CN111859710A - Quantitative and safe cumulative risk model modeling method - Google Patents
Quantitative and safe cumulative risk model modeling method Download PDFInfo
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Abstract
The invention relates to the field of system safety, and particularly discloses a quantitative safety accumulative risk model modeling method, which comprises the following steps: s1, collecting a running value of an observation signal changing along with time; s2, determining the size of an observation time window and the variation range of the observation signal; s3, calculating the steady-state probability of the observation signal at any moment in the observation time window; and S4, calculating the cumulative risk probability of the observation signal in the observation time window. The method obtains the accumulated risk probability of the observation signal through accumulated integral calculation of the risk along with the time, so that the safety risk is quantized into a specific numerical value.
Description
Technical Field
The invention relates to the field of system safety, in particular to an accumulative risk model modeling method for quantitative safety.
Background
Many similar failures or problems currently occur in the time dimension, from quantitative to qualitative changes. For example, in the use process of a new energy electric vehicle, battery failure or thermal runaway is a main form of safety hazard, core parameters influencing the battery failure or thermal runaway mainly include current, voltage, temperature, resistance, cell voltage, SOC and the like, and when the parameters are used for judging the safety of a power utilization system, a threshold judgment method, an outlier method, an information entropy method and the like are usually adopted.
For this reason, a cumulative risk model modeling method capable of quantifying the degree of safety is required.
Disclosure of Invention
The invention provides an accumulative risk model modeling method for quantitative security, which solves the technical problems that: the existing method for judging the safety of the power utilization system cannot quantify the safety degree.
The basic scheme provided by the invention is as follows:
a quantitative and safe cumulative risk model modeling method comprises the following steps:
s1, collecting a running value of an observation signal changing along with time;
s2, determining the size of an observation time window and the variation range of the observation signal;
s3, calculating the steady-state probability of the observation signal at any moment in the observation time window;
and S4, calculating the cumulative risk probability of the observation signal in the observation time window.
The working principle and the advantages of the invention are as follows:
the accumulated risk probability of the observation signal is obtained through accumulated integral calculation of the risk along with the time, so that the safety risk is quantized into a specific numerical value.
Further, the observed signal x (t) obeys a probability distribution with a location parameter u and a scale parameter σ, and its probability density function is:
wherein σ represents a standard deviation of the observed signal within the observed time window, u represents an average of the operational values within the observed time window, and t represents any time.
Has the advantages that: the normal distribution reflects the distribution rule of random variables, the observation signals in the method are just random variables, the operation values of the observation signals are random, and the operation rule of the observation signals in the scheme basically conforms to the normal distribution. Therefore, the scheme expresses the distribution probability of the observation signals based on the probability density function, so that the accumulated risk probability is convenient to quantize in the later period, and the quantization result has no large error.
Further, the step S4 includes the steps of:
s41, calculating the accumulated steady-state probability of the observation signal in the observation time window;
and S42, calculating the accumulative risk probability according to the accumulative steady-state probability.
Has the advantages that: the cumulative risk probability can be calculated more easily by calculating the cumulative steady-state probability of the observed signal and then obtaining the cumulative risk probability by using the theorem of cumulative steady-state probability + cumulative risk probability being 1.
Further, the calculation formula adopted in step S41 is as follows:
wherein p (T) represents the cumulative steady-state probability, T representing the observation time window;
the calculation formula adopted in step S42 is: r (t) ═ 1-p (t), r (t) represents the cumulative risk probability.
Has the advantages that: and (3) integrating the steady-state probability of any time t in the observation time window by adopting an integral formula to obtain the accumulated steady-state probability in the observation time window and finally obtain the accumulated risk probability.
Further, T is 3 times the variance of the full life cycle of the observed signal.
Has the advantages that: t is usually obtained by big data statistics, and usually T is taken as 3 times of the total variance of the parameter full life cycle, and the total variance is calculated by big data statistics. That is, the observation time windows T are different for different observation signals, and even for the same observation signal, the observation time windows T are different due to the difference in the entire life cycle. And limiting the observation time window T to be 3 times of the total variance of the whole full life cycle, so that the solved accumulative risk probability error is smaller.
Further, the observation signal is a working voltage or a working current.
Has the advantages that: the working voltage or the working current is used as the most basic parameter for judging whether the power utilization system works normally, the corresponding accumulated risk probability is calculated for the working voltage or the working current, and the risk condition of the power utilization system can be reflected most visually.
Further, the observation signal is the voltage range of the new energy electric automobile.
Has the advantages that: the new energy electric automobile is an example of an electricity utilization system, the voltage range is an important factor influencing the safety of the new energy electric automobile, the corresponding accumulated risk probability is calculated for the voltage range, the working stability of the new energy electric automobile can be intuitively reflected, and the more stable the accumulated risk probability is, the safer the new energy electric automobile is.
Further, the observation signal is the voltage of a single cell of the new energy electric vehicle.
Has the advantages that: the new energy electric automobile is an example of a power utilization system, the single cell voltage is an important factor influencing the safety of the new energy electric automobile, the corresponding accumulated risk probability is calculated for the single cell voltage, the power supply stability of the new energy electric automobile can be intuitively reflected, and the more stable the accumulated risk probability is, the safer the new energy electric automobile is.
Further, the observation signal is the battery temperature of the new energy electric automobile.
Has the advantages that: the new energy electric automobile is an example of an electricity utilization system, the battery temperature is an important factor influencing the safety of the new energy electric automobile, the corresponding accumulated risk probability is calculated for the battery temperature, the working temperature stability of the new energy electric automobile can be intuitively reflected, and the more stable the accumulated risk probability is, the safer the new energy electric automobile is.
Further, the observation signal is the internal resistance of the battery of the new energy electric automobile.
Has the advantages that: the new energy electric automobile is an example of a power utilization system, the internal resistance of the battery reflects the loss of the battery and is an important factor influencing the safety of the new energy electric automobile, the corresponding accumulated risk probability is calculated for the internal resistance of the battery, the battery loss condition of the new energy electric automobile can be intuitively reflected, and the lower the loss is, the lower the accumulated risk probability is, and the safer the new energy electric automobile is.
Drawings
Fig. 1 is a flowchart illustrating steps of a cumulative risk model modeling method for quantifying safety according to embodiment 1 of the present invention;
FIG. 2 is a waveform of the cumulative risk probability of FIG. 1 versus life cycle;
fig. 3 is a waveform relationship between the cumulative risk probability and the life cycle according to embodiment 2 of the present invention.
Detailed Description
The following is further detailed by the specific embodiments:
example 1
In order to quantify the safety degree of the observed signal, the present embodiment of the invention provides a cumulative risk model modeling method for quantifying safety, and the present embodiment of the invention provides a cumulative risk model modeling method for quantifying safety, as shown in fig. 1, including steps S1-S4.
S1, collecting a running value of an observation signal changing along with time.
The observation signal in this step differs depending on the power system (hereinafter referred to as system). Generally, a system is safe if it is stable, and the stability of the system depends on the intrinsic cause of the system, i.e., the stability of the core parameters characterizing the safety of the system. It is first necessary to determine the security element of a system, i.e. the observed signal in this step.
And S2, determining the size of an observation time window and the variation range of the observation signal.
The step is to determine the accumulated time period of the accumulated risk probability and determine each operation value of the observation signal in the time period, so as to obtain the corresponding standard deviation, the operation average value and the like through statistics.
And S3, calculating the steady-state probability of the observation signal at any moment in the observation time window.
In this embodiment, the observed signal x (t) obeys a probability distribution with a location parameter u and a scale parameter σ, and its probability density function (the steady-state probability of the observed signal at any time within the observation time window) is:
wherein σ represents a standard deviation of the observed signal within the observed time window, u represents an average of the operational values within the observed time window, and t represents any time.
The normal distribution reflects the distribution rule of the random variables, the observation signals in the embodiment are just random variables, the operation values of the observation signals are random, and the operation rule of the observation signals in the scheme basically conforms to the normal distribution. Therefore, the scheme expresses the distribution probability of the observation signals based on the probability density function, so that the accumulated risk probability is convenient to quantize in the later period, and the quantization result has no large error.
And S4, calculating the cumulative risk probability of the observation signal in the observation time window.
The step S4 includes the steps of:
s41, calculating the accumulated steady-state probability of the observation signal in the observation time window;
and S42, calculating the accumulative risk probability according to the accumulative steady-state probability.
Wherein, the calculation formula adopted in the step S41 is as follows:
wherein p (T) represents the cumulative steady-state probability, T representing the observation time window;
the calculation formula adopted in step S42 is: r (t) ═ 1-p (t), r (t) represents the cumulative risk probability.
In this embodiment, T is 3 times the variance of the full life cycle of the observed signal. T is usually obtained by big data statistics, and usually T is taken as 3 times of the total variance of the parameter full life cycle, and the total variance is calculated by big data statistics. That is, the observation time windows T are different for different observation signals, and even for the same observation signal, the observation time windows T are different due to the difference in the entire life cycle. And limiting the observation time window T to be 3 times of the total variance of the whole full life cycle, so that the solved accumulative risk probability error is smaller.
In this step, the cumulative steady-state probability of the observation signal is calculated first, and then the cumulative risk probability is obtained by using the theorem that the cumulative steady-state probability + the cumulative risk probability is 1, so that the cumulative risk probability can be calculated more easily.
In the new energy automobile as an example, it is assumed that some important physical parameters of the automobile operation process which we can observe are known, such as voltage, current, temperature, internal resistance and the like, and usually, the physical parameters should be stable or a constant state, and of course, the physical parameters may fluctuate or change due to interference of some uncertain factors. It is assumed that fluctuation of the physical parameters in an allowable range of a constant is subject to normal distribution, the smaller the fluctuation range is, the more stable the fluctuation is, the more in a certain normal state, the higher the steady-state probability is, and conversely, the larger the fluctuation range is, the smaller the probability in a certain stable state is, and the higher the safety risk is. And a large number of facts show that the risk of potential safety hazards is usually low at the beginning in the whole vehicle life cycle, then the vehicle enters a stable operation period, the risk is relatively low, and the safety risk is increased to the later stage of the vehicle life cycle, because the stability of the system is worse and worse, the parameter fluctuation range is enlarged, and the probability of being in a certain stable state is smaller and smaller, the possible safety problem of the vehicle can be judged by accumulating the probability of the occurrence of the risk in the operation of the vehicle, and therefore safety early warning is realized. Thus, the risk of the vehicle at different life cycle stages is different, while the degree of risk accumulation can predict the safety of the vehicle.
The system of the embodiment is a new energy electric vehicle as an example, and the system may be other in other embodiments. In the use process of the new energy electric automobile, battery failure or thermal runaway is a main form of safety hazard, and core parameters influencing the battery failure or thermal runaway mainly include current, voltage, temperature, resistance, monomer voltage, voltage range and the like, which can be used as observation signals of the embodiment, and the operation values of the new energy electric automobile changing along with time are corresponding current values, voltage values, temperature values, resistance values and the like.
In the present embodiment, the observation signal used is a voltage range (the difference between the maximum voltage and the minimum voltage of the single battery at a certain time), the mathematical expectation of the range voltage is 0, and the observation time window T is the total variance of the range over the entire life cycle × 3. 33 normal vehicles which are not in accident at present and 6 vehicles which are in accident are randomly extracted, and after the steps S1-S4 are executed, the obtained accumulated risk probability is shown in FIG. 2. It can be seen that the cumulative risk probability of a vehicle with an accident is generally higher than the cumulative risk probability of a vehicle without an accident. The voltage range is an important factor influencing the safety of the new energy electric automobile, the corresponding accumulated risk probability is calculated for the voltage range, the working stability of the new energy electric automobile can be intuitively reflected, and the more stable the accumulated risk probability is, the safer the new energy electric automobile is.
In summary, the working principle and advantages of the embodiment of the invention are as follows:
the accumulated risk probability of extremely poor voltage is obtained through accumulated integral calculation of risks along with time, so that the corresponding safety risks are quantized into a specific numerical value. The calculation of the accumulated risk probability has great reference value for the user, the user can be reminded of paying attention to the place with high accumulated risk probability, accidents are likely to occur easily, and the user is preferably overhauled in advance to avoid the accidents; the method has great reference value for maintainers, is convenient for the maintainers to find out the reasons causing accidents, can carry out investigation from high accumulated risk probability to low accumulated risk probability, and saves maintenance time.
Example 2
The present embodiment is different from embodiment 1 in that: the observation signal is the voltage of a single cell of the new energy electric automobile. The calculation object is 88 single battery cells of an accident vehicle, and the calculated cumulative risk probability is shown in fig. 3. As can be seen from fig. 3, before an accident occurs in a single battery cell, the cumulative risk probability of the single battery cell increases continuously with time, and finally, the total risk probability of the single battery cell reaches one hundred percent when the accident occurs. It can be seen that the trend of the accumulated risk probability has great reference significance for the possibility of accidents, time nodes and the like.
The single cell voltage is an important factor influencing the safety of the new energy electric automobile, the corresponding accumulated risk probability is calculated for the single cell voltage, the power supply stability of the new energy electric automobile can be intuitively reflected, and the more stable the accumulated risk probability is, the safer the new energy electric automobile is.
In other embodiments, the observed signal is:
the battery temperature of the new energy electric automobile. The new energy electric automobile is an example of an electricity utilization system, the battery temperature is an important factor influencing the safety of the new energy electric automobile, the corresponding accumulated risk probability is calculated for the battery temperature, the working temperature stability of the new energy electric automobile can be intuitively reflected, and the more stable the accumulated risk probability is, the safer the new energy electric automobile is.
The battery internal resistance of the new energy electric automobile. The new energy electric automobile is an example of a power utilization system, the internal resistance of the battery reflects the loss of the battery and is an important factor influencing the safety of the new energy electric automobile, the corresponding accumulated risk probability is calculated for the internal resistance of the battery, the battery loss condition of the new energy electric automobile can be intuitively reflected, and the lower the loss is, the lower the accumulated risk probability is, and the safer the new energy electric automobile is.
The working voltage or the working current of the new energy electric automobile. The working voltage or the working current is used as the most basic parameter for judging whether the power utilization system works normally, the corresponding accumulated risk probability is calculated for the working voltage or the working current, and the risk condition of the power utilization system can be reflected most visually.
In other embodiments, the observed signal varies from one power system to another.
The foregoing is merely an example of the present invention, and common general knowledge in the field of known specific structures and characteristics is not described herein in any greater extent than that known in the art at the filing date or prior to the priority date of the application, so that those skilled in the art can now appreciate that all of the above-described techniques in this field and have the ability to apply routine experimentation before this date can be combined with one or more of the present teachings to complete and implement the present invention, and that certain typical known structures or known methods do not pose any impediments to the implementation of the present invention by those skilled in the art. It should be noted that, for those skilled in the art, without departing from the structure of the present invention, several changes and modifications can be made, which should also be regarded as the protection scope of the present invention, and these will not affect the effect of the implementation of the present invention and the practicability of the patent. The scope of the claims of the present application shall be determined by the contents of the claims, and the description of the embodiments and the like in the specification shall be used to explain the contents of the claims.
Claims (10)
1. A quantitative and safe cumulative risk model modeling method is characterized by comprising the following steps:
s1, collecting a running value of an observation signal changing along with time;
s2, determining the size of an observation time window and the variation range of the observation signal;
s3, calculating the steady-state probability of the observation signal at any moment in the observation time window;
and S4, calculating the cumulative risk probability of the observation signal in the observation time window.
2. The method of claim 1, wherein the observed signal x (t) obeys a probability distribution with a position parameter u and a scale parameter σ, and has a probability density function of:
wherein σ represents a standard deviation of the observed signal within the observed time window, u represents an average of the operational values within the observed time window, and t represents any time.
3. The quantitative security cumulative risk model modeling method of claim 2, wherein said step S4 comprises the steps of:
s41, calculating the accumulated steady-state probability of the observation signal in the observation time window;
and S42, calculating the accumulative risk probability according to the accumulative steady-state probability.
4. The modeling method of a quantitative safety cumulative risk model according to claim 3, wherein the calculation formula adopted in the step S41 is:
wherein p (T) represents the cumulative steady-state probability, T representing the observation time window;
the calculation formula adopted in step S42 is: r (t) ═ 1-p (t), r (t) represents the cumulative risk probability.
5. The cumulative risk model modeling method of quantified safety of claim 2, characterized by: t is 3 times the variance of the full life cycle of the observed signal.
6. The cumulative risk model modeling method of quantified safety of claim 1, characterized by: the observation signal is working voltage or working current.
7. The cumulative risk model modeling method of quantified safety of claim 1, characterized by: the observation signal is the voltage range of the new energy electric automobile.
8. The cumulative risk model modeling method of quantified safety of claim 1, characterized by: the observation signal is the voltage of a single cell of the new energy electric automobile.
9. The cumulative risk model modeling method of quantified safety of claim, characterized by: the observation signal is the battery temperature of the new energy electric automobile.
10. The cumulative risk model modeling method of quantified safety of claim 1, characterized by: the observation signal is the internal resistance of the battery of the new energy electric automobile.
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