CN111881031A - Intelligent transportation software and hardware precision disturbance method library and risk index construction method - Google Patents
Intelligent transportation software and hardware precision disturbance method library and risk index construction method Download PDFInfo
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- CN111881031A CN111881031A CN202010714656.5A CN202010714656A CN111881031A CN 111881031 A CN111881031 A CN 111881031A CN 202010714656 A CN202010714656 A CN 202010714656A CN 111881031 A CN111881031 A CN 111881031A
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- 238000004364 calculation method Methods 0.000 claims description 9
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Abstract
The method adopts the metamorphic testing technology to test the correctness of the intelligent traffic model, designs the metamorphic relation applied to the intelligent traffic model and judges the correctness of the program function from multiple aspects, tests the intelligent traffic model by using the original case and the derived case generated based on the metamorphic relation, and checks whether the output of the original case and the derived case meets the corresponding metamorphic relation to obtain the test result. The invention can adopt a new software testing method to reasonably measure and evaluate the correctness of the intelligent traffic model.
Description
Technical Field
The invention belongs to the field of software and hardware testing, and particularly relates to testing of operation precision of software and hardware at the bottom layer of intelligent traffic. A disturbance method library of a series of processing is designed, a disturbance method is selected and disturbance parameters are set for specific software and hardware scenes, a precision test sample set is generated, and a condition protocol is adopted to generate a precision evaluation threshold value for precision analysis.
Background
The data-driven test based on disturbance is an important method for guaranteeing the reliability of scientific computing software. There is a detection method for value and code stability, which includes two perturbation strategies: numerical perturbation and expression perturbation. The numerical disturbance mainly replaces non-precision key byte positions or intermediate values in numerical representation, the expression disturbance replaces an original numerical expression by an equivalent numerical expression, and then the numerical stability of the program is detected by comparing the difference of program output before and after disturbance.
The intelligent transportation software system is essentially a large-scale complex numerical calculation program. Such programs introduce errors due to inherent problems with floating point type precision, which are often reduced to a large extent during large scale training. However, in the application of the intelligent transportation software system, the further adoption of fixed-point operation or lower floating-point operation precision may bring potential risks, and thus strict theoretical analysis or a great amount of test verification is required. Currently, part of input physical devices (such as laser radar) are expensive, and whether a model trained in a high-precision environment can be suitable for lower-precision input data is also to be further researched. Although it has been demonstrated that some intelligent traffic software models satisfy the condition of lepichz continuity, the accuracy fluctuation is also extremely dependent on the magnitude of the lepichz constant. At present, a test scheme specially aiming at each software and hardware bottom layer optimization method under multiple scenes of intelligent traffic V2X does not exist, and along with interactive complication of application scenes and diversification of interactive equipment suppliers, the difference of software and hardware of different specific bottom layers under the same scene brings implicit compatibility defects, possibly causes system abnormity and causes safety problems.
The method is mainly based on an angle design precision disturbance method library such as precision abnormal values, operation rules, scale scaling, precision truncation, data interference and the like, and adopts a proper disturbance method and disturbance parameters to adjust the existing test sample set in combination with specific software and hardware scenes to obtain a precision test sample set, and meanwhile, a precision risk index is constructed for accumulated errors calculated by bottom layer software and hardware to serve as a reference standard in the precision evaluation process.
Disclosure of Invention
The invention aims to solve the problems that: in the practical application of the intelligent transportation software system, the input precision and the operation precision are influenced due to the limited resources. The method can be combined with specific software and hardware scenes, and find the precision problem of the software and hardware at the bottom layer of the intelligent traffic V2X by constructing a disturbance method library and risk indexes.
The technical scheme of the invention is as follows: a disturbance method library and a risk index construction technology for the operational accuracy of bottom layer software and hardware are characterized in that the accuracy disturbance method library is constructed from multiple angles such as accuracy abnormal values, accuracy truncation and the like, and risk indexes are constructed according to condition protocols. The generation method comprises the following two modules/steps:
1) generating a precision disturbance method library: firstly, designing a precision disturbance method library from the angles of operation rules, precision truncation, data interference and the like, and then adjusting the existing test sample set by adopting a proper disturbance method and disturbance parameters according to the specific software and hardware scenes related to the test task, including vehicle-mounted equipment, crowd equipment, cloud equipment and the like to obtain a precision test sample set.
2) And (3) generating a precision risk index: firstly, tracking the calculation process of software and hardware in a specific scene, then collecting the error amplitude generated in the calculation process of the software and the hardware, and finally converting the risk assessment value into the cumulative sum of each part of reduction errors and corresponding weight by using condition number reduction error amplitude.
The invention is characterized in that:
1. in order to better find the operation problem of the software and hardware at the bottom layer of the intelligent traffic V2X, a plurality of software and hardware scenes are considered when a disturbance method library is constructed.
2. In order to meet the requirement of precision error testing cost, a precision risk index is constructed based on the idea of error accumulation.
Drawings
Fig. 1 is a general flow chart of the implementation of the present invention.
Figure 2 is a flow chart of key step 1.
Figure 3 is a flow chart of key step 2.
Detailed Description
The intelligent transportation bottom layer software and hardware operation precision test is implemented through data disturbance, and a precision disturbance method library and a precision risk index construction technology are mainly adopted.
(1) Precision perturbation method library design
In the invention, an accuracy disturbance method library D is designed from five angles of an accuracy abnormal value, an operation rule, scale scaling, accuracy truncation and data interference. The method comprises the steps that related data contents such as precision-related abnormal data types, abnormal ranges and the like are introduced into precision abnormal disturbance, interference is added according to an operation rule aiming at a data precision operation process and an operation method, conditional amplification and reduction are carried out according to the scale of data in the intelligent transportation field through scale scaling, the operation rule can be divided into multiple precision interference modes such as local and overall scaling, high-precision numerical values are cut into low-precision numerical values through precision cutting, data interference is directly oriented to data, and test data are processed through typical data interference modes such as noise data adding and data damage.
(2) Precision test sample set generation
According to specific application scenes, according to specific software and hardware scenes related to a test task, including vehicle-mounted equipment, crowd equipment, cloud equipment and the like, a corresponding appropriate precision disturbance method F is selected from a disturbance library, and an existing test sample set is adjusted by adopting an appropriate disturbance method and disturbance parameters to obtain a precision test sample set.
(3) Precision risk indicator construction
In the invention, the calculation process of software and hardware under a specific scene is tracked, the condition number reduction error amplitude is adopted, the reduced calculation errors are accumulated, and the risk evaluation function F (pi) = Sigma pi-is used for evaluating the risk in the precision test set, wherein the occupied weight is calculated correspondingly by pi according to the error of the condition number reduction, so that the risk evaluation value is converted into the accumulated sum of each part of reduction error and the corresponding weight.
In this example, a relatively complete precision disturbance method library and precision risk indexes are constructed by combining specific software and hardware scenes, and precision analysis is performed on precision of software and hardware operation of the bottom layer of the intelligent traffic V2X, so that inherent precision errors and precision adaptability of the software and hardware are found.
Claims (3)
1. An intelligent transportation software and hardware precision disturbance method library and a risk index construction method are characterized in that the precision disturbance method library is designed, a specific software and hardware scene is combined to adjust an existing test sample set, and meanwhile, a precision risk index is constructed for accumulated errors calculated by bottom layer software and hardware and serves as a reference standard in a precision evaluation process; firstly, designing a precision disturbance method library from the angles of operation rules, precision truncation, data interference and the like, then adjusting an existing test sample set by adopting a proper disturbance method and disturbance parameters according to a specific software and hardware scene related to a test task, including vehicle-mounted equipment, crowd equipment, cloud equipment and the like to obtain a precision test sample set, tracking the calculation process of the software and hardware under the specific scene, then acquiring the error amplitude generated in the calculation process of the software and hardware, and finally reducing the error amplitude by adopting condition numbers to convert a risk evaluation value into the sum of each part of protocol errors and corresponding weights.
2. According to the intelligent transportation software and hardware accuracy disturbance method library and the risk indicator construction method in claim 1, the method is characterized in that a precision disturbance method library is designed from five angles of precision abnormal values, an operation rule, scale scaling, precision truncation and data interference, the precision abnormal disturbance introduces precision-related abnormal data types and abnormal range data contents, the operation rule adds interference aiming at a data precision operation process and an operation method, the scale scaling conditionally amplifies and reduces the data scale in the intelligent transportation field, the method can be divided into various precision interference modes such as local and overall scaling, the precision truncation is used for truncating a high-precision numerical value into a low-precision numerical value, the data interference is directly oriented to the data, and typical data interference modes such as noise data addition and data damage are used for processing test data.
3. The intelligent transportation software and hardware precision disturbance method library and the risk index construction method according to claim 1 are characterized in that the intelligent transportation system software and hardware-oriented precision risk index construction is performed, the software and hardware calculation process under a specific scene is tracked, the condition number reduction error amplitude is adopted, the reduced calculation errors are accumulated, and the risk evaluation function is used for calculation:
F(π) = ∑π·𝑘
the above equation is used to evaluate the risk in the precision test set, where pi corresponds to the occupied weight calculated for the error using the condition number convention, thereby converting the risk evaluation value into the cumulative sum of each part of the convention error and the corresponding weight.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20180232300A1 (en) * | 2015-05-28 | 2018-08-16 | International Business Machines Corporation | Generation of test scenarios based on risk analysis |
| CN108874678A (en) * | 2018-06-28 | 2018-11-23 | 北京顺丰同城科技有限公司 | A kind of automatic test approach and device of intelligent program |
| CN109084992A (en) * | 2018-07-27 | 2018-12-25 | 长安大学 | Method based on engine bench test unmanned vehicle intelligence |
| CN109902018A (en) * | 2019-03-08 | 2019-06-18 | 同济大学 | A kind of acquisition methods of intelligent driving system test cases |
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- 2020-07-23 CN CN202010714656.5A patent/CN111881031A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20180232300A1 (en) * | 2015-05-28 | 2018-08-16 | International Business Machines Corporation | Generation of test scenarios based on risk analysis |
| CN108874678A (en) * | 2018-06-28 | 2018-11-23 | 北京顺丰同城科技有限公司 | A kind of automatic test approach and device of intelligent program |
| CN109084992A (en) * | 2018-07-27 | 2018-12-25 | 长安大学 | Method based on engine bench test unmanned vehicle intelligence |
| CN109902018A (en) * | 2019-03-08 | 2019-06-18 | 同济大学 | A kind of acquisition methods of intelligent driving system test cases |
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