HARA exposure degree analysis method for vehicle function safety and related equipment

Abstract

The invention discloses an HARA exposure degree analysis method for vehicle function safety and related equipment, relates to the field of function safety hazard analysis and risk assessment, and mainly aims to solve the problem that function safety controllability analysis excessively depends on artificial experience. The method comprises the steps that a database is established based on historical judgment data, and the database is related to all feature values of the HARA exposure degree; training an empirical probability model of each characteristic value based on the database, the empirical probability model being established based on a naive Bayes algorithm; and analyzing the HARA exposure degree of the vehicle function safety based on the empirical probability model of each characteristic value. The method is used for the HARA exposure degree analysis process of vehicle function safety.

Description

Technical Field

[0001] The present invention relates to the field of functional safety hazard analysis and risk assessment, and in particular to a HARA exposure analysis method for vehicle functional safety and related equipment. Background Art

[0002] Functional safety is an important part of systems engineering, which focuses on preventing injuries caused by system failures or errors. In many safety-critical systems, such as aerospace, automobiles, medical equipment, etc., functional safety analysis is an essential link to identify and evaluate potential safety risks. For the functional safety of road vehicles, the focus is on addressing unreasonable risks that endanger personal safety due to failures in the vehicle's electronic and electrical systems. When conducting functional safety analysis at the conceptual stage, hazard analysis and risk assessment (hereinafter referred to as "HARA analysis") is required, which requires combining hazard scenarios and failures to model and analyze the exposure, controllability and severity of the hazards, and finally combining these three indicators to give a level judgment of the functional safety target.

[0003] However, HARA analysis is a complex process that requires a lot of evaluation and human resources, and the accuracy of the results depends largely on the experience and expertise of the analysts. This results in a highly subjective judgment result that lacks objectivity and accuracy. It also requires a lot of computing resources, which is a challenge for systems with limited computing power. Finally, existing methods often cannot effectively handle complex safety issues such as system interactions and human factors, which may lead to inaccurate analysis results. Summary of the invention

[0004] In view of the above problems, the present invention provides a HARA exposure analysis method for vehicle functional safety and related equipment, the main purpose of which is to solve the problem that functional safety controllability analysis is overly dependent on manual experience.

[0005] In order to solve at least one of the above technical problems, in a first aspect, the present invention provides a HARA exposure analysis method for vehicle functional safety, the method comprising:

[0006] Establishing a database based on the historical determination data, wherein the database is associated with each characteristic value of HARA exposure;

[0007] Training an empirical probability model of each characteristic value based on the database, wherein the empirical probability model is established based on a naive Bayes algorithm;

[0008] The HARA exposure of vehicle functional safety is analyzed based on the empirical probability model of the various characteristic values.

[0009] Optionally, establishing a database based on historical determination data includes:

[0010] Acquiring initial historical determination data, wherein the historical determination data is used to determine the functional safety HARA analysis exposure of historical samples;

[0011] Based on the Python pandas library, the initial historical determination data is cleaned and deduplicated to determine the second historical determination data;

[0012] An extraction and fusion operation is performed on each characteristic value of the second historical determination data to establish a database, wherein the characteristic value is related to the HARA exposure, and the database includes characteristic values ​​corresponding to different characteristics of at least two historical samples.

[0013] Optionally, the training of the empirical rate model of each characteristic value based on the database includes:

[0014] The prior probability is calculated based on the following formula:

[0015]

[0016] Wherein, the P(Y=c k ) represents the prior probability; the c k Represents the exposure classification of a sample given by empirical data; N represents the number of samples, i represents the i-th sample, and k represents the exposure level.

[0017] Optionally, the above method further includes:

[0018] The conditional probability is calculated based on the following formula:

[0019]

[0020] j=1,2,…,n;l=1,2,…,S j ; k = 1, 2, ..., K

[0021] Wherein, the P(X (j) =a jl |Y=c k ) represents the calculation of conditional probability; the x i (j) represents the jth feature of the i-th sample, a jl It means that the j-th eigenvalue may take the l-th value.

[0022] Optionally, the analyzing the HARA exposure of vehicle functional safety based on the empirical probability model of each characteristic value includes:

[0023] Determine target analysis instance data;

[0024] The HARA exposure of vehicle functional safety is analyzed based on the target analysis example data by using the empirical probability model of each characteristic value.

[0025] Optionally, the above method further includes:

[0026] The exposure classification probability is calculated based on the following formula:

[0027]

[0028] Wherein, the P(Y=c k ) represents the exposure classification probability.

[0029] Optionally, analyzing the HARA exposure of vehicle functional safety through the empirical probability model of each characteristic value based on the target analysis instance data includes:

[0030] The HARA exposure of vehicle functional safety is determined based on the following formula:

[0031]

[0032] In a second aspect, an embodiment of the present invention further provides a HARA exposure analysis device for vehicle functional safety, comprising:

[0033] an establishing unit, configured to establish a database based on the historical determination data, wherein the database is associated with each characteristic value of the HARA exposure;

[0034] A training unit, used for training an empirical probability model of each characteristic value based on the database, wherein the empirical probability model is established based on a naive Bayes algorithm;

[0035] An analyzing unit is used to analyze the HARA exposure of vehicle functional safety based on the empirical probability model of each characteristic value.

[0036] In order to achieve the above-mentioned purpose, according to the third aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium includes a stored program, wherein when the above-mentioned program is executed by a processor, the steps of the above-mentioned HARA exposure analysis method for vehicle functional safety are implemented.

[0037] In order to achieve the above-mentioned purpose, according to the fourth aspect of the present invention, there is provided an electronic device, comprising at least one processor and at least one memory connected to the processor; wherein the above-mentioned processor is used to call the program instructions in the above-mentioned memory to execute the steps of the above-mentioned HARA exposure analysis method for vehicle functional safety.

[0038] Through the above technical solution, the HARA exposure analysis method and related equipment for vehicle functional safety provided by the present invention solve the problem that functional safety controllability analysis is too dependent on manual experience. The present invention establishes a database based on historical judgment data, wherein the database is related to each characteristic value of HARA exposure; trains an empirical probability model for each characteristic value based on the database, wherein the empirical probability model is established based on the naive Bayes algorithm; and analyzes the HARA exposure of vehicle functional safety based on the empirical probability model for each characteristic value. In the above solution, the machine learning model can automatically identify and extract potential factors affecting exposure by learning a large amount of historical data, thereby improving the accuracy of exposure judgment, reducing evaluation bias caused by subjective factors, and realizing automated exposure judgment, which greatly improves the efficiency of functional safety HARA analysis and reduces the time consumption and error rate of manual operation.

[0039] Correspondingly, the HARA exposure analysis device, equipment and computer-readable storage medium for vehicle functional safety provided by the embodiments of the present invention also have the above-mentioned technical effects.

[0040] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, it can be implemented according to the contents of the specification. In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0042] Figure 1 A schematic flow chart of a HARA exposure analysis method for vehicle functional safety provided by an embodiment of the present invention is shown;

[0043] Figure 2 A schematic diagram of a naive Bayes model provided by an embodiment of the present invention is shown;

[0044] Figure 3 A schematic block diagram showing the composition of a HARA exposure analysis device for vehicle functional safety provided by an embodiment of the present invention is shown;

[0045] Figure 4 A schematic block diagram of the composition of an electronic device for analyzing HARA exposure of vehicle functional safety provided by an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0046] The exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present invention and to enable the scope of the present invention to be fully communicated to those skilled in the art.

[0047] In order to solve the problem that functional safety controllability analysis is too dependent on manual experience, an embodiment of the present invention provides a HARA exposure analysis method for vehicle functional safety, such as Figure 1 As shown, the method includes:

[0048] S101, establishing a database based on historical determination data, wherein the database is related to each characteristic value of HARA exposure;

[0049] The above-mentioned step S101 also includes S1011, obtaining initial historical judgment data, wherein the historical judgment data is used to determine the functional safety HARA analysis exposure of historical samples; based on Python's pandas library, the initial historical judgment data is cleaned and deduplicated to determine second historical judgment data; each eigenvalue of the second historical judgment data is extracted and fused to establish a database, wherein the eigenvalue is related to the HARA exposure, and the database includes eigenvalues ​​corresponding to different features of at least two historical samples.

[0050] Exemplarily, considering the different focus factors of data sources, the above steps are one of the important differences from other HARA analysis and evaluation factors (such as controllability and severity). First, this application collects and organizes database samples related to exposure determination and functional safety HARA analysis, namely the above initial historical determination data: including standard case database determination exposure files, such as ISO26262, IEC61508 and other standard databases; engineering files: including the determination results of exposure in the actual functional safety project development by experts in related fields; Ministry of Transport's monitoring report on vehicle-related data, etc.

[0051] Furthermore, the original data samples are cleaned and deduplicated, and this step can be performed using the pandas library in Python.

[0052] Finally, the relevant features related to exposure determination in the data samples are extracted and integrated, such as duration (average percentage of operating time), road type, road surface type, vehicle status, status of other traffic participants, driving control type, and other feature values ​​closely related to exposure to establish a standard database.

[0053] S102, training an empirical probability model of each characteristic value based on the database, wherein the empirical probability model is established based on a naive Bayes algorithm;

[0054] like Figure 2 As shown, the Bayesian method is based on the Bayesian principle and uses the knowledge of probability statistics to classify the sample database. Its characteristic is that it combines prior probability and posterior probability, which not only avoids the subjective bias of using only prior probability, but also avoids the overfitting phenomenon of using sample information alone. The naive Bayesian method is a corresponding simplification based on the Bayesian algorithm, that is, it is assumed that the attributes are conditionally independent of each other when the target value is given. However, because the characteristic values ​​are independent of each other when evaluating the exposure rate, the characteristics of the naive Bayesian method are more suitable for the application background of this application.

[0055] The above step S102 also includes S1021 and S1022:

[0056] S1021. Calculate the prior probability based on the following formula:

[0057]

[0058] Specifically, the P(Y=c k ) represents the prior probability; the c k Represents the exposure classification of a sample given by empirical data. N represents the number of samples, i represents the i-th sample, and k represents the exposure (E0 / 1 / 2 / 3 / 4) corresponding to the level (c1 / c2 / c3 / c4 / c5).

[0059] Specifically, the c k For the example X given by empirical data, the exposure classification is set to y(E0 / E1 / E2 / E3 / E4) corresponding to {c1, c2, c3, c4, c5}, y i ∈{c1,c2,…,c k}.

[0060] Furthermore, the present application comprehensively considers the characteristic values ​​of the exposure-related database in S1011 (element such as duration (average percentage of operating time), road type, road surface type, vehicle status, status of other traffic participants, driving control type, etc.).

[0061] Database T = {(x1, y1), (x2, y2), ... (x N ,y N )},in is the jth feature of the i-th sample, a jlThe jth eigenvalue may take the lth value, j = 1, 2, 3 ... n, l = 1, 2, 3, ..., S j ,y i ∈{c1,c2,…,c k}; For example, the exposure classification of a given example X is set to y(E0 / E1 / E2 / E3 / E4), corresponding to {c1, c2, c3, c4, c5} respectively.

[0062] S1022. Calculate the conditional probability based on the following formula:

[0063]

[0064] j=1,2,…,n;l=1,2,…,S j ; k = 1, 2, ..., K

[0065] Wherein, the P(X (j) =a jl |Y=c k ) represents the calculated conditional probability; Characterize the jth feature of the i-th sample, a jl Characterizes that the j-th eigenvalue may take the l-th value.

[0066] Specifically,

[0067] S103 , analyzing the HARA exposure of vehicle functional safety based on an empirical probability model of each characteristic value.

[0068] The above step S103 also includes S1031, S1032 and S1033:

[0069] S1031. Determine target analysis instance data; and analyze the HARA exposure of vehicle functional safety through the empirical probability model of each characteristic value based on the target analysis instance data.

[0070] By introducing machine learning technology, automated exposure determination can be achieved, greatly improving the efficiency of functional safety HARA analysis.

[0071] S1032. Calculate the exposure classification probability based on the following formula:

[0072]

[0073] Wherein, the P(Y=c k ) represents the exposure classification probability.

[0074] Specifically, the exposure classification is set to y(E0 / E1 / E2 / E3 / E4) corresponding to the probability of {c1, c2, c3, c4, c5} respectively.(1) ,x (2) ,…,x (n) ) T When the exposure needs to be calculated, the is the jth feature of the i-th sample, a jl The jth eigenvalue may take the lth value, Y = c k Same as above.

[0075] S1033. Determine the HARA exposure of vehicle functional safety based on the following formula:

[0076]

[0077] Wherein, y represents the HARA exposure determination value (approximately) of vehicle functional safety.

[0078] It can be understood that for all Y=c k , the values ​​in the denominators are the same, so the denominator part can be ignored, and the final form is expressed. argmax is a function that finds the parameter (set) of a function. When we have another function y=f(x), if the result x0=argmax(f(x)), it means that when the function f(x) takes x=x0, the maximum value of the value range of f(x) is obtained; if there are multiple points that make f(x) obtain the same maximum value, then the result of argmax(f(x)) is a point set. In other words, argmax(f(x)) is the variable point x corresponding to the maximum value of f(x). The Y is (E0 / E1 / E2 / E3 / E4) corresponding to {c1, c2, c3, c4, c5} respectively, and the instance X is the most likely instance to obtain c1, c2, c3, c4, c5.

[0079] Based on the above scheme, by introducing machine learning technology, automated exposure determination can be achieved, which greatly improves the efficiency of functional safety HARA analysis and reduces the time and error rate of manual operation. The stability of the enhanced model and the fitting of empirical probability are repeatedly verified through application examples, and verified by the practical results of experts in the engineering field. The performance of the model can also be evaluated by the K-fold cross-validation method. It can effectively solve the problems of difficulty in acquiring and annotating exposure determination data for functional safety HARA analysis and high consumption of computing resources. The optimized machine learning model is integrated into the existing functional safety analysis tools to achieve a seamless combination of machine learning and traditional functional safety analysis methods, improving the quality and efficiency of the entire functional safety analysis.

[0080] Furthermore, as a response to the above Figure 1In order to realize the method shown in the figure, the embodiment of the present invention also provides a HARA exposure analysis device for vehicle functional safety, which is used to analyze the above Figure 1 The device embodiment corresponds to the aforementioned method embodiment. For ease of reading, this device embodiment will not repeat the details of the aforementioned method embodiment one by one, but it should be clear that the device in this embodiment can correspond to all the contents of the aforementioned method embodiment. Figure 3 As shown, the device includes: a first acquisition unit 21, a determination unit 22, a second acquisition unit 23 and a generation unit 24, wherein

[0081] An establishing unit 21, configured to establish a database based on the historical determination data, wherein the database is associated with each characteristic value of the HARA exposure;

[0082] A training unit 22, used for training an empirical probability model of each characteristic value based on the database, wherein the empirical probability model is established based on a naive Bayes algorithm;

[0083] The analyzing unit 23 is used to analyze the HARA exposure of the vehicle functional safety based on the empirical probability model of each characteristic value.

[0084] The processor includes a kernel, which calls the corresponding program unit from the memory. One or more kernels can be set, and a HARA exposure analysis method for vehicle functional safety is implemented by adjusting kernel parameters, which can solve the problem that functional safety controllability analysis is too dependent on manual experience.

[0085] An embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium includes a stored program, and when the program is executed by a processor, the HARA exposure analysis method for vehicle functional safety is implemented.

[0086] An embodiment of the present invention provides a processor, which is used to run a program, wherein the HARA exposure analysis method for vehicle functional safety is executed when the program is running.

[0087] An embodiment of the present invention provides an electronic device, the electronic device comprising at least one processor and at least one memory connected to the processor; wherein the processor is used to call program instructions in the memory to execute the HARA exposure analysis method for vehicle functional safety as described above

[0088] An embodiment of the present invention provides an electronic device 30, such as Figure 4As shown, the electronic device includes at least one processor 301, and at least one memory 302 and a bus 303 connected to the processor; wherein the processor 301 and the memory 302 communicate with each other through the bus 303; the processor 301 is used to call the program instructions in the memory to execute the above-mentioned HARA exposure analysis method for vehicle functional safety.

[0089] The intelligent electronic devices in this article can be PCs, PADs, mobile phones, etc.

[0090] The present application also provides a computer program product, which, when executed on a process management electronic device, is suitable for executing a program that initializes the steps of the HARA exposure analysis method for vehicle functional safety.

[0091] It should be noted that in the above embodiments, the description of each embodiment has its own emphasis, and for parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0092] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0093] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0094] These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0095] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0096] The present application also provides a computer program product, which includes computer software instructions. When the computer software instructions are executed on a processing device, the processing device is caused to execute the following Figure 1 This corresponds to the flow of control of the memory in the embodiment.

[0097] The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on the computer, the process or function according to the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from a website site, a computer, a server or a data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (digital subscriber line, DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center. The computer-readable storage medium can be any available medium that a computer can store or a data storage device such as a server or a data center that includes one or more available media integration. The available medium can be a magnetic medium, (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid state drive (SSD)), etc.

[0098] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0099] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0100] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0101] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0102] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, ROM), random access memory (Random Access Memory, RAM), disk or optical disk and other media that can store program codes.

[0103] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some of the technical features therein by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A HARA exposure analysis method for vehicle functional safety, It is characterized in that include: Establishing a database based on the historical determination data, wherein the database is associated with each characteristic value of HARA exposure; Training an empirical probability model of each characteristic value based on the database, wherein the empirical probability model is established based on a naive Bayes algorithm; The HARA exposure of vehicle functional safety is analyzed based on the empirical probability model of the various characteristic values.

2. The method according to claim 1, It is characterized in that The step of establishing a database based on historical determination data includes: Acquiring initial historical determination data, wherein the historical determination data is used to determine the functional safety HARA analysis exposure of historical samples; Based on the Python pandas library, the initial historical determination data is cleaned and deduplicated to determine the second historical determination data; An extraction and fusion operation is performed on each characteristic value of the second historical determination data to establish a database, wherein the characteristic value is related to the HARA exposure, and the database includes characteristic values ​​corresponding to different characteristics of at least two historical samples.

3. The method according to claim 1, It is characterized in that The empirical rate model for training each characteristic value based on the database includes: The prior probability is calculated based on the following formula: Wherein, the P(Y=c k ) represents the prior probability; the c k Represents the exposure classification of a sample given by empirical data; N represents the number of samples, i represents the i-th sample, and k represents the exposure level.

4. The method according to claim 3, It is characterized in that Also includes: The conditional probability is calculated based on the following formula: Wherein, the P(X (j) =a jl |Y=c k ) represents the calculation of conditional probability; represents the jth feature of the i-th sample, a jl It means that the j-th eigenvalue may take the l-th value.

5. The method according to claim 4, It is characterized in that The HARA exposure of vehicle functional safety is analyzed based on the empirical probability model of each characteristic value, including: Determine target analysis instance data; The HARA exposure of vehicle functional safety is analyzed based on the target analysis example data by using the empirical probability model of each characteristic value.

6. The method according to claim 5, It is characterized in that Also includes: The exposure classification probability is calculated based on the following formula: Wherein, the P(Y=c k ) represents the exposure classification probability.

7. The method according to claim 6, It is characterized in that The analyzing the HARA exposure of vehicle functional safety through the empirical probability model of each characteristic value based on the target analysis instance data includes: The HARA exposure of vehicle functional safety is determined based on the following formula:

8. A HARA exposure analysis device for vehicle functional safety, It is characterized in that Also includes: an establishing unit, configured to establish a database based on the historical determination data, wherein the database is associated with each characteristic value of the HARA exposure; A training unit, used for training an empirical probability model of each characteristic value based on the database, wherein the empirical probability model is established based on a naive Bayes algorithm; An analyzing unit is used to analyze the HARA exposure of vehicle functional safety based on the empirical probability model of each characteristic value.

9. A computer-readable storage medium, It is characterized in that The computer-readable storage medium includes a stored program, wherein when the program is executed by a processor, the steps of the HARA exposure analysis method for vehicle functional safety according to any one of claims 1 to 7 are implemented.

10. An electronic device, It is characterized in that The electronic device includes at least one processor and at least one memory connected to the processor; wherein the processor is used to call program instructions in the memory to execute the steps of the HARA exposure analysis method for vehicle functional safety as described in any one of claims 1 to claim 7.

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