Sensor design method and system for realizing braking

Abstract

The invention relates to the technical field of intelligent transportation equipment, and discloses a sensor design method and system under braking, and the method comprises the steps: determining the working environment elements of a brake sensor under the braking of a brake system, analyzing the application scene characteristics of the sensor, and formulating the core design parameters of the brake sensor; the signal propagation topology of the brake sensor is determined, and the cooperative coupling effect between the signal propagation topology and the physical efficiency representation is evaluated; performing simulation construction on the brake sensor to obtain a sensor prototype, and analyzing signal detection characteristics of the sensor prototype; calculating the life prediction equivalent value of the sensor prototype, evaluating the performance stability of the sensor prototype, and analyzing the adaptation compatibility of the sensor prototype in the braking system; and screening out an optimal sensing material from the core sensing material and the alternative sensing material to generate the design method of the brake sensor. According to the invention, the reliability of sensor design under braking can be improved.

Description

Technical Field

[0001] The present invention relates to a method and system for designing a sensor under braking, belonging to the technical field of intelligent transportation equipment. Background Art

[0002] In the field of modern transportation, the braking system is a key component to ensure the safe driving of vehicles. The quality of its performance is directly related to the life and property safety of passengers and drivers. With the rapid development of the automotive industry and the continuous improvement of people's requirements for driving safety, more stringent standards have been put forward for the performance and reliability of sensors in the braking system.

[0003] Most of the existing brake sensor designs adopt traditional design ideas and methods. In the selection of sensors, often only a single performance index, such as sensitivity or response time, is considered, while ignoring the comprehensive requirements of the braking system for various aspects of sensor performance under complex working conditions. In the design process, insufficient consideration is given to the compatibility between the sensor and other components of the braking system, resulting in problems such as signal interference and unstable mechanical connection when the sensor is actually installed and used, affecting the overall performance of the braking system. These design methods lack in-depth research on the long-term stability and reliability of the sensor. When the vehicle travels for a long time, brakes frequently, and faces various harsh environments (such as high temperature, high humidity, strong vibration, etc.), the performance of the sensor is prone to attenuation and cannot work stably continuously. There is an urgent need for a method to improve the reliability of sensor design under braking. Summary of the Invention

[0004] The present invention provides a method and system for designing a sensor under braking, and its main purpose is to improve the reliability of sensor design under braking.

[0005] To achieve the above object, a method for designing a sensor under braking provided by the present invention includes:

[0006] Obtain the operating condition parameters and dynamic efficiency parameters of the braking system. Based on the operating condition parameters and the dynamic efficiency parameters, clarify the working environment elements of the brake sensor in the braking system. Based on the working environment elements, analyze the application scenario characteristics of the sensor, collect the existing fault information of the brake sensor, and combine the application scenario characteristics and the existing fault information to formulate the core design parameters of the brake sensor;

[0007] Schedule the material intrinsic data and structural design data of the brake sensor, analyze the physical performance characterization corresponding to the material intrinsic data, determine the signal propagation topology of the brake sensor according to the structural design data, evaluate the cooperative coupling effect between the signal propagation topology and the physical performance characterization, and determine the parameter priority sequence of the core design parameters according to the cooperative coupling effect;

[0008] Query the core sensing material and alternative sensing materials of the brake sensor, combine the core sensing material and the alternative sensing materials, perform a simulation build on the brake sensor to obtain a sensor prototype, collect the induction accuracy data and durability test data of the sensor prototype, and analyze the signal detection characteristics of the sensor prototype based on the induction accuracy data;

[0009] Calculate the life prediction equivalent value of the sensor prototype according to the durability test data, evaluate the performance stability of the sensor prototype based on the life prediction equivalent value, determine the installation position requirements and working condition constraints of the brake sensor in the braking system, collect the adaptability parameters of the sensor prototype, and analyze the adaptability and compatibility of the sensor prototype in the braking system in combination with the installation position requirements, the working condition constraints, and the adaptability parameters;

[0010] Combine the signal detection characteristics, the performance stability, and the adaptability and compatibility, screen out the best sensing material from the core sensing material and the alternative sensing materials, and generate the design method of the brake sensor based on the core design parameters, the parameter priority sequence, and the best sensing material.

[0011] Optionally, the formulating the core design parameters of the brake sensor by combining the application scenario characteristics and the existing fault information includes:

[0012] Extract the characteristics of the application scenario features to obtain scenario feature factors;

[0013] Perform information classification processing on the existing fault information to obtain a fault category set;

[0014] Analyze the correlation relationship between the scenario feature factors and the fault category set to obtain a correlation mapping matrix;

[0015] Based on the correlation mapping matrix, determine the key scenario features and key fault categories of the brake sensor;

[0016] Analyze the design constraint conditions of the brake sensor based on the key scenario features and key fault categories;

[0017] Formulate the core design parameters of the brake sensor based on the design constraint conditions.

[0018] Optionally, the physical performance characterization corresponding to the analysis of the material intrinsic data includes:

[0019] Perform normalization processing on the material intrinsic data to obtain normalized material data;

[0020] Extract the material physical properties corresponding to the normalized material data, perform screening processing on the material physical properties, and obtain key physical properties;

[0021] Calculate the physical performance indicators corresponding to the key physical properties, and generate the physical performance characterization corresponding to the material intrinsic data based on the physical performance indicators.

[0022] Optionally, determining the signal propagation topology of the brake sensor according to the structural design data includes:

[0023] Perform data preprocessing on the structural design data to obtain target structural design data;

[0024] Extract the structural parameter set of the brake sensor from the target structural design data;

[0025] Perform material property correlation processing on the structural parameter set to obtain an attribute correlation parameter set;

[0026] Based on the attribute correlation parameter set, construct a signal propagation numerical model corresponding to the brake sensor;

[0027] Perform simulation calculation processing on the signal propagation numerical model to obtain signal propagation dynamic data;

[0028] Perform topological abstraction processing on the signal propagation dynamic data to generate the signal propagation topology of the brake sensor.

[0029] Optionally, evaluating the cooperative coupling effect between the signal propagation topology and the physical performance characterization includes:

[0030] Extract the signal propagation characteristics corresponding to the signal propagation topology, perform dimensionality reduction processing on the signal propagation characteristics, and obtain dimensionality-reduced signal propagation characteristics;

[0031] Calculate the feature similarity index between the dimensionality-reduced signal propagation characteristics, and calculate the characterization similarity index between the physical performance characterizations;

[0032] Calculate the correlation factor between the signal propagation topology and the physical performance characterization;

[0033] Calculate the synergy coupling degree between the signal propagation topology and the physical performance characterization in combination with the associated factor, the feature similarity index, and the representation similarity index;

[0034] Evaluate the synergy coupling effect between the signal propagation topology and the physical performance characterization based on the synergy coupling degree.

[0035] Optionally, calculating the associated factor between the signal propagation topology and the physical performance characterization includes:

[0036] Perform vectorization processing on the signal propagation topology and the physical performance characterization respectively to obtain a propagation topology vector and a performance characterization vector;

[0037] Calculate the vector cosine value between the propagation topology vector and the performance characterization vector;

[0038] Calculate the vector mutual information between the propagation topology vector and the performance characterization vector;

[0039] Combine the vector cosine value and the vector mutual information, and calculate the associated factor between the signal propagation topology and the physical performance characterization through the following formula:

[0040]

[0041] Among them, D represents the associated factor between the signal propagation topology and the physical performance characterization, represents the weight adjustment coefficient, represents the vector cosine value between the Xth propagation topology vector in the signal propagation topology and the Yth performance characterization vector in the physical performance characterization, represents the vector mutual information between the Xth propagation topology vector in the signal propagation topology and the Yth performance characterization vector in the physical performance characterization.

[0042] Optionally, analyzing the signal detection characteristics of the sensor prototype based on the induction accuracy data includes:

[0043] Perform data cleaning processing on the induction accuracy data to obtain cleaned induction accuracy data;

[0044] Analyze and extract the accuracy time-domain characteristics and accuracy frequency-domain characteristics corresponding to the cleaned induction accuracy data;

[0045] Generate a signal characteristic descriptor corresponding to the sensor prototype based on the accuracy time-domain characteristics and the accuracy frequency-domain characteristics;

[0046] Analyze the signal detection characteristics of the sensor prototype based on the signal characteristic descriptor.

[0047] Optionally, calculating the life prediction equivalent value of the sensor prototype based on the durability test data includes:

[0048] Analyze the test performance parameters corresponding to the durability test data, and query the failure mode and failure mechanism corresponding to the brake sensor;

[0049] Conduct a variable analysis of the failure mode and the failure mechanism to obtain the failure variables corresponding to the brake sensor;

[0050] Based on the failure variables, formulate the life evaluation index corresponding to the brake sensor, and based on the life evaluation index, extract the life-related parameters of the brake sensor from the test performance parameters;

[0051] Calculate the life score value corresponding to the life-related parameters based on the durability test data;

[0052] Allocate the parameter contribution degree corresponding to the life-related parameters, and combine the life score value and the parameter contribution degree to calculate the life prediction equivalent value of the sensor prototype.

[0053] Optionally, analyzing the adaptation and compatibility degree of the sensor prototype in the braking system by combining the installation position requirements, the working condition constraints, and the adaptability parameters includes:

[0054] Based on the installation position requirements, determine the installation area of the sensor prototype in the braking system, and measure the area size parameters of the installation area and the corresponding area of the sensor prototype and the sensor size parameters;

[0055] Based on the area size parameters and the sensor size parameters, calculate the size adaptability of the sensor prototype in the braking system through the following formula:

[0056]

[0057] where G represents the size adaptability of the sensor prototype in the braking system, , , respectively represent the length, width, and height size parameters in the area size parameters, , , respectively represent the length, width, and height size parameters in the sensor size parameters, represents the maximum value of the length selected from the area size parameters and the sensor size parameters, represents the maximum value of the width selected from the area size parameters and the sensor size parameters, represents the maximum value of the height selected from the area size parameters and the sensor size parameters;

[0058] Analyze the adaptation factors corresponding to the adaptation parameters, and calculate the factor adaptation degrees corresponding to the adaptation factors based on the working condition constraints and the adaptation parameters.

[0059] Calculate the working adaptation degree of the sensor prototype in the braking system based on the factor adaptation degrees.

[0060] Analyze the adaptation compatibility degree of the sensor prototype in the braking system by combining the size adaptation degree and the working adaptation degree.

[0061] To solve the above problems, the present invention also provides a sensor design system for realizing braking, and the system includes:

[0062] A core design parameter formulation module, configured to obtain the operating condition parameters and dynamic performance parameters of the braking system, clarify the working environment elements of the brake sensor in the braking system based on the operating condition parameters and the dynamic performance parameters, analyze the application scenario characteristics of the sensor based on the working environment elements, collect the existing fault information of the brake sensor, and formulate the core design parameters of the brake sensor by combining the application scenario characteristics and the existing fault information.

[0063] A parameter priority sequence determination module, configured to schedule the material intrinsic data and structural design data of the brake sensor, analyze the physical performance characteristics corresponding to the material intrinsic data, determine the signal propagation topology of the brake sensor according to the structural design data, evaluate the cooperative coupling effect between the signal propagation topology and the physical performance characteristics, and determine the parameter priority sequence of the core design parameters according to the cooperative coupling effect.

[0064] A signal detection characteristic analysis module, configured to query the core sensing material and alternative sensing materials of the brake sensor, simulate and build the brake sensor by combining the core sensing material and the alternative sensing materials to obtain a sensor prototype, collect the induction accuracy data and durability test data of the sensor prototype, and analyze the signal detection characteristics of the sensor prototype based on the induction accuracy data.

[0065] An adaptation compatibility analysis module, configured to calculate the life prediction equivalent value of the sensor prototype according to the durability test data, evaluate the performance stability of the sensor prototype based on the life prediction equivalent value, determine the installation position requirements and working condition constraints of the brake sensor in the braking system, collect the adaptation parameters of the sensor prototype, and analyze the adaptation compatibility degree of the sensor prototype in the braking system by combining the installation position requirements, the working condition constraints and the adaptation parameters.

[0066] A design method generation module, configured to screen out the optimal sensing material from the core sensing material and the alternative sensing materials by combining the signal detection characteristics, the performance stability, and the adaptation compatibility, and generate a design method for the brake sensor based on the core design parameters, the parameter priority sequence, and the optimal sensing material.

[0067] Compared with the problems described in the background art, the present invention formulates the core design parameters of the brake sensor by combining the application scenario characteristics and the existing fault information, and can obtain the key quantitative criteria for the design of the brake sensor, thereby laying a foundation for subsequent optimization of the performance of the brake sensor and improvement of its reliability and stability. Further, the present invention analyzes the physical effect characterization corresponding to the material intrinsic data by scheduling the material intrinsic data and the structural design data of the brake sensor, and can deeply understand the performance of the brake sensor at the material level, providing a basis for subsequent signal propagation topology analysis and co-coupling effect evaluation. The present invention simulates and constructs the brake sensor by combining the core sensing material and the alternative sensing materials, and can obtain brake sensors with different material combinations, thereby providing a diverse sample basis and rich data source for subsequent analysis of the signal detection characteristics of the sensor prototype. Further, the present invention calculates the life prediction equivalent value of the sensor prototype based on the durability test data, and evaluates the performance stability of the sensor prototype according to the life prediction equivalent value, and can effectively evaluate the reliability of the sensor prototype during long-term use. Further, the present invention screens out the optimal sensing material from the core sensing material and the alternative sensing materials by combining the signal detection characteristics, the performance stability, and the adaptation compatibility, and can accurately match the material that meets the complex working conditions requirements of the brake sensor to ensure its efficient and stable operation. Based on the core design parameters, the parameter priority sequence, and the optimal sensing material, a design method for the brake sensor is generated, thereby improving the reliability of the sensor design under braking. Therefore, a method and system for implementing a sensor design method under braking provided by an embodiment of the present invention can improve the reliability of the sensor design under braking. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] Figure 1 FIG. is a schematic flowchart of a method for implementing a sensor design method under braking provided by an embodiment of the present invention;

[0069] Figure 2 FIG. is a schematic block diagram of a module for implementing the method for implementing a sensor design method under braking provided by an embodiment of the present invention.

[0070] The implementation of the object, functional features, and advantages of the present invention will be further described with reference to the embodiments and the accompanying drawings. Detailed Implementation Modes

[0071] It should be understood that the specific embodiments described herein are merely used to explain the present invention and are not intended to limit the present invention.

[0072] The embodiments of the present application provide a sensor design method for realizing braking. The execution subjects of the sensor design method for realizing braking include, but are 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 embodiments of the present application. In other words, the sensor design method for realizing braking 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.

[0073] Embodiment 1:

[0074] Referring to Figure 1 As shown, it is a schematic flowchart of a sensor design method for realizing braking provided by an embodiment of the present invention. In this embodiment, the sensor design method for realizing braking includes:

[0075] S1. Obtain the operating condition parameters and dynamic performance parameters of the braking system, based on the operating condition parameters and the dynamic performance parameters, clarify the working environment elements of the brake sensor in the braking system, based on the working environment elements, analyze the application scenario characteristics of the sensor, collect the existing fault information of the brake sensor, and combine the application scenario characteristics and the existing fault information to formulate the core design parameters of the brake sensor.

[0076] By combining the application scenario characteristics and the existing fault information, the present invention formulates the core design parameters of the brake sensor, and can obtain the key quantitative criteria for the brake sensor design, thereby laying a foundation for subsequent optimization of the performance of the brake sensor, improvement of its reliability and stability.

[0077] It should be explained that the operating condition parameters refer to various quantitative indicators of the braking system under different working states, such as braking pressure, braking speed, braking time, etc. The dynamic performance parameters are parameters that reflect the effects and performance generated by the braking system during dynamic operation, such as braking efficiency, energy loss, etc. The working environment factors include environmental conditions such as temperature, humidity, vibration, and electromagnetic interference where the brake sensor is located. The application scenario characteristics refer to the usage characteristics of the brake sensor in different braking scenarios, such as daily urban driving, high-speed driving, mountain downhill driving, etc. The existing fault information refers to various fault records that occurred during the past use of the brake sensor, such as inaccurate signals, response delays, short circuits, and open circuits. The core design parameters refer to the key quantitative indicators during the design process of the brake sensor, such as sensitivity, accuracy, anti-interference ability, response time, etc.

[0078] Furthermore, the operating condition parameters and dynamic performance parameters of the braking system can be obtained by real-time collection through various sensors installed in the system and transmitted to the data processing center for analysis; the working environment factors of the brake sensor can be measured by environmental monitoring equipment; the application scenario characteristics can be determined through big data analysis of different driving scenarios; the existing fault information can be collected from the vehicle's fault diagnosis system and maintenance records.

[0079] Specifically, formulating the core design parameters of the brake sensor by combining the application scenario characteristics and the existing fault information includes:

[0080] Extract the feature of the application scenario characteristics to obtain scenario feature factors;

[0081] Conduct information classification processing on the existing fault information to obtain a set of fault categories;

[0082] Analyze the correlation relationship between the scenario feature factors and the set of fault categories to obtain a correlation mapping matrix;

[0083] Based on the correlation mapping matrix, determine the key scenario characteristics and key fault categories of the brake sensor;

[0084] Based on the key scenario characteristics and key fault categories, analyze the design constraint conditions of the brake sensor;

[0085] Based on the design constraint conditions, formulate the core design parameters of the brake sensor.

[0086] It should be noted that the scenario feature factor is a representative feature parameter extracted from the application scenario features, such as the braking frequency, braking intensity, etc. under different scenarios; the fault category set is a set of different fault types obtained by classifying and sorting the existing fault information, such as electrical faults, mechanical faults, etc.; the association mapping matrix is used to describe the degree of association between the scenario feature factor and the fault category set; the key scenario features and key fault categories refer to the scenario features and fault categories with a relatively high degree of association in the association mapping matrix, which have a greater impact on the performance of the brake sensor; the design constraint conditions are the restrictions and requirements for the design of the brake sensor determined according to the key scenario features and key fault categories, such as the heat resistance requirements for the sensor material in a high-temperature scenario, etc.

[0087] Furthermore, the feature extraction of the application scenario features can be achieved through data dimensionality reduction algorithms such as principal component analysis; the classification of the existing fault information can be completed by using clustering algorithms in machine learning; the analysis of the association relationship between the scenario feature factor and the fault category set can be realized through association rule mining algorithms; determining the corresponding design constraint conditions based on the key scenario features and key fault categories requires comprehensive consideration in combination with the working principle of the brake sensor and relevant industry standards; formulating the core design parameters of the brake sensor based on the design constraint conditions. For example, if the key scenario feature is frequent braking and the key fault category is response delay, then improving the response speed of the sensor can be used as a core design parameter, and specific response time indicators can be determined according to factors such as the braking frequency.

[0088] S2. Schedule the material intrinsic data and structural design data of the brake sensor, analyze the physical efficacy characterization corresponding to the material intrinsic data, determine the signal propagation topology of the brake sensor according to the structural design data, evaluate the synergistic coupling effect between the signal propagation topology and the physical efficacy characterization, and determine the parameter priority sequence of the core design parameters according to the synergistic coupling effect.

[0089] Through scheduling the material intrinsic data and structural design data of the brake sensor and analyzing the physical efficacy characterization corresponding to the material intrinsic data, the present invention can deeply understand the performance of the brake sensor at the material level, providing a basis for subsequent signal propagation topology analysis and synergistic coupling effect evaluation. It should be noted that the material intrinsic data refers to the inherent data that can reflect the essential characteristics of the material, such as the density, hardness, dielectric constant, etc. of the material; the structural design data refers to various parameters involved in the structural design process of the brake sensor, including size specifications, shape characteristics, internal layout, etc.; the physical efficacy characterization is a comprehensive description of the physical properties exhibited by the material based on these intrinsic data, covering the performance characteristics of the material in multiple fields such as mechanical, electrical, and thermal.

[0090] Specifically, the physical performance characterization corresponding to the analysis of the material's intrinsic data includes:

[0091] Perform normalization processing on the material's intrinsic data to obtain normalized material data;

[0092] Extract the material physical properties corresponding to the normalized material data, perform screening processing on the material physical properties to obtain key physical properties;

[0093] Calculate the physical performance indicators corresponding to the key physical properties, and based on the physical performance indicators, generate the physical performance characterization corresponding to the material's intrinsic data.

[0094] It should be explained that the normalized material data is the data obtained after the material's intrinsic data undergoes normalization or standardization processing. The material physical properties are the inherent characteristics exhibited by the material under physical conditions and are used to describe the performance and behavior of the material in the fields of mechanics, thermotics, electricity, magnetism, etc. The key physical properties are the physical parameters in the material's intrinsic data that have the greatest impact on the performance of the brake sensor, and the physical performance characterization is the performance manifestation of the key physical properties in practical applications.

[0095] Furthermore, the normalization processing of the material's intrinsic data can be achieved through data normalization or standardization methods; the extraction of the material physical properties corresponding to the normalized material data can be achieved through the partial least squares regression (PLSR) method; the screening processing of the material physical properties can be performed through the correlation analysis method to obtain key physical properties; the calculation of the physical performance indicators corresponding to the key physical properties can be achieved through simulation tools such as finite element analysis (FEA) or computational fluid dynamics (CFD); summarize the physical performance indicators, and finally generate the physical performance characterization corresponding to the material's intrinsic data.

[0096] Based on the structural design data, the present invention determines the signal propagation topology of the brake sensor, clearly presenting the signal transmission path and nodes, providing an intuitive basis for deeply understanding the working mechanism of the sensor, optimizing the signal transmission efficiency, and accurately positioning potential fault points, and helping to improve the overall performance and reliability of the brake sensor design. It should be explained that the signal propagation topology is the propagation path and network structure of the signal in the brake sensor, including the signal start point, end point, key nodes, and path branches.

[0097] Specifically, the determination of the signal propagation topology of the brake sensor according to the structural design data includes:

[0098] Perform preprocessing on the structural design data to obtain target structural design data;

[0099] Extract the structural parameter set of the brake sensor from the target structural design data;

[0100] Perform material property correlation processing on the structural parameter set to obtain an attribute correlation parameter set;

[0101] Based on the attribute correlation parameter set, construct a signal propagation numerical model corresponding to the brake sensor;

[0102] Perform simulation calculation processing on the signal propagation numerical model to obtain signal propagation dynamic data;

[0103] Perform topological abstraction processing on the signal propagation dynamic data to generate the signal propagation topology of the brake sensor.

[0104] It should be explained that the target structural design data is the part of the structural design data that has been screened, sorted, and extracted to be closely related to the current analysis requirements. The structural parameter set is a set of parameters describing the structural characteristics such as the geometric shape, size, and layout of each component of the brake sensor in the target structural design data. The attribute correlation parameter set is a set of parameters formed by combining the structural parameter set with the material selection information of different components of the brake sensor and matching and correlating the corresponding material characteristic parameters (such as conductivity, elastic modulus, etc.) from the material intrinsic database. The signal propagation numerical model is a mathematical model corresponding to the brake sensor, constructed based on the signal propagation principle, the structural parameter set, and the attribute correlation parameter set, and used to simulate the signal propagation process. The signal propagation dynamic data is the data of the propagation state parameters (such as signal strength, propagation speed, etc.) of the signal at different positions and different times recorded during the simulation calculation process of the signal propagation numerical model.

[0105] Furthermore, the structural design data can be pre-processed through professional data conversion tools to obtain target structural design data, such as CAD files, 3D modeling files, etc.; the structural parameter set of the brake sensor can be extracted from the target structural design data through a targeted parameter extraction algorithm, and the parameter extraction algorithm can be compiled through programming languages; the structural parameter set can be processed for material property association by matching and associating corresponding material property parameters in the material intrinsic database, and the material intrinsic database is a pre-constructed feature property set library, such as property libraries of conductivity, elastic modulus, dielectric constant, etc.; based on the property association parameter set, a signal propagation numerical model corresponding to the brake sensor can be constructed through finite element analysis software in combination with the basic principle of signal propagation; the signal propagation numerical model can be processed by simulation calculation through numerical calculation methods to obtain signal propagation dynamic data, such as the finite difference method; the signal propagation dynamic data can be topologically abstracted by defining the key positions of signal propagation as nodes, the propagation paths as edges and assigning edge attributes to generate the signal propagation topology of the brake sensor.

[0106] By evaluating the synergistic coupling effect between the signal propagation topology and the physical performance characterization, the present invention can accurately grasp the interaction law between the internal structure and material properties of the sensor, provide a scientific basis for optimizing the design and improving the overall performance and reliability of the brake sensor. According to the synergistic coupling effect, the priority sequence of the core design parameters is determined, which is convenient for the subsequent generation of the design method of the brake sensor. It should be noted that the synergistic coupling effect represents the interaction relationship between the signal propagation topology and the physical performance characterization, such as the influence of material thermal conductivity on signal transmission delay. The priority sequence of parameters is the order formed after the core design parameters are sorted according to specific criteria (such as importance, influence degree on system performance, difficulty of implementation, etc.), which is used to guide the attention and processing order of each core design parameter in the design process. Furthermore, according to the synergistic coupling effect, the priority sequence of the core design parameters is determined. For example, according to the level of the synergistic coupling effect, the core design parameters closely related to the strong synergistic coupling effect are ranked in the front, and those weakly related are ranked behind, so as to determine the priority sequence of parameters.

[0107] Specifically, the evaluation of the synergistic coupling effect between the signal propagation topology and the physical performance characterization includes:

[0108] Extract the signal propagation characteristics corresponding to the signal propagation topology, and perform dimensionality reduction processing on the signal propagation characteristics to obtain the dimensionality-reduced signal propagation characteristics;

[0109] Calculate the feature similarity index between the dimension-reduced signal propagation features, and calculate the representation similarity index between the physical efficiency characterizations;

[0110] Calculate the correlation factor between the signal propagation topology and the physical efficiency characterizations;

[0111] Combine the correlation factor, the feature similarity index, and the representation similarity index to calculate the synergy coupling degree between the signal propagation topology and the physical efficiency characterizations;

[0112] Based on the synergy coupling degree, evaluate the synergy coupling effect between the signal propagation topology and the physical efficiency characterizations.

[0113] It should be explained that the signal propagation features are a set of parameters corresponding to the signal propagation topology that can reflect the signal propagation characteristics. The dimension-reduced signal propagation features are a set of features obtained by performing dimension reduction processing on the signal propagation features to simplify the data while retaining key information. The feature similarity index represents the measure of the similarity degree in feature attributes between the dimension-reduced signal propagation features. The representation similarity index represents the measure of the similarity degree in performance attributes between the physical efficiency characterizations. The correlation factor represents a quantitative index of the degree of tight mutual connection between the signal propagation topology and the physical efficiency characterizations. The synergy coupling degree represents the comprehensive quantitative result of the overall synergy effect and the degree of mutual coupling tightness between the signal propagation topology and the physical efficiency characterizations.

[0114] Further, the signal propagation features corresponding to the signal propagation topology can be extracted by the principal component analysis (PCA) method. The dimension reduction algorithm of linear discriminant analysis (LDA) can be used to perform dimension reduction processing on the signal propagation features to obtain the dimension-reduced signal propagation features. The feature similarity index between the dimension-reduced signal propagation features can be calculated by the cosine similarity algorithm. The representation similarity index between the physical efficiency characterizations can be calculated by the above cosine similarity algorithm. Combining the correlation factor, the feature similarity index, and the representation similarity index, the synergy coupling degree between the signal propagation topology and the physical efficiency characterizations can be calculated by the geometric mean calculation method. The calculation formula is: Synergy coupling degree = , where A, B, and C respectively represent the correlation factor, the feature similarity index, and the representation similarity index. Based on the value of the synergy coupling degree, evaluate the synergy coupling effect between the signal propagation topology and the physical efficiency characterizations. For example, the higher the value of the synergy coupling degree, the higher the synergy coupling effect.

[0115] Further, as an optional embodiment of the present invention, the calculation of the correlation factor between the signal propagation topology and the physical efficiency characterizations includes:

[0116] Vectorize the signal propagation topology and the physical performance characterization respectively to obtain a propagation topology vector and a performance characterization vector;

[0117] Calculate the cosine value of the vectors between the propagation topology vector and the performance characterization vector;

[0118] Calculate the mutual information of the vectors between the propagation topology vector and the performance characterization vector;

[0119] Combine the cosine value of the vectors and the mutual information of the vectors, and calculate the correlation factor between the signal propagation topology and the physical performance characterization through the following formula:

[0120]

[0121] where D represents the correlation factor between the signal propagation topology and the physical performance characterization, represents the weight adjustment coefficient, represents the cosine value of the vectors between the Xth propagation topology vector in the signal propagation topology and the Yth performance characterization vector in the physical performance characterization, represents the mutual information of the vectors between the Xth propagation topology vector in the signal propagation topology and the Yth performance characterization vector in the physical performance characterization.

[0122] Furthermore, is a weight adjustment coefficient, and its value range is between 0 and 1, which is used to adjust the proportion of the vector space similarity and the mutual information in information theory in the calculation of the correlation factor. When = 1, the correlation factor is only determined by the cosine value of the angle between the vectors in the vector space, emphasizing the similarity of the vector space; when = 0, the correlation factor is only determined by the normalized mutual information, emphasizing the dependence relationship in information theory. The weight adjustment coefficient can be reasonably set by referring to the relevant literature in the related field, referring to the value of the adjustment parameter in the similar problems, and combining the characteristics and differences of the current research.

[0123] It should be explained that the propagation topology vector and the performance characterization vector are respectively the vector form representations obtained after the signal propagation topology and the physical performance characterization are processed by feature extraction and dimensionality reduction, etc. The cosine value of the vectors is a measure of the direction similarity degree between the propagation topology vector and the performance characterization vector in the vector space, and the mutual information of the vectors is a measure of the degree of mutual dependence of information between the propagation topology vector and the performance characterization vector.

[0124] Furthermore, the signal propagation topology and the physical performance characterization can be vectorized by the above-mentioned principal component analysis (PCA) method to obtain a propagation topology vector and a performance characterization vector; the vector cosine value between the propagation topology vector and the performance characterization vector can be calculated by combining the vector dot product formula with the calculation of the vector norm; the probability distribution can be calculated based on kernel density estimation, and then the vector mutual information between the propagation topology vector and the performance characterization vector can be calculated according to the mutual information definition formula.

[0125] S3. Query the core sensing material and alternative sensing materials of the brake sensor, and combine the core sensing material and the alternative sensing materials to perform a simulation construction on the brake sensor to obtain a sensor prototype, collect the induction accuracy data and durability test data of the sensor prototype, and analyze the signal detection characteristics of the sensor prototype based on the induction accuracy data.

[0126] By combining the core sensing material and the alternative sensing materials of the present invention to perform a simulation construction on the brake sensor, brake sensors with different material combinations can be obtained, thereby providing a diverse sample basis and rich data source for the subsequent analysis of the signal detection characteristics of the sensor prototype.

[0127] It should be explained that the core sensing material is the key material that determines the main sensing performance of the brake sensor, has unique physical and chemical properties, can accurately sense the physical changes generated during braking and convert them into electrical signals; the alternative sensing material is an alternative choice for the core sensing material and may have different advantages in terms of cost, processing difficulty, environmental adaptability, etc., and is used to optimize the sensor performance in different scenarios; the sensor prototype is a preliminary model built according to the design principle of the brake sensor based on the combination of the core sensing material and the alternative sensing materials, the induction accuracy data is the numerical record of the accuracy of the sensor's perception of physical quantity changes under simulated braking conditions, reflecting the sensor's ability to distinguish tiny changes; the durability test data is the relevant data recording the stable performance of the sensor during long-term and multi-frequency simulated braking tests, such as the number of failures, performance degradation indicators, etc. Furthermore, the query of the core sensing material and alternative sensing materials of the brake sensor can be obtained from the Internet through a human-computer interaction method; the simulation construction of the brake sensor can be realized by three-dimensional modeling software, such as SolidWorks; the collection of the induction accuracy data and durability test data of the sensor prototype can be realized by the simulation method, such as setting simulation condition parameters, running the simulation based on the simulation condition parameters and collecting data, thereby obtaining the induction accuracy data and durability test data of the sensor prototype.

[0128] Based on the induction accuracy data, the present invention analyzes the signal detection characteristics of the sensor prototype, and can accurately evaluate the ability of the sensor to capture and transmit brake signals under different working conditions, providing strong data support for optimizing the performance of the brake sensor and improving the braking safety of the vehicle. It should be noted that the signal detection characteristics are the ability and related performance manifestations of the sensor prototype to convert the change of the brake physical quantity into an electrical signal and accurately transmit it, including the comprehensive embodiment of aspects such as detection accuracy, sensitivity, response speed, and anti-interference ability.

[0129] Specifically, the analysis of the signal detection characteristics of the sensor prototype based on the induction accuracy data includes:

[0130] Perform data cleaning on the induction accuracy data to obtain cleaned induction accuracy data;

[0131] Analyze and extract the accuracy time-domain characteristics and accuracy frequency-domain characteristics corresponding to the cleaned induction accuracy data;

[0132] Generate a signal characteristic descriptor corresponding to the sensor prototype based on the accuracy time-domain characteristics and the accuracy frequency-domain characteristics;

[0133] Analyze the signal detection characteristics of the sensor prototype based on the signal characteristic descriptor.

[0134] It should be noted that the cleaned induction accuracy data is a clean data set obtained by removing noise, filling missing values, and smoothing the induction accuracy data. The accuracy time-domain characteristics and the accuracy frequency-domain characteristics are respectively the time-domain statistical characteristics (such as mean, variance, peak value, etc.) and frequency-domain analysis characteristics (such as spectrum, main frequency, bandwidth, etc.) corresponding to the cleaned induction accuracy data. The signal characteristic descriptor is a comprehensive description of the signal strength, stability, frequency characteristics, and dynamic response corresponding to the sensor prototype.

[0135] Furthermore, the box plot method can be used to perform data cleaning on the induction accuracy data to obtain cleaned induction accuracy data;

[0136] The accuracy time-domain features and accuracy frequency-domain features corresponding to the cleaned induction accuracy data can be extracted respectively by methods of time-domain statistical analysis (such as mean, variance, peak value) and frequency-domain transformation (such as Fourier transform, power spectral density analysis); based on the accuracy time-domain features and the accuracy frequency-domain features, a signal characteristic descriptor corresponding to the sensor prototype is generated through comprehensive feature description and quantitative analysis, such as signal strength, stability, frequency distribution, and dynamic response characteristics, where the signal strength is described by the root mean square value (RMS) in the time domain and the power spectral density (PSD) in the frequency domain, the stability is reflected by the variance in the time domain and the bandwidth in the frequency domain, the frequency distribution is characterized by the main frequency and spectral features, and the dynamic response characteristics are characterized by parameters such as rise time, fall time, and overshoot; based on the signal characteristic descriptor, the signal detection characteristics of the sensor prototype, such as detection sensitivity, detection accuracy, noise immunity, and response speed, are analyzed by setting detection thresholds, verification rules, and performance evaluations, where the detection sensitivity is measured by the minimum detectable signal strength, the detection accuracy is evaluated by the consistency between the detection result and the actual signal, the noise immunity is reflected by the detection stability in a noisy environment, and the response speed is characterized by the time from the signal appearance to the detection completion.

[0137] S4. According to the durability test data, calculate the life prediction equivalent value of the sensor prototype, evaluate the performance stability of the sensor prototype based on the life prediction equivalent value, determine the installation position requirements and working condition constraints of the brake sensor in the braking system, collect the adaptability parameters of the sensor prototype, and analyze the adaptation compatibility of the sensor prototype in the braking system in combination with the installation position requirements, the working condition constraints, and the adaptability parameters.

[0138] In the present invention, by calculating the life prediction equivalent value of the sensor prototype based on the durability test data and evaluating the performance stability of the sensor prototype according to the life prediction equivalent value, the reliability of the sensor prototype in long-term use can be effectively evaluated. It should be noted that the life prediction equivalent value is a life prediction index shown by the sensor prototype in the durability test and is used to quantify its service life; the performance stability is the degree of performance fluctuation shown by the sensor prototype in the durability test; further, based on the life prediction equivalent value, the performance stability of the sensor prototype is evaluated. For example, by comparing the life prediction equivalent value with the industry standard life, the smaller the deviation, the higher the performance stability; by analyzing the fluctuation range of the life prediction equivalent value under different working conditions, the smaller the fluctuation, the higher the performance stability.

[0139] Specifically, calculating the life prediction equivalent value of the sensor prototype according to the durability test data includes:

[0140] Analyze the test performance parameters corresponding to the durability test data, and query the failure modes and failure mechanisms corresponding to the brake sensor;

[0141] Conduct a variable analysis of the failure modes and failure mechanisms to obtain the failure variables corresponding to the brake sensor;

[0142] Based on the failure variables, formulate the life evaluation index corresponding to the brake sensor. Based on the life evaluation index, extract the life-related parameters of the brake sensor from the test performance parameters;

[0143] Based on the durability test data, calculate the life score value corresponding to the life-related parameter;

[0144] Allocate the contribution degree of the life-related parameter, and combine the life score value and the contribution degree of the parameter to calculate the life prediction equivalent value of the sensor prototype.

[0145] It should be explained that the test performance parameter is the quantitative measurement index corresponding to the durability test data, the failure mode and the failure mechanism are the fault manifestation forms and internal causes corresponding to the brake sensor, the failure variable is the key variable factor corresponding to the brake sensor that causes failure, the life evaluation index is the standard scale corresponding to the brake sensor for evaluating the life, the life-related parameter is the parameter closely related to the life of the brake sensor in the test performance parameter, the life score value is the quantitative score obtained according to the scoring system corresponding to the life-related parameter, and the contribution degree of the parameter represents the relative importance degree of the life-related parameter corresponding to the influence on the sensor life.

[0146] Furthermore, the test performance parameters corresponding to the durability test data can be analyzed through data mining and statistical analysis techniques. The failure modes and failure mechanisms corresponding to the brake sensor can be queried by referring to product technical documents, industry research reports, and failure case databases. The failure variables corresponding to the brake sensor can be obtained by analyzing the variables of the failure modes and the failure mechanisms through causal analysis and mathematical statistics methods. Based on the failure variables, the life evaluation index corresponding to the brake sensor can be formulated by combining expert experience with industry standards. Based on the life evaluation index, the life-related parameters of the brake sensor can be extracted from the test performance parameters through a correlation analysis algorithm. Based on the durability test data, the life score value corresponding to the life-related parameter can be calculated through a pre-set scoring rule and quantization model. The scoring system established by the industry can be referred to. For each life-related parameter, a grade interval is divided according to the degree of its influence on the sensor life and a score is assigned. For example, for the life-related parameter of temperature, if it is within ±5% of the normal operating temperature, the life score value is 10 points; in the range of ±5% - ±10%, the score value is 8 points, etc. Then, according to the actual values of each life-related parameter in the durability test data, its corresponding life score value is determined. The contribution degree of the life-related parameter can be allocated through the analytic hierarchy process or the principal component analysis method. For example, through historical data statistical analysis, a large amount of data of the same type of sensor is collected, the relationship between the changes of each life-related parameter and the changes of the sensor life is analyzed, and the quantization coefficient of the influence of each parameter on the life is calculated as the contribution degree of the parameter. Or experts can be invited to evaluate, and methods such as the Delphi method can be used to let experts score the importance of different life-related parameters. After multiple rounds of feedback and adjustment, the contribution degree of the parameter is determined. Combining the life score value and the contribution degree of the parameter, the life prediction equivalent value of the sensor prototype is calculated using the weighted summation formula. Multiply the life score value of each life-related parameter by its corresponding contribution degree of the parameter, and then accumulate and sum to get a comprehensive score. Then, according to historical data or experience, a corresponding relationship between the comprehensive score and the actual life is established, and the comprehensive score is converted into the life prediction equivalent value through this relationship. For example, through the analysis of historical data, the actual average life corresponding to the comprehensive score in the range of 6 - 7 points is between 2000 - 3000 hours, and finally the life prediction equivalent value is determined.

[0147] By combining the installation position requirements, the working condition constraints, and the adaptability parameters, and analyzing the adaptability and compatibility degree of the sensor prototype in the braking system, it is possible to effectively avoid failures caused by the mismatch between the sensor and the braking system, ensure the stable operation of the braking system, and improve the safety and reliability of vehicle driving. It should be noted that the installation position requirements refer to the physical position and environmental conditions that the brake sensor needs to meet when installed in the braking system; the working condition constraints refer to the environmental and operating restrictions that the brake sensor needs to meet when working in the braking system; the adaptability parameters refer to the compatibility types exhibited by the sensor prototype during installation and operation in the braking system, and the adaptability and compatibility degree indicates the degree to which the sensor prototype adapts to, cooperates with, and can work stably in the braking system in terms of the installation position, working conditions, and other related components of the braking system.

[0148] Specifically, the combination of the installation position requirements, the working condition constraints, and the adaptability parameters, and the analysis of the adaptability and compatibility degree of the sensor prototype in the braking system include:

[0149] Based on the installation position requirements, determine the installation area of the sensor prototype in the braking system, and measure the area size parameters corresponding to the installation area and the sensor prototype and the sensor size parameters;

[0150] Based on the area size parameters and the sensor size parameters, calculate the size adaptability degree of the sensor prototype in the braking system through the following formula:

[0151]

[0152] where G represents the size adaptability degree of the sensor prototype in the braking system, , , respectively represent the length, width, and height size parameters in the area size parameters, , , respectively represent the length, width, and height size parameters in the sensor size parameters, represents the maximum value of the length selected from the area size parameters and the sensor size parameters, represents the maximum value of the width selected from the area size parameters and the sensor size parameters, represents the maximum value of the height selected from the area size parameters and the sensor size parameters;

[0153] Analyze the adaptation factors corresponding to the adaptability parameters, and calculate the factor adaptability degree corresponding to the adaptation factors based on the working condition constraints and the adaptability parameters;

[0154] Calculate the working fitness of the sensor prototype in the braking system based on the factor fitness;

[0155] Analyze the adaptation compatibility of the sensor prototype in the braking system by combining the size fitness and the working fitness.

[0156] It should be noted that the area size parameter and the sensor size parameter are respectively the specific values of the length, width, height and other key dimensions corresponding to the installation area and the sensor prototype. The size fitness represents the quantitative index of the adaptation degree of the sensor prototype in the braking system based on the comparison of the two sizes. The adaptation factor is the specific aspect corresponding to the adaptation parameter that affects the adaptation compatibility between the sensor and the braking system. The factor fitness is the quantitative value measuring the adaptation degree corresponding to the adaptation factor. The working fitness represents the adaptation degree index of the sensor prototype in the braking system considering the constraints of working conditions.

[0157] Furthermore, the area description information in the installation position requirement can be extracted, and the installation area of the sensor prototype in the braking system can be determined based on the description information. The area size parameter corresponding to the installation area and the sensor size parameter of the sensor prototype can be measured by a high-precision measuring tool. The parameter meaning corresponding to the adaptation parameter can be analyzed by semantic analysis, and the adaptation factor can be determined according to the parameter meaning. The constraint limit condition corresponding to the adaptation parameter can be determined from the working condition constraints. Combining the constraint limit condition and the corresponding standard operating condition of the sensor, the factor fitness corresponding to the adaptation factor can be calculated. Taking temperature as a reference, the temperature of the constraint limit condition is m, and the temperature of the standard operating condition of the sensor is t. The specific calculation formula is: factor fitness of temperature = 1 - ; Calculate the average value of the corresponding factor fitness to obtain the working fitness of the sensor prototype in the braking system. Combine the size fitness and the working fitness, and use the analytic hierarchy process to analyze the adaptation compatibility of the sensor prototype in the braking system. For example, construct a judgment matrix, compare the size fitness and the working fitness pairwise according to the 1-9 scale method to determine the relative importance, calculate the eigenvector to obtain the weight. For example, the weight of the size fitness is 0.4, and the weight of the working fitness is 0.6. Calculate the comprehensive adaptation compatibility score by weighted summation, and judge the adaptation situation accordingly.

[0158] S5. Screen out the best sensing material from the core sensing material and the alternative sensing materials by combining the signal detection characteristics, the performance stability and the adaptation compatibility. Generate the design method of the brake sensor based on the core design parameters, the parameter priority sequence and the best sensing material.

[0159] By combining the signal detection characteristics, the performance stability, and the adaptation compatibility, the present invention screens out the best sensing material from the core sensing material and the alternative sensing materials, which can accurately match the materials that meet the complex working conditions requirements of the brake sensor, ensuring its efficient and stable operation. Based on the core design parameters, the parameter priority sequence, and the best sensing material, the design method of the brake sensor is generated, thereby improving the design reliability of the sensor under braking. It should be noted that the best sensing material is the material that comprehensively considers various indicators such as signal detection characteristics, performance stability, and adaptation compatibility among the core sensing material and the alternative sensing materials. After quantitative evaluation and comparison, it has the best performance in all aspects and most meets the design requirements of the brake sensor. Further, by combining the signal detection characteristics, the performance stability, and the adaptation compatibility, the core sensing material and the alternative sensing materials are scored in sequence, and the material with the highest score is used as the best sensing material. Based on the core design parameters, the parameter priority sequence, and the best sensing material, in accordance with the parameter priority order, the characteristics of the best sensing material are used to match the requirements of each core design parameter, and the design method of the brake sensor is gradually refined from aspects such as structural layout, circuit design to packaging process.

[0160] Compared with the problems described in the background art, the present invention combines the application scenario features and the existing fault information to formulate the core design parameters of the brake sensor, and can obtain the key quantitative criteria for the design of the brake sensor, thereby laying a foundation for subsequent optimization of the performance of the brake sensor and improving its reliability and stability. Further, the present invention schedules the material intrinsic data and the structural design data of the brake sensor, and analyzes the physical efficacy characterization corresponding to the material intrinsic data, so as to deeply understand the performance of the brake sensor at the material level, providing a basis for subsequent signal propagation topology analysis and co-coupling effect evaluation. The present invention combines the core sensing material and the alternative sensing material to simulate and build the brake sensor, and can obtain brake sensors with different material combinations, thereby providing a diverse sample basis and rich data source for subsequent analysis of the signal detection characteristics of the sensor prototype. Further, the present invention calculates the life prediction equivalent value of the sensor prototype based on the durability test data, and evaluates the performance stability of the sensor prototype according to the life prediction equivalent value, which can effectively evaluate the reliability of the sensor prototype during long-term use. Further, the present invention combines the signal detection characteristics, the performance stability and the adaptation compatibility, and screens out the best sensing material from the core sensing material and the alternative sensing material, which can accurately match the material that meets the complex working conditions requirements of the brake sensor and ensure its efficient and stable operation. Based on the core design parameters, the parameter priority sequence and the best sensing material, the design method of the brake sensor is generated, thereby improving the reliability of the sensor design under braking. Therefore, a method and system for realizing the sensor design under braking provided by the embodiments of the present invention can improve the reliability of the sensor design under braking.

[0161] Embodiment 2:

[0162] As Figure 2 shown, it is a functional module diagram of a system for realizing the sensor design under braking according to the present invention.

[0163] The system 200 for realizing the sensor design under braking according to the present invention can be installed in an electronic device. According to the functions to be realized, the system for realizing the sensor design under braking can include a core design parameter formulation module 201, a parameter priority sequence determination module 202, a signal detection characteristic analysis module 203, an adaptation compatibility analysis module 204, and a design method generation module 205. The modules in the present invention can also be referred to as units, which refer to a series of computer program segments that can be executed by a processor of an electronic device and can complete fixed functions, and are stored in the memory of the electronic device.

[0164] In the embodiments of the present invention, the functions of each module / unit are as follows:

[0165] The core design parameter formulation module 201 is configured to obtain the operating condition parameters and dynamic efficiency parameters of the braking system, clarify the working environment elements of the brake sensor under braking based on the operating condition parameters and the dynamic efficiency parameters, analyze the application scenario characteristics of the sensor based on the working environment elements, collect the existing fault information of the brake sensor, and formulate the core design parameters of the brake sensor by combining the application scenario characteristics and the existing fault information;

[0166] The parameter priority sequence determination module 202 is configured to schedule the material intrinsic data and structural design data of the brake sensor, analyze the physical efficiency characterization corresponding to the material intrinsic data, determine the signal propagation topology of the brake sensor according to the structural design data, evaluate the cooperative coupling effect between the signal propagation topology and the physical efficiency characterization, and determine the parameter priority sequence of the core design parameters according to the cooperative coupling effect;

[0167] The signal detection characteristic analysis module 203 is configured to query the core sensing material and alternative sensing materials of the brake sensor, perform a simulation construction on the brake sensor by combining the core sensing material and the alternative sensing materials to obtain a sensor prototype, collect the induction accuracy data and durability test data of the sensor prototype, and analyze the signal detection characteristics of the sensor prototype based on the induction accuracy data;

[0168] The adaptation compatibility analysis module 204 is configured to calculate the life prediction equivalent value of the sensor prototype according to the durability test data, evaluate the performance stability of the sensor prototype based on the life prediction equivalent value, determine the installation position requirements and working condition constraints of the brake sensor in the braking system, collect the adaptation parameters of the sensor prototype, and analyze the adaptation compatibility of the sensor prototype in the braking system by combining the installation position requirements, the working condition constraints and the adaptation parameters;

[0169] The design method generation module 205 is configured to screen out the best sensing material from the core sensing material and the alternative sensing materials by combining the signal detection characteristics, the performance stability and the adaptation compatibility, and generate the design method of the brake sensor based on the core design parameters, the parameter priority sequence and the best sensing material.

[0170] Specifically, each module in the sensor design system 200 for realizing braking in the embodiment of the present invention adopts the same as the above-mentioned Figure 1The same technical means as the sensor design method for implementing braking described in [reference], and can produce the same technical effects, which will not be elaborated here.

[0171] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above exemplary embodiments, and without departing from the spirit or basic characteristics of the present invention, the present invention can be implemented in other specific forms.

[0172] 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 sensor design method for implementing braking, characterized in that: The method comprises: Obtaining the operating condition parameters and dynamic performance parameters of the brake system, clarifying the working environment factors of the brake sensor of the brake system under braking based on the operating condition parameters and the dynamic performance parameters, analyzing the application scenario characteristics of the sensor based on the working environment factors, collecting the existing fault information of the brake sensor, and formulating the core design parameters of the brake sensor in combination with the application scenario characteristics and the existing fault information; Dispatching material intrinsic data and structural design data of the brake sensor, analyzing the physical performance characterization corresponding to the material intrinsic data, determining the signal propagation topology of the brake sensor according to the structural design data, evaluating the synergistic coupling effect between the signal propagation topology and the physical performance characterization, and determining the parameter priority sequence of the core design parameters according to the synergistic coupling effect; Query the core sensing material and the alternative sensing material of the brake sensor, combine the core sensing material and the alternative sensing material, simulate and build the brake sensor to obtain a sensor prototype, collect sensing accuracy data and durability test data of the sensor prototype, and analyze the signal detection characteristics of the sensor prototype based on the sensing accuracy data; According to the durability test data, the life prediction equivalent value of the sensor prototype is calculated, and based on the life prediction equivalent value, the performance stability of the sensor prototype is evaluated, the installation position requirements and working condition constraints of the brake sensor in the brake system are determined, and the adaptability parameters of the sensor prototype are collected. In combination with the installation position requirements, the working condition constraints and the adaptability parameters, the adaptability compatibility of the sensor prototype in the brake system is analyzed; In combination with the signal detection characteristics, the performance stability and the adaptation compatibility, the best sensing material is selected from the core sensing material and the alternative sensing materials, and the design method of the brake sensor is generated based on the core design parameters, the parameter priority sequence and the best sensing material.

2. A sensor design method for implementing braking according to claim 1, characterized in that: The core design parameters of the brake sensor are formulated in combination with the application scenario characteristics and the existing fault information, including: Extracting the application scene features to obtain scene feature factors; Performing information classification processing on the existing fault information to obtain a fault category set; Analyze the correlation between the scenario characteristic factors and the fault category set to obtain a correlation mapping matrix; Based on the association mapping matrix, determining key scenario features and key fault categories of the brake sensor; Based on the key scenario characteristics and key fault categories, analyzing the design constraints of the brake sensor; Based on the design constraints, core design parameters of the brake sensor are formulated.

3. A sensor design method for implementing braking according to claim 1, characterized in that: The physical performance characterization corresponding to the analysis of the intrinsic data of the material includes: Performing standardization processing on the intrinsic data of the material to obtain standardized material data; Extracting material physical properties corresponding to the standardized material data, screening the material physical properties, and obtaining key physical properties; The physical performance index corresponding to the key physical property is calculated, and based on the physical performance index, the physical performance characterization corresponding to the material intrinsic data is generated.

4. A sensor design method for implementing braking according to claim 1, characterized in that: Determining the signal propagation topology of the brake sensor according to the structural design data includes: Performing data pre-processing on the structural design data to obtain target structural design data; extracting a structural parameter set of the brake sensor from the target structural design data; Performing material attribute association processing on the structural parameter set to obtain an attribute association parameter set; Based on the attribute association parameter set, construct a signal propagation numerical model corresponding to the brake sensor; Performing simulation calculation processing on the signal propagation numerical model to obtain signal propagation dynamic data; Topological abstract processing is performed on the signal propagation dynamic data to generate a signal propagation topology of the brake sensor.

5. A sensor design method for implementing braking according to claim 1, characterized in that: The evaluating the synergistic coupling effect between the signal propagation topology and the physical performance characterization includes: Extracting the signal propagation features corresponding to the signal propagation topology, and performing dimensionality reduction processing on the signal propagation features to obtain dimensionality-reduced signal propagation features; Calculating a feature similarity index between the reduced-dimensionality signal propagation features, and calculating a representation similarity index between the physical effectiveness representations; calculating a correlation factor between the signal propagation topology and the physical performance representation; Calculate the degree of synergistic coupling between the signal propagation topology and the physical effectiveness representation by combining the correlation factor, the feature similarity index, and the representation similarity index; Based on the cooperative coupling degree, the cooperative coupling effect between the signal propagation topology and the physical performance characterization is evaluated.

6. A sensor design method for implementing braking according to claim 5, characterized in that: The calculating the correlation factor between the signal propagation topology and the physical performance characterization includes: Performing vector processing on the signal propagation topology and the physical performance representation respectively to obtain a propagation topology vector and a performance representation vector; Calculating a vector cosine value between the propagation topology vector and the effectiveness characterization vector; Calculating vector mutual information between the propagation topology vector and the effectiveness representation vector; Combining the vector cosine value and the vector mutual information, the correlation factor between the signal propagation topology and the physical performance representation is calculated by the following formula: ； Where D represents the correlation factor between the signal propagation topology and the physical performance characterization, represents the weight adjustment coefficient, represents the vector cosine value between the Xth propagation topology vector in the signal propagation topology and the Yth performance characterization vector in the physical performance characterization, Represents the vector mutual information between the Xth propagation topology vector in the signal propagation topology and the Yth performance representation vector in the physical performance representation.

7. A sensor design method for implementing braking as claimed in claim 1, characterized in that: The analyzing the signal detection characteristics of the sensor prototype based on the sensing accuracy data includes: Performing data cleaning processing on the sensing accuracy data to obtain cleaned sensing accuracy data; Analyze and extract the precision time domain features and precision frequency domain features corresponding to the cleaning sensing precision data; Based on the precision time domain features and the precision frequency domain features, generating a signal characteristic descriptor corresponding to the sensor prototype; Based on the signal characteristic descriptor, the signal detection characteristics of the sensor prototype are analyzed.

8. A sensor design method for implementing braking as claimed in claim 1, characterized in that: The calculating, according to the durability test data, the life prediction equivalent value of the sensor prototype comprises: Analyzing the test performance parameters corresponding to the durability test data, and querying the failure mode and failure mechanism corresponding to the brake sensor; Performing variable analysis on the failure mode and the failure mechanism to obtain a failure variable corresponding to the brake sensor; Based on the failure variable, a life evaluation index corresponding to the brake sensor is formulated, and based on the life evaluation index, a life-related parameter of the brake sensor is extracted from the test performance parameter; Based on the durability test data, calculating a life score value corresponding to the life-related parameter; The parameter contribution corresponding to the life-related parameter is allocated, and the life prediction equivalent value of the sensor prototype is calculated by combining the life score value and the parameter contribution.

9. A sensor design method for implementing braking as claimed in claim 1, characterized in that: The analyzing the adaptability compatibility of the sensor prototype in the braking system in combination with the installation position requirement, the working condition constraint and the adaptability parameter includes: Based on the installation position requirement, determining the installation area of ​​the sensor prototype in the brake system, and measuring area size parameters and sensor size parameters corresponding to the installation area and the sensor prototype; Based on the area size parameter and the sensor size parameter, the size adaptability of the sensor prototype in the brake system is calculated by the following formula: ； Where G represents the size fit of the sensor prototype in the braking system, , , Respectively represent the length, width, and height size parameters in the area size parameters. , , Respectively represent the length, width, and height size parameters of the sensor. Indicates the maximum length of the selected area size parameter and sensor size parameter. Indicates the maximum width of the selected area size parameter and sensor size parameter. Indicates the maximum height value among the selected area size parameters and sensor size parameters; Analyzing the adaptation factor corresponding to the adaptability parameter, and calculating the factor adaptability corresponding to the adaptation factor based on the working condition constraint and the adaptability parameter; Based on the factor fitness, calculating the working fitness of the sensor prototype in the braking system; The adaptability compatibility of the sensor prototype in the brake system is analyzed in combination with the size adaptability and the working adaptability.

10. A sensor design system for implementing braking, characterized in that: The system comprises: A core design parameter formulation module is used to obtain the operating condition parameters and dynamic performance parameters of the brake system, clarify the working environment factors of the brake sensor of the brake system under braking based on the operating condition parameters and the dynamic performance parameters, analyze the application scenario characteristics of the sensor based on the working environment factors, collect the existing fault information of the brake sensor, and formulate the core design parameters of the brake sensor in combination with the application scenario characteristics and the existing fault information; a parameter priority sequence determination module, for scheduling material intrinsic data and structural design data of the brake sensor, analyzing the physical performance representation corresponding to the material intrinsic data, determining the signal propagation topology of the brake sensor according to the structural design data, evaluating the synergistic coupling effect between the signal propagation topology and the physical performance representation, and determining the parameter priority sequence of the core design parameters according to the synergistic coupling effect; A signal detection characteristic analysis module, used to query the core sensing material and the alternative sensing material of the brake sensor, combine the core sensing material and the alternative sensing material to simulate and build the brake sensor to obtain a sensor prototype, collect sensing accuracy data and durability test data of the sensor prototype, and analyze the signal detection characteristics of the sensor prototype based on the sensing accuracy data; An adaptability compatibility analysis module is used to calculate the life prediction equivalent value of the sensor prototype according to the durability test data, evaluate the performance stability of the sensor prototype according to the life prediction equivalent value, determine the installation position requirements and working condition constraints of the brake sensor in the brake system, collect the adaptability parameters of the sensor prototype, and analyze the adaptability compatibility of the sensor prototype in the brake system in combination with the installation position requirements, the working condition constraints and the adaptability parameters; A design method generation module is used to select the best sensing material from the core sensing material and the alternative sensing materials based on the signal detection characteristics, the performance stability and the adaptation compatibility, and generate a design method for the brake sensor based on the core design parameters, the parameter priority sequence and the best sensing material.

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