CN110097219B - Electric vehicle operation and maintenance optimization method based on safety tree model - Google Patents
Electric vehicle operation and maintenance optimization method based on safety tree model Download PDFInfo
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Abstract
The invention relates to an electric vehicle operation and maintenance optimization method based on a safety tree model, which comprises the following steps: s1, constructing a safety tree; s2, sequencing the safety importance of each bottom layer event based on the safety tree; s3, judging whether the safety state of the electric vehicle reaches a threshold value, if so, executing a step S4, otherwise, continuing to evaluate; and S4, checking the branches with high safety importance in the safety tree, eliminating faults, and returning to the step S3. The electric vehicle operation and maintenance optimization method based on the safety tree model can evaluate the safety of the electric vehicle with constantly changing safety performance in real time, accurately and digitally in the actual operation process, so that safety fault states are integrated according to the safety tree module, the safety state of the electric vehicle is described in a fixed time and quantity mode, the guidance of electric vehicle manufacturing, maintenance, operation and maintenance is realized, and the safety of the electric vehicle is improved.
Description
Technical Field
The invention relates to a transportation tool, in particular to an electric vehicle operation and maintenance optimization method based on a safety tree model.
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 safety management and control method for an electric vehicle, which can combine effective theoretical analysis of a system with engineering experience, is still lacked; and lack of a method for quantitatively describing the safety state of an electric vehicle, accurately embodying the safety characteristics of each system.
Disclosure of Invention
The technical problem to be solved by the present invention is to provide an electric vehicle operation and maintenance optimization method based on a safety tree model, aiming at the above-mentioned defects in the prior art.
The technical scheme adopted by the invention for solving the technical problems is as follows: an electric vehicle operation and maintenance optimization method based on a safety tree model is constructed, and 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 relations among the bottom layer events, the middle layer events and the top layer events and safety importance degrees;
s2, sequencing the safety importance of each bottom layer event based on the safety tree;
s3, judging whether the safety state of the electric vehicle reaches a threshold value, if so, executing a step S4, otherwise, continuing to evaluate;
and S4, checking the branches with high safety importance in the safety tree, eliminating faults, and returning to the step S3.
In the method for optimizing the operation and maintenance of the electric vehicle based on the safety tree model of the present invention, the step S4 further includes:
s41, selecting unmarked branches with high safety importance in the safety tree for marking according to the safety importance sequence of each bottom layer event, so as to obtain an event meridian from the bottom layer event to the top layer event of the branches with high safety importance;
s42, analyzing the event veins to obtain bottom layer event parameters of the event veins;
s43, troubleshooting is carried out on the bottom layer event based on the bottom layer event parameter, the fault is eliminated, and then the step S3 is returned.
In the method for optimizing the operation and maintenance of the electric vehicle based on the safety tree model of the present invention, the step S42 further includes:
s421, analyzing the event collaterals according to the change rule of the importance degree of the bottom layer event, the middle layer event or the top layer event on the event collaterals;
s422, for the event with abnormal change according to the importance degree in the bottom layer event, the middle layer event or the top layer event on the event vein, finding out the actually occurring bottom layer event according to the upstream and downstream of the event vein, determining the causal relationship of the bottom layer event and obtaining the bottom layer event parameter of the bottom layer event.
In the method for optimizing the operation and maintenance of the electric vehicle based on the safety tree model, the fault elimination comprises repairing, replacing parts, and redesigning functions and/or structures.
In the method for optimizing the operation and maintenance of the electric vehicle based on the safety tree model of the present invention, the step S1 further includes:
s11, collecting safety fault data of the electric vehicle;
s12, mapping and classifying the safety fault data of the electric vehicle into different safety event groups, and respectively counting frequency data of each safety event group;
s13, classifying the electric vehicle safety fault data in each safety event group by adopting a joint analysis method to construct a safety tree.
In the method for optimizing the operation and maintenance of the electric vehicle based on the safety tree model of the present invention, the step S13 further includes:
s131, dividing the safety fault data of the electric vehicle into at least a first fault category, a second fault category, a third fault category and a fourth fault category;
s132, analyzing the electric 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 a hierarchical relationship among the electric vehicle safety fault data so as to determine a bottom layer event, a middle layer event and a top layer event and a logic causal relationship and a safety importance degree among the bottom layer event, the middle layer event and the top layer event;
and S133, establishing fault cause-and-effect relations layer by layer until all the electric vehicle safety fault data are traversed to complete the construction of the safety tree of the electric vehicle.
In the method for optimizing the operation and maintenance of the electric vehicle based on the safety tree model of the present invention, the step S2 further includes
S21, analyzing parameter deviation of the middle layer event through collection and statistics of the middle layer event, and converting original frequency data of the middle layer event into standardized middle event frequency data of each level;
s22, obtaining the occurrence probability of each bottom layer event through the logic causal relationship and the result analysis and statistics of the intermediate events;
s23, 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;
s24, calculating 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;
and S25, sequencing the safety importance of each bottom layer event based on the influence probability of each bottom layer event on each top layer event.
In the method for optimizing the operation and maintenance of the electric vehicle based on the safety tree model, the step S21 includes:
s211, 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;
s212, 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; and/or
The step S22 includes: and counting the standardized frequency data of the intermediate events at all levels in the scene of field application, test and inspection, and respectively calculating the occurrence probability of each corresponding bottom event.
In the method for optimizing the operation and maintenance of the electric vehicle based on the safety tree model, in step S23, the occurrence probability of the top-level event is calculated according to the occurrence frequency statistics and distribution of the intermediate events and the risk values of the intermediate events; and/or
In step S24, a bayesian algorithm is used to calculate the influence probability of each bottom-layer event on the top-layer event.
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, wherein the computer program, when executed by a processor, implements the method for optimizing the operation and maintenance of an electric vehicle based on a safety tree model.
By implementing the electric vehicle operation and maintenance optimization method based on the safety tree model and the computer readable storage medium, the safety of the electric vehicle with constantly changing safety performance can be evaluated in real time, accurately and digitally in the actual operation process, so that each safety fault state is synthesized according to the safety tree model, the safety state of the electric vehicle is described in a fixed time and a fixed quantity, the guidance of the electric vehicle manufacturing, maintenance, operation and maintenance is realized, and the safety of the electric vehicle is improved.
Drawings
The invention will be further described with reference to the accompanying drawings and examples, in which:
FIG. 1 is a schematic flow chart of a safety tree model-based electric vehicle operation and maintenance optimization method according to a first preferred embodiment of the present invention;
FIG. 2 is a schematic diagram of the classification of electric vehicle safety fault data of the electric vehicle operation and maintenance optimization method based on the safety tree model according to the preferred embodiment of the invention;
fig. 3a-3c are schematic diagrams of a part of a safety tree of the electric vehicle operation and maintenance optimization method based on the safety tree model according to the preferred embodiment of the 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 operation and maintenance optimization method based on a safety tree model, 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 relations among the bottom layer events, the middle layer events and the top layer events and safety importance degrees; s2, sequencing the safety importance of each bottom layer event based on the safety tree; s3, judging whether the safety state of the electric vehicle reaches a threshold value, if so, executing a step S4, otherwise, continuing to evaluate; and S4, checking the branches with high safety importance in the safety tree, eliminating faults, and returning to the step S3. By implementing the electric vehicle operation and maintenance optimization method based on the safety tree model and the computer readable storage medium, the safety of the electric vehicle with constantly changing safety performance can be evaluated in real time, accurately and digitally in the actual operation process, so that each safety fault state is synthesized according to the safety tree model, the safety state of the electric vehicle is described in a fixed time and a fixed quantity, the guidance of the electric vehicle manufacturing, maintenance, operation and maintenance is realized, and the safety of the electric vehicle is improved.
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 electric vehicle safety requirement analysis and the electric 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 graphic representation and qualitative description are carried out 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 tree safety importance is a main measure 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 electric 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.
In the actual operation process of the electric vehicle, the safety performance of the electric vehicle is continuously changed along with time. When the electric vehicle runs for a certain time or experiences a certain condition, the parts or subsystems of the electric vehicle may have a significant impact on the safety performance of the electric vehicle due to aging loss of the parts and the like. This information, if not timely obtained, can cause significant losses for electric vehicles, and therefore real-time, accurate, digital assessment of the safety of electric vehicles is essential. The safety state of the electric vehicle refers to the overall safety tree model of the electric vehicle, integrates all safety fault states, and calculates important parameters of the electric vehicle, which have indicating significance and are uniformly embodied on the safety of the electric vehicle, based on the real-time quantitative description of the safety tree model on the safety condition of the electric vehicle.
On the basis of automobile safety state evaluation, safety maintenance can be carried out on the vehicle according to the real-time evaluation value of the safety state of the electric vehicle. And when the safety state evaluation value of the electric vehicle reaches a dangerous threshold value, the vehicle is timely repaired and maintained, and dangerous parts are updated and replaced. Before the electric vehicle safety system is in a bad state, the safety state of the electric vehicle can be monitored and evaluated in real time based on the safety tree, so that whether maintenance is needed or not can be determined. Through the safety tree model, events with high structural importance and key importance of the basic fault of the safety tree of the electric vehicle can be checked first, and branches with high importance in the associated branches are labeled, so that an event meridian from the top event of the safety fault to the bottom event of the basic fault is obtained. And further analyzing the event context line to obtain fault information such as safety fault, basic fault parameters and the like corresponding to the fault context line. Aiming at the actual problem of the safety fault, the safety fault can be eliminated and checked, the fault range is reduced, meanwhile, the corresponding part is maintained or replaced, and the rationality of the maintenance scheme is evaluated. After the maintenance is finished, the safety state of the automobile needs to be evaluated again, when the service life of the automobile under the new safety state estimation value cannot meet the actual target requirement, the automobile needs to be maintained again, and dangerous parts are continuously searched until the predicted maintenance target is finished. In addition, according to the change rule of the importance degree of the fault of each level on the event venation line, the important core basic fault problem can be analyzed by judging the fault node with the jumping importance degree. And for the safety fault with abnormal importance evaluation, further analyzing the upstream and downstream of the safety fault according to the safety tree pulse network structure of the safety fault to find out the actual fault occurrence node and determine the causal relationship. According to the logical relationship of the fault module node, the fault or damage of the part can be repaired or replaced; if a failure occurs because there is an unreasonable part in the design of its structure or function, it is necessary to redesign the part.
Fig. 1 is a flow chart of an electric vehicle operation and maintenance optimization method based on a safety tree model according to a first preferred embodiment of the 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 electric vehicle safety fault data of an electric vehicle; mapping and classifying the safety fault data of the electric vehicle into different safety event groups, and counting and calculating frequency data of each safety event group; and classifying the electric 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 electric vehicle safety failure data of an electric vehicle may further include transmitting data in an electric vehicle controller, a safety controller and a tachograph of the electric vehicle to a platform database through a CAN bus; and then obtaining electric vehicle safety fault data of the electric vehicle from the data. For example, the electric vehicle safety fault data can be mapped and classified into a plurality of subsystems or components such as a braking system, a steering system, a vehicle body part and the like, so that the electric vehicle safety fault data are counted into different groups according to the mapping 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 electric 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 electric vehicle safety fault data in each safety event group by using a joint analysis method to construct a safety tree further includes: classifying the electric 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 electric 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 a hierarchical relationship among the electric vehicle safety fault data so as to determine a bottom layer event, a middle layer event and a top layer event and a logic causal relationship and a safety importance degree among the bottom layer event, the middle layer event and the top layer event; and establishing fault causal relations layer by layer until all the electric 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 electric vehicle safety fault data of a first fault category is divided into a top layer event, a middle layer event and a bottom layer event according to a mechanism; analyzing the fault factor correlation of the electric vehicle safety fault data of the second fault category by adopting a Bayesian inference method, so as to divide the electric vehicle safety fault data of the second fault category into a top layer event, a middle layer event and a bottom layer event based on an analysis result; analyzing the fault factor correlation of the electric vehicle safety fault data of the third fault category by adopting a machine learning method, so as to divide the electric 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 electric vehicle safety fault data of the fourth fault category by adopting an interpretation structure method, so that the electric vehicle safety fault data of the fourth fault category are 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 electric 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, analyzing the fault factor correlation of the electric vehicle safety fault data of the second fault type by adopting a Bayesian inference method, and dividing the electric vehicle safety fault data of the second fault type into a top layer event, a middle layer event and a bottom layer event based on an 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 supported by operation data, a machine learning method may be adopted to analyze the fault factor correlation of the electric vehicle safety fault data of the third fault category, so as to divide the electric 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 electric vehicle safety fault data of the fourth fault category by adopting an interpretation structure method, so that the electric vehicle safety fault data of the fourth fault category are 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 individual underlying events are sorted for security importance based on the security tree. In a preferred embodiment of the present invention, the step S2 may further include S21. analyzing the existing 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 normalized middle event frequency data of each stage; s22, obtaining the occurrence probability of each bottom layer event through the logic causal relationship and the result analysis and statistics of the intermediate events; s23, 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; s24, calculating 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; and S25, sequencing the safety importance of each bottom layer event based on the influence probability of each bottom layer event on each top layer event.
Preferably, in step S21, the acquired and counted intermediate layer events are used to analyze the parameter deviation of the intermediate layer events, and the raw frequency data of the intermediate layer events are 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. Preferably, in the step S22, 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 respectively. Preferably, in step S22, calculating the occurrence probability of the top-level event according to the statistics and distribution of the occurrence frequency of the intermediate events and the risk value of each intermediate event; preferably, in the step S24, 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 may 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 can be constructed based on the normalized and corrected occurrence probability of the underlying event, and a minimum structure function can be constructedAnd (4) a cut set is collected, and the structural safety importance of the bottom layer event is calculated according to a safety tree safety importance formula. For example, assume that there are i underlying events, each with an occurrence probability of XiBuilding a structure function
A minimal cut set is then created as { X }1}，{X2}，{X3},……,{Xi}. Safety importance formula based on safety tree
The security importance of the security tree structure can be calculated
In step S3, it is determined whether the safety state of the electric vehicle has reached a threshold value. The threshold value may be set, for example, according to actual operating parameters of the electric vehicle, such as a maximum operating speed, a maximum power consumption per hour, and the like. The threshold value can be set by a person skilled in the art on the basis of actual manufacturing, maintenance experience. The threshold value may be one or more values, but is of course preferably a plurality of values, in particular a plurality of values which are characteristic for an adverse state of the electric vehicle. As previously mentioned, electric vehicle safety performance is constantly changing over time. When the running time of the electric vehicle reaches a certain value or experiences a certain condition, parts or subsystems of the electric vehicle may have a great influence on the safety performance of the electric vehicle due to aging loss of the parts and the like, and if the parts or subsystems are not processed at the moment, serious faults or even accidents may occur, so that huge losses are caused. Therefore, when the safety state of the electric vehicle must reach the threshold value, step S4 needs to be executed to perform the subsequent processing. If the safety state of the electric vehicle is not met, the safety state of the electric vehicle is proved to be acceptable, and then the real-time monitoring is continuously carried out without carrying out subsequent processing.
In step S4, the branch with high security importance in the security tree is checked and the failure is eliminated. After troubleshooting, the process may return to step S3 to continue monitoring. If the monitoring finds whether the safety state of the electric vehicle has reached a threshold value, the fault is proved to be incomplete, and further troubleshooting is needed. In this way, one, two or even more branches in the safety tree can be checked according to the safety importance degree sequence until all faults are eliminated, or most faults are eliminated until the safety state of the electric vehicle is lower than the threshold value. The troubleshooting includes repairing, replacing parts, functional and/or structural redesign. According to the logical relationship of the fault node, the fault or damage of the part can be repaired or replaced; if the failure occurs because there is an unreasonable part in the design of the structure or function, the part needs to be redesigned
In a preferred embodiment of the present invention, the branch with no labeling and high security importance in the security tree may be selected for labeling according to the security importance ranking of each bottom-level event, so as to obtain an event loop from the bottom-level event to the top-level event of the branch with high security importance; then analyzing the event collaterals to obtain underlying event parameters of the event collaterals; and finally, troubleshooting the bottom event and eliminating the fault based on the bottom event parameter. After troubleshooting, the process may return to step S3 to continue monitoring. If the monitoring finds whether the safety state of the electric vehicle has reached a threshold value, the fault is proved to be incomplete, and further troubleshooting is needed. In this way, one, two or even more branches in the safety tree can be checked according to the safety importance degree sequence until all faults are eliminated, or most faults are eliminated until the safety state of the electric vehicle is lower than the threshold value.
In a further preferred embodiment of the present invention, the analyzing the event skeleton to obtain the bottom layer event parameters of the event skeleton further comprises analyzing the event skeleton according to a change rule of importance of each level event in the bottom layer event, the middle layer event or the top layer event on the event skeleton; for an event with abnormal importance change in a bottom layer event, a middle layer event or a top layer event on the event collaterals, finding out an actually occurring bottom layer event according to the upstream and downstream of the event collaterals, determining the causal relationship of the bottom layer event and obtaining bottom layer event parameters of the bottom layer event; and directly acquiring the bottom layer event parameters of the bottom layer events for the events without abnormal change of the importance.
The invention will now be described with reference to the embodiments shown in figures 2-3 b. For example, in the operation process of an electric vehicle, we find that the safety state of the electric vehicle has reached a dangerous threshold, such as the braking distance is too large and exceeds the dangerous threshold, we will analyze the event context thereof according to the safety tree, and we find that the event context in which the braking distance exceeds the dangerous threshold is a structural safety event-a braking pipeline oil leakage event/a hydraulic sensor abnormal event according to the importance degree thereof, so we label the two event contexts. Then, the event pulse line is analyzed according to the change rule of the importance degree of each level event in the middle layer event (brake pipeline oil leakage event/hydraulic sensor abnormal event) or the top layer event (structure safety event) of the bottom layer event (brake pipeline oil leakage event/hydraulic sensor abnormal event) on the event pulse line, and the fact that no abnormal change exists in the bottom layer event, the middle layer event or the top layer event on the event pulse line is found, so that the possibility of the occurrence of the brake pipeline oil leakage event/hydraulic sensor abnormal event is considered to be the maximum, therefore, bottom layer event parameters of the bottom layer event (brake pipeline and hydraulic sensor) are obtained, and based on the bottom layer event parameters, whether the brake pipeline and the hydraulic sensor are repaired or replaced, or redesigned and the like are determined. After the troubleshooting is finished, it is judged again that the safety state of the electric vehicle has reached the danger threshold. If the safety state of the electric vehicle has been less than the danger threshold at this time, it proves that our troubleshooting is very successful, and if the safety state of the electric vehicle has also reached the danger threshold at this time, it proves that our troubleshooting is not successful, and it may actually be a problem other underlying event. We therefore return to the safety tree again, at which point we will select an additional event context from the safety tree based on its importance, e.g. we can select a structural safety event-a brake pressure anomaly-a brake oil seal failure, because this event context has been labeled. Similarly, we can repeat the above operations until the safety state of the electric vehicle has been less than the hazard threshold.
In one case, for an event with abnormal importance change in a bottom layer event, a middle layer event or a top layer event on the event meridian, an actually occurring bottom layer event is found out according to the upstream and downstream of the event meridian, the causal relationship of the bottom layer event is determined, and the bottom layer event parameter of the bottom layer event is obtained. For example, suppose we can select a structure safety event-a braking pressure anomaly-a braking oil seal damage according to an event cycle, but we find that the safety degree of the braking oil seal damage is abnormal, which aims at the braking pressure anomaly, at this time, we will continue to consider other bottom layer events with the braking pressure anomaly, brake oil leakage, deformation of the brake bottom plate, and the like, and finally, those skilled in the art can know that the actually occurring bottom layer event is actually the deformation of the brake bottom plate according to the actual vehicle running condition and a priori experience, at this time, a bottom layer event parameter of the bottom layer event (the deformation of the brake bottom plate) is obtained, and based on the bottom layer event parameter, it is determined whether to repair or replace the brake bottom plate, or to perform redesign, and the like. After the troubleshooting is finished, it is judged again that the safety state of the electric vehicle has reached the danger threshold. If the safety state of the electric vehicle has been less than the danger threshold at this time, it proves that our troubleshooting is very successful, and if the safety state of the electric vehicle has also reached the danger threshold at this time, it proves that our troubleshooting is not successful, and it may actually be a problem other underlying event. We therefore return to the safety tree again, now that since the event context line of structural safety event-braking pressure anomaly-brake shoe deformation has been marked, we will select another event context line from the safety tree according to its importance, and the above operation can be repeated until the safety state of the electric vehicle has been less than the hazard threshold.
The electric vehicle operation and maintenance optimization method based on the safety tree model can evaluate the safety of the electric vehicle with constantly changing safety performance in real time, accurately and digitally in the actual operation process, so that safety fault states are integrated according to the safety tree module, the safety state of the electric vehicle is described in a fixed time and quantity mode, the guidance of electric vehicle manufacturing, maintenance, operation and maintenance is realized, and the safety of the electric vehicle is improved.
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, wherein the computer program, when executed by a processor, implements the method for optimizing the operation and maintenance of an electric vehicle based on a safety tree model.
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 operation and maintenance optimization method based on the safety tree model and the computer readable storage medium, the safety of the electric vehicle with constantly changing safety performance can be evaluated in real time, accurately and digitally in the actual operation process, so that each safety fault state is synthesized according to the safety tree model, the safety state of the electric vehicle is described in a fixed time and a fixed quantity, the guidance of the electric vehicle manufacturing, maintenance, operation and maintenance is realized, and the safety of the electric vehicle is improved.
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 (6)
1. An electric vehicle operation and maintenance optimization method based on a safety tree model 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 relations among the bottom layer events, the middle layer events and the top layer events and safety importance degrees;
s2, sequencing the safety importance of each bottom layer event based on the safety tree;
s3, judging whether the safety state of the electric vehicle reaches a threshold value, if so, executing a step S4, otherwise, continuing to evaluate;
s4, checking branches with high safety importance in the safety tree, eliminating faults, and returning to the step S3;
the step S1 further includes:
s11, collecting safety fault data of the electric vehicle;
s12, mapping and classifying the safety fault data of the electric vehicle into different safety event groups, and respectively counting frequency data of each safety event group;
s13, classifying the electric vehicle safety fault data in each safety event group by adopting a joint analysis method to construct a safety tree;
the step S2 further comprises
S21, analyzing parameter deviation of the middle layer event through collection and statistics of the middle layer event, and converting original frequency data of the middle layer event into standardized middle event frequency data of each level;
s22, obtaining the occurrence probability of each bottom layer event through the logic causal relationship and the result analysis and statistics of the intermediate events;
s23, 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;
s24, calculating 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;
s25, sequencing the safety importance of each bottom layer event based on the influence probability of each bottom layer event on each top layer event;
the step S21 includes: s211, 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; s212, 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;
the step S22 includes: counting standardized frequency data of intermediate events at all levels in scene of field application, test and inspection, and respectively calculating occurrence probability corresponding to each bottom event;
in step S23, calculating the occurrence probability of the top-level event according to the occurrence frequency statistics and distribution of the intermediate events and the risk value of each intermediate event;
in step S24, a bayesian algorithm is used to calculate the influence probability of each bottom-layer event on the top-layer event.
2. The safety tree model-based electric vehicle operation and maintenance optimization method of claim 1, wherein the step S4 further comprises:
s41, selecting unmarked branches with high safety importance in the safety tree for marking according to the safety importance sequence of each bottom layer event, so as to obtain an event meridian from the bottom layer event to the top layer event of the branches with high safety importance;
s42, analyzing the event veins to obtain bottom layer event parameters of the event veins;
s43, troubleshooting is carried out on the bottom layer event based on the bottom layer event parameter, the fault is eliminated, and then the step S3 is returned.
3. The safety tree model-based electric vehicle operation and maintenance optimization method according to claim 2, wherein the step S42 further comprises:
s421, analyzing the event collaterals according to the change rule of the importance degree of the bottom layer event, the middle layer event or the top layer event on the event collaterals;
s422, for the event with abnormal change according to the importance degree in the bottom layer event, the middle layer event or the top layer event on the event vein, finding out the actually occurring bottom layer event according to the upstream and downstream of the event vein, determining the causal relationship of the bottom layer event and obtaining the bottom layer event parameter of the bottom layer event; and directly acquiring the bottom layer event parameters of the bottom layer events for the events without abnormal change of the importance.
4. The safety tree model-based electric vehicle operation and maintenance optimization method according to claim 3, wherein the fault elimination comprises repair, replacement of parts, functional and/or structural redesign.
5. The safety tree model-based electric vehicle operation and maintenance optimization method of claim 1, wherein the step S13 further comprises:
s131, dividing the safety fault data of the electric vehicle into at least a first fault category, a second fault category, a third fault category and a fourth fault category;
s132, analyzing the electric 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 a hierarchical relationship among the electric vehicle safety fault data so as to determine a bottom layer event, a middle layer event and a top layer event and a logic causal relationship and a safety importance degree among the bottom layer event, the middle layer event and the top layer event;
and S133, establishing fault cause-and-effect relations layer by layer until all the electric 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 model-based electric vehicle operation and maintenance optimization method according to any one of claims 1 to 5.
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