CN113537662A - Risk detection method and device - Google Patents
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Abstract
The embodiment of the application provides a risk detection method and a device, the risk detection method can quantify the risk of the problem to be evaluated and the risk of the project to be evaluated in a unified mode by acquiring sampling data corresponding to the problem to be evaluated in the project to be evaluated, determining the risk value of the problem to be evaluated according to the risk value of the problem to be evaluated, not only can easily identify the actual risk value of the problem to be evaluated which is not completely verified, but also can detect the whole risk of the project to be evaluated more perfectly and accurately.
Description
Technical Field
The embodiment of the application relates to the technical field of automobiles, in particular to a risk detection method and device.
Background
With the acceleration of the development rhythm of the vehicle, the verification period of the vehicle is continuously compressed, which results in that the sufficiency of the verification of many vehicle problems cannot be ensured. And the management of hundreds of thousands of problems of the same vehicle project depends on manual qualitative analysis, and the project risk and the problem risk are difficult to quantify. Taking risk control of problems found when vehicles are tested on roads as an example, the severity of the risk is generally qualitatively analyzed mainly by manual identification, for example, all problems are tabulated by EXCEL, the severity of a single problem is identified by red, yellow and green, and key problems are obtained by screening and simple statistics of the condition of problem closure. However, this approach is not perfect and less accurate.
Disclosure of Invention
In view of the above, one of the technical problems solved by the embodiments of the present invention is to provide a risk detection method and apparatus for quantifying problem risks and project risks, so as to detect the overall risk of a project more perfectly and accurately.
The embodiment of the application provides a risk detection method, which comprises the following steps:
acquiring sampling data corresponding to a problem to be evaluated in a project to be evaluated, wherein the sampling data comprises at least one item of verification data, stage data, problem severity data and time data, the verification data indicates an analysis verification condition of the problem to be evaluated, the stage data indicates a stage of the problem to be evaluated, the problem severity data indicates a severity of the problem to be evaluated, the time data indicates a risk increase degree corresponding to problem opening time of the problem to be evaluated, and the number of the problems to be evaluated is greater than or equal to 2;
determining a risk value of the problem to be evaluated according to the sampling data;
and determining the risk value of the project to be evaluated according to the risk value of the problem to be evaluated.
Optionally, in an embodiment of the present application, acquiring sample data corresponding to a problem to be evaluated in a project to be evaluated includes:
and acquiring the verification data according to the reason quantization data, the measure quantization data and the verification quantization data of the problem to be evaluated, wherein the reason quantization data indicates the reason identification condition of the problem to be evaluated, the measure quantization data indicates the measure use condition of the problem to be evaluated, and the verification quantization data indicates the verification state of the problem to be evaluated.
Optionally, in an embodiment of the present application, acquiring sample data corresponding to a problem to be evaluated in a project to be evaluated includes:
and acquiring the problem severity data according to the safety quantization data, the high-frequency quantization data, the functional quantization data and the perception quantization data of the problem to be evaluated, wherein the safety quantization data indicates whether the problem to be evaluated belongs to a safety problem, the high-frequency quantization data indicates whether the problem to be evaluated belongs to a high-frequency problem, the functional data indicates whether the problem to be evaluated belongs to a functional problem, and the perception quantization data indicates whether the problem to be evaluated belongs to a perception problem.
Optionally, in an embodiment of the present application, acquiring the problem severity data according to the safety quantization data, the high-frequency quantization data, the functional quantization data, and the perception quantization data of the problem to be evaluated includes:
and carrying out weighted summation on the safety quantization data, the high-frequency quantization data, the functional quantization data and the perception quantization data of the problem to be evaluated to obtain the problem severity data.
Optionally, in an embodiment of the present application, acquiring sample data corresponding to a problem to be evaluated in a project to be evaluated includes:
and acquiring the time data according to the verification data corresponding to the problem to be evaluated, the problem opening time of the problem to be evaluated and a preset risk increment.
Optionally, in an embodiment of the present application, obtaining the time data according to the verifiability data corresponding to the problem to be evaluated, the problem opening time of the problem to be evaluated, and a preset risk increment includes:
when the verification data belongs to a first data set, determining the product of the time interval of the problem opening time of the problem to be evaluated and a preset time threshold and the preset risk increment as the time data;
when the verification data belongs to a second data set, 0 is determined as the time data.
Optionally, in an embodiment of the present application, determining a risk value of the problem to be evaluated according to the sampling data includes:
calculating a product of the confirmatory data, the staging data, and the problem severity data;
and determining the sum of the product and the time data as the risk value of the problem to be evaluated.
Optionally, in an embodiment of the present application, determining a risk value of the item to be evaluated according to the risk value of the problem to be evaluated includes:
and determining the weighted average value of the risk values of at least two problems to be evaluated as the risk value of the project to be evaluated.
Optionally, in an embodiment of the present application, the risk values of the item to be evaluated include risk values at least two time points, and the method further includes:
and determining a relation curve between the risk value of the item to be evaluated and the time point according to each time point and the risk value of the item to be evaluated corresponding to each time point, wherein the relation curve is used for representing the condition that the risk value of the item to be evaluated changes along with time.
The embodiment of the present application further provides a risk detection device, which includes:
the system comprises a data acquisition module, a data processing module and a data processing module, wherein the data acquisition module is used for acquiring sampling data corresponding to a problem to be evaluated in a project to be evaluated, the sampling data comprises at least one of verification data, stage data, problem severity data and time data, the verification data indicates the analysis and verification condition of the problem to be evaluated, the stage data indicates the stage of the problem to be evaluated, the problem severity data indicates the severity of the problem to be evaluated, the time data indicates the risk increase degree corresponding to the problem opening time of the problem to be evaluated, and the number of the problems to be evaluated is greater than or equal to 2;
the problem risk determining module is used for determining a risk value of the problem to be evaluated according to the sampling data;
and the project risk determining module is used for determining the risk value of the project to be evaluated according to the risk value of the problem to be evaluated.
Optionally, in an embodiment of the present application, the data obtaining module is specifically configured to:
and acquiring the verification data according to the reason quantization data, the measure quantization data and the verification quantization data of the problem to be evaluated, wherein the reason quantization data indicates the reason identification condition of the problem to be evaluated, the measure quantization data indicates the measure use condition of the problem to be evaluated, and the verification quantization data indicates the verification state of the problem to be evaluated.
Optionally, in an embodiment of the present application, the data obtaining module is specifically configured to:
and acquiring the problem severity data according to the safety quantization data, the high-frequency quantization data, the functional quantization data and the perception quantization data of the problem to be evaluated, wherein the safety quantization data indicates whether the problem to be evaluated belongs to a safety problem, the high-frequency quantization data indicates whether the problem to be evaluated belongs to a high-frequency problem, the functional data indicates whether the problem to be evaluated belongs to a functional problem, and the perception quantization data indicates whether the problem to be evaluated belongs to a perception problem.
Optionally, in an embodiment of the present application, the data obtaining module is specifically configured to:
and carrying out weighted summation on the safety quantization data, the high-frequency quantization data, the functional quantization data and the perception quantization data of the problem to be evaluated to obtain the problem severity data.
Optionally, in an embodiment of the present application, the data obtaining module is specifically configured to:
and acquiring the time data according to the verification data corresponding to the problem to be evaluated, the problem opening time of the problem to be evaluated and a preset risk increment.
Optionally, in an embodiment of the present application, the data obtaining module is specifically configured to:
when the verification data belongs to a first data set, determining the product of the time interval of the problem opening time of the problem to be evaluated and a preset time threshold and the preset risk increment as the time data;
when the verification data belongs to a second data set, 0 is determined as the time data.
Optionally, in an embodiment of the present application, the problem risk determination module:
calculating a product of the confirmatory data, the staging data, and the problem severity data;
and determining the sum of the product and the time data as the risk value of the problem to be evaluated.
Optionally, in an embodiment of the present application, the item risk determination module:
and determining the weighted average value of the risk values of at least two problems to be evaluated as the risk value of the project to be evaluated.
Optionally, in an embodiment of the present application, the risk value of the item to be evaluated includes risk values at least two time points, the apparatus further includes a relationship curve determining module, configured to determine a relationship curve between the risk value of the item to be evaluated and a time point according to each time point and the risk value of the item to be evaluated corresponding to each time point, where the relationship curve is used to characterize a time-varying condition of the risk value of the item to be evaluated.
In the embodiment, the risk value of the problem to be evaluated and the risk of the project to be evaluated can be quantified in a unified manner by acquiring the sampling data corresponding to the problem to be evaluated in the project to be evaluated, determining the risk value of the project to be evaluated according to the risk value of the problem to be evaluated, not only can the risk value of the problem to be evaluated, which is not completely verified actually, be easily identified, but also the overall risk of the project to be evaluated can be detected perfectly and accurately.
Drawings
Some specific embodiments of the present application will be described in detail hereinafter by way of illustration and not limitation with reference to the accompanying drawings. The same reference numbers in the drawings identify the same or similar elements or components. Those skilled in the art will appreciate that the drawings are not necessarily drawn to scale. In the drawings:
fig. 1 is a method for risk detection according to an embodiment of the present disclosure;
fig. 2 is a method for determining a preset risk increment according to an embodiment of the present disclosure;
FIG. 3 is a graph illustrating a relationship between a risk value and a time point of a project to be evaluated according to an embodiment of the present disclosure;
FIG. 4 is another risk detection method provided by embodiments of the present application;
fig. 5 is a risk detection apparatus according to an embodiment of the present application.
Detailed Description
The following further describes specific implementation of the embodiments of the present invention with reference to the drawings.
Example one
An embodiment of the present application provides a risk detection method, as shown in fig. 1, and fig. 1 is a flowchart of the risk detection method provided in the embodiment of the present application. The risk detection method comprises the following steps:
s101, acquiring sampling data corresponding to the problem to be evaluated in the project to be evaluated.
The sampled data may include at least one of confirmatory data, staging data, problem severity data, and time data. The verification data indicates the analysis and verification condition of the problem to be evaluated, the stage data indicates the stage of the problem to be evaluated, the problem severity data indicates the severity of the problem to be evaluated, and the time data indicates the risk increase degree corresponding to the problem opening time of the problem to be evaluated. The number of questions to be evaluated is greater than or equal to 2.
In this embodiment, the items to be evaluated may be vehicle complete vehicle road test items, and the problems to be evaluated may be problems occurring in a vehicle complete vehicle road test process. Of course, the item to be evaluated may also be another item, and correspondingly, the problem to be evaluated may be a problem of the item to be evaluated in the process of the experiment or the test.
In this embodiment, the verification data may be data for quantifying an analysis and verification condition of the problem to be evaluated, the staged data may be data for quantifying a stage of the problem to be evaluated, the problem severity data may be data for quantifying a severity of the problem to be evaluated, and the time data may be data for quantifying a risk increase degree corresponding to a problem opening time of the problem to be evaluated. The sampled data may include validation data, staging data, problem severity data, time data, or a combination thereof. Optionally, in order to enable more complete and accurate risk detection, the sampling data includes verification data, staging data, problem severity data, and time data.
Optionally, in order to consider an analysis and verification situation of a problem to be evaluated and accurately quantify a risk of the problem to be evaluated, in an implementation manner of the present application, acquiring sample data corresponding to the problem to be evaluated in a project to be evaluated may include:
and obtaining the verification data according to the reason quantization data, the measure quantization data and the verification quantization data of the problem to be evaluated. The reason quantized data can indicate the reason identification condition of the problem to be evaluated, the measure quantized data can indicate the measure use condition of the problem to be evaluated, and the verification quantized data can indicate the verification state of the problem to be evaluated.
Specifically, the cause quantization data may be data that quantizes whether the cause of the problem to be evaluated is identified and whether the identified cause is reasonable. The value of the reason quantization data is not limited, and may be set according to actual requirements, for example, 0 may be used to indicate that the reason of the problem to be evaluated is identified and the identified reason is correct, that is, the reason is reasonable, 1 may be used to indicate that the reason of the problem to be evaluated is not identified, that is, no reason is found, and 2 may be used to indicate that the reason of the problem to be evaluated is identified but the identified reason is wrong, that is, the reason is wrong.
The measure quantization data may be data that quantizes whether a measure to solve the problem to be evaluated is found and whether the found measure is reasonable. The value of the measure quantized data is not limited, and may be set according to actual requirements, for example, 0 may be used to indicate that a measure for solving the problem to be evaluated has been found and the found measure is correct, that is, the measure is reasonable, 1 may be used to indicate that a measure for solving the problem to be evaluated has not been found, that is, no measure is available, and 2 may be used to indicate that a measure for solving the problem to be evaluated has been found but the found measure cannot correctly solve the problem to be evaluated, that is, the measure is wrong.
The verification quantized data may be data quantized to verify whether or not the problem to be solved is verified and whether or not the verification is passed. The value of the verification quantized data is not limited, and may be set according to actual requirements, for example, 0 may be used to indicate that the problem to be solved has been verified and passes verification, i.e., verification passes or does not need verification, 1 may be used to indicate that the problem to be solved is being verified, i.e., verification, 3 may be used to indicate that the problem to be evaluated has not been verified, i.e., unverified, and 6 may be used to indicate that the problem to be evaluated does not appear and is not verified, i.e., unverified or not reproduced.
Further optionally, in order to simplify the calculation process, obtaining the verification data according to the quantitative data of the cause of the problem to be evaluated, the quantitative data of the measure, and the verification quantitative data may include:
and obtaining the verification data by summing the reason quantization data, the measure quantization data and the verification quantization data of the problem to be evaluated.
Specifically, the verification data can be obtained by substituting the formula (1) with the verification data obtained by summing the cause quantization data, the measure quantization data and the verification quantization data of the problem to be evaluated.
C＝∫(R，M，P)＝R+M+P (1)
Wherein R denotes cause quantization data, M denotes measure quantization data, P denotes verification quantization data, and C denotes verification data.
For example, if the quantitative data of the cause of the problem to be evaluated is 0, the quantitative data of the measure is 0, and the quantitative data of the verification is 1, the obtained verification data is 1. For another example, if the quantitative data of the cause of the problem to be evaluated is 2, the quantitative data of the measure is 2, and the quantitative data of the verification is 4, the verifiability data is 8.
Optionally, in order to consider the severity of the problem to be evaluated and accurately quantify the risk of the problem to be evaluated, in an implementation manner of the present application, acquiring the sample data corresponding to the problem to be evaluated in the project to be evaluated may include:
the method comprises the steps of obtaining problem severity data according to safety quantization data, high-frequency quantization data, functional quantization data and perception quantization data of a problem to be evaluated, wherein the safety quantization data indicate whether the problem to be evaluated belongs to a safety problem or not, the high-frequency quantization data indicate whether the problem to be evaluated belongs to a high-frequency problem or not, the functional data indicate whether the problem to be evaluated belongs to a functional problem or not, and the perception quantization data indicate whether the problem to be evaluated belongs to a perception problem or not.
Specifically, the security-like problem may be a problem that may cause casualties, the high-frequency problem may be a problem that has a high occurrence probability, the function-like problem may be a problem related to the function of the item to be evaluated, and the perception-like problem may be a problem that causes the user to feel uncomfortable psychologically or physiologically. For example, taking the problem of the vehicle in road test as an example, the safety problem may be a failure that may cause casualties or cause failure of main assembly components of the vehicle or failure of the vehicle to operate normally, such as steering failure, braking failure, etc. The high frequency class of problems may be failures that are potential design problems or supplier lot quality problems or factory assembly problems, etc. The functional problem may be a problem with a mechanical function of the vehicle, such as a stuck seat belt, a stuck eye-box, etc., or a problem with an electrical function of the vehicle, such as a failure of a window lift function, etc., or a problem with an intelligent networking function of the vehicle, such as an Advanced Driver Assistance System (ADAS) failure. The perception-like problem can be a problem causing visual discomfort of a user, such as instrument light reflection, rusting and the like, or a problem causing auditory discomfort of the user, such as wind noise, tire noise or chassis abnormal sound, or tactile discomfort of the user, such as burrs, hurdles and the like, or a problem causing olfactory discomfort of the user, such as a gasoline smell or a strong interior smell.
The safety quantitative data is data for quantifying whether the problem to be evaluated belongs to a safety problem, the value of the safety quantitative data is not limited, and the safety quantitative data can be set according to actual requirements, for example, 0 can be used to indicate that the problem to be evaluated belongs to the safety problem, and 1 can be used to indicate that the problem to be evaluated does not belong to the safety problem. Similarly, the high-frequency quantized data is data for quantizing whether the problem to be evaluated belongs to a high-frequency problem, and the value of the high-frequency quantized data is not limited and can be set according to actual requirements, for example, 0 may be used to indicate that the problem to be evaluated belongs to a high-frequency problem, and 1 may be used to indicate that the problem to be evaluated does not belong to a high-frequency problem. The functional data is data for quantifying whether the problem to be evaluated belongs to a functional problem, the value of the functional data is not limited, and the functional data can be set according to actual requirements, for example, 0 can be used to indicate that the problem to be evaluated belongs to the functional problem, and 1 can be used to indicate that the problem to be evaluated does not belong to the functional problem. The perception quantization data indicate whether the problem to be evaluated belongs to a perception problem or not, the value of the perception quantization data is not limited, and the perception quantization data can be set according to actual requirements, for example, 0 can be used for indicating that the problem to be evaluated belongs to the perception problem, and 1 is used for indicating that the problem to be evaluated does not belong to the perception problem.
Further optionally, in order to improve the degree of influence of some types of problems, such as security problems, on the severity of the problem, and to facilitate a final risk detection result, in an embodiment of the present application, obtaining problem severity data according to security quantitative data, high-frequency quantitative data, functional quantitative data, and perception quantitative data of the problem to be evaluated may include:
and carrying out weighted summation on the safety quantized data, the high-frequency quantized data, the functional quantized data and the perception quantized data of the problem to be evaluated to obtain problem severity data.
Specifically, the corresponding weight values may be set for the security class problem, the high frequency class problem, the function class problem, and the perception class problem in advance. For example, the weight value may be set according to actual application, for example, when a security problem occurs, casualties may be caused, the consequences are serious, a large weight value may be set for the security problem, and when a perception problem occurs, the influence is relatively small, and a small weight value may be set for the perception problem. And then, substituting safety quantization data, high-frequency quantization data, functional quantization data and perception quantization data of the problem to be evaluated into formula (2) to obtain problem severity data. In this way it is possible to provide a solution,
S＝∫(S1+F1+F2+S2)＝K1×S1+K2×F1+K3×F2+K4×S2 (2)
wherein, K1Represents a security weight value, K2Representing high frequency weight value, K3Represents a functional weight value, K4Representing perceptual weight value, S1Representing securely quantized data, F1Representing high frequency quantized data, F2Representing functional weight quantized data, S2Representing perceptual weight quantized data.
Optionally, in order to consider the degree of influence of the problem opening time on the risk value of the problem to be evaluated and accurately quantify the risk of the problem to be evaluated, in an embodiment of the present application, obtaining sampling data corresponding to the problem to be evaluated in the project to be evaluated may include:
and acquiring time data according to the verifiability data corresponding to the problem to be evaluated, the problem opening time of the problem to be evaluated and the preset risk increment.
Here, how to obtain the time data is illustrated, and of course, this is only an exemplary illustration and does not represent that the present application is limited thereto.
Optionally, in an implementation manner of the present application, obtaining time data according to the verifiability data corresponding to the problem to be evaluated, the problem opening time of the problem to be evaluated, and the preset risk increment may include:
when the verification data belongs to the first data set, determining the product of the time interval of the problem opening time of the problem to be evaluated and the preset time threshold and the preset risk increment as time data;
when the verification data belongs to the second data set, 0 is determined as the time data.
Specifically, firstly, the verification data may be divided into a first data set and a second data set according to the analysis and verification condition of the problem to be evaluated specifically indicated by the verification data, for example, the second data set may include verification data indicating that the cause and measure of the problem to be evaluated have been determined and verification passes, verification data that the cause and measure of the problem to be evaluated have been determined and verification is being performed, and/or verification data that the cause of the problem to be evaluated has been determined but no measure is performed and verification is not required, and the like, and the first data set may include all verification data except the verification data in the second data set. Then, the time threshold is determined by equation (3) according to which data set the verification data belongs to.
Where Δ R represents a preset risk increment, t1Indicating the problem opening time, T indicating a preset time threshold, C indicating the verification data and T indicating the time data.
How to determine the preset risk increment is illustrated with reference to fig. 2, which is only an exemplary illustration and does not represent that the present application is limited thereto.
Optionally, if the corresponding risk value at the time threshold t is R1The corresponding risk value at the question turn-on time of the question to be evaluated is R2Then a preset risk increment Δ R for the problem to be evaluated may be set to R2-R1. Similarly, if the corresponding risk value at time threshold t is R1Problem under evaluationThe corresponding risk value at the problem turn-on time of (2) is R3Then a preset risk increment Δ R for the problem to be evaluated may be set to R3-R1。
And S102, determining a risk value of the problem to be evaluated according to the sampling data.
Specifically, the risk value of the corresponding problem to be evaluated is determined according to the sampling data of each problem to be evaluated in the project to be evaluated, namely at least one of the verification data, the stage data, the problem severity data and the time data.
Optionally, in order to simplify the process of determining the risk value of the problem to be evaluated, determining the risk value of the problem to be evaluated according to the verification data, the stage data, the problem severity data, and the time data may include: calculating a product of the confirmatory data, the staging data, and the problem severity data; and determining the sum of the product and the time data as a risk value of the problem to be evaluated.
For example, the risk value of each problem to be evaluated in the project to be evaluated can be obtained by substituting the verification data, the stage data, the problem severity data and the time data of the problem to be evaluated into the formula (4).
R＝C×J×S+T (4)
Wherein, R represents the risk value of the problem to be evaluated, C represents the verifiability data of the problem to be evaluated, J represents the stage data of the problem to be evaluated, S represents the problem severity data of the problem to be evaluated, and T represents the time data of the problem to be evaluated.
S103, determining a risk value of the project to be evaluated according to the risk value of the problem to be evaluated.
Specifically, the project to be evaluated may include a plurality of problems to be evaluated, and after the risk value of each problem to be evaluated is obtained, the risk value of the entire project to be evaluated may be determined according to the risk value of each problem to be evaluated.
Here, two possible implementations are listed to illustrate how to determine the preset risk increment, which is only an exemplary illustration and does not represent that the present application is limited thereto.
Optionally, in a possible implementation manner, in order to simplify the calculation process, optionally, determining a risk value of the item to be evaluated according to the risk value of the problem to be evaluated may include: and determining the average value of the risk values of at least two problems to be evaluated as the risk value of the project to be evaluated.
Specifically, if the item to be evaluated includes n problems to be evaluated, the risk value of each problem to be evaluated is RiI is 1, 2, …, n, the risk value of the item to be evaluated is
Optionally, in another possible implementation manner, in order to reflect the degree of influence of the risk values of different problems to be evaluated on the risk value of the whole item to be evaluated, the risk value of the item to be evaluated is further determined more accurately. Optionally, determining the risk value of the item to be evaluated according to the risk value of the problem to be evaluated may include: and determining the weighted average value of the risk values of at least two problems to be evaluated as the risk value of the project to be evaluated.
Specifically, if the item to be evaluated includes n problems to be evaluated, the risk value of each problem to be evaluated is RiI is 1, 2, …, n, the risk value of the item to be evaluated is
Optionally, in order to more intuitively reflect the change of the risk value of the entire item to be evaluated over time, in an implementation manner of the present application, the risk value of the item to be evaluated includes risk values at least two time points, and after determining the risk value of the item to be evaluated according to the risk value of the problem to be evaluated, the method further includes:
and determining a relation curve between the risk value of the item to be evaluated and the time point according to each time point and the risk value of the item to be evaluated corresponding to each time point, wherein the relation curve is used for representing the condition that the risk value of the item to be evaluated changes along with time.
At each time point, the number of the problems to be evaluated of the project to be evaluated may be different, and the risk value of each problem to be evaluated may be changed. The method comprises the steps of determining the risk value of a problem to be evaluated of a project to be evaluated at preset time intervals, and determining the risk value of the project to be evaluated according to the risk value of the problem to be evaluated, so that the risk values of the project to be evaluated at least two time points can be obtained, and a relation curve between the risk value of the project to be evaluated and the time points can be determined according to each time point and the risk value of the project to be evaluated. For example, a relation curve between the risk value of the item to be evaluated and the time point can be obtained by substituting formula (5) for the risk value of the item to be evaluated corresponding to each time point and the time point. The change condition of the item to be evaluated can be judged through the change of the slope of the relation curve.
Rallt＝∫(Rall，t) (5)
Wherein R isalltRepresenting the change situation of the risk value of the whole vehicle road test project along with the time point t, RallAnd representing the risk value of the whole vehicle road test project at the time point t.
The relationship between the risk value and the time point of the item to be evaluated is shown in FIG. 3, wherein R isalltRepresenting the risk value, R, of the item to be assessed at a certain point in timestartIndicates that the first vehicle road test problem is at t1Risk value at time point, RendIndicates that the item to be evaluated is at t4Risk value at time point, t1Indicating the point in time, t, at which the first question to be evaluated starts2Time point, t, at which the risk value of the item to be evaluated reaches the peak3Time point, t, at which any risk value of the item to be evaluated is located4Representing the point in time at which the risk value for the item under evaluation begins to converge. The change of the risk of the item to be evaluated can be judged through the change of the relation curve. The slope increase represents that the risk is increased, the slope decrease represents that the response measures are taken to achieve the purpose of reducing the risk, the slope tends to be stable, and the risk value of the project to be evaluated is at a lower value level, so that the risk of the project is controllable.
Optionally, in order to determine the important to-be-evaluated problem of the to-be-evaluated item, after determining the risk values of all corresponding to-be-evaluated problems according to all the sampling data, the method further includes: and sequencing the risk values of the problems to be evaluated, and determining the problems to be evaluated corresponding to the first risk values with higher risk values as important problems to be evaluated.
In the embodiment, the risk value of the problem to be evaluated and the risk of the project to be evaluated can be quantified in a unified manner by acquiring the sampling data corresponding to the problem to be evaluated in the project to be evaluated, determining the risk value of the project to be evaluated according to the risk value of the problem to be evaluated, not only can the risk value of the problem to be evaluated, which is not completely verified actually, be easily identified, but also the overall risk of the project to be evaluated can be detected perfectly and accurately.
Example two
Based on the risk detection method provided by the first embodiment of the present application, the second embodiment of the present application describes in detail the risk detection method provided by the embodiment shown in fig. 1 by taking a test of the whole vehicle on a road as an example. As shown in fig. 3, the risk detection method provided in this embodiment may include:
s301, acquiring sampling data of the finished automobile road test problem in the finished automobile road test project.
The whole vehicle road test problem refers to a problem occurring in a road test process of a whole vehicle. The sampled data may include confirmatory data, stage data, problem severity, and time data. The number of the road test problems of the whole vehicle is more than or equal to 2.
In the present embodiment, the verification data C is first obtained by substituting the reason quantization data R, the measure quantization data M, and the verification quantization data P into the above-described equation (1). In this embodiment, specific values and specific contents of instructions of the reason quantized data R, the measure quantized data M, and the verification quantized data P are shown in the second column, the third column, and the fourth column of table 1, respectively, and the obtained verification data C is shown in the first column of table 1. In addition, a remark column is added to table 1 to help understand the analysis and verification conditions specifically indicated by the verification data C.
TABLE 1
Next, staged data of the vehicle road test problem is obtained, and in this embodiment, values of the staged data and indicated stages are shown in table 2, respectively.
TABLE 2
Next, the safety quantized data, the high frequency quantized data, the functional quantized data, and the perception quantized data are substituted into the above-described equation (2) to obtain the problem severity data of the full vehicle road test problem. In this embodiment, if the security weight value K1High frequency weight value K2Functional weight value K3And a perceptual weight value K48, 4, 2, 1, respectively, and safety quantified data S for the case of the complete vehicle road test problem as shown in table 3, column 61High frequency quantized data F1Functional weight quantization data F2And perceptual weight quantized data S2The obtained problem severity data S of the full vehicle road test problem are shown in the first column of table 3, respectively, as shown in the second, third, fourth and fifth columns of table 3.
TABLE 3
Finally, 8, 6, 5, 4, and 3 of the values of the verification data C are divided into a first data set, 1, 2, and 7 are divided into a first data set, and the time data T is obtained according to the above-described formula (3).
And S302, determining a risk value of the whole vehicle road test problem according to the sampling data.
In this embodiment, the risk value R of the corresponding complete vehicle road test problem is obtained by substituting the verifiability data C, the stage data J, the problem severity data S, and the time data T of each complete vehicle road test problem into the formula (4) described above.
And S303, determining the risk value of the finished automobile road test project according to the risk value of the finished automobile road test problem.
In this embodiment, the average value of the risk values of at least two finished automobile road test problems is determined as the risk value of the finished automobile road test project. Specifically, if the whole vehicle road test project includes n whole vehicle road test problems, the risk value of each whole vehicle road test problem is RiAnd i is 1, 2, …, n, the risk value of the whole vehicle road test item is
S304, the risk values of the finished automobile road test items comprise risk values at least two time points, a relation curve of the risk values of the finished automobile road test items and the time points is determined according to each time point and the risk values of the finished automobile road test items corresponding to each time point, and the relation curve is used for representing the condition that the risk values of the finished automobile road test items change along with time.
Specifically, the risk value of the finished automobile road test problem in the finished automobile road test project is determined at preset time intervals, and the risk value of the finished automobile road test project is determined according to the risk value of the finished automobile road test problem, so that the risk values of the finished automobile road test project at least two time points can be obtained, and further the risk value of the finished automobile road test project corresponding to each time point and the time point can be substituted into the formula (5) to obtain the relation curve between the risk value and the time point of the finished automobile road test project,
optionally, in order to determine the important problem of the whole vehicle road test project, after determining the risk values of all corresponding whole vehicle road test problems according to all the sampling data, the method further includes: and sequencing the risk values of the whole vehicle road test problems, and determining the whole vehicle road test problems corresponding to the first plurality of risk values with higher risk values as important problems.
In this embodiment, by obtaining sampling data corresponding to the complete vehicle road test problem in the complete vehicle road test project, determining the risk value of the complete vehicle road test problem according to the sampling data, and then determining the risk value of the complete vehicle road test project according to the risk value of the complete vehicle road test problem, the risk of the complete vehicle road test problem and the risk of the complete vehicle road test project can be quantified in a unified manner, so that not only can the risk value of the complete vehicle road test problem which is not completely verified actually be easily identified, but also the complete vehicle road test problem which is not completely verified can be favorably transmitted to relevant departments for processing, the risk of the complete vehicle road test project is decentralized, attention of various business lines is attracted, and the overall risk of the complete vehicle road test problem can be more perfectly and accurately detected.
EXAMPLE III
Fig. 5 is a schematic structural diagram of a risk detection device according to an embodiment of the present application, and as shown in fig. 5, the risk detection device according to the embodiment may include:
the data obtaining module 501 is configured to obtain sample data corresponding to a problem to be evaluated in a project to be evaluated, where the sample data includes at least one of verification data, stage data, problem severity data, and time data, the verification data indicates an analysis verification condition of the problem to be evaluated, the stage data indicates a stage of the problem to be evaluated, the problem severity data indicates a severity of the problem to be evaluated, the time data indicates a risk increase degree corresponding to a problem opening time of the problem to be evaluated, and the number of the problems to be evaluated is greater than or equal to 2;
a problem risk determination module 502, configured to determine a risk value of a problem to be evaluated according to the sample data;
and the project risk determining module 503 is configured to determine a risk value of the project to be evaluated according to the risk value of the problem to be evaluated.
Optionally, the data obtaining module 501 is specifically configured to:
and acquiring verification data according to the reason quantized data, the measure quantized data and the verification quantized data of the problem to be evaluated, wherein the reason quantized data indicate the reason identification condition of the problem to be evaluated, the measure quantized data indicate the measure use condition of the problem to be evaluated, and the verification quantized data indicate the verification state of the problem to be evaluated.
Optionally, the data obtaining module 501 is specifically configured to:
the method comprises the steps of obtaining problem severity data according to safety quantization data, high-frequency quantization data, functional quantization data and perception quantization data of a problem to be evaluated, wherein the safety quantization data indicate whether the problem to be evaluated belongs to a safety problem or not, the high-frequency quantization data indicate whether the problem to be evaluated belongs to a high-frequency problem or not, the functional data indicate whether the problem to be evaluated belongs to a functional problem or not, and the perception quantization data indicate whether the problem to be evaluated belongs to a perception problem or not.
Optionally, the data obtaining module 501 is specifically configured to:
and carrying out weighted summation on the safety quantized data, the high-frequency quantized data, the functional quantized data and the perception quantized data of the problem to be evaluated to obtain problem severity data.
Optionally, the data obtaining module 501 is specifically configured to:
and acquiring time data according to the verifiability data corresponding to the problem to be evaluated, the problem opening time of the problem to be evaluated and the preset risk increment.
Optionally, the data obtaining module 501 is specifically configured to:
when the verification data belongs to the first data set, determining the product of the time interval of the problem opening time of the problem to be evaluated and the preset time threshold and the preset risk increment as time data;
when the verification data belongs to the second data set, 0 is determined as the time data.
Optionally, the problem risk determining module 502 is specifically configured to:
calculating a product of the confirmatory data, the staging data, and the problem severity data;
and determining the sum of the product and the time data as a risk value of the problem to be evaluated.
Optionally, the item risk determining module 503 is specifically configured to:
and determining the weighted average value of the risk values of at least two problems to be evaluated as the risk value of the project to be evaluated.
Optionally, the risk value of the item to be evaluated includes risk values at least two time points, the risk detection device further includes a relationship curve determination module, configured to determine, according to the risk value of the item to be evaluated corresponding to each time point and each time point, a relationship curve between the risk value of the item to be evaluated and the relationship curve between the determined time point and the risk value of the item to be evaluated, where the relationship curve is used to represent a time-varying condition of the risk value of the item to be evaluated.
The risk detection apparatus provided in this embodiment is used to execute the risk detection methods provided in the first and second embodiments, and the technical principle and the technical effect are similar, which are not described herein again.
Thus, particular embodiments of the present subject matter have been described. Other embodiments are within the scope of the following claims. In some cases, the actions recited in the claims can be performed in a different order and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may be advantageous.
The above description is only an example of the present application and is not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
Claims (10)
1. A method of risk detection, comprising:
acquiring sampling data corresponding to a problem to be evaluated in a project to be evaluated, wherein the sampling data comprises at least one item of verification data, stage data, problem severity data and time data, the verification data indicates an analysis verification condition of the problem to be evaluated, the stage data indicates a stage of the problem to be evaluated, the problem severity data indicates a severity of the problem to be evaluated, the time data indicates a risk increase degree corresponding to problem opening time of the problem to be evaluated, and the number of the problems to be evaluated is greater than or equal to 2;
determining a risk value of the problem to be evaluated according to the sampling data;
and determining the risk value of the project to be evaluated according to the risk value of the problem to be evaluated.
2. The risk detection method according to claim 1, wherein obtaining the sample data corresponding to the problem to be evaluated in the item to be evaluated comprises:
and acquiring the verification data according to the reason quantization data, the measure quantization data and the verification quantization data of the problem to be evaluated, wherein the reason quantization data indicates the reason identification condition of the problem to be evaluated, the measure quantization data indicates the measure use condition of the problem to be evaluated, and the verification quantization data indicates the verification state of the problem to be evaluated.
3. The risk detection method according to claim 1, wherein obtaining the sample data corresponding to the problem to be evaluated in the item to be evaluated comprises:
and acquiring the problem severity data according to the safety quantization data, the high-frequency quantization data, the functional quantization data and the perception quantization data of the problem to be evaluated, wherein the safety quantization data indicates whether the problem to be evaluated belongs to a safety problem, the high-frequency quantization data indicates whether the problem to be evaluated belongs to a high-frequency problem, the functional data indicates whether the problem to be evaluated belongs to a functional problem, and the perception quantization data indicates whether the problem to be evaluated belongs to a perception problem.
4. The risk detection method according to claim 3, wherein obtaining the problem severity data from the safety quantitative data, the high frequency quantitative data, the functional quantitative data and the perception quantitative data of the problem to be evaluated comprises:
and carrying out weighted summation on the safety quantization data, the high-frequency quantization data, the functional quantization data and the perception quantization data of the problem to be evaluated to obtain the problem severity data.
5. The risk detection method according to claim 1, wherein obtaining the sample data corresponding to the problem to be evaluated in the item to be evaluated comprises:
and acquiring the time data according to the verification data corresponding to the problem to be evaluated, the problem opening time of the problem to be evaluated and a preset risk increment.
6. The risk detection method according to claim 5, wherein obtaining the time data according to the verifiability data corresponding to the problem to be evaluated, the problem opening time of the problem to be evaluated, and a preset risk increment comprises:
when the verification data belongs to a first data set, determining the product of the time interval of the problem opening time of the problem to be evaluated and a preset time threshold and the preset risk increment as the time data;
when the verification data belongs to a second data set, 0 is determined as the time data.
7. The risk detection method of claim 1, wherein determining the risk value for the problem to be assessed from the sampled data comprises:
calculating a product of the confirmatory data, the staging data, and the problem severity data;
and determining the sum of the product and the time data as the risk value of the problem to be evaluated.
8. The risk detection method according to claim 1, wherein determining the risk value of the item to be evaluated according to the risk value of the question to be evaluated comprises:
and determining the weighted average value of the risk values of at least two problems to be evaluated as the risk value of the project to be evaluated.
9. The risk detection method according to claim 1, wherein the risk values of the item to be assessed include risk values at least two points in time, the method further comprising:
and determining a relation curve between the risk value of the item to be evaluated and the time point according to each time point and the risk value of the item to be evaluated corresponding to each time point, wherein the relation curve is used for representing the condition that the risk value of the item to be evaluated changes along with time.
10. A risk detection device, comprising:
the system comprises a data acquisition module, a data processing module and a data processing module, wherein the data acquisition module is used for acquiring sampling data corresponding to a problem to be evaluated in a project to be evaluated, the sampling data comprises at least one of verification data, stage data, problem severity data and time data, the verification data indicates the analysis and verification condition of the problem to be evaluated, the stage data indicates the stage of the problem to be evaluated, the problem severity data indicates the severity of the problem to be evaluated, the time data indicates the risk increase degree corresponding to the problem opening time of the problem to be evaluated, and the number of the problems to be evaluated is greater than or equal to 2;
the problem risk determining module is used for determining a risk value of the problem to be evaluated according to the sampling data;
and the project risk determining module is used for determining the risk value of the project to be evaluated according to the risk value of the problem to be evaluated.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2005149414A (en) * | 2003-11-19 | 2005-06-09 | Hitachi Software Eng Co Ltd | Retrieval method for project risk, evaluation system and common database utilization method |
| US20090299804A1 (en) * | 2003-10-08 | 2009-12-03 | Bank Of America Corporation | Operational risk assessment and control |
| CN106251085A (en) * | 2016-08-10 | 2016-12-21 | 中交公局海威工程建设有限公司 | A kind of methods of risk assessment and device |
| CN107172004A (en) * | 2016-03-08 | 2017-09-15 | 中兴通讯股份有限公司 | The methods of risk assessment and device of a kind of Network Security Device |
| CN109816221A (en) * | 2019-01-07 | 2019-05-28 | 平安科技（深圳）有限公司 | Decision of Project Risk method, apparatus, computer equipment and storage medium |
| CN110334012A (en) * | 2019-05-31 | 2019-10-15 | 口碑(上海)信息技术有限公司 | A kind of methods of risk assessment and device |
-
2020
- 2020-04-15 CN CN202010293732.XA patent/CN113537662A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20090299804A1 (en) * | 2003-10-08 | 2009-12-03 | Bank Of America Corporation | Operational risk assessment and control |
| JP2005149414A (en) * | 2003-11-19 | 2005-06-09 | Hitachi Software Eng Co Ltd | Retrieval method for project risk, evaluation system and common database utilization method |
| CN107172004A (en) * | 2016-03-08 | 2017-09-15 | 中兴通讯股份有限公司 | The methods of risk assessment and device of a kind of Network Security Device |
| CN106251085A (en) * | 2016-08-10 | 2016-12-21 | 中交公局海威工程建设有限公司 | A kind of methods of risk assessment and device |
| CN109816221A (en) * | 2019-01-07 | 2019-05-28 | 平安科技（深圳）有限公司 | Decision of Project Risk method, apparatus, computer equipment and storage medium |
| CN110334012A (en) * | 2019-05-31 | 2019-10-15 | 口碑(上海)信息技术有限公司 | A kind of methods of risk assessment and device |
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