this force point; the weight coefficient refers to the relative importance of the j-th optimized force point in the entire force balance calculation; the direction angle refers to the angle between the direction of the j-th optimized force point and the preset reference direction; the interaction coefficient refers to the interaction degree between the j-th optimized force point and the v-th optimized force point; the reference adjustment factor refers to an adjustable parameter used to adjust the calculation of the force balance value according to actual situations. It can comprehensively consider factors such as the characteristics of the system and the operating environment, correct the calculation results, and make the force balance value more in line with actual needs and the actual operating state of the system; the balance reference value refers to a preset reference value, which serves as a basic standard for measuring the force balance of the system.

[0125] Based on the force balance value, the present invention determines the sensor structure that the simulated vehicle conforms to, can accurately match the vehicle's force monitoring requirements, helps the sensor to efficiently capture the force data of the vehicle under different working conditions, ensures the accuracy and reliability of the data, and thus enhances the vehicle performance and safety.

[0126] Among them, the sensor structure refers to the various components of the sensor and their connection and combination methods. It includes a sensitive element for directly sensing the measured quantity (such as the force, pressure, acceleration, etc. of the vehicle) and outputting other quantities with a definite relationship; a conversion element for converting the non-electrical signal output by the sensitive element into an electrical signal for subsequent processing; a signal conditioning circuit for amplifying, filtering, etc. the converted electrical signal to improve the signal quality; and auxiliary components such as a housing, a bracket, etc., which play a role in protecting and fixing the internal components. Optionally, determining the sensor structure that the simulated vehicle conforms to can be achieved through a multi-objective optimization algorithm. For example, the optimal solution can be searched through the NSGA-II algorithm to obtain the sensor structure with the best comprehensive performance.

[0127] S5. Query the structural design process adapted to the sensor structure, determine the material structure attributes corresponding to the structural design process, analyze the braking demand target corresponding to the simulated vehicle based on the material structure attributes, and generate a sensor design scheme for the braking force of the simulated vehicle based on the braking demand target in combination with the real-time braking situation of the simulated vehicle.

[0128] By querying the structural design process adapted to the sensor structure, the present invention can improve the accuracy and stability of sensor installation, ensuring its normal operation under complex working conditions; on the other hand, the appropriate process helps to optimize the sensor performance and improve the accuracy and reliability of data acquisition.

[0129] Among them, the structural design process refers to a series of design methods and manufacturing process measures adopted to enable the sensor structure to meet the performance requirements and operating conditions in the simulated vehicle, including material selection of sensor components, shape design, dimensional accuracy control, manufacturing processes such as casting, forging, machining accuracy control, as well as assembly processes, quality inspection processes, etc.

[0130] As an embodiment of the present invention, the querying of the structural design process adapted to the sensor structure includes: analyzing the connection relationship between the components corresponding to the sensor structure; constructing a structural topology diagram corresponding to the connection relationship; querying the operating conditions of each component in the structural topology diagram; comprehensively scoring the operating conditions to obtain a condition scoring index; and querying the structural design process adapted to the sensor structure based on the condition scoring index.

[0131] Among them, the connection relationship refers to the physical connection method and interaction relationship between each component in the sensor structure, including mechanical connections such as welding, riveting, bolt connection, etc., and electrical connections such as wire connection, solder joint connection, etc., and also includes the relationship in aspects such as signal transmission and energy transfer between components; the structural topology diagram refers to a schematic diagram that graphically shows the connection relationship between each component of the sensor, using nodes to represent each component of the sensor and lines to represent the connections between components, without considering the actual shape, size, and spatial position of the components, mainly used to intuitively analyze and understand the composition and connection logic of the sensor structure; the operating condition refers to the working conditions and environmental conditions faced by each component of the sensor during the operation of the simulated vehicle, including environmental factors such as temperature, humidity, vibration, shock, electromagnetic interference, etc., and working parameters such as the force, pressure, current, voltage, etc. borne by the components; the condition scoring index refers to a numerical value obtained by quantifying the comprehensive evaluation result of the operating conditions of each component of the sensor through certain evaluation criteria and algorithms.

[0132] Furthermore, the analysis of the connection relationships between the components corresponding to the sensor structure can be achieved through an incidence matrix algorithm, such as the incidence matrix algorithm in graph theory. The sensor components are abstracted as nodes, and the connection relationships are regarded as edges to construct an incidence matrix to represent the connection relationships between the components. The construction of the structural topology graph corresponding to the connection relationships can be realized through a generation algorithm based on a graph data structure. For example, with the incidence matrix or other data representing the connection relationships as the input, using graph traversal algorithms such as depth-first search or breadth-first search to generate a visual structural topology graph of nodes and edges. The query of the operating conditions of each component in the structural topology graph can be achieved through a finite element algorithm. For example, the sensor structure is discretized into a finite number of elements, and by solving partial differential equations, the stress, strain, temperature distribution and other condition data of each component under given operating conditions are calculated. The comprehensive scoring of the operating conditions can be realized through a fuzzy comprehensive evaluation algorithm. For example, the value ranges of the various factors of the operating conditions are fuzzified, a fuzzy relation matrix is established, and fuzzy operations are combined with the weight vector to obtain the fuzzy value of the comprehensive score, and then the condition scoring index is obtained through defuzzification processing. The query of the structural design process suitable for the sensor structure can be achieved through a genetic algorithm. For example, with the performance objectives of the sensor and the constraints of the operating conditions as the conditions, the parameters of the structural design process such as materials, dimensions, and machining accuracy are used as genes, and through genetic operations such as selection, crossover, and mutation, the optimal structural design process plan is searched.

[0133] By determining the material structure attributes corresponding to the structural design process, the present invention helps to select the most suitable materials during production, give full play to the process advantages, and avoid quality problems caused by the incompatibility between materials and processes.

[0134] Among them, the material structure attributes refer to the key elements that comprehensively reflect the material characteristics, covering the atomic arrangement, crystal structure, phase composition and defect conditions at the microscopic level, as well as the porosity, internal fiber or layered distribution and other characteristics at the macroscopic level. Optionally, the determination of the material structure attributes corresponding to the structural design process can be achieved through a molecular dynamics simulation algorithm. For example, based on Newton's laws of motion, the motion of atoms and molecules in the material is simulated to predict the microscopic structure attributes such as the structural changes and diffusion behavior of the material under different conditions.

[0135] Furthermore, based on the material structure attributes, the present invention analyzes the braking demand objectives corresponding to the simulated vehicle, and can clarify which materials are more suitable for the components of the braking system to ensure meeting the requirements of braking intensity, stability and durability.

[0136] Among them, the braking demand target refers to a set of indicators aiming to achieve the expected vehicle braking performance, aiming to ensure the safe and efficient operation of the vehicle. It covers braking effectiveness, that is, the ability of the braking system to decelerate and stop the vehicle at a specified initial speed, usually measured by braking distance and braking deceleration. For example, a short braking distance is required for emergency braking during high-speed driving. Braking stability is also crucial, requiring that the vehicle does not deviate or skid abnormally during braking to ensure that the driving direction is controllable. Optionally, the analysis of the braking demand target corresponding to the simulated vehicle can be achieved by the theoretical calculation method. For example, based on parameters such as the mass, driving speed, and road surface conditions of the vehicle, using mechanical principles such as Newton's laws of motion, calculate the braking force, braking distance, braking deceleration, etc. during braking to determine the braking effectiveness demand target.

[0137] Furthermore, based on the braking demand target and combined with the real-time braking situation of the simulated vehicle, the present invention generates a sensor design scheme for the braking force of the simulated vehicle, which can ensure that the sensor precisely meets the vehicle braking monitoring requirements and guarantee its effective monitoring of the core indicators of braking force.

[0138] Among them, the real-time braking situation refers to the actual working state presented by the braking system at the current moment during the operation of the simulated vehicle. It covers multiple key dimensions, including the real-time depression depth of the brake pedal, which directly reflects the intensity of the driver's immediate demand for braking force; the dynamic change value of the braking pressure, which reflects the real-time fluctuation of the pressure in the hydraulic or pneumatic circuit of the braking system and is closely related to the generation of braking force; the change situation of the wheel speed. By monitoring the wheel speed, it can be judged whether the vehicle is in a tendency of locking and whether the speed difference between each wheel is normal, thus reflecting the balance of braking; and the actual deceleration of the vehicle. This parameter intuitively shows the degree of speed reduction of the vehicle due to braking and is a key indicator for measuring the braking effect. The sensor design scheme refers to the detailed planning of how to construct a system architecture that can effectively sense, collect, and transmit information related to braking force for the monitoring requirements of the simulated vehicle braking system. It includes the selection of sensor types. For example, according to the measurement requirements, a strain gauge force sensor may be selected to accurately measure the magnitude of the braking force, or a Hall effect sensor may be used to monitor the current or magnetic field changes in the braking system to indirectly reflect the braking force situation. After determining the type, the installation position of the sensor needs to be determined, and factors such as facilitating the acquisition of accurate data, avoiding interference, and the convenience of installation and maintenance need to be comprehensively considered. For example, the sensor for measuring the wheel braking force is installed between the wheel hub and the brake caliper. At the same time, the setting of sensor performance parameters is also involved in the scheme, such as the measurement range should cover the maximum braking force that may be generated during vehicle braking, the accuracy should meet the requirement of accurately capturing the subtle changes in the braking force, and the response time should be short enough to provide real-time feedback on the braking situation. Optionally, the generation of the sensor design scheme related to the braking force of the simulated vehicle can be achieved through scheme generation tools, such as: tools like ANSYS, MATLAB, etc.

[0139] Compared with the problems described in the background art, the present invention can accurately determine the braking engineering data by obtaining the braking system information corresponding to the simulated vehicle, provide a basis for real-time monitoring of the braking force, help obtain accurate braking monitoring data, and can be used to explore the braking mode requirements, and then construct a braking adjustment system, laying a foundation for improving the performance of the vehicle braking system. The present invention explores the braking mode requirements corresponding to the braking optimization value, analyzes the demand quantitative relationship corresponding to the braking mode requirement, explores the braking mode requirements corresponding to the braking optimization value, and analyzes the demand quantitative relationship corresponding to the braking mode requirement. Further, based on the braking adjustment system, the present invention determines the braking adjustment module corresponding to the simulated vehicle, which can accurately adapt to the complex and diverse driving conditions of the vehicle. Through the targeted optimization of each link of the braking system, it can greatly improve the braking performance and ensure the safety and reliability of braking in different scenarios. Further, by identifying the core parameter points in the braking trend map, the present invention helps to accurately diagnose potential problems in the braking system, optimize the system design, avoid blind adjustment. At the same time, during the actual operation of the vehicle, based on these core parameter points, braking abnormalities can be detected in a timely manner, improving driving safety and ensuring the stable and reliable operation of the vehicle. Finally, by querying the structural design process adapted to the sensor structure, the present invention can improve the accuracy and stability of sensor installation, ensuring its normal operation under complex working conditions; on the other hand, the appropriate process helps to optimize the sensor performance and improve the accuracy and reliability of data acquisition. Therefore, the novel compact braking force sensor design method and system provided by the embodiments of the present invention can improve the overall performance of the vehicle braking system.

[0140] Embodiment 2:

[0141] As Figure 2 shown, it is a functional module diagram of a novel compact braking force sensor design system of the present invention.

[0142] The novel compact braking force sensor design system 200 described in the present invention can be installed in an electronic device. According to the functions achieved, the novel compact braking force sensor design system may include an optimization value calculation module 201, a system construction module 202, a map construction module 203, a structure determination module 204, and a scheme generation module 205. The modules described in the present invention may also be referred to as units, which refer to a series of computer program segments that can be executed by the processor of an electronic device and can complete fixed functions, and are stored in the memory of the electronic device.

[0143] In the embodiments of the present invention, the functions of each module / unit are as follows:

[0144] The optimization value calculation module 201 is used to obtain brake system information corresponding to the simulated vehicle, determine brake engineering data corresponding to the simulated vehicle based on the brake system information, monitor the brake force of the simulated vehicle in real time based on the brake engineering data, obtain brake monitoring data, and calculate the brake optimization value corresponding to the simulated vehicle based on the brake monitoring data;

[0145] The system construction module 202 is used to mine the braking mode requirements corresponding to the braking optimization value, analyze the quantitative relationship of requirements corresponding to the braking mode requirements, and combine the quantitative relationship of requirements with the driving state of the simulated vehicle to construct a braking adjustment system corresponding to the simulated vehicle;

[0146] The map construction module 203 is used to determine the brake adjustment module corresponding to the simulated vehicle based on the brake adjustment system, identify the key adjustment features in the brake adjustment module, analyze the adjustment improvement trend corresponding to the brake adjustment module based on the key adjustment features, and construct the brake trend map corresponding to the simulated vehicle based on the adjustment improvement trend;

[0147] The structure determination module 204 is used to identify the core parameter points in the braking trend map, query the force optimization direction corresponding to the braking trend map based on the core parameter points, extract the optimized force points in the force optimization direction, calculate the force balance value corresponding to the optimized force point, and determine the sensor structure that the simulated vehicle meets based on the force balance value;

[0148] The solution generation module 205 is used to query the structural design process that the sensor structure is adapted to, determine the material structure properties corresponding to the structural design process, analyze the braking demand target corresponding to the simulated vehicle based on the material structure properties, and generate a sensor design solution for the braking force of the simulated vehicle based on the braking demand target combined with the real-time braking condition of the simulated vehicle.

[0149] In detail, each module in the novel compact brake force sensor design system 200 of the embodiment of the present invention is used in the same manner as described above. Figure 1 The new compact brake force sensor design method described in the invention has the same technical means and can produce the same technical effects, so it will not be repeated here.

[0150] It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0151] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A design method for a new type of compact braking force sensor, characterized in that, The method includes: Obtaining the braking system information corresponding to the simulated vehicle, determining the braking engineering data corresponding to the simulated vehicle based on the braking system information, monitoring the braking force of the simulated vehicle in real time based on the braking engineering data to obtain braking monitoring data, and calculating the braking optimization value corresponding to the simulated vehicle based on the braking monitoring data; Mining the braking mode requirements corresponding to the braking optimization value, analyzing the demand quantitative relationship corresponding to the braking mode requirements, and constructing the braking adjustment system corresponding to the simulated vehicle by combining the demand quantitative relationship with the driving state of the simulated vehicle; Determining the braking adjustment module corresponding to the simulated vehicle based on the braking adjustment system, identifying the key adjustment features in the braking adjustment module, analyzing the adjustment improvement trend corresponding to the braking adjustment module based on the key adjustment features, and constructing the braking trend map corresponding to the simulated vehicle based on the adjustment improvement trend; Identifying the core parameter points in the braking trend map, querying the force optimization direction corresponding to the braking trend map based on the core parameter points, extracting the optimized force points in the force optimization direction, calculating the force balance value corresponding to the optimized force points, and determining the sensor structure that the simulated vehicle conforms to based on the force balance value; Querying the structural design process adapted to the sensor structure, determining the material structure attributes corresponding to the structural design process, analyzing the braking demand target corresponding to the simulated vehicle based on the material structure attributes, and generating a sensor design scheme for the braking force of the simulated vehicle by combining the braking demand target with the real-time braking situation of the simulated vehicle.

2. The novel compact brake braking force sensor design method according to claim 1, characterized in that, The monitoring the braking force of the simulated vehicle in real time based on the braking engineering data to obtain braking monitoring data includes: Analyzing the braking influencing factors in the braking engineering data; Identifying the pressure fluctuation characteristics of the simulated vehicle during braking based on the braking influencing factors; Performing characteristic analysis on the pressure fluctuation characteristics to obtain braking response characteristics; Extracting the resonance frequency points of the simulated vehicle during braking based on the braking response characteristics; Monitoring the braking force of the simulated vehicle in real time based on the resonance frequency points to obtain braking monitoring data.

3. The novel compact brake force sensor design method according to claim 1, characterized in that The calculating the braking optimization value corresponding to the simulated vehicle based on the braking monitoring data includes: Calculating the braking optimization value corresponding to the simulated vehicle using the following formula: Among them, Qy represents the braking optimization value corresponding to the simulated vehicle, N represents the number of segments into which the braking process of the simulated vehicle is divided, k represents the quantity index corresponding to the number of segments, T k represents the braking time corresponding to the k-th segment, α represents the braking force change coefficient, represents the instantaneous change rate corresponding to the k-th segment, β represents the braking force deviation coefficient, F k (t) represents the braking force change function corresponding to the k-th segment, represents the average braking force corresponding to all segments.

4. The novel compact brake braking force sensor design method according to claim 1, wherein The constructing the braking adjustment system corresponding to the simulated vehicle by combining the demand quantitative relationship with the driving state of the simulated vehicle includes: Analyzing the association rules corresponding to the demand quantitative relationship and the driving state; Quantitatively evaluating the braking demand of the simulated vehicle in the current driving state based on the association rules to obtain a quantitative evaluation result; Screening out potential braking strategies that adapt to the current braking demand from the quantitative evaluation result; Performing dynamic simulation on the potential braking strategies to obtain a dynamic simulation effect; Constructing the braking adjustment system corresponding to the simulated vehicle according to the dynamic simulation effect.

5. The novel compact brake braking force sensor design method according to claim 1, characterized in that, Identifying the key adjustment features in the brake adjustment module includes: Analyzing the corresponding operation process link of the brake adjustment module; Defining the detailed process parameters corresponding to the operation process link; Calculating the parameter sensitivity of the detailed process parameters under different braking conditions; Based on the parameter sensitivity, screening the key parameter factors in the detailed process parameters; Querying the collaborative adjustment relationship between the key parameter factors; Based on the collaborative adjustment relationship, identifying the key adjustment features in the brake adjustment module.

6. The novel compact brake force sensor design method according to claim 1, characterized in that, Constructing the braking trend map corresponding to the simulated vehicle based on the adjustment improvement trend includes: Analyzing the trend change nodes in the adjustment improvement trend; Based on the trend change nodes, performing feature annotation on the simulated vehicle under different braking conditions to obtain the annotated condition features; Performing curve fitting on the annotated engineering data to obtain the annotated engineering curve; Extracting the curve fluctuation points in the annotated engineering curve; Based on the curve fluctuation points, constructing the braking trend map corresponding to the simulated vehicle.

7. The novel compact brake braking force sensor design method according to claim 1, characterized in that, Identifying the core parameter points in the braking trend map includes: Querying the multi-dimensional feature distribution in the braking trend map; Based on the multi-dimensional feature distribution, locating the mutation regions in the braking trend map that exceed the preset threshold; Performing cluster analysis on the mutation regions to obtain the regional analysis results; Extracting the central points of the sub-regions in the regional analysis results; Identifying the core parameter points in the central points of the sub-regions.

8. The novel compact brake braking force sensor design method according to claim 1, characterized in that Calculating the force balance value corresponding to the optimized force point includes: Calculating the force balance value corresponding to the optimized force point using the following formula: Among them, EZ represents the force balance value corresponding to the optimized force point, M represents the total quantity corresponding to the optimized force point, j represents the quantity index corresponding to the optimized force point, F j represents the detailed force value corresponding to the j-th force point, W j represents the weight coefficient corresponding to the j-th force point, θ j represents the direction angle corresponding to the direction of the j-th force point and the reference direction, I jv represents the interaction coefficient corresponding to between the j-th force point and the v-th force point, λ represents the reference adjustment factor, and B represents the balance reference value.

9. The novel compact brake braking force sensor design method according to claim 1, characterized in that, Querying the structural design process adapted to the sensor structure includes: Analyzing the connection relationship between the components corresponding to the sensor structure; Constructing the structural topology corresponding to the connection relationship; Querying the operating conditions of the components in the structural topology; Performing a comprehensive evaluation on the operating conditions to obtain the condition evaluation index; Based on the condition evaluation index, querying the structural design process adapted to the sensor structure.

10. A novel compact braking force sensor design system, characterized in that, The system includes: An optimization value calculation module for obtaining the brake system information corresponding to the simulated vehicle, determining the braking engineering data corresponding to the simulated vehicle based on the brake system information, monitoring the braking force of the simulated vehicle in real time based on the braking engineering data to obtain braking monitoring data, and calculating the braking optimization value corresponding to the simulated vehicle based on the braking monitoring data; A system construction module for mining the braking mode requirements corresponding to the braking optimization value, analyzing the demand quantitative relationship corresponding to the braking mode requirements, and constructing the braking adjustment system corresponding to the simulated vehicle by combining the demand quantitative relationship with the driving state of the simulated vehicle; A map construction module for determining the brake adjustment module corresponding to the simulated vehicle based on the braking adjustment system, identifying the key adjustment features in the brake adjustment module, analyzing the adjustment improvement trend corresponding to the brake adjustment module based on the key adjustment features, and constructing the braking trend map corresponding to the simulated vehicle based on the adjustment improvement trend; A structure determination module, configured to identify core parameter points in the braking trend map, query the force optimization direction corresponding to the braking trend map based on the core parameter points, extract optimized force points in the force optimization direction, calculate the force balance value corresponding to the optimized force points, and determine the sensor structure that the simulated vehicle conforms to based on the force balance value; A solution generation module, configured to query the structural design process adapted to the sensor structure, determine the material structure attributes corresponding to the structural design process, analyze the braking demand target corresponding to the simulated vehicle based on the material structure attributes, and generate a sensor design solution for the braking force of the simulated vehicle based on the braking demand target in combination with the real-time braking condition of the simulated vehicle.

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