Hazard exposure degree calculation method based on multi-system three-safety fusion analysis

Abstract

The invention discloses a hazard exposure degree calculation method based on multi-system three-safety fusion analysis, and relates to the technical field of automobile safety, and the method comprises the steps: determining a to-be-evaluated vehicle function, and determining a safety analysis category to which the to-be-evaluated vehicle function belongs, the safety analysis category being selected from function safety, expected function safety and information safety; external factors related to the vehicle function operation scene are recognized, the external factors are selected from human factors, environmental factors, other vehicle factors and social factors, and the scene triggering probability of the vehicle function is calculated based on the occurrence probability of the recognized external factors and the probability that the vehicle function is triggered under the external factors; decomposing the vehicle function into dependent parts, and calculating to obtain a function failure probability of the vehicle function based on the used probability and the failure probability of each part; and multiplying the scene triggering probability by the function failure probability to obtain the quantitative hazard exposure degree of the vehicle function.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of automobile safety, in particular to a hazard exposure degree calculation method based on multi-system three-an integration analysis. BACKGROUND

[0002] In the functional safety of automobiles, for the evaluation of potential defects of vehicle electronic and electrical components, the traditional functional safety analysis method is to combine the defects with hazard scenarios to determine the potential hazards caused thereby, to perform hazard analysis and risk assessment on the hazards, to obtain the severity level, exposure level and controllability of the hazards, and to obtain the automobile safety integrity level. The exposure level refers to the possibility of the occurrence of the hazards. However, with the continuous development and extension of intelligent networked automobile technology, the vehicle electronic and electrical components and their embedded code functions are constantly enriched and expanded, and defects of vehicle key parts and nodes, especially defects of high-coupling intelligent driving systems, may cause a large number of potential functional failures, and the potential hazards cover information security, functional safety and intended functional safety.

[0003] The traditional automobile safety analysis method has the defect that the three fields of functional safety, intended functional safety and information security are isolated from each other, different evaluation standards and level division systems are adopted in each field, and the risk assessment results lack a unified quantitative benchmark, making it difficult to compare the safety performance across fields and make system-level decisions. SUMMARY

[0004] The purpose of the present application is to provide a hazard exposure degree calculation method based on multi-system three-an integration analysis, which at least solves one of the technical problems that the risk assessment results lack a unified quantitative benchmark and it is difficult to compare the safety performance across fields and make system-level decisions.

[0005] The present application provides the following solutions:

[0006] According to one aspect of the present application, a hazard exposure degree calculation method based on multi-system three-an integration analysis is provided, comprising:

[0007] determining a vehicle function to be evaluated and specifying the safety analysis category to which the vehicle function belongs, the safety analysis category being selected from functional safety, intended functional safety and information safety;

[0008] identifying external factors related to the vehicle function operation scenario, the external factors being selected from human factors, environmental factors, other vehicle factors and social factors, and calculating a scenario triggering probability of the vehicle function based on the occurrence probability of the identified external factors and the probability of the vehicle function being triggered under the external factors;

[0009] decompose the vehicle function into dependent components, calculate a function failure probability of the vehicle function based on a usage probability of each component and a failure probability of each component;

[0010] multiply the scenario triggering probability and the function failure probability to obtain a quantitative hazard exposure degree of the vehicle function.

[0011] Further, the calculation of the scenario triggering probability of the vehicle function comprises:

[0012] for each type of identified external factor, multiply its occurrence probability and its corresponding vehicle function triggering probability to obtain a triggering contribution value of the factor;

[0013] sum and average the triggering contribution values of all identified external factors to obtain the scenario triggering probability.

[0014] Further, the calculation of the function failure probability of the vehicle function comprises:

[0015] for each component on which the vehicle function depends, multiply its usage probability and its failure probability to obtain a failure contribution value of the component;

[0016] sum and average the failure contribution values of all components to obtain the function failure probability.

[0017] Further, the external factors comprise:

[0018] The human factors comprise overtake behavior data or sudden braking behavior data of a driver;

[0019] The environmental factors comprise rainfall data, heavy fog weather data or road rockfall data of a vehicle running area;

[0020] The other vehicle factors comprise overtake behavior data of other vehicles or acceleration behavior data of a following vehicle when the ego vehicle changes lanes;

[0021] The social factors comprise traffic laws and regulations of a vehicle running area, and the traffic laws and regulations comprise driving lane direction rules, lane speed limit rules or turning and straight passing priority rules.

[0022] Further, the usage probability of each component is determined based on a vehicle running working condition; and events causing function failure of each component comprise internal defects of the component and malicious intrusion from outside.

[0023] Further, in the information security analysis category, the function failure probability is calculated by weighting based on weights of different attack paths.

[0024] According to the two aspects of the present application, a hazard exposure degree calculation system based on multi-system three-an integration analysis is provided, comprising:

[0025] The definition module is used to identify the vehicle functions to be evaluated and to clarify the safety analysis scope to which they belong. The safety analysis scope is selected from functional safety, expected functional safety, and information security.

[0026] The scenario analysis module is used to identify external factors related to the vehicle function operation scenario. The external factors are selected from human factors, environmental factors, other vehicle factors and social factors. Based on the occurrence probability of the identified external factors and the probability of the vehicle function being triggered under the external factors, the scenario trigger probability of the vehicle function is calculated.

[0027] The failure analysis module is used to decompose the vehicle function into the components it depends on, and calculate the functional failure probability of the vehicle function based on the usage probability and failure probability of each component.

[0028] The risk synthesis module is used to multiply the scenario trigger probability by the function failure probability to obtain the quantitative hazard exposure of the vehicle function.

[0029] According to three aspects of the present invention, an electronic device is provided, comprising: a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus;

[0030] The memory stores a computer program, which, when executed by the processor, causes the processor to perform the steps of a hazard exposure calculation method based on multi-system three-safety fusion analysis.

[0031] According to four aspects of the present invention, a computer-readable storage medium is provided, comprising: storing a computer program executable by an electronic device, wherein when the computer program is run on the electronic device, the electronic device performs the steps of a hazard exposure calculation method based on multi-system three-safety fusion analysis.

[0032] According to five aspects of the present invention, a vehicle is provided, comprising:

[0033] Electronic equipment, used to implement a method for calculating hazard exposure based on multi-system three-safety fusion analysis;

[0034] The processor runs a program, and when the program runs, it executes the steps of a hazard exposure calculation method based on multi-system three-safety fusion analysis from the data output by the electronic device.

[0035] Storage medium for storing programs that, when running, execute steps of a hazard exposure calculation method based on multi-system three-safety fusion analysis on data output from electronic devices.

[0036] The above solution achieves the following beneficial technical effects:

[0037] This application solves the problem of the isolation between the three safety analyses in traditional methods by establishing a unified analysis framework that simultaneously covers functional safety, expected functional safety and information security; the hazard exposure calculation based on the probability model provides comparable quantitative indicators for risks in different safety categories, supporting cross-domain safety decision-making.

[0038] This application establishes a risk assessment model closely related to real driving scenarios by introducing four external factors: human factors, environmental factors, other vehicle factors, and social factors. Based on probability calculations for specific driving conditions, the assessment results can accurately reflect the actual risk levels in different regions and usage scenarios.

[0039] This application achieves risk tracing from system function to specific component by decomposing vehicle functions to the component level and performing failure probability analysis; based on the dynamic adjustment mechanism of component usage probability, it accurately characterizes the contribution of each component to the overall risk under different operating conditions.

[0040] This application can identify the contribution of external scenario risks and internal system reliability risks by separately calculating the probability of scenario triggering and the probability of functional failure; the output of quantitative hazard exposure provides data support for prioritizing safety measures and optimizing resource allocation. Attached Figure Description

[0041] Figure 1 This is a flowchart of a hazard exposure calculation method based on multi-system three-safety fusion analysis provided by one or more embodiments of the present invention.

[0042] Figure 2 This is a functional execution flowchart of a hazard exposure calculation method based on multi-system three-safety fusion analysis provided by one or more embodiments of the present invention.

[0043] Figure 3 This is a functional execution flowchart of a hazard exposure calculation method based on multi-system three-safety fusion analysis provided by one or more embodiments of the present invention.

[0044] Figure 4 This is a multi-functional, multi-component execution flowchart of a hazard exposure calculation method based on multi-system three-safety fusion analysis provided by one or more embodiments of the present invention.

[0045] Figure 5 This is an architecture diagram of a hazard exposure calculation system based on multi-system three-safety fusion analysis provided by one or more embodiments of the present invention. Detailed Implementation

[0046] The technical solution of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0047] Figure 1 This is a flowchart of a hazard exposure calculation method based on multi-system three-safety fusion analysis provided by one or more embodiments of the present invention.

[0048] Figure 2 This is a functional execution flowchart of a hazard exposure calculation method based on multi-system three-safety fusion analysis provided by one or more embodiments of the present invention.

[0049] Figure 3 This is a functional execution flowchart of a hazard exposure calculation method based on multi-system three-safety fusion analysis provided by one or more embodiments of the present invention.

[0050] Figure 4 This is a multi-functional, multi-component execution flowchart of a hazard exposure calculation method based on multi-system three-safety fusion analysis provided by one or more embodiments of the present invention.

[0051] like Figures 1-4 The hazard exposure calculation method based on multi-system three-safety fusion analysis shown includes:

[0052] Identify the vehicle functions to be evaluated and define their respective safety analysis categories, which are selected from functional safety, expected functional safety, and information security.

[0053] Specifically, the selection of vehicle functions is based on the system design documents or function list of intelligent connected vehicles. For example, automatic emergency braking system, lane keeping assist system or wiper control system are extracted from the vehicle's electronic and electrical architecture as functions to be evaluated. This selection process can be completed through engineering review meetings or automated requirements management tools to ensure that the selected functions represent the vehicle's key operating behaviors.

[0054] The scope of safety analysis is clearly based on international standards and industry practices, and is selected from functional safety, expected functional safety, and information security. The functional safety scope comes from the ISO 26262 standard, which focuses on hazards caused by random hardware failures or systemic malfunctions of electronic and electrical components, such as abnormal controller signals or actuator jamming. The expected functional safety scope comes from the ISO / PAS 21448 standard, which deals with risks caused by system performance limitations or reasonably foreseeable misuse, such as sensor perception errors or the inadequacy of decision-making algorithms in specific scenarios. The information security scope comes from the ISO / SAE 21434 standard, which addresses threats caused by malicious attacks, unauthorized access, or data tampering, such as intrusion into communication channels or exploitation of software vulnerabilities.

[0055] In terms of operation, the scope of safety analysis is clearly defined through multidisciplinary team collaboration, including functional safety engineers, expected functional safety experts, and information security analysts. The team uses classification matrices or decision trees to classify categories based on functional definition documents and operational scenario descriptions. For example, for the automatic emergency braking function, if the analysis focuses on radar sensor hardware failure, it is classified under the functional safety category; if the analysis focuses on the system misidentifying obstacles in severe weather, it is classified under the expected functional safety category; if the analysis focuses on the communication bus being maliciously injected with false signals, it is classified under the information security category.

[0056] Logically, the three security analysis categories together form the basic framework of the three-security integrated analysis. Their division ensures that hazard identification covers all potential risk sources. The categories are not mutually exclusive, but rather enhance the completeness of the analysis through complementary relationships. For example, a car door lock function may simultaneously involve actuator failure for functional safety, user misoperation for expected functional safety, and remote hacking attacks for information security. Therefore, it is necessary to analyze in parallel under multiple categories.

[0057] At the application level, once the scope of security analysis is clearly defined, it provides targeted input for subsequent steps such as scenario trigger probability calculation and functional failure probability analysis. This process is achieved through a security management platform integrated into the vehicle development process, which automatically calls the corresponding analysis modules and data sources according to the scope.

[0058] External factors related to vehicle function operation scenarios are identified. These external factors are selected from human factors, environmental factors, other vehicle factors, and social factors. Based on the probability of occurrence of the identified external factors and the probability of vehicle functions being triggered under these external factors, the scenario trigger probability of vehicle functions is calculated.

[0059] Furthermore, external factors include:

[0060] Human factors include data on the driver's overtaking behavior or emergency braking behavior;

[0061] Environmental factors include rainfall, fog data, or rockfall data in the areas where the vehicles operate;

[0062] Other vehicle factors include data on other vehicles' overtaking behavior or data on the acceleration behavior of vehicles behind when one vehicle changes lanes;

[0063] Social factors include traffic laws and regulations in the area where the vehicle travels, including lane direction rules, lane speed limits, and priority rules for turning and going straight.

[0064] Furthermore, the calculated scenario trigger probabilities for vehicle functions include:

[0065] For each identified external factor, its probability of occurrence is multiplied by the probability of its corresponding vehicle function being triggered to obtain the trigger contribution value of that factor.

[0066] The scene trigger probability is obtained by summing and averaging the trigger contribution values ​​of all identified external factors.

[0067] Specifically, the identification of external factors is based on the analysis of the operating scenarios of the target vehicle's functions. Four types of factors are extracted through scenario modeling tools or data acquisition systems, including human factors, environmental factors, other vehicle factors, and social factors. The selection of these factors is based on the practice of intelligent connected vehicle safety analysis and aims to cover the multi-dimensional external conditions that affect function triggering.

[0068] Regarding human factors, driver overtaking behavior data can be collected through steering wheel angle sensors and accelerometers, while emergency braking behavior data can be obtained through brake pressure sensors. These data are preprocessed to form behavioral feature vectors, the probability of which is calculated based on historical driving data statistical models, such as analyzing driver behavior patterns under specific road conditions using machine learning algorithms. In terms of environmental factors, rainfall data in the vehicle's operating area is obtained through access to meteorological department application interfaces, fog data is obtained through visibility sensors combined with weather forecast data, and road rockfall data comes from the traffic management department's road disaster early warning system. The probability of these environmental factors is derived from geographic information systems and real-time monitoring data statistics. Regarding other vehicle factors, overtaking behavior data is obtained through vehicle-to-everything (V2X) communication to acquire the motion status of surrounding vehicles, and acceleration behavior data of following vehicles when the vehicle changes lanes is obtained through millimeter-wave radar monitoring relative speed changes. The probability of these occurrences is calculated based on traffic flow models and historical interaction data. Regarding social factors, traffic laws and regulations data are obtained from standardized traffic rule databases, including lane direction rules, lane speed limits, and priority rules for turning and straight-going traffic. The probability of these rules occurring is based on localized configuration and assessment of law enforcement.

[0069] Based on the identified external factors, the process of calculating the scene trigger probability adopts a probability fusion method; assuming that the identified external factors have been identified Class-related external factors, the probability of occurrence of each class of external factors is denoted as . ,in For factor type indexes, The value range is 1 to The conditional probability of the vehicle function being triggered under this factor is denoted as . First, calculate the trigger contribution value for each type of external factor, which is: and The product of the factors is then used; subsequently, the trigger contribution values ​​of all identified external factors are summed, and the sum is divided by the total number of factor types. That is, the scene trigger probability is obtained by arithmetic average. The calculation formula is as follows:

[0070]

[0071] This formula achieves a balanced consideration of the influence of multiple factors through arithmetic average, avoiding the dominance of a single factor in the evaluation result and reflecting the rationality of probability fusion; at the same time, the assumption of independence between factors ensures the simplicity and feasibility of the calculation model.

[0072] In terms of operation, this calculation process is implemented through a probability calculation module in the embedded system. The module receives input probabilities from various data sources and performs multiplication and averaging operations. For example, when evaluating the wiper function, the probability of occurrence of multiple rainfall intensity ranges corresponding to rainfall data in the environmental factors is considered. And the conditional probability of a driver turning on the windshield wipers under various rainfall intensities. The probability of triggering the wiper function in a given scenario is calculated using the above formula. Logically, this calculation method transforms discrete external influences into a unified probability index, providing standardized input for subsequent risk analysis.

[0073] At the application level, the calculated scenario trigger probability is used as an input parameter for hazard exposure analysis and is used together with the functional failure probability for risk assessment. This probability value is transmitted to the safety monitoring unit through the vehicle bus to provide a quantitative basis for functional safety decisions. For example, in the analysis of automatic emergency braking systems, the scenario trigger probability is used to assess the likelihood of the system being activated in complex traffic environments.

[0074] The vehicle function is decomposed into the components it depends on, and the functional failure probability of the vehicle function is calculated based on the probability of each component being used and the probability of each component failing.

[0075] Furthermore, the probability of each component being used is determined based on the vehicle's operating conditions; events that cause each component to malfunction include internal defects and malicious intrusions from the outside.

[0076] Furthermore, the calculated probability of vehicle function failure includes:

[0077] For each component on which the vehicle function depends, multiply its probability of use by its probability of failure to obtain the failure contribution value of that component.

[0078] The failure contribution values ​​of all components are summed and averaged to obtain the functional failure probability.

[0079] Specifically, the decomposition of vehicle functions is based on the system architecture design document. The target function is broken down into the set of components it depends on through a function-component mapping table. These components include, but are not limited to, controllers, sensors, and actuators, and their selection is based on the system design specifications of the vehicle's electronic and electrical architecture.

[0080] The probability of each component being used is determined based on the vehicle's operating conditions, including vehicle speed, load, ambient temperature, and operating mode. For example, in an automatic emergency braking system, the probability of the brake controller being used is dynamically adjusted with the vehicle speed, with a higher probability of use at high speeds than at low speeds. One possible implementation is to collect vehicle operating parameters in real time through a condition monitoring module and determine the probability of each component being used based on a preset condition-use probability mapping table.

[0081] Events that cause the malfunction of various components include internal defects and malicious intrusions from the outside. Internal defects include hardware aging, material fatigue, and manufacturing process deviations, and their probability data comes from the reliability test reports of component suppliers. External malicious intrusions refer to intentional attacks on the vehicle network, including communication protocol cracking, software vulnerability exploitation, and unauthorized access to hardware interfaces. Their probability data is based on statistics from network security logs and threat intelligence databases.

[0082] The process of calculating the probability of functional failure uses a probability-weighted method; assuming vehicle function dependency... There are 10 components, and the probability of each component being used is denoted as . ,in For component indexes, The value range is 1 to The failure probability of this component is denoted as . First, calculate the failure contribution value of each component, which is: and The product of the components; then, the failure contribution values ​​of all components are summed, and the sum is divided by the total number of components. That is, the probability of functional failure is obtained by arithmetic mean. The calculation formula is as follows:

[0083]

[0084] This formula achieves a balanced assessment of the failure risk of multiple components through arithmetic averaging, reflecting a comprehensive consideration of the overall reliability of the system;

[0085] In terms of operation, the calculation process is implemented through a reliability analysis module. The module receives input data from the operating condition monitoring module and the reliability database of each component, and performs probability multiplication and averaging operations. For example, when analyzing the door lock function, it is necessary to calculate the usage probability and failure probability of the hardware key controller, the remote software key, and the door lock actuator, and obtain the overall functional failure probability through the above formula. Logically, this calculation method aggregates component-level failure risks into system-level functional failure probabilities, establishing a quantitative bridge from component reliability to system security.

[0086] At the application level, the calculated functional failure probability serves as a key input parameter for hazard exposure analysis, and together with the scenario trigger probability, constitutes a complete risk assessment model. This probability value is output to the safety management system through the vehicle diagnostic interface, providing data support for preventive maintenance and safety strategy optimization.

[0087] The quantitative hazard exposure of the vehicle function is obtained by multiplying the probability of scenario triggering by the probability of function failure.

[0088] Specifically, the quantitative hazard exposure is calculated based on the scenario trigger probability and functional failure probability obtained in the aforementioned steps, and the coupling analysis of the two types of risk factors is achieved through probability multiplication. The mathematical basis of this calculation process is the joint probability theory in probability theory, which reflects the combined impact of external scenario risks and internal system reliability risks.

[0089] In the specific calculation process, let the scene trigger probability be... The probability of functional failure is Then the quantitative hazard exposure The calculation formula is:

[0090]

[0091] in, This indicates the probability that a vehicle function will be triggered under a specific operating scenario, and its value comes from the output of the scenario trigger probability analysis module. This represents the probability that a vehicle function will fail due to a defect in its component. Its value comes from the output of the function failure probability analysis module. This formula achieves a combined evaluation of two independent probability events through multiplication, which conforms to the calculation rules of the joint probability of independent events in probability theory.

[0092] In terms of operation, the calculation is implemented through a risk synthesis module, which receives probability data from the scenario analysis module and the failure analysis module, performs multiplication operations, and outputs a quantitative hazard exposure degree. One possible implementation is to use an arithmetic logic unit in an embedded system to perform the multiplication calculation, and the calculation result is transmitted to the decision system through a data bus. Logically, this calculation method establishes a complete risk assessment chain from scenario analysis to system reliability analysis, ensuring the comprehensiveness of risk measurement.

[0093] At the application level, the calculated quantitative hazard exposure rate serves as a key indicator for vehicle safety assessment, supporting safety decisions and risk management. For example, this value can be used for safety integrity level assessment, classifying risk levels by setting threshold ranges, and providing a basis for prioritizing safety measures. One application scenario is to input this value into a vehicle safety monitoring system, triggering an early warning mechanism when it exceeds a preset threshold, reminding the system to take corresponding safety mitigation measures.

[0094] Through the above implementation method, this embodiment fully discloses the technical features of quantitative hazard exposure calculation. Its technical rationality is reflected in the mathematical basis of the probability model and the collaborative working mechanism of each analysis module. This implementation method ensures the quantification and standardization of risk assessment results, and provides a reliable technical means for the development of vehicle functional safety.

[0095] In this embodiment, within the scope of information security analysis, the probability of functional failure is calculated by weighting different attack paths.

[0096] Specifically, given the unique nature of information security risks, the calculation results of the probability of functional failure are optimized by assigning corresponding weights to different attack paths, thereby more accurately reflecting the actual security threats.

[0097] First, potential information security attack paths are identified, based on the vehicle system's network architecture and security threat model. For example, for remote vehicle control functions, attack paths may include cloud server interface intrusion, hijacking of communication channels between mobile terminals and servers, exploitation of vulnerabilities in in-vehicle T-Boxes, and in-vehicle network penetration. The probability of each attack path being exploited is derived from statistical analysis of historical data from network security logs, vulnerability databases, and threat intelligence platforms.

[0098] When determining the weight of each attack path, an evaluation method based on threat level and attack cost is adopted; assuming k effective attack paths are identified, the weight of each attack path is denoted as . ,in This is an index of the attack path. The value range is from 1 to k; weight The determination of the attack path comprehensively considers the attack difficulty, required resources, and potential benefits. Its value is obtained through expert evaluation or analysis of historical attack data using machine learning models. The failure probability of each attack path is denoted as... This represents the conditional probability that the attacker will successfully achieve their attack objective through this path;

[0099] Based on the above parameters, a weighted calculation of the functional failure probability is performed; let the basic functional failure probability be... The weighted probability of functional failure is... The calculation formula is:

[0100]

[0101] Among them, weight Satisfy normalization conditions This ensures the probabilistic validity of the weighted calculation results; the formula highlights the impact of high-threat attack paths through weight allocation, making the risk assessment results more consistent with the actual threat distribution in the field of information security;

[0102] In terms of operation, the weighted calculation is implemented through the information security analysis module, which is integrated into the vehicle security monitoring system. The module receives path weight data and failure probability of each path from the threat intelligence database and performs a weighted summation operation. For example, when evaluating the door lock system, different weight values ​​are assigned to the three attack paths of hardware key cracking, remote signal forgery and actuator control cracking, and the weighted functional failure probability is calculated by the above formula.

[0103] Logically, this weighted calculation method overcomes the shortcomings of traditional average calculation, which may underestimate the risk of high-threat paths, by quantifying the relative importance of different attack paths, thus reflecting the heterogeneity of attack paths in information security risk assessment. At the application level, the weighted functional failure probability serves as a key input for calculating the hazard exposure in the field of information security, providing a quantitative basis for formulating targeted security protection strategies.

[0104] Figure 5 This is an architecture diagram of a hazard exposure calculation system based on multi-system three-safety fusion analysis provided by one or more embodiments of the present invention.

[0105] like Figure 5 The hazard exposure calculation system shown includes:

[0106] The definition module is used to identify the vehicle functions to be evaluated and to clarify the scope of safety analysis to which they belong. The scope of safety analysis is selected from functional safety, expected functional safety, and information security.

[0107] The scenario analysis module is used to identify external factors related to the vehicle's function operation scenarios. These external factors are selected from human factors, environmental factors, other vehicle factors, and social factors. Based on the probability of occurrence of the identified external factors and the probability of the vehicle function being triggered under these external factors, the scenario trigger probability of the vehicle function is calculated.

[0108] The failure analysis module is used to decompose vehicle functions into the components they depend on, and calculate the functional failure probability of the vehicle functions based on the usage probability and failure probability of each component.

[0109] The risk synthesis module is used to multiply the scenario trigger probability by the function failure probability to obtain the quantitative hazard exposure of the vehicle function.

[0110] It is worth noting that although this system / device only discloses the definition module, scenario analysis module, failure analysis module, and risk synthesis module, it does not mean that this device is limited to the above-mentioned basic functional modules. On the contrary, what this invention intends to express is that, based on the above-mentioned basic functional modules, those skilled in the art can arbitrarily add one or more functional modules in combination with existing technology to form an infinite number of embodiments or technical solutions. That is to say, this system / device is open rather than closed. The fact that this embodiment only discloses a few basic functional modules should not be taken as a reason to believe that the scope of protection of the claims of this invention is limited to the above-disclosed basic functional modules.

[0111] In one specific embodiment, the definition module receives vehicle function parameters configured by the user. Taking the windshield wiper system as an example, the safety analysis scope of this function is determined to be functional safety. The scene analysis module automatically selects relevant external factors based on the functional characteristics. In the windshield wiper function, it mainly identifies environmental factors, including rainfall data and its probability of occurrence. Based on meteorological data statistics, for example, the probability of hourly rainfall being less than 0.1 mm is 60%, corresponding to the probability of this function being triggered. The probability of rainfall of 0.1-1.5mm is 10%, corresponding to The probability of rainfall of 1.6-6.9 mm is 10%, corresponding to The probability of rainfall of 7-14.9 mm is 10%, corresponding to The probability of rainfall >15mm is 10%, corresponding to The module is based on the formula:

[0112]

[0113] The calculated scene trigger probability is C=18%, where m=1 indicates that only environmental factors are considered.

[0114] The failure analysis module decomposes the wiper function into components, identifying two key components: the wiper switch controller and the wiper rotation mechanism; the operating condition monitoring unit obtains the probability of component usage. In the windshield wiper function, the probability of use for both components is 1; the component failure probability is obtained based on a reliability database. The failure probability of the switch controller is 1%, and the failure probability of the rotating parts is 2%; according to the formula:

[0115]

[0116] The calculated probability of functional failure is P=1.5%, where n=2 represents the total number of components;

[0117] The risk synthesis module receives C=18% from the scenario analysis module and P=1.5% from the failure analysis module, according to the formula:

[0118]

[0119] The calculated quantitative hazard exposure level R is 0.27%.

[0120] In the expected functional safety analysis scenario, taking the automatic emergency braking system as an example, the definition module marks its safety analysis scope as expected functional safety; the scenario analysis module identifies four types of external factors, of which human factors have a 2% probability of occurrence, environmental factors have a 3% probability of occurrence, other vehicle factors have a 3% probability of occurrence, and social factors are not considered; since AEB is an automatic triggering system, the functional trigger probability corresponding to each factor is... All values ​​are set to 1; according to the calculation formula, the scene trigger probability C = 2%;

[0121] The failure analysis module decomposes the AEB function into a vehicle speed detection module, an AEB control module, and a torque output module. The torque output module includes two sub-components: electric braking and mechanical braking. Based on operating condition data, it is determined that the mechanical braking utilization probability is 100% when the vehicle speed is ≥60kph, and the utilization probabilities of electric braking and mechanical braking are each 50% when the vehicle speed is <60kph. Combining the failure probability data of each component, a weighted calculation yields a functional failure probability P=2.45%. The risk synthesis module ultimately calculates a quantitative hazard exposure R=0.049%.

[0122] In the information security analysis scenario, taking a car door lock system as an example, the definition module marks its security analysis scope as information security; the scenario analysis module identifies the probability of malicious attacks occurring in environmental factors as 1%, and the probability of function triggering as... The calculated scenario trigger probability C = 1%; the failure analysis module performs weighted calculations based on attack path weights, identifying three attack paths: remote software key cracking (80% weight, 2% failure probability), vehicle lock actuator cracking (20% weight, 1% failure probability), and hardware key cracking (0% weight, 0% failure probability); according to the weighting formula:

[0123]

[0124] The calculated probability of functional failure is P=0.6%; the risk synthesis module ultimately calculates the quantitative hazard exposure rate R=0.006%.

[0125] Each module exchanges data through a standardized data interface. The scenario analysis module and the failure analysis module support parallel computing, and the risk synthesis module uses a double-precision floating-point arithmetic unit to ensure calculation accuracy. When the system is running, the definition module first initializes the analysis parameters, the scenario analysis module and the failure analysis module execute the calculation tasks synchronously, and finally the risk synthesis module completes the data fusion.

[0126] This application provides an electronic device, including: a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus;

[0127] The memory stores a computer program, which, when executed by the processor, causes the processor to perform steps of a hazard exposure calculation method based on multi-system three-safety fusion analysis.

[0128] This application also provides a computer-readable storage medium storing a computer program executable by an electronic device, which, when run on the electronic device, causes the electronic device to perform steps of a hazard exposure calculation method based on multi-system three-safety fusion analysis.

[0129] This application also provides a vehicle, including:

[0130] Electronic equipment, used to implement a method for calculating hazard exposure based on multi-system three-safety fusion analysis;

[0131] The processor runs a program, and when the program runs, it executes the steps of a hazard exposure calculation method based on multi-system three-safety fusion analysis from the data output by the electronic device.

[0132] Storage medium for storing programs that, when running, execute steps of a hazard exposure calculation method based on multi-system three-safety fusion analysis on data output from electronic devices.

[0133] The communication bus mentioned in the aforementioned electronic devices can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This communication bus can be divided into address bus, data bus, control bus, etc.

[0134] The electronic device comprises a hardware layer, an operating system layer running on top of the hardware layer, and an application layer running on the operating system. The hardware layer includes hardware such as a central processing unit (CPU), a memory management unit (MMU), and memory. The operating system can be any one or more computer operating systems that control the electronic device through processes, such as Linux, Unix, Android, iOS, or Windows. Furthermore, in this embodiment of the invention, the electronic device can be a smartphone, tablet computer, or other handheld device, or a desktop computer, portable computer, or other electronic device; there is no particular limitation in this embodiment.

[0135] In this embodiment of the invention, the executing entity for electronic device control can be an electronic device itself, or a functional module within an electronic device capable of calling and executing a program. The electronic device can obtain the firmware corresponding to the storage medium. This firmware is provided by the supplier, and different storage media may have the same or different firmware; no limitation is made here. After obtaining the firmware corresponding to the storage medium, the electronic device can write this firmware into the storage medium; specifically, it burns the firmware corresponding to the storage medium into the storage medium. The process of burning the firmware into the storage medium can be implemented using existing technology, and will not be elaborated upon in this embodiment of the invention.

[0136] Electronic devices can also obtain reset commands corresponding to the storage media. The reset commands corresponding to the storage media are provided by the supplier. The reset commands corresponding to different storage media can be the same or different, and no restrictions are imposed here.

[0137] At this time, the storage medium of the electronic device is a storage medium on which the corresponding firmware has been written. The electronic device can respond to the reset command corresponding to the storage medium on which the corresponding firmware has been written, thereby resetting the storage medium on which the corresponding firmware has been written according to the reset command. The process of resetting the storage medium according to the reset command can be implemented by existing technology and will not be described in detail in this embodiment of the invention.

[0138] For ease of description, the above devices are described separately by function as various units and modules. Of course, in implementing this application, the functions of each unit and module can be implemented in one or more software and / or hardware.

[0139] It will be understood by those skilled in the art that, unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. It should also be understood that terms such as those defined in general dictionaries should be understood to have the meaning consistent with their meaning in the context of the prior art, and should not be interpreted in an idealized or overly formal sense unless specifically defined.

[0140] For the sake of simplicity, the method embodiments are described as a series of actions. However, those skilled in the art should understand that the embodiments of the present invention are not limited to the described order of actions, because according to the embodiments of the present invention, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions involved are not necessarily essential to the embodiments of the present invention.

[0141] As can be seen from the above description of the embodiments, those skilled in the art can clearly understand that this application can be implemented by means of software plus necessary general-purpose hardware platforms. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in various embodiments or some parts of the embodiments of this application.

[0142] 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 foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A hazard exposure degree calculation method based on multi-system three-an integration analysis, characterized in that, The hazard exposure degree calculation method based on the multi-system three-an integration analysis comprises: determining a vehicle function to be evaluated and specifying a safety analysis category to which the vehicle function belongs, the safety analysis category being selected from functional safety, intended function safety, and information safety; identifying external factors related to a vehicle function operation scenario, the external factors being selected from human factors, environmental factors, other vehicle factors, and social factors, and calculating a scenario triggering probability of the vehicle function based on occurrence probabilities of the identified external factors and probabilities of the vehicle function being triggered under the external factors; decomposing the vehicle function into dependent components, and calculating a function failure probability of the vehicle function based on usage probabilities of the components and failure probabilities of the components; multiplying the scenario triggering probability and the function failure probability to obtain a quantitative hazard exposure degree of the vehicle function.

2. The hazard exposure degree calculation method based on multi-system three-an integration analysis according to claim 1, characterized in that, The calculation of the scenario triggering probability of the vehicle function comprises: multiplying, for each type of identified external factor, an occurrence probability of the external factor and a triggering probability of the vehicle function corresponding to the external factor to obtain a triggering contribution value of the external factor; summing and averaging triggering contribution values of all identified external factors to obtain the scenario triggering probability. 3.The hazard exposure degree calculation method based on multi-system three-an integration analysis according to claim 1, wherein, The calculation of the function failure probability of the vehicle function comprises: multiplying, for each component on which the vehicle function depends, a usage probability of the component and a failure probability of the component to obtain a failure contribution value of the component; summing and averaging failure contribution values of all components to obtain the function failure probability. 4.The hazard exposure degree calculation method based on multi-system three-an integration analysis according to claim 1, wherein, The external factors comprise: the human factors include overtake behavior data or sudden braking behavior data of a driver; the environmental factors include rainfall data, heavy fog weather data, or road rockfall data of a vehicle operation area; the other vehicle factors include overtake behavior data of another vehicle or acceleration behavior data of a following vehicle when a subject vehicle changes lanes; the social factors include traffic laws and regulations of a vehicle driving area, the traffic laws and regulations including driving lane direction rules, lane speed limit rules, or turning and straight passing priority rules. 5.The hazard exposure degree calculation method based on multi-system three-an integration analysis according to claim 1, wherein, The usage probabilities of the components are determined based on vehicle operation conditions; events causing function failure of the components include internal defects of the components and malicious intrusion from outside. 6.The hazard exposure degree calculation method based on multi-system three-an integration analysis according to claim 1, wherein, In the information safety analysis category, the function failure probability is calculated by weighting based on weights of different attack paths.

7. A hazard exposure degree calculation system based on multi-system three-an integration analysis, characterized by, The hazard exposure degree calculation system based on the multi-system three-an integration analysis comprises: a definition module configured to determine a vehicle function to be evaluated and specify a safety analysis category to which the vehicle function belongs, the safety analysis category being selected from functional safety, intended function safety, and information safety; a scenario analysis module configured to identify external factors related to a vehicle function operation scenario, the external factors being selected from human factors, environmental factors, other vehicle factors, and social factors, and calculate a scenario triggering probability of the vehicle function based on occurrence probabilities of the identified external factors and probabilities of the vehicle function being triggered under the external factors; a failure analysis module configured to decompose the vehicle function into dependent components, and calculate a function failure probability of the vehicle function based on usage probabilities of the components and failure probabilities of the components; and a hazard exposure degree calculation module configured to multiply the scenario triggering probability and the function failure probability to obtain a quantitative hazard exposure degree of the vehicle function. A risk synthesis module is configured to multiply the scenario trigger probability and the functional failure probability to obtain a quantitative hazard exposure degree of the vehicle function.

8. An electronic device, comprising: The method comprises the following steps: The processor, the communication interface, and the memory are in communication with each other through the communication bus. The memory stores a computer program executable by the processor, and when the computer program is executed by the processor, the processor executes the steps of the hazard exposure degree calculation method based on multi-system three-an integration analysis according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The memory stores a computer program executable by the electronic device, and when the computer program runs on the electronic device, the electronic device executes the steps of the hazard exposure degree calculation method based on multi-system three-an integration analysis according to any one of claims 1 to 6.

10. A vehicle characterized by comprising: The method comprises the following steps: The electronic device is configured to execute the steps of the hazard exposure degree calculation method based on multi-system three-an integration analysis according to any one of claims 1 to 6. The processor runs the program, and when the program runs, the data output from the electronic device executes the steps of the hazard exposure degree calculation method based on multi-system three-an integration analysis according to any one of claims 1 to 6. The storage medium is configured to store the program, and when the program runs, the data output from the electronic device executes the steps of the hazard exposure degree calculation method based on multi-system three-an integration analysis according to any one of claims 1 to 6.

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