CN110084500B - Electric vehicle safety control method based on safety tree probability and safety importance degree and electric vehicle - Google Patents
Electric vehicle safety control method based on safety tree probability and safety importance degree and electric vehicle Download PDFInfo
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Abstract
The invention relates to an electric vehicle safety control method based on safety tree probability and safety importance, which comprises the following steps: constructing a safety tree; analyzing the parameter deviation of the intermediate layer event, and converting the original frequency data of the intermediate layer event into standardized intermediate event frequency data of each level; analyzing and counting the logic causal relationship and the intermediate event result to obtain the occurrence probability of each bottom layer event; acquiring the occurrence probability of each top-level event based on the safety tree and the middle-level event and the frequency data statistics of the middle-level events; obtaining the influence probability of each bottom layer event on the top layer event; sequencing the safety importance of each bottom layer event based on the influence probability of each bottom layer event on each top layer event; and performing safety control on the electric vehicle based on the safety importance of the safety tree. The method can calculate the probability and the safety importance of the safety tree, and the system can effectively analyze, quantitatively describe and accurately reflect the safety state of the electric vehicle.
Description
Technical Field
The present invention relates to transportation vehicles, and more particularly, to a safety control method for an electric vehicle and an electric vehicle based on safety tree probability and safety importance.
Background
With the rapid development of the world economy and the attention on environmental awareness, the popularization rate of automobiles is higher and higher, the requirement on automobile exhaust emission is higher and higher, and energy-saving, safe and pollution-free electric vehicles are the development trend in the future. However, electric vehicles generally have electrical systems up to hundreds of volts, which exceed the safe voltage range of dc, and if not properly designed and protected, high voltage safety problems such as electric shock may occur. Further, the electric vehicle includes a plurality of component parts such as a steering system, a brake system, a safety control system, and the like, each of which includes a plurality of component parts. Failure or malfunction of any component may result in the entire vehicle being out of control, or malfunctioning, thereby causing the driver or passengers to be at risk. However, at present, a whole electric vehicle safety management and control method which can combine effective theoretical analysis of a system and engineering experience is still lacked; and a method for quantitatively describing the safety state of the whole vehicle and accurately representing the safety state of each system of the electric vehicle is lacked.
Disclosure of Invention
The technical problem to be solved by the present invention is to provide a safety control method for an electric vehicle based on safety tree probability and safety importance, aiming at the above defects in the prior art, by constructing a safety tree of the electric vehicle, analyzing the safety tree probability and the safety importance, and effectively analyzing, quantitatively describing and accurately embodying the safety state of the electric vehicle by a system.
The technical scheme adopted by the invention for solving the technical problems is as follows: an electric vehicle safety control method based on safety tree probability calculation and safety importance is constructed, and the method comprises the following steps:
s1, the safety tree comprises a plurality of bottom layer events, middle layer events, top layer events, logic causal relationships among the bottom layer events, the middle layer events and the top layer events and safety importance degrees;
s2, analyzing the parameter deviation of the middle layer event through the collection and statistics of the middle layer event, and converting the original frequency data of the middle layer event into standardized frequency data of middle events at all levels;
s3, obtaining the occurrence probability of each bottom layer event through the logic causal relationship and the result analysis and statistics of the intermediate events;
s4, obtaining the occurrence probability of each top-level event based on the safety tree, the collection of the middle-level events and the statistics of the frequency data of the middle events;
s5, calculating to obtain the influence probability of each bottom layer event on the top layer event based on the probability of each bottom layer event on each middle event and the occurrence probability of each top layer event;
s6, sequencing the safety importance of each bottom layer event based on the influence probability of each bottom layer event on each top layer event;
and S7, carrying out safety control on the electric vehicle based on the safety importance of the safety tree.
In the electric vehicle safety control method based on the safety tree probability and the safety importance of the present invention, the step S2 includes:
s21, collecting fault data of a middle event of the electric vehicle, performing statistical decoupling, and analyzing existing parameter deviation according to dynamic change of operation parameters of the electric vehicle; taking the parameter deviation and the emergency failure alarm event in the fault data as the original frequency data of the middle layer event;
and S22, converting the original frequency data into standardized intermediate event frequency data of each level aiming at the working environment corresponding to the original frequency data of the intermediate events of each level.
In the electric vehicle safety control method based on the safety tree probability and the safety importance of the present invention, in step S3, normalized intermediate event frequency data of each level in the scene of field application, test, and inspection are counted, and the occurrence probability corresponding to each underlying event is calculated respectively.
In the electric vehicle safety control method based on the safety tree probability and the safety importance of the present invention, in step S4, the occurrence probability of the top event is calculated according to the statistics and distribution of the occurrence frequency of the intermediate events and the risk value of each intermediate event.
In the electric vehicle safety control method based on the safety tree probability and the safety importance according to the present invention, in step S5, the influence probability of each bottom-level event on the top-level event is calculated using a bayesian algorithm.
In the electric vehicle safety control method based on the safety tree probability and the safety importance degree, in the step S7, the contribution degree of each bottom layer event to the occurrence probability of each top layer event is calculated to evaluate the influence of each bottom layer event on each top layer event, so as to provide a quantitative basis for the design, production, process improvement and operation, maintenance of the electric vehicle.
In the electric vehicle safety control method based on the safety tree probability and the safety importance of the present invention, the step S1 further includes:
s11, collecting the whole vehicle safety fault data of the electric vehicle;
s12, mapping and classifying the whole vehicle safety fault data into different safety event groups, and respectively counting frequency data of each safety event group;
s13, classifying the whole vehicle safety fault data in each safety event group by adopting a joint analysis method to construct a safety tree;
in the electric vehicle safety control method based on the safety tree probability and the safety importance of the present invention, the step S13 further includes:
s131, dividing the whole vehicle safety fault data into at least a first fault category, a second fault category, a third fault category and a fourth fault category;
s132, analyzing the whole vehicle safety fault data of the first fault category, the second fault category, the third fault category and the fourth fault category by adopting different analysis methods to determine the hierarchical relationship among the whole vehicle safety fault data;
and S133, establishing fault causal relations layer by layer until all the whole vehicle safety fault data are traversed to complete the construction of the safety tree of the electric vehicle. Another technical solution adopted by the present invention to solve the technical problem is to construct a computer-readable storage medium on which a computer program is stored, the program, when executed by a processor, implementing the electric vehicle safety control method based on the safety tree probability and the safety importance.
Another technical solution to solve the technical problem of the present invention is to configure an electric vehicle, including a processor, and a computer program stored in the processor, wherein the program, when executed by the processor, implements the electric vehicle safety control method based on the safety tree probability and the safety importance.
By implementing the electric vehicle safety control method based on the safety tree probability and the safety importance, the computer readable storage medium and the electric vehicle, the safety tree probability and the safety importance can be analyzed by constructing the safety tree of the electric vehicle, and the system can effectively analyze, quantitatively describe and accurately embody the safety state of the electric vehicle.
Drawings
The invention will be further described with reference to the accompanying drawings and examples, in which:
FIG. 1 is a flow chart of a method for electric vehicle safety control based on safety tree probability and safety importance of the preferred embodiment of the present invention;
FIG. 2 is a schematic diagram of classification of vehicle safety fault data of an electric vehicle safety control method based on safety tree probability and safety importance for the electric vehicle in accordance with a preferred embodiment of the present invention;
3a-3c are schematic diagrams of a partial safety tree of an electric vehicle safety control method based on safety tree probability and safety importance in accordance with a preferred embodiment of the present invention;
fig. 4a-b are a probability list and safety importance results of the underlying events of the electrical safety events of the electric vehicle safety control method based on safety tree probabilities and safety importance of the electric vehicle according to the preferred embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The invention relates to an electric vehicle safety control method based on safety tree probability and safety importance, which comprises the following steps: s1, constructing a safety tree, wherein the safety tree comprises a plurality of bottom layer events, middle layer events, top layer events, logic causal relationships among the bottom layer events, the middle layer events and the top layer events and safety importance degrees; s2, analyzing the parameter deviation of the middle layer event through the collection and statistics of the middle layer event, and converting the original frequency data of the middle layer event into standardized frequency data of middle events at all levels; s3, obtaining the occurrence probability of each bottom layer event through the logic causal relationship and the result analysis and statistics of the intermediate events; s4, obtaining the occurrence probability of each top-level event based on the safety tree, the collection of the middle-level events and the statistics of the frequency data of the middle events; s5, calculating to obtain the influence probability of each bottom layer event on the top layer event based on the probability of each bottom layer event on each middle event and the occurrence probability of each top layer event; s6, sequencing the safety importance of each bottom layer event based on the influence probability of each bottom layer event on each top layer event; and S7, carrying out safety control on the electric vehicle based on the safety importance of the safety tree. The electric vehicle safety control method based on the safety tree probability and the safety importance can analyze the safety tree probability and the safety importance by constructing the safety tree of the electric vehicle, and effectively analyze, quantitatively describe and accurately reflect the safety state of the electric vehicle by a system.
In the invention, the safety tree of the electric vehicle is a system method for comprehensively solving the safety problem of the electric vehicle, a related logic system is established through a top layer event, a bottom layer event, related logic and data, a safety event model is established through the whole vehicle safety requirement analysis and the whole vehicle system to establish a tree diagram, the tree diagram is used for describing the logic relationship among different layers of events of the vehicle, and the tree diagram is used for carrying out graphic representation and qualitative description on a plurality of subsystems or parts such as a braking system, a steering system, vehicle body parts and the like. The safety tree is focused on the real occurrence of events, a barrier is set by tracking a penetration system, and the system is designed into a modularized open type system. In the invention, the safety importance of the safety tree is a main measurement for quantitatively analyzing and evaluating the influence importance degree of bottom events on top events, and reflects the weight of each bottom event on the safety influence of the whole vehicle. In the invention, the security importance of the security tree includes the probability of each bottom-level event, the differentiation of each intermediate event and the risk degree factor of each top-level event, and is a quantitative evaluation of the influence of each bottom-level event on each top-level event. The safety importance represents the safety weight of each underlying event of the electric vehicle. In the present invention, the bottom-level events can be understood as base faults, and the top-level events can be understood as surface faults. There is a direct causal relationship, or an indirect causal relationship, between the bottom-level event and the top-level event. Between the bottom layer events and the top layer events, there may be middle layer events. In the invention, the safety importance degree endows each bottom layer event with statistical characteristics, is quantitative description on the system safety, and is a tool for quantitatively analyzing the system safety of the electric vehicle. Fig. 1 is a flowchart of an electric vehicle safety control method based on safety tree probability and safety importance in accordance with a preferred embodiment of the present invention. As shown in fig. 1, in step S1, a security tree is constructed. The safety tree comprises a plurality of bottom layer events, middle layer events, top layer events, logic causal relations among the bottom layer events, the middle layer events and the top layer events and safety importance degrees. In the preferred embodiment of the present invention, the security tree can be constructed by any known method, and the existing security tree can also be used.
A method of constructing a security tree according to a preferred embodiment of the present invention is described below. Those skilled in the art will appreciate that in other preferred embodiments of the present invention, other methods may be used to construct the security tree. The invention is not limited to this particular method of construction.
In a preferred embodiment of the present invention, the step of constructing the security tree comprises: collecting the whole vehicle safety fault data of the electric vehicle; mapping and classifying the whole vehicle safety fault data into different safety event groups, and counting and calculating frequency data of each safety event group; and classifying the whole vehicle safety fault data in each safety event group by adopting a joint analysis method to construct a safety tree.
In a preferred embodiment of the present invention, the step of collecting vehicle safety failure data of the electric vehicle may further include transmitting data in a vehicle controller, a safety controller and a tachograph of the electric vehicle to a platform database through a CAN bus; and then acquiring the whole vehicle safety fault data of the electric vehicle from the data. For example, the whole vehicle safety fault data can be mapped and classified into a plurality of subsystems or components such as a braking system, a steering system, vehicle body parts and the like, so that the whole vehicle safety fault data is counted into different groups according to the mapping and classification principle, and the occurrence batches of each safety event group are counted.
As shown in fig. 2, in a preferred embodiment of the present invention, the vehicle safety fault data may be mapped to a structural safety event, an electrical safety event, a functional logic safety event, a collision safety event, a thermal safety event, an explosion-proof safety event, an operation and maintenance safety event, an environmental safety event, and a full life cycle safety event, respectively. And according to data classification, analysis and calculation, the probability of the basic level events of the system can be obtained to be 30% of a structure safety event, 10% of an electrical safety event, 20% of a functional logic safety event, 5% of a collision safety event, 5% of a thermal safety event, 8% of an explosion-proof safety event, 9% of an operation and maintenance safety event, 8% of an environment safety event and 5% of a full life cycle safety event. The above-described inductive analysis process may employ various methods known in the art, may also employ known methods to calculate the probability of each safety event group accounting for all safety faults, and may also employ individual measurements and collected empirical data from the electric vehicle manufacturer.
In a preferred embodiment of the present invention, the step of classifying the vehicle safety fault data in each safety event group by using a joint analysis method to construct a safety tree further includes: dividing the whole vehicle safety fault data into at least a first fault category, a second fault category, a third fault category and a fourth fault category; analyzing the whole vehicle safety fault data of the first fault category, the second fault category, the third fault category and the fourth fault category by adopting different analysis methods to determine the hierarchical relationship among the whole vehicle safety fault data; and establishing fault causal relationships layer by layer until all the whole vehicle safety fault data are traversed to complete the construction of the safety tree of the electric vehicle. The first fault category is faults with clear mechanisms or verifiable mechanisms, the second fault category is faults with unclear mechanisms but empirical verification bases, and the third fault category is faults with unclear mechanisms but operation data support; the fourth type of fault is a fault with clear mechanism but complex system structure. For example, the whole vehicle safety fault data of the first fault category is divided into a top layer event, a middle layer event and a bottom layer event according to the mechanism; analyzing the fault factor correlation of the whole vehicle safety fault data of the second fault category by adopting a Bayesian inference method, so that the whole vehicle safety fault data of the second fault category is divided into a top layer event, a middle layer event and a bottom layer event based on the analysis result; analyzing the fault factor correlation of the whole vehicle safety fault data of the third fault category by adopting a machine learning method, so as to divide the whole vehicle safety fault data of the third fault category into a top layer event, a middle layer event and a bottom layer event based on an analysis result; and analyzing the fault factor correlation of the whole vehicle safety fault data of the fourth fault category by adopting an interpretation structure method, so that the whole vehicle safety fault data of the fourth fault category is divided into a top layer event, a middle layer event and a bottom layer event based on the analysis result.
In a preferred embodiment of the present invention, the step of classifying the vehicle safety fault data in each safety event group by using a joint analysis method to construct a safety tree further includes: aiming at a top-level event and all corresponding bottom-level events thereof, establishing an 'IF … THEN …' rule to describe the causal relationship between the events layer by layer according to the multilayer causal relationship until all pairs of 'top-level event-bottom-level event' are traversed; generating a rule set expressing a logical relationship of the top layer event and the bottom layer event based on the top layer event, the bottom layer event and a causal relationship and an experienced middle layer event between the bottom layer event and the top layer event; constructing the security tree based on the rule set, the top-level event, the bottom-level event, and the middle-level event, and the security tree module; the rule set is validated to remove logical relationship errors or event errors.
Fig. 3a-3c are schematic diagrams of a portion of a security tree of a preferred embodiment of the present invention. As shown in fig. 3a-3c, three intermediate events, namely a braking safety event, a driving safety event, and a steering safety event, can be subdivided below the structural safety event, and a safety tree can be constructed for each event. We will next describe a brake safety event as an example. Referring to fig. 3b, the braking safety event is taken as a top level event, and we find that there is actually a causal relationship between the plurality of intermediate safety events and the plurality of bottom level safety events. For the first category, the mechanism is clear or the mechanism can verify fault events, such as brake valve damage X14, pipeline joint damage X16, hydraulic controller abnormity X21, insufficient hydraulic oil X24 and hydraulic motor abnormity X22, the causal relationship of the brake valve damage X14, pipeline joint damage X16, hydraulic controller abnormity X21, insufficient hydraulic oil X24 and hydraulic motor abnormity X22 can be directly obtained, at this time, the causal relationship among the events can be directly determined according to the mechanism, and the cause and effect relationship among the events is described by adopting an IF … THEN … rule, namely IF the brake valve damage X14, the pipeline joint damage X16, the hydraulic controller abnormity X21, the insufficient hydraulic oil X24 and the hydraulic motor abnormity X22, a brake safety event occurs.
For the second type of faults with unclear mechanism but empirical verification basis, the fault factor correlation of the whole vehicle safety fault data of the second fault type is analyzed by adopting a Bayesian inference method, so that the whole vehicle safety fault data of the second fault type is divided into a top layer event, a middle layer event and a bottom layer event based on the analysis result. As shown in fig. 3c, taking the braking safety event as a top-level event, we can find out, through a bayesian algorithm, that the steering safety event is taken as a first middle-level event, and is causally associated with a steering operating mechanism fault, a steering engine fault, and a steering actuator fault of a second middle-level event. And the faults of the steering operation mechanism are respectively directly related to abnormal fastening of a steering wheel, damage of a bearing of a steering pipe, abrasion of a spline of the steering pipe column, fastening of a screw and a thread and insufficient lubrication of the spline in a plurality of bottom events. The diverter failures are directly causally linked to multiple bottom events diverter lube shortage X6, diverter spline damage X7, diverter gear wear damage X8, diverter fastening screw loosening X9, diverter flooding X10, respectively. The steering actuator failures are directly causally linked to a number of floor event knuckle arm failures X11, steering bulb failures X12, steering knuckle deformation/break X13, steering stabilizer bar break X14, and steering interference X15, respectively.
For the third category, for faults with unclear mechanisms but operation data support, a machine learning method may be adopted to analyze the fault factor correlation of the vehicle safety fault data of the third fault category, so as to divide the vehicle safety fault data of the third fault category into a top layer event, a middle layer event and a bottom layer event based on the analysis result. As shown in fig. 3b, with the brake safety event as the top level event, we can find by a similar state comparison that the parking brake fault can actually be the first level intermediate event, which is causally related to the second level intermediate event brake pressure anomaly as the service brake fault is the first level intermediate event. The brake pressure anomaly has a causal relationship with a plurality of bottom events, namely damage X6 of a brake oil seal, oil leakage X5 of a brake and deformation X8 of a brake base plate. Meanwhile, the parking brake fault also has a causal relationship with a plurality of bottom layer event handle damage X8, friction sheet abrasion X1, brake cylinder clamping stagnation X2, brake spring damage X3 and transmission shaft damage X12 directly.
For the fourth category, the mechanism is clear but the system structure is complex; and analyzing the fault factor correlation of the whole vehicle safety fault data of the fourth fault category by adopting an interpretation structure method, so that the whole vehicle safety fault data of the fourth fault category is divided into a top layer event, a middle layer event and a bottom layer event based on the analysis result. As shown in fig. 3b, taking the braking safety event as the top-level event, we can find, by explaining the structural method, that the service braking fault can actually be the first-level intermediate event, and it has a causal relationship directly with the friction sheet wear X1, the brake cylinder clamping stagnation X2, the brake spring damage X3, and the bracket bearing damage X4 of the multiple bottom-level events, and also has a causal relationship with the second-level intermediate event braking pressure abnormality. And the abnormal brake pressure has a causal relationship with the damage X6 of the brake oil seal of the bottom event and the oil leakage X5 of the brake.
Therefore, one skilled in the art can construct the entire safety tree of the electric vehicle according to the above teachings, and/or a part of the safety tree in the preferred embodiment of the present invention, after constructing the safety tree, the rule set is verified to remove the logical relationship error or the event error. And searching for errors of event logic relations in the rule set of 'IF … THEN …' describing the safety tree, wherein the common event relation errors are found.
The safety tree is a comprehensive, open and full-period safety system based on data driving, probability calculation and safety importance analysis, is a system model for evaluating the safety state of a vehicle, and is a powerful tool for quantitatively analyzing the safety of a system. The safety tree system can be designed aiming at different safety fault classifications, the limitation of safety analysis aiming at each system component independently is broken through, and the safety condition of the electric vehicle can be better reflected. The safety tree is established for safety domain fault data, and the correlation among safety fault data of each hierarchy is determined by the statistical characteristics and data of fault events besides logic deduction. The safety tree model is focused on the real fault event, tracks and penetrates through a system to set barriers according to a design thought or system development, and is designed into a modular open system. The safety tree can be updated in real time based on new fault data, a virtuous circle is formed, and continuous optimization is carried out. The application of the safety tree is oriented to the actual design, production, operation and maintenance process, and the requirements of engineering practice are better met.
In step S2, the parameter deviation of the intermediate layer event is analyzed by collecting and counting the intermediate layer event, and the raw frequency data of the intermediate layer event is converted into normalized intermediate event frequency data of each stage. In a preferred embodiment of the present invention, intermediate event fault data of the electric vehicle may be collected for statistical decoupling, and possible parameter deviations are analyzed for dynamic changes in operating parameters. Parameter deviation and sudden failure alarm are carried out to form original data of intermediate events of each stage, and finally frequency data are converted; and converting the original frequency data into standardized intermediate event frequency data of each stage aiming at the working environment corresponding to the original frequency data of the intermediate events of each stage. One skilled in the art will appreciate that any method known in the art may be used to count the frequency of occurrence of each intermediate event and perform the normalization correction.
In step S3, the normalized intermediate event frequency data at each level in the scene of field application, test, and inspection are counted, and the occurrence probability corresponding to each underlying event is calculated.
In step S4, in step S4, calculating the occurrence probability of the top event according to the occurrence frequency statistics and distribution of the intermediate events and the risk value of each intermediate event;
in step S5, based on the probability of each bottom-layer event to each intermediate event and the occurrence probability of each top-layer event, the influence probability of each bottom-layer event to the top-layer event can be obtained through bayesian calculation; those skilled in the art will appreciate that in addition to the following calculation methods, those skilled in the art may also use other calculation formulas to perform the calculation according to actual situations. The invention is not limited herein by a particular calculation method.
In a preferred embodiment of the present invention, the importance of the bottom layer event is equal to the deviation of the occurrence probability of the top layer event from the normalized and corrected occurrence probability of the bottom layer event. In a further preferred embodiment of the invention, the security importance of the underlying event may be calculated based on the following formula:
wherein, IG(i) Is the underlying event XiThe security importance of; q. q.siIs the normalized corrected occurrence probability of the underlying event; g is the probability of occurrence of the top-level event, which is with respect to q1,q2,...qi,...,qNThe set of cutsets.
In a further preferred embodiment of the present invention, a structure function and a minimum cut set may be constructed based on the occurrence probability of the normalized and corrected bottom layer event, and the structural security importance of the bottom layer event may be calculated according to a security tree security importance formula. For example, assume there are i underlying events, each baseThe probability of occurrence of a layer event is XiBuilding a structure function
A minimal cut set is then created as { X }1}，{X2}，{X3},……,{Xi}. Safety importance formula based on safety tree
Formula capable of calculating safety importance of safety tree structure
In step S7, the contribution of each bottom layer event to the occurrence probability of each top layer event is calculated to evaluate the influence of each bottom layer event on each top layer event, so as to provide a quantitative basis for the design, production, process improvement, operation and maintenance of the electric vehicle. Fig. 4a-b are a probability list and safety importance results of the underlying events of the electrical safety events of the electric vehicle safety control method based on safety tree probabilities and safety importance of the electric vehicle according to the preferred embodiment of the present invention. For example, as shown in fig. 4a-4b, the calculated safety importance of X2 power limit anomaly, X29 current safety fault, and X33 insulation resistance fault are 0.1122, which indicates the greatest impact on the top electrical fault. Then the person skilled in the art, after obtaining such parameters, may perform safety control of the electric vehicle by, for example, using high quality fittings, high frequency service, or other control safety control methods known in the art. For another example, after an electrical fault occurs, a person skilled in the art can first check the underlying events in these several aspects based on the above-mentioned safety importance, so that maintenance time can be saved as much as possible.
Accordingly, the present invention can be realized in hardware, software, or a combination of hardware and software. The present invention can be realized in a centralized fashion in at least one computer system, or in a distributed fashion where different elements are spread across several interconnected computer systems. Any kind of computer system or other apparatus adapted for carrying out the methods of the present invention is suited. A typical combination of hardware and software could be a general purpose computer system with a computer program that, when being loaded and executed, controls the computer system such that it carries out the methods described herein.
The present invention may also be implemented by a computer program product, comprising all the features enabling the implementation of the methods of the invention, when loaded in a computer system. The computer program in this document refers to: any expression, in any programming language, code or notation, of a set of instructions intended to cause a system having an information processing capability to perform a particular function either directly or after either or both of the following: a) conversion to other languages, codes or symbols; b) reproduced in a different format.
The invention therefore also relates to a computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the method for building a safety tree of an electric vehicle.
The invention also relates to an electric vehicle comprising a processor, a computer program stored in said processor, said program, when executed by the processor, implementing said electric vehicle's safety tree construction method.
By implementing the electric vehicle safety control method based on the safety tree probability calculation and the safety importance, the computer readable storage medium and the electric vehicle, the safety tree probability and the safety importance can be analyzed by constructing the safety tree of the electric vehicle, and the system can effectively analyze, quantitatively describe and accurately embody the safety state of the electric vehicle.
While the invention has been described with reference to specific embodiments, it will be understood by those skilled in the art that various changes may be made and equivalents may be substituted without departing from the scope of the invention. In addition, many modifications may be made to adapt a particular situation or material to the teachings of the invention without departing from its scope. Therefore, it is intended that the invention not be limited to the particular embodiment disclosed, but that the invention will include all embodiments falling within the scope of the appended claims.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.
Claims (7)
1. An electric vehicle safety control method based on safety tree probability and safety importance is characterized by comprising the following steps:
s1, constructing a safety tree, wherein the safety tree comprises a plurality of bottom layer events, middle layer events, top layer events, logic causal relationships among the bottom layer events, the middle layer events and the top layer events and safety importance degrees;
s2, analyzing the parameter deviation of the middle layer event through the collection and statistics of the middle layer event, and converting the original frequency data of the middle layer event into standardized frequency data of middle events at all levels;
s3, obtaining the occurrence probability of each bottom layer event through the logic causal relationship and the result analysis and statistics of the intermediate events;
s4, obtaining the occurrence probability of each top-level event based on the safety tree, the collection of the middle-level events and the statistics of the frequency data of the middle events;
s5, calculating to obtain the influence probability of each bottom layer event on the top layer event based on the probability of each bottom layer event on each middle event and the occurrence probability of each top layer event;
s6, sequencing the safety importance of each bottom layer event based on the influence probability of each bottom layer event on each top layer event;
s7, carrying out safety control on the electric vehicle based on the safety importance of the safety tree; the step S2 includes:
s21, collecting fault data of a middle event of the electric vehicle, performing statistical decoupling, and analyzing existing parameter deviation according to dynamic change of operation parameters of the electric vehicle; taking the parameter deviation and the emergency failure alarm event in the fault data as the original frequency data of the middle layer event;
s22, converting the original frequency data into standardized intermediate event frequency data of each level aiming at the working environment corresponding to the original frequency data of the intermediate events of each level; in step S3, counting the normalized intermediate event frequency data of each level in the scene of field application, test, and inspection, and calculating the occurrence probability corresponding to each bottom-level event respectively; in step S4, the occurrence probability of the top-level event is calculated according to the statistics and distribution of the occurrence frequency of the intermediate events and the risk value of each intermediate event.
2. The safety tree probability and safety importance based electric vehicle safety control method of claim 1, wherein in the step S5, a bayesian algorithm is used to calculate the influence probability of each bottom-level event on the top-level event.
3. The safety tree probability and safety importance based electric vehicle safety control method of claim 1, wherein in step S7, the contribution of each bottom event to the occurrence probability of each top event is calculated to evaluate the influence of each bottom event on each top event, so as to provide quantitative basis for design, production, process improvement and operation and maintenance of the electric vehicle.
4. The electric vehicle safety control method based on the safety tree probability and the safety importance degree according to claim 1, wherein the step S1 further comprises:
s11, collecting the whole vehicle safety fault data of the electric vehicle;
s12, mapping and classifying the safety fault data of the whole vehicle into different safety event groups, and respectively counting frequency data of each safety event group;
and S13, classifying the whole vehicle safety fault data in each safety event group by adopting a joint analysis method to construct a safety tree.
5. The electric vehicle safety control method based on the safety tree probability and the safety importance degree according to claim 4, wherein the step S13 further comprises:
s131, dividing the whole vehicle safety fault data into at least a first fault category, a second fault category, a third fault category and a fourth fault category;
s132, analyzing the whole vehicle safety fault data of the first fault category, the second fault category, the third fault category and the fourth fault category by adopting different analysis methods to determine the hierarchical relationship among the whole vehicle safety fault data;
and S133, establishing fault causal relations layer by layer until all the whole vehicle safety fault data are traversed to complete the construction of the safety tree of the electric vehicle.
6. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, implements a safety tree probability and safety importance based electric vehicle safety control method according to any one of claims 1 to 5.
7. An electric vehicle comprising a processor, a computer program stored in the processor, the program when executed by the processor implementing the safety tree probability and safety importance based electric vehicle safety control method of any of claims 1-5.
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