CN113627711B - Risk assessment method and related equipment for vehicle functional safety - Google Patents
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Abstract
The application discloses a risk assessment method and related equipment for vehicle function safety, wherein the method comprises the following steps: performing data processing on the existing vehicle function experience data to obtain corresponding feature data and association relations among the feature data; constructing a vehicle evaluation data network according to the characteristic data and the association relation between the characteristic data; and inputting the vehicle functional characteristics to be evaluated into the vehicle evaluation data network, and outputting an evaluation result. The method can solve the problems that the existing ASIL evaluation criteria are high in subjectivity and objective and effective ASIL evaluation results are difficult to obtain in the actual vehicle function development process.
Description
Technical Field
The application relates to the technical field of vehicles, in particular to a risk assessment method and related equipment for vehicle function safety.
Background
Road vehicle functional safety is the reduction of the safety risk caused by failure of the vehicle control system to a socially acceptable level by analysis and design, wherein the concept of the Automotive safety integrity level (automatic SAFETY INTEGRITY LEVEL, ASIL) is adopted, which is used to describe the failure level as well as the system functional reliability level. Therefore, whether the hazard analysis and the risk level evaluation are correct or not plays a vital role in developing the safety of the vehicle functions.
However, in the process of developing actual vehicle functions, the existing ASIL evaluation criteria are relatively subjective, and objective and effective ASIL evaluation results are difficult to obtain.
Disclosure of Invention
The embodiment of the application provides a risk assessment method and related equipment for vehicle function safety, which can solve the problems that in the process of developing actual vehicle functions, the subjectivity of the existing ASIL assessment criteria is strong, and objective and effective ASIL assessment results are difficult to obtain.
In a first aspect of an embodiment of the present application, there is provided a risk assessment method for vehicle functional safety, including:
Performing data processing on existing vehicle function experience data to obtain corresponding feature data and an association relation between the feature data, wherein the existing vehicle function experience data comprises vehicle function description data, vehicle hazard analysis data corresponding to the vehicle function description data, risk assessment data corresponding to the vehicle function description data and safety state data corresponding to the vehicle hazard analysis data, and the types of the feature data comprise vehicle function features, vehicle hazard features, risk grade features and safety state features;
constructing a vehicle evaluation data network according to the feature data and the association relation between the feature data, wherein the vehicle evaluation data network is constructed based on a knowledge graph algorithm;
And inputting the vehicle functional characteristics to be evaluated into the vehicle evaluation data network, and outputting an evaluation result, wherein the evaluation result comprises an evaluation result corresponding to the vehicle hazard characteristics, an evaluation result corresponding to the risk level characteristics and an evaluation result corresponding to the safety state characteristics.
In some embodiments, the step of performing data processing on the existing vehicle function experience data to obtain corresponding feature data and an association relationship between the feature data includes:
extracting feature information of the existing vehicle function experience data and an association relation between the feature information to obtain corresponding feature data and an association relation between the feature data;
And calculating the similarity between the characteristic data of the same category, and merging the characteristic data of the same category when the similarity does not exceed a set threshold value.
In some embodiments, the step of extracting feature information of the existing vehicle function experience data and an association relationship between the feature information to obtain corresponding feature data and an association relationship between the feature data includes:
Extracting feature information of the existing vehicle function experience data to obtain the feature data, wherein the categories of the feature data comprise vehicle function features, vehicle hazard features, risk level features and safety state features;
extracting a functional failure mode as an association relationship between the vehicle functional characteristic and the vehicle hazard characteristic;
extracting a hazard reason as an association relationship between the vehicle functional characteristic and the risk level characteristic;
extracting a hazard degradation execution action as an association relationship between the vehicle hazard feature and the safety state feature;
and extracting risk correspondence as an association relationship between the risk level characteristics and the vehicle hazard characteristics.
In some embodiments, before the step of constructing the vehicle evaluation data network according to the feature data and the association relationship between the feature data, the method further includes:
extracting attribute information of the existing vehicle function experience data, wherein the attribute information comprises function attribute information, hazard attribute information, risk attribute information and safety state attribute information;
And attaching the attribute information to the corresponding feature data, wherein the function attribute information is attached to the vehicle function feature, the hazard attribute information is attached to the vehicle hazard feature, the risk attribute information is attached to the risk level feature, and the safety state attribute information is attached to the safety state feature.
In some embodiments, the step of calculating the similarity between the feature data of the same category, and merging the feature data of the same category when the similarity does not exceed a set threshold value, includes:
calculating a similarity vector of the feature data;
Calculating the similarity between the feature data of the same category according to the similarity vector of the feature data of the same category;
and merging the characteristic data of the same category when the similarity does not exceed the set threshold value.
In some embodiments, the step of extracting feature information of the existing vehicle function experience data and an association relationship between the feature information to obtain corresponding feature data and an association relationship between the feature data includes:
judging whether two or more pieces of same characteristic information exist;
And if the same characteristic information corresponds to different existing vehicle function experience data, extracting the characteristic information again from the same characteristic information corresponding to different existing vehicle function experience data.
In some embodiments, after the step of constructing a vehicle evaluation data network according to the feature data and the association relationship between the feature data, the method further includes:
and supplementing and establishing an association relation in the vehicle evaluation data network according to an inference rule, wherein the vehicle function features comprise at least one function group, the function group comprises at least one vehicle function feature, the inference rule comprises a hazard inference rule and a risk inference rule, the failure modes corresponding to the vehicle function features in the same function group are the same, and the risk inference rule comprises the same hazard cause corresponding to the vehicle function features in the same function group.
In a second aspect of the embodiment of the present application, there is provided a risk assessment device for vehicle functional safety, including:
The system comprises a data processing module, a data processing module and a data processing module, wherein the data processing module is used for performing data processing on existing vehicle function experience data to obtain corresponding feature data and an association relation between the feature data, the existing vehicle function experience data comprises vehicle function description data, vehicle hazard analysis data corresponding to the vehicle function description data, risk assessment data corresponding to the vehicle function description data and safety state data corresponding to the vehicle hazard analysis data, and the categories of the feature data comprise vehicle function features, vehicle hazard features, risk grade features and safety state features;
The construction module is used for constructing a vehicle evaluation data network according to the characteristic data and the association relation between the characteristic data, wherein the vehicle evaluation data network is constructed based on a knowledge graph algorithm;
The evaluation module is used for inputting the vehicle function characteristics to be evaluated into the vehicle evaluation data network and outputting evaluation results, wherein the evaluation results comprise evaluation results corresponding to the vehicle hazard characteristics, evaluation results corresponding to the risk level characteristics and evaluation results corresponding to the safety state characteristics.
In a third aspect of an embodiment of the present application, there is provided an electronic device including: a memory, a processor and a computer program stored in the memory and executable on the processor for implementing the steps of the vehicle functional safety risk assessment method according to the first aspect when the computer program stored in the memory is executed.
In a fourth aspect of embodiments of the present application, there is provided a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the risk assessment method for vehicle functional safety of the first aspect.
According to the risk assessment method and the related equipment for vehicle function safety, the existing vehicle function experience data are subjected to data processing to obtain the corresponding feature data and the association relation between the feature data, the existing vehicle function experience data can comprise actually-occurring event data caused by the vehicle function or experience data obtained through long-term experience accumulation, the vehicle function features, the vehicle hazard features, the risk level features and the safety state features obtained through the data processing are subjected to the correlation relation to obtain the vehicle assessment data network, the vehicle function features to be assessed in the actual vehicle function development process are subjected to risk assessment through the vehicle assessment data network, and the obtained assessment result is based on the actually-occurring time data or the data accumulated through long-term experience, so that the assessment result is objective and accurate compared with the prior art, and the vehicle function design is further optimized on the basis of the assessment result.
Drawings
FIG. 1 is a schematic flow chart of a risk assessment method for vehicle functional safety according to an embodiment of the present application;
FIG. 2 is a schematic diagram of a vehicle evaluation data network according to an embodiment of the present application;
FIG. 3 is a schematic block diagram of a risk assessment device for vehicle functional safety according to an embodiment of the present application;
FIG. 4 is a schematic block diagram of an electronic device according to an embodiment of the present application;
fig. 5 is a schematic block diagram of a computer readable storage medium according to an embodiment of the present application.
Detailed Description
In order to better understand the technical solutions provided by the embodiments of the present specification, the following detailed description of the technical solutions of the embodiments of the present specification is made through the accompanying drawings and the specific embodiments, and it should be understood that the specific features of the embodiments of the present specification are detailed descriptions of the technical solutions of the embodiments of the present specification, and not limit the technical solutions of the present specification, and the technical features of the embodiments of the present specification may be combined with each other without conflict.
In this document, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element. The term "two or more" includes two or more cases.
Road vehicle functional safety is the reduction of the safety risk caused by failure of the vehicle control system to a socially acceptable level by analysis and design, wherein the concept of an automotive safety integrity level is adopted, which is used to describe the failure level as well as the size of the functional reliability of the system. Therefore, whether the hazard analysis and the risk level evaluation are correct or not plays a vital role in developing the safety of the vehicle functions. However, in the process of developing actual vehicle functions, the existing ASIL evaluation criteria are relatively subjective, and objective and effective ASIL evaluation results are difficult to obtain.
In view of the above, the embodiment of the application provides a risk assessment method and related equipment for vehicle function safety, which can solve the problem that the existing ASIL assessment criteria are relatively strong in subjectivity and difficult to obtain objective and effective ASIL assessment results in the process of developing actual vehicle functions.
In a first aspect of the embodiment of the present application, a risk assessment method for vehicle functional safety is provided, and fig. 1 is a schematic flowchart of a risk assessment method for vehicle functional safety provided in the embodiment of the present application. As shown in fig. 1, the risk assessment method for vehicle function safety provided by the embodiment of the application includes:
s100: and carrying out data processing on the existing vehicle function experience data to obtain corresponding feature data and association relation among the feature data.
Wherein the existing vehicle function experience data comprises vehicle function description data, vehicle hazard analysis data corresponding to the vehicle function description data, risk assessment data corresponding to the vehicle function description data and safety state data corresponding to the vehicle hazard analysis data, and the categories of the feature data comprise vehicle function features, vehicle hazard features, risk level features and safety state features. By way of example, existing vehicle function empirical data may include actual occurrence event data caused by vehicle functions or empirical data obtained through long-term experience accumulation, and the present application is not particularly limited. By way of example, the vehicle functional features may include a functional description of the vehicle, such as an acceleration function, a braking function, or a steering function, etc.; vehicle hazard characteristics may include impact, tire damage or puncture, etc.; the risk level features may include individual risk levels, such as low risk, medium risk, and high risk; the safety state features may include a full safety state, a relative safety state (in need of risk avoidance), an unsafe state, etc., and the present application is not particularly limited. The association relationship between the feature data may be understood as a correspondence relationship, for example, the acceleration function may correspond to an impact, a damaged tire or a blown tire, and the association relationship between the vehicle function feature and the vehicle hazard feature determines under what condition the acceleration function corresponds to an impact, or whether the acceleration function is damaged or blown tire; the association relation between the vehicle function characteristics and the risk level characteristics determines whether the acceleration function corresponds to low risk, medium risk or high risk under the condition; the association of the vehicle hazard feature with the safety state feature determines in what case the crash corresponds to a full safety state, or a relative safety state, or an unsafe state, as exemplified above, without being a specific limitation of the present application.
S200: and constructing a vehicle evaluation data network according to the feature data and the association relation between the feature data.
The vehicle evaluation data network is constructed based on a knowledge graph algorithm. Knowledge Graph (knowledgegraph), also called scientific Knowledge Graph, is a series of different graphs showing the Knowledge development process and structural relationship, and uses visualization technology to describe Knowledge resources and their carriers, and excavate, analyze, construct, draw and display Knowledge and their interrelationships. A knowledge graph forms a semantic network graph, nodes represent entities, and edges are formed by relationships. The semantic network diagram formed by the knowledge graph expresses various entities, concepts and various associations between the entities and the concepts. The feature data can be regarded as an entity, and an algorithm used for constructing the knowledge graph can be called a knowledge graph algorithm. Fig. 2 is a schematic diagram of a vehicle evaluation data network according to an embodiment of the present application. As shown in fig. 2, a knowledge graph algorithm is utilized, based on the feature data obtained by the data processing in step S100 and the association relationship between the feature data, a vehicle evaluation data network 100 can be constructed, and the vehicle evaluation data network 100 can be used for performing risk evaluation on vehicle functions developed by a vehicle in the process of developing actual vehicle functions, so that the design of the vehicle functions can be further optimized according to the evaluation result.
S300: and inputting the vehicle functional characteristics to be evaluated into a vehicle evaluation data network, and outputting an evaluation result.
The evaluation results comprise evaluation results corresponding to the vehicle hazard characteristics, evaluation results corresponding to the risk level characteristics and evaluation results corresponding to the safety state characteristics. The vehicle function feature to be evaluated is a vehicle function feature to be evaluated in actual vehicle function development, for example, the vehicle function feature to be evaluated may be an acceleration function, the acceleration function is input into a vehicle evaluation data network, the vehicle evaluation data network outputs a corresponding evaluation result according to the association relationship between feature data, the evaluation result generally includes an evaluation result corresponding to a vehicle hazard feature associated with the acceleration function, an evaluation result corresponding to a risk level feature and an evaluation result corresponding to a safety state feature, and the evaluation result corresponding to the vehicle hazard feature may include collision, tire damage or tire burst; the evaluation results corresponding to the risk level features may include low risk, medium risk, and high risk; the evaluation result corresponding to the safety state feature may include a complete safety state, a relative safety state (risk avoidance is required), an unsafe state, and the like, which is not particularly limited in the present application.
According to the risk assessment method for vehicle function safety, the existing vehicle function experience data are subjected to data processing to obtain the corresponding feature data and the association relation between the feature data, the existing vehicle function experience data can comprise actually-occurring event data caused by the vehicle function or experience data are obtained through long-term experience accumulation, the vehicle function features, the vehicle hazard features, the risk level features and the safety state features obtained through the data processing are subjected to correlation relation to obtain the vehicle assessment data network, the vehicle assessment data network is used for carrying out risk assessment on the vehicle function features to be assessed in the process of developing the actual vehicle function, and the obtained assessment result is based on the actually-occurring time data or the data accumulated through long-term experience, so that the assessment result is objective and accurate compared with the prior art, and the design of the vehicle function is further optimized on the basis of the assessment result.
In some embodiments, step S100 may include:
And extracting feature information of the existing vehicle function experience data and an association relation between the feature information to obtain corresponding feature data and the association relation between the feature data. The feature information extraction may be implemented by extracting keywords, and the present application is not particularly limited.
And calculating the similarity between the feature data of the same category, and merging the feature data of the same category when the similarity does not exceed a set threshold value.
In some embodiments, the step of calculating the similarity between the feature data of the same category and merging the feature data of the same category when the similarity does not exceed a set threshold value includes:
and calculating the similarity vector of the characteristic data.
By way of example, the similarity vector for the feature data may be calculated as follows:
P＝w1×sim(x1,y1)+w2×sim(x2,y2)+…+wn×sin(xn,yn),
Wherein P is a similarity vector of the feature data, x and y are two data types in the feature data, x n and y n are example values corresponding to x and y on the nth feature value, n is a natural number greater than 0, sim (x n,yn) is a similarity vector of the nth feature value, and w n is a weighting coefficient of the similarity vector of the nth feature value. The data type of the feature data may include text or characters, and the characters may be numerals, letters, formulas, etc., and the present application is not particularly limited.
And calculating the similarity between the feature data of the same category according to the similarity vector of the feature data of the same category.
By way of example, the similarity between the same class of feature data may be calculated as follows:
Wherein S is the similarity between two feature data of the same category, P 1 is the similarity vector of the first feature data of the same category, P 2 is the similarity vector of the second feature data of the same category, and the first feature data and the second feature data are any two feature data in the feature data of the same category. All feature data in the same category can be subjected to similarity calculation between two or more feature data, and the application is not particularly limited.
And when the similarity does not exceed the set threshold, combining the characteristic data of the same category. The setting threshold value may be set differently according to a specific feature data category, and the present application is not limited in particular. The similarity does not exceed the set threshold, which indicates that two or more feature data are relatively close or similar, and the two or more feature data can be combined into one feature data, so that convergence and differentiation of the feature data are facilitated. And if the similarity exceeds the set threshold, indicating that the two or more characteristic data have obvious differences, and not processing.
According to the risk assessment method for vehicle function safety provided by the embodiment of the application, by calculating the similarity between the feature data of the same category and judging the size between the similarity and the set threshold value, whether the feature data of the same category are similar or close can be distinguished, the similar or close feature data are combined into one feature data, the function of data convergence can be achieved, the data arrangement is facilitated, and the establishment of a subsequent vehicle assessment data network is facilitated.
In some embodiments, the step of extracting feature information of existing vehicle function experience data and an association relationship between the feature information to obtain corresponding feature data and an association relationship between the feature data includes:
And extracting feature information of the existing vehicle function experience data to obtain feature data, wherein the categories of the feature data comprise vehicle function features, vehicle hazard features, risk level features and safety state features.
And extracting the functional failure mode as an association relation between the vehicle functional characteristics and the vehicle hazard characteristics. Failure mode refers to the general term for the entire failure process from the factors responsible for failure, the mechanism of failure, the progress of failure to the arrival of the critical state of failure, etc. Illustratively, the deformation failure mode includes local deformation or global deformation, elastic deformation, plastic deformation, or creep deformation. For example, in the vehicle technical field, when the vehicle function feature is an acceleration function, the function failure mode may include acceleration failure, and the acceleration failure may be non-acceleration, under acceleration, overspeed acceleration, flameout, or the like, and the present application is not particularly limited. For example, the corresponding vehicle hazard feature of the acceleration function in the event of an underacceleration may be a crash.
And extracting the hazard reasons as the association relation between the vehicle function characteristics and the risk grade characteristics. For example, the hazard reasons corresponding to the acceleration function may include a malfunction of the accelerator or a malfunction of a gear, and the risk level corresponding to the acceleration function under the hazard reasons of the malfunction of the accelerator may be a risk of wind.
The hazard downgraded execution action is extracted as an association between the vehicle hazard feature and the safety state feature. The hazard downgrade performing action may be understood as not reducing the extent of vehicle hazard, may perform the hazard downgrade action, and may include braking, slowing down, or other operational action. For example, the vehicle hazard feature corresponding to the acceleration function in the event of under acceleration may be a crash, and the crash may correspond to a safety state feature of a relative safety state (requiring avoidance of danger) in the event that the hazard degrading action is braking.
And extracting the risk correspondence as an association relationship between the risk level characteristics and the vehicle hazard characteristics. The risk correspondence may be a one-to-one correspondence between risk level characteristics and vehicle hazard characteristics, and for example, the acceleration function may correspond to a risk under the hazard cause of a throttle failure as a risk in which case the vehicle hazard characteristic corresponding to the risk is a crash.
According to the risk assessment method for vehicle function safety, a vehicle assessment data network is formed through various failure modes of different vehicle function characteristics, vehicle hazard characteristics, risk grade characteristics corresponding to hazard reasons and safety state characteristics corresponding to hazard degradation actions. When the vehicle function evaluation is carried out, searching is carried out in a vehicle evaluation data network according to the vehicle function characteristics to be evaluated, the obtained evaluation result is more objective and accurate compared with the prior art, and the design of the vehicle function can be further optimized according to the evaluation result.
In some embodiments, prior to step S200, further comprising:
attribute information of existing vehicle function experience data is extracted, wherein the attribute information comprises function attribute information, hazard attribute information, risk attribute information and safety state attribute information.
Attribute information is attached to the corresponding feature data, wherein the function attribute information is attached to the vehicle function feature, the hazard attribute information is attached to the vehicle hazard feature, the risk attribute information is attached to the risk level feature, and the safety state attribute information is attached to the safety state feature. By way of example, the functional attribute information may include driving scenes, environments, personnel, etc., the driving scenes may include highways, overpasses, rural roads, etc., the environments may be climatic environments, such as rainy, snowy, foggy, sunny, etc., the personnel may define the level of driving age, etc.; the hazard attribute information may include vehicle acceleration information, displacement information, and the like; the risk attribute information may include severity, controllability, or exposure, etc.; the security state attribute information may include state and time, etc. The foregoing is illustrative of the present application and is not to be construed as limiting thereof.
According to the risk assessment method for vehicle function safety, which is provided by the embodiment of the application, the attribute information is extracted and is additionally arranged in the feature data, so that the scene or degree information of the feature data can be further refined, the assessment result obtained by the constructed vehicle assessment data network in the vehicle function assessment process is closer to the actual situation, and the assessment result is more accurate.
In some embodiments, the step of extracting feature information of existing vehicle function experience data and an association relationship between the feature information to obtain corresponding feature data and an association relationship between the feature data includes:
it is determined whether two or more pieces of identical characteristic information exist.
And if the same characteristic information corresponds to different existing vehicle function experience data, extracting the characteristic information again for the same characteristic information corresponds to different existing vehicle function experience data. For example, the extracted feature information may be extracted keywords, when extracting keywords of existing vehicle function experience data, two or more existing vehicle function experience data extract the same keywords, but two or more existing vehicle function experience data represent different scene meanings or function meanings, and the like, at this time, re-extraction of keywords is required to be performed on the part of existing vehicle function experience data until it is known that different keywords are extracted, and the process of extracting keywords may slightly adjust the extraction rule to avoid re-extraction of the same keywords.
According to the risk assessment method for vehicle function safety provided by the embodiment of the application, whether two or more pieces of same characteristic information exist is judged, if the same characteristic information corresponds to different existing vehicle function experience data, the characteristic information is extracted again for the same characteristic information corresponds to different existing vehicle function experience data, so that missing of part of existing vehicle function experience data can be avoided, the built vehicle assessment data network can be ensured to contain more comprehensive characteristic data of the existing vehicle function experience data and association relation among the characteristic data, and further the assessment result obtained by using the vehicle assessment data network in the vehicle function assessment process is more close to the actual situation, and the assessment result is more accurate.
In some embodiments, after step S200, further comprising:
And supplementing and establishing an association relation in the vehicle evaluation data network according to an inference rule, wherein the vehicle function features comprise at least one function group, the function group comprises at least one vehicle function feature, the inference rule comprises a hazard inference rule and a risk inference rule, failure modes corresponding to the vehicle function features in the same function group are the same, and the risk inference rule comprises the same hazard reasons corresponding to the vehicle function features in the same function group.
For example, the functions of the vehicle function features in the same function group are similar, and the function group can be established by putting the newly developed function features into the existing function groups with similar functions for the newly developed vehicle function features. The hazard reasoning rule may specifically cause a vehicle hazard feature for a functional failure mode corresponding to any vehicle functional feature in any functional group, and enter a safety state feature, and then other vehicle functional features in the same functional group corresponding to the same functional failure mode are associated with the same vehicle hazard feature and the same safety state feature. The risk reasoning rule specifically may be that the hazard cause corresponding to any vehicle function feature in any function group corresponds to a risk level feature, and causes a vehicle hazard feature and enters a safety state feature, and then other vehicle function features in the same function group corresponding to the same hazard cause are associated with the same risk level feature, the same vehicle hazard feature and the same safety state feature.
According to the risk assessment method for vehicle function safety provided by the embodiment of the application, the association relation in the vehicle assessment data network is additionally established by setting the inference rule, so that newly developed vehicle function characteristics can be dealt with, and generally, the newly developed vehicle function characteristics have no corresponding failure mode, vehicle hazard characteristics, risk grade characteristics and safety state characteristics in actual occurrence event and experience accumulation, so that the newly developed vehicle function characteristics are put into the existing function group with similar functions according to the inference rule, the association relation of the newly developed vehicle function characteristics in the vehicle assessment data network is additionally established based on the function similarity, and an assessment result which is relatively close to the real situation can be obtained during function assessment, so that the assessment result is relatively objective and accurate compared with the prior art, and the design of the vehicle function is further optimized on the basis of the assessment result.
In a second aspect of the embodiment of the present application, a risk assessment device for vehicle functional safety is provided, and fig. 3 is a schematic block diagram of a risk assessment device for vehicle functional safety according to an embodiment of the present application.
As shown in fig. 3, includes:
The database module 200 is configured to perform a database process on existing vehicle function experience data to obtain corresponding feature data and an association relationship between the feature data, where the existing vehicle function experience data includes vehicle function description data, vehicle hazard analysis data corresponding to the vehicle function description data, risk assessment data corresponding to the vehicle function description data, and safety state data corresponding to the vehicle hazard analysis data, and the category of the feature data includes vehicle function features, vehicle hazard features, risk level features, and safety state features.
The construction module 300 is configured to construct a vehicle evaluation data network according to the feature data and the association relationship between the feature data, where the vehicle evaluation data network is constructed based on a knowledge graph algorithm.
The evaluation module 400 is configured to input a vehicle function feature to be evaluated into the vehicle evaluation data network, and output an evaluation result, where the evaluation result includes an evaluation result corresponding to a vehicle hazard feature, an evaluation result corresponding to a risk level feature, and an evaluation result corresponding to a safety state feature.
According to the risk assessment device for vehicle function safety, provided by the embodiment of the application, the existing vehicle function experience data is subjected to data processing to obtain the corresponding feature data and the association relation between the feature data, the existing vehicle function experience data can comprise actually-occurring event data caused by the vehicle function or experience data obtained through long-term experience accumulation, the vehicle function features, the vehicle hazard features, the risk level features and the safety state features obtained through the data processing are subjected to the correlation relation to obtain the vehicle assessment data network, the vehicle function features to be assessed in the process of developing the actual vehicle function are subjected to risk assessment through the vehicle assessment data network, and the obtained assessment result is based on the actually-occurring time data or the data accumulated through long-term experience, so that the assessment result is more objective and accurate compared with the prior art, and the design of the vehicle function is further optimized on the basis of the assessment result.
In a third aspect of the embodiment of the present application, fig. 4 is a schematic block diagram of an electronic device according to an embodiment of the present application. As shown in fig. 4, there is provided an electronic device 500 including: memory 510, processor 520, and a computer program stored in memory 510 and executable on processor 520, processor 520 being configured to implement the steps of the vehicle functional safety risk assessment method according to the first aspect when executing the computer program stored in memory 510:
and carrying out data processing on the existing vehicle function experience data to obtain corresponding feature data and an association relation between the feature data, wherein the existing vehicle function experience data comprises vehicle function description data, vehicle hazard analysis data corresponding to the vehicle function description data, risk assessment data corresponding to the vehicle function description data and safety state data corresponding to the vehicle hazard analysis data, and the categories of the feature data comprise vehicle function features, vehicle hazard features, risk grade features and safety state features.
And constructing a vehicle evaluation data network according to the feature data and the association relation between the feature data, wherein the vehicle evaluation data network is constructed based on a knowledge graph algorithm.
And inputting the vehicle functional characteristics to be evaluated into a vehicle evaluation data network, and outputting evaluation results, wherein the evaluation results comprise evaluation results corresponding to vehicle hazard characteristics, evaluation results corresponding to risk level characteristics and evaluation results corresponding to safety state characteristics.
In a third aspect of the present embodiment, fig. 5 is a schematic block diagram of a computer readable storage medium according to an embodiment of the present application. As shown in fig. 5, there is provided a computer-readable storage medium 600 having stored thereon a computer program 610, which when executed by a processor, implements the steps of the vehicle functional safety risk assessment method of the first aspect:
and carrying out data processing on the existing vehicle function experience data to obtain corresponding feature data and an association relation between the feature data, wherein the existing vehicle function experience data comprises vehicle function description data, vehicle hazard analysis data corresponding to the vehicle function description data, risk assessment data corresponding to the vehicle function description data and safety state data corresponding to the vehicle hazard analysis data, and the categories of the feature data comprise vehicle function features, vehicle hazard features, risk grade features and safety state features.
And constructing a vehicle evaluation data network according to the feature data and the association relation between the feature data, wherein the vehicle evaluation data network is constructed based on a knowledge graph algorithm.
And inputting the vehicle functional characteristics to be evaluated into a vehicle evaluation data network, and outputting evaluation results, wherein the evaluation results comprise evaluation results corresponding to vehicle hazard characteristics, evaluation results corresponding to risk level characteristics and evaluation results corresponding to safety state characteristics.
While preferred embodiments of the present description have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the disclosure.
It will be apparent to those skilled in the art that various modifications and variations can be made in the present specification without departing from the spirit or scope of the specification. Thus, if such modifications and variations of the present specification fall within the scope of the claims and the equivalents thereof, the present specification is also intended to include such modifications and variations.
Claims (8)
1. A risk assessment method for vehicle functional safety, comprising:
Performing data processing on existing vehicle function experience data to obtain corresponding feature data and an association relation between the feature data, wherein the existing vehicle function experience data comprises vehicle function description data, vehicle hazard analysis data corresponding to the vehicle function description data, risk assessment data corresponding to the vehicle function description data and safety state data corresponding to the vehicle hazard analysis data, and the types of the feature data comprise vehicle function features, vehicle hazard features, risk grade features and safety state features;
constructing a vehicle evaluation data network according to the feature data and the association relation between the feature data, wherein the vehicle evaluation data network is constructed based on a knowledge graph algorithm;
Inputting the vehicle functional characteristics to be evaluated into the vehicle evaluation data network, and outputting an evaluation result, wherein the evaluation result comprises an evaluation result corresponding to the vehicle hazard characteristics, an evaluation result corresponding to the risk level characteristics and an evaluation result corresponding to the safety state characteristics;
The step of performing data processing on the existing vehicle function experience data to obtain corresponding feature data and association relations between the feature data comprises the following steps:
extracting feature information of the existing vehicle function experience data and an association relation between the feature information to obtain corresponding feature data and an association relation between the feature data;
calculating the similarity between the characteristic data of the same category, and merging the characteristic data of the same category when the similarity does not exceed a set threshold value;
The step of calculating the similarity between the feature data of the same category, and merging the feature data of the same category when the similarity does not exceed a set threshold value, includes: calculating a similarity vector for the feature data based on: Wherein, As the similarity vector of the feature data,Both of the two data types in the feature data,Is thatThe corresponding example value on the nth eigenvalue, n is a natural number greater than 0,Is the similarity vector of the nth eigenvalue,A weighting coefficient of a similarity vector which is the nth characteristic value;
calculating the similarity between the feature data of the same category according to the similarity vector of the feature data of the same category;
Similarity between the feature data of the same class is calculated based on the following formula:
Wherein, For the similarity between two feature data of the same class,Is a similarity vector of first feature data of the same class,The first characteristic data and the second characteristic data are any two characteristic data in the characteristic data of the same category.
2. The risk assessment method for vehicle functional safety according to claim 1, wherein the step of extracting feature information of the existing vehicle functional experience data and an association relationship between the feature information to obtain corresponding feature data and an association relationship between the feature data comprises:
Extracting feature information of the existing vehicle function experience data to obtain the feature data, wherein the categories of the feature data comprise vehicle function features, vehicle hazard features, risk level features and safety state features;
extracting a functional failure mode as an association relationship between the vehicle functional characteristic and the vehicle hazard characteristic;
extracting a hazard reason as an association relationship between the vehicle functional characteristic and the risk level characteristic;
extracting a hazard degradation execution action as an association relationship between the vehicle hazard feature and the safety state feature;
and extracting risk correspondence as an association relationship between the risk level characteristics and the vehicle hazard characteristics.
3. The risk assessment method for vehicle functional safety according to claim 1, wherein before the step of constructing a vehicle assessment data network according to the feature data and the association relationship between the feature data, further comprising:
extracting attribute information of the existing vehicle function experience data, wherein the attribute information comprises function attribute information, hazard attribute information, risk attribute information and safety state attribute information;
And attaching the attribute information to the corresponding feature data, wherein the function attribute information is attached to the vehicle function feature, the hazard attribute information is attached to the vehicle hazard feature, the risk attribute information is attached to the risk level feature, and the safety state attribute information is attached to the safety state feature.
4. The risk assessment method for vehicle functional safety according to claim 1, wherein the step of extracting feature information of the existing vehicle functional experience data and an association relationship between the feature information to obtain corresponding feature data and an association relationship between the feature data comprises:
judging whether two or more pieces of same characteristic information exist;
And if the same characteristic information corresponds to different existing vehicle function experience data, extracting the characteristic information again from the same characteristic information corresponding to different existing vehicle function experience data.
5. The risk assessment method for vehicle functional safety according to claim 2, wherein after the step of constructing a vehicle assessment data network according to the feature data and the association relationship between the feature data, further comprising:
and supplementing and establishing an association relation in the vehicle evaluation data network according to an inference rule, wherein the vehicle function features comprise at least one function group, the function group comprises at least one vehicle function feature, the inference rule comprises a hazard inference rule and a risk inference rule, the failure modes corresponding to the vehicle function features in the same function group are the same, and the risk inference rule comprises the same hazard cause corresponding to the vehicle function features in the same function group.
6. A risk assessment device for safety of a vehicle function, comprising:
The system comprises a data processing module, a data processing module and a data processing module, wherein the data processing module is used for performing data processing on existing vehicle function experience data to obtain corresponding feature data and an association relation between the feature data, the existing vehicle function experience data comprises vehicle function description data, vehicle hazard analysis data corresponding to the vehicle function description data, risk assessment data corresponding to the vehicle function description data and safety state data corresponding to the vehicle hazard analysis data, and the categories of the feature data comprise vehicle function features, vehicle hazard features, risk grade features and safety state features;
The construction module is used for constructing a vehicle evaluation data network according to the characteristic data and the association relation between the characteristic data, wherein the vehicle evaluation data network is constructed based on a knowledge graph algorithm;
The evaluation module is used for inputting the vehicle function characteristics to be evaluated into the vehicle evaluation data network and outputting evaluation results, wherein the evaluation results comprise evaluation results corresponding to the vehicle hazard characteristics, evaluation results corresponding to the risk level characteristics and evaluation results corresponding to the safety state characteristics;
The step of performing data processing on the existing vehicle function experience data to obtain corresponding feature data and association relations between the feature data comprises the following steps:
extracting feature information of the existing vehicle function experience data and an association relation between the feature information to obtain corresponding feature data and an association relation between the feature data;
calculating the similarity between the characteristic data of the same category, and merging the characteristic data of the same category when the similarity does not exceed a set threshold value;
The step of calculating the similarity between the feature data of the same category, and merging the feature data of the same category when the similarity does not exceed a set threshold value, includes: calculating a similarity vector for the feature data based on:
Wherein, As the similarity vector of the feature data,Both of the two data types in the feature data,Is thatThe corresponding example value on the nth eigenvalue, n is a natural number greater than 0,Is the similarity vector of the nth eigenvalue,A weighting coefficient of a similarity vector which is the nth characteristic value;
calculating the similarity between the feature data of the same category according to the similarity vector of the feature data of the same category;
Similarity between the feature data of the same class is calculated based on the following formula:
Wherein, For the similarity between two feature data of the same class,Is a similarity vector of first feature data of the same class,The first characteristic data and the second characteristic data are any two characteristic data in the characteristic data of the same category.
7. An electronic device, comprising: memory, a processor and a computer program stored in the memory and executable on the processor for performing the steps of the vehicle functional safety risk assessment method according to any one of claims 1-5 when the computer program stored in the memory is executed.
8. A computer-readable storage medium, characterized in that a computer program is stored thereon, which, when being executed by a processor, implements the steps of the risk assessment method of vehicle functional safety according to any one of claims 1-5.
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