CN115184055A - Method and system for determining test set with optimized hierarchical testability - Google Patents

Abstract

The invention provides a method and a system for determining a test set of hierarchical testability optimization, which relate to the technical field of computer aided design and are used for carrying out testability modeling on equipment to obtain an equipment testability model; performing testability analysis on the equipment based on the equipment testability model to obtain an initial fault-test correlation matrix; performing built-in test level optimization on the initial fault-test correlation matrix to obtain a built-in fault-test correlation matrix; performing automatic test-level optimization on the initial fault-test correlation matrix to obtain an automatic fault-test correlation matrix; performing manual test-level optimization on the initial fault-test correlation matrix to obtain a manual fault-test correlation matrix, and further determining a test set and a fault set; according to the invention, the built-in test level optimization, the automatic test level optimization and the manual test level optimization are carried out on the initial fault-test correlation matrix, so that a test set and a fault set are respectively constructed for different tests, and the testability optimization capability is further improved.

Description

Method and system for determining test set with optimized hierarchical testability

Technical Field

The invention relates to the technical field of computer aided design, in particular to a method and a system for determining a test set of hierarchical testability optimization.

Background

Testability (Testability) is a design characteristic that a product can timely and accurately determine the state (operable, inoperable or performance-degraded) of the product and effectively isolate internal faults of the product, and is a key of quality characteristics of equipment. Design for testability (DFT) refers to a Design method that comprehensively considers all test resources in the product Design process, such as Built-in test (BIT), automatic Test Equipment (ATE), manual Test (MT), etc., ensures that a product obtains sufficient tests with minimum workload through a careful plan, and ensures that test results have higher confidence. In the process of designing the testability of the equipment system, due to the increasing performance and the increasing complexity of the equipment system, the difficulty of fault detection and diagnosis is increasing. In order to improve the testability level and the diagnostic capability of the equipment, a large number of tests are generally required to be set on the basis of the comprehensive analysis of the faults of the equipment. But so many tests are not of the same importance from the standpoint of meeting the testability requirements of the device, there is redundancy. One of the important tasks of a testability design is therefore test optimization selection.

At present, the design of internal test and external test equipment of large-scale equipment such as airplanes, missiles and the like basically depends on the experience of designers, a unified method and a unified flow do not exist, and an integrated design platform is not referred to. Usually, after the built-in test design is completed, the design of the external test equipment is started in the shaping stage of the product. Even the built-in test and the external test equipment are designed separately by two departments and are independent of each other, so that the division of the built-in test and the external test equipment is unclear, the difference of the design level is large, scientific bases are lacked, the design levels of different designers are uneven, and the testability design level of the equipment is severely restricted. At present, technicians usually improve the test performance of the equipment by optimizing test equipment or improving a testability optimization method, however, the optimization method adopts test sets (including a test set and a fault set) provided by an equipment design stage in all built-in test, automatic test and manual test, and the test set provided by the equipment design stage has data redundancy aiming at each test, so that the optimization effect of the testability is low.

Disclosure of Invention

The invention aims to provide a method and a system for determining a test set with graded testability optimization, which can optimize the test set respectively aiming at built-in test, automatic test and manual test, thereby improving the testability optimization capability.

In order to achieve the purpose, the invention provides the following scheme:

a method for determining a test set of hierarchical testability optimization comprises the following steps:

carrying out testability modeling on the equipment to obtain an equipment testability model;

performing testability analysis on the equipment based on the equipment testability model to obtain an initial fault-test correlation matrix; the initial fault-test correlation matrix is used for describing the correlation between the fault mode of the equipment and the test;

performing built-in test level optimization on the initial fault-test correlation matrix to obtain a built-in fault-test correlation matrix;

performing automatic test-level optimization on the initial fault-test correlation matrix based on the built-in fault-test correlation matrix to obtain an automatic fault-test correlation matrix;

performing manual test-level optimization on the initial fault-test correlation matrix based on the automatic fault-test correlation matrix to obtain a manual fault-test correlation matrix;

determining a test set and a fault set according to the fault-test correlation finalization matrix; the test set and the fault set are used for corresponding tests of the fault-test related finalization matrix; the fault-test correlation conclusion matrix includes a built-in fault-test correlation matrix, and a built-in fault-test correlation matrix.

Optionally, the initial fault-test correlation matrix is an m × n-order 0-1 matrix; wherein m represents the total number of failure modes of the device; n represents the total number of test types of the equipment;

when the element of the ith row and the jth column in the initial fault-test correlation matrix is 1, the correlation between the ith fault mode and the jth test is represented; i =1,2,. M; j =1,2,. N; and when the element of the ith row and the jth column in the initial fault-test correlation matrix is 0, the ith fault mode is irrelevant to the jth test.

Optionally, the performing internal test-level optimization on the initial fault-test correlation matrix to obtain an internal fault-test correlation matrix includes:

determining a built-in cost matrix, a test weight matrix and a reliability matrix when the device is tested in the machine based on the device testability model; the built-in cost matrix is used for expressing the cost of each test when the device is tested in the built-in test; the test weight matrix is used for expressing the weight of each test device when the device is tested in the machine; the reliability matrix is used for expressing the reliability of each test device when the device is tested in the machine;

respectively determining a row vector array and a column vector array of the initial fault-test correlation matrix;

determining any proper subset of the column vector array as a set of column vectors in the to-be-determined machine;

determining an identification vector of the to-be-determined inner column vector group as a first identification vector; the first identification vector is a 1 Xn-order 0-1 matrix; when the jth element in the first identification vector is 1, the jth element in the first identification vector indicates that the jth column vector in the column vector array is included in the built-in column vector group; when the jth element in the first identification vector is 0, the jth element in the first identification vector indicates that the jth column vector in the column vector array is not included in the intra-column vector group;

determining the fault detection rate of the column vector group in the machine to be determined as a first fault detection rate according to the row vector array and the first identification vector;

determining the fault isolation rate of the column vector group to be determined in the machine as a first fault isolation rate according to the row vector array and the first identification vector;

according to the first identification vector and the built-in cost matrix, a formula is utilized

Determining an optimization coefficient of a column vector group in the undetermined machine as a first optimization coefficient; wherein A is a first optimization coefficient;

is the ith element in the first identification vector;

is the ith element in the first price matrix;

updating the to-be-determined internal column vector group, and returning to the step of determining that the identification vector of the to-be-determined internal column vector group is a first identification vector until all proper subsets of the column vector array are traversed to obtain a first optimization coefficient, a first fault detection rate and a first fault isolation rate of each to-be-determined internal column vector group;

constructing a first constraint condition;

determining the undetermined internal column vector group with the minimum first optimization coefficient as an optimal undetermined internal column vector group based on the first constraint condition, the first fault detection rate, the first fault isolation rate, the test weight matrix and the reliability matrix;

converting the optimal undetermined internal column vector group into a matrix form to obtain an internal fault-test related undetermined matrix;

and deleting the row vectors of which all elements are 0 in the internal fault-test correlation undetermined matrix to obtain the internal fault-test correlation matrix.

Optionally, the determining, according to the row vector array and the first identification vector, that the fault detection rate of the to-be-determined inner column vector group is a first fault detection rate includes:

determining a row vector which meets the detection condition of a column vector group to be determined in the row vector array as a machine row vector;

acquiring the fault rates of faults corresponding to all the row vectors in the row vector array;

determining the sum of the failure rates of the faults corresponding to all the built-in row vectors as a first related quantity;

determining the sum of the failure rates of the failures corresponding to all the row vectors in the row vector array as a second correlation quantity;

and determining the ratio of the first correlation quantity to the second correlation quantity as a first fault detection rate.

Optionally, the determining, according to the row vector array and the first identification vector, that the fault isolation rate of the column vector group in the to-be-determined machine is a first fault isolation rate includes:

determining any row vector in the row vector array as a current row vector;

multiplying the current row vector by the corresponding element of the first identification vector to obtain a current row identification matrix;

traversing all row vectors in the row vector array to obtain a plurality of row identification matrixes;

determining any row identification matrix as a current first row identification matrix;

determining any row identification matrix except the current first row identification matrix as a current second row identification matrix;

determining a norm value after the exclusive or operation is carried out on the current first row identification matrix and the current second row identification matrix;

updating the current second row identifier matrix and returning to the step of determining norm values after XOR operation is carried out on the current first row identifier matrix and the current second row identifier matrix until all row identifier matrices except the current first row identifier matrix are traversed to obtain a plurality of norm values corresponding to the current first row identifier matrix as a current norm set;

performing a continuous product operation on the elements in the current norm group to obtain an isolation parameter of a fault corresponding to the first row of the identification matrix;

updating the current first row identification matrix, and returning to the step of determining that any row identification matrix except the current first row identification matrix is the current second row identification matrix until all row identification matrices are traversed to obtain the isolation parameters of the faults corresponding to each row identification matrix;

determining the fault corresponding to the row identification matrix with the isolation parameter larger than 1 as an isolation fault;

determining the sum of the fault rates of all the isolation faults as a third correlation quantity;

and determining the ratio of the third correlation quantity to the second correlation quantity as a first fault isolation rate.

Optionally, the performing, based on the built-in fault-test correlation matrix, automatic test-level optimization on the initial fault-test correlation matrix to obtain an automatic fault-test correlation matrix includes:

determining an automatic cost matrix when the equipment is automatically tested based on the equipment testability model; the automatic cost matrix is used for expressing the cost of each test when the equipment is automatically tested;

determining any proper subset of the column vector array as a pending automatic column vector group according to the built-in fault-test correlation matrix; the built-in fault-test correlation matrix is a proper subset of the set of undetermined automatic column vectors;

determining an identification vector of the undetermined automatic column vector group as a second identification vector; the second identification vector is a 1 Xn-order 0-1 matrix;

determining the fault detection rate of the to-be-determined automatic column vector group as a second fault detection rate according to the row vector array and the second identification vector;

determining the fault isolation rate of the to-be-determined automatic column vector group as a second fault isolation rate according to the row vector array and the second identification vector;

using a formula according to the second identification vector and the automatic cost matrix

Determining pending auto columnsThe optimization coefficient of the vector group is a second optimization coefficient; wherein B is a second optimization coefficient;

identifying the ith element in the vector for the second label;

is the ith element in the second price matrix;

updating the undetermined automatic column vector groups, and returning to the step of determining that the identification vectors of the undetermined automatic column vector groups are second identification vectors until all proper subsets of the column vector array are traversed to obtain a second optimization coefficient, a second fault detection rate and a second fault isolation rate of each undetermined automatic column vector group;

constructing a second constraint condition;

determining a pending automatic column vector group with the minimum second optimization coefficient as an optimal pending automatic column vector group based on the second constraint condition, the second fault detection rate and the second fault isolation rate;

converting the optimal undetermined automatic column vector group into a matrix form to obtain an automatic fault-test related undetermined matrix;

and deleting the row vectors of which all elements are 0 in the automatic fault-test correlation undetermined matrix to obtain an automatic fault-test correlation matrix.

Optionally, the initial fault-test correlation matrix is subjected to manual test-level optimization based on the automatic fault-test correlation matrix, so as to obtain a manual fault-test correlation matrix;

determining an artificial cost matrix when the equipment is manually tested based on the equipment testability model; the manual cost matrix is used for expressing the cost of each test when the equipment is manually tested;

determining any proper subset of the column vector array as a group of undetermined manual column vectors according to the automatic fault-test correlation matrix; the automatic fault-test correlation matrix is a proper subset of the set of pending artificial column vectors;

determining an identification vector of the undetermined artificial column vector group as a third identification vector; the third identification vector is a 1 Xn-order 0-1 matrix;

determining the fault detection rate of the to-be-determined artificial column vector group as a third fault detection rate according to the row vector array and the third identification vector;

determining the fault isolation rate of the to-be-determined artificial column vector group as a third fault isolation rate according to the row vector array and the third identification vector;

according to the third identification vector and the artificial cost matrix, a formula is utilized

Determining an optimization coefficient of the undetermined artificial column vector group as a third optimization coefficient; wherein D is a third optimization coefficient;

identifying the ith element in the vector for the third;

is the ith element in the third price matrix;

updating the undetermined artificial column vector groups, and returning to the step of determining that the identification vectors of the undetermined artificial column vector groups are third identification vectors until all proper subsets of the column vector array are traversed to obtain a third optimization coefficient, a third fault detection rate and a third fault isolation rate of each undetermined artificial column vector group;

constructing a third constraint condition;

determining a pending artificial column vector group with the minimum third optimization coefficient as an optimal pending artificial column vector group based on the third constraint condition, the third fault detection rate and the third fault isolation rate;

converting the optimal undetermined manual column vector group into a matrix form to obtain a manual fault-test related undetermined matrix;

and deleting the row vectors of which the elements are all 0 in the artificial fault-test correlation undetermined matrix to obtain the artificial fault-test correlation matrix.

Optionally, the first constraint condition is:

wherein, w _B Represents the sum of all elements in the test weight matrix; r is _B Representing the sum of all elements in the reliability matrix;

a first failure detection rate is indicated and,

a first fault isolation rate is indicated and,

represents the lower weight limit of the test specimen;

represents a lower reliability limit;

representing a first lower fault detection rate limit;

representing a first lower fault isolation rate limit;

the second constraint condition is as follows:

wherein,

a second failure detection rate is indicated that is,

a second fault isolation rate is indicated and,

representing a second lower fault detection rate limit;

representing a second lower fault isolation rate limit;

the third constraint condition is as follows:

wherein,

a third failure detection rate is indicated and,

a third fault isolation rate is indicated and,

a third lower fault detection rate limit is indicated,

representing a third lower fault isolation rate limit.

Optionally, the determining a test set and a fault set according to the fault-test correlation completion matrix specifically includes:

determining a test corresponding to each column vector in the fault-test correlation completion matrix to obtain the test set;

and determining the fault corresponding to each row vector in the fault-test related finalization matrix to obtain the fault set.

A hierarchical testability-optimized test set determination system, comprising:

the device testability model building module is used for carrying out testability modeling on the device to obtain a device testability model;

the initial fault-test correlation matrix determining module is used for carrying out testability analysis on the equipment based on the equipment testability model to obtain an initial fault-test correlation matrix; the initial fault-test correlation matrix is used for describing the correlation between the fault mode of the equipment and the test;

the internal fault-test correlation matrix determining module is used for carrying out internal test level optimization on the initial fault-test correlation matrix to obtain an internal fault-test correlation matrix;

an automatic fault-test correlation matrix determination module, configured to perform automatic test-level optimization on the initial fault-test correlation matrix based on the in-machine fault-test correlation matrix to obtain an automatic fault-test correlation matrix;

the artificial fault-test correlation matrix determining module is used for carrying out artificial test level optimization on the initial fault-test correlation matrix based on the automatic fault-test correlation matrix to obtain an artificial fault-test correlation matrix;

a test set and fault set determination module for determining a test set and a fault set according to the fault-test correlation finalization matrix; the test set and the fault set are used for corresponding tests of the fault-test related finalization matrix; the fault-test correlation conclusion matrix includes a built-in fault-test correlation matrix, and a built-in fault-test correlation matrix.

According to the specific embodiment provided by the invention, the invention discloses the following technical effects:

the invention provides a method and a system for determining a test set of hierarchical testability optimization, which are used for carrying out testability modeling on equipment to obtain an equipment testability model; performing testability analysis on the equipment based on the equipment testability model to obtain an initial fault-test correlation matrix; performing built-in test level optimization on the initial fault-test correlation matrix to obtain a built-in fault-test correlation matrix; performing automatic test-level optimization on the initial fault-test correlation matrix based on the built-in fault-test correlation matrix to obtain an automatic fault-test correlation matrix; performing manual test-level optimization on the initial fault-test correlation matrix based on the automatic fault-test correlation matrix to obtain a manual fault-test correlation matrix; determining a test set and a fault set according to the fault-test correlation completion matrix; according to the invention, the built-in test level optimization, the automatic test level optimization and the manual test level optimization are carried out on the initial fault-test correlation matrix, so that a test set and a fault set suitable for built-in test, automatic test and manual test are obtained, and the testability optimization capability is further improved.

Drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.

FIG. 1 is a flowchart of a method for determining a test set for hierarchical testability optimization according to an embodiment of the present invention;

FIG. 2 is a flowchart of a method for determining a test set for hierarchical testability optimization according to a second embodiment of the present invention;

FIG. 3 is a flowchart illustrating an in-machine test level optimization process according to a second embodiment of the present invention;

FIG. 4 is a flowchart illustrating an automatic test level optimization process according to a second embodiment of the present invention;

FIG. 5 is a flowchart of a manual test-level optimization according to a second embodiment of the present invention.

Detailed Description

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, belong to the protection scope of the present invention.

The invention aims to provide a method and a system for determining a test set with graded testability optimization, which can optimize the test set respectively aiming at built-in test, automatic test and manual test, thereby improving the testability optimization capability.

In order to make the aforementioned objects, features and advantages of the present invention more comprehensible, the present invention is described in detail with reference to the accompanying drawings and the detailed description thereof.

Example one

As shown in fig. 1, the present embodiment provides a method for determining a test set of hierarchical testability optimization, including:

step 101: and performing testability modeling on the equipment to obtain an equipment testability model.

Step 102: performing testability analysis on the equipment based on the equipment testability model to obtain an initial fault-test correlation matrix; the initial fault-test correlation matrix is used for describing the correlation between the fault mode of the equipment and the test; the initial fault-test correlation matrix is an m multiplied by n order 0-1 matrix; wherein m represents the total number of failure modes of the device; n represents the total number of test types of the equipment; when the element of the ith row and the jth column in the initial fault-test correlation matrix is 1, the ith fault mode is related to the jth test; i =1,2,. M; j =1,2,. N; when the element in the ith row and jth column of the initial failure-test correlation matrix is 0, it indicates that the ith failure mode is not correlated with the jth test.

Step 103: and carrying out built-in test-level optimization on the initial fault-test correlation matrix to obtain a built-in fault-test correlation matrix.

Step 104: and performing automatic test-level optimization on the initial fault-test correlation matrix based on the built-in fault-test correlation matrix to obtain an automatic fault-test correlation matrix.

Step 105: based on the automatic fault-test correlation matrix, carrying out manual test-level optimization on the initial fault-test correlation matrix to obtain a manual fault-test correlation matrix;

step 106: determining a test set and a fault set according to the fault-test correlation completion matrix; the test set and the fault set are used for corresponding tests of the fault-test correlation completion matrix; the fault-test correlation finalization matrix includes a built-in fault-test correlation matrix, and a built-in fault-test correlation matrix.

Step 103, comprising:

step 1031: determining a built-in cost matrix, a test weight matrix and a reliability matrix when the device is tested in a built-in manner based on the device testability model; the built-in cost matrix is used for expressing the cost of each test when the built-in test is carried out on the equipment; the test weight matrix is used for expressing the weight of each test device when the device is tested in the machine; the reliability matrix is used for expressing the reliability of each test device when the device is tested in the machine.

Step 1031: a row vector array and a column vector array of the initial fault-test correlation matrix are determined, respectively.

Step 1032: any proper subset of the column vector array is determined to be a set of column vectors within the machine to be determined.

Step 1033: determining an identification vector of a column vector group in the undetermined machine as a first identification vector; the first identification vector is a 1 Xn-order 0-1 matrix; when the jth element in the first identification vector is 1, the jth element in the first identification vector represents that the jth column vector in the column vector array is included in the built-in column vector group; when the jth element in the first identification vector is 0, the jth column vector in the column vector array is not included in the built-in column vector group.

Step 1034: and determining the fault detection rate of the column vector group in the to-be-determined machine as a first fault detection rate according to the row vector array and the first identification vector.

Step 1035: and determining the fault isolation rate of the column vector group in the to-be-determined machine as a first fault isolation rate according to the row vector array and the first identification vector.

Step 1036: according to the first identification vector and the built-in cost matrix, a formula is utilized

Determining an optimization coefficient of a column vector group in the undetermined machine as a first optimization coefficient; wherein A is a first optimization coefficient;

is the ith element in the first identification vector;

is the ith element in the first price matrix.

Step 1037: and updating the column vector group in the to-be-determined machine, and returning to the step of determining that the identification vector of the column vector group in the to-be-determined machine is the first identification vector until all proper subsets of the column vector array are traversed to obtain a first optimization coefficient, a first fault detection rate and a first fault isolation rate of each column vector group in the to-be-determined machine.

Step 1038: a first constraint is constructed.

Step 1039: and determining the undetermined internal column vector group with the minimum first optimization coefficient as an optimal undetermined internal column vector group based on the first constraint condition, the first fault detection rate, the first fault isolation rate, the test weight matrix and the reliability matrix.

Step 10310: and converting the optimal undetermined internal column vector group into a matrix form to obtain an internal fault-test related undetermined matrix.

Step 10311: and deleting row vectors of which all elements are 0 in the internal fault-test correlation undetermined matrix to obtain the internal fault-test correlation matrix.

The method comprises the following steps of determining the fault detection rate of a column vector group in an undetermined machine as a first fault detection rate according to a row vector array and a first identification vector, and comprises the following steps: determining a row vector which meets the detection condition of the undetermined built-in column vector group in the row vector array as a built-in row vector; acquiring the fault rates of faults corresponding to all the row vectors in the row vector array; determining the sum of the failure rates of the failures corresponding to all the built-in row vectors as a first correlation quantity; determining the sum of the failure rates of the corresponding failures of all the row vectors in the row vector array as a second correlation quantity; and determining the ratio of the first correlation quantity to the second correlation quantity as a first fault detection rate.

The method for determining the fault isolation rate of the column vector group in the undetermined machine as the first fault isolation rate comprises the following steps of: determining any row vector in the row vector array as a current row vector; multiplying the current row vector by the corresponding element of the first identification vector to obtain a current row identification matrix; traversing all row vectors in the row vector array to obtain a plurality of row identification matrixes; determining any row identification matrix as a current first row identification matrix; determining any row identification matrix except the current first row identification matrix as a current second row identification matrix; determining a norm value after the exclusive or operation is carried out on the current first row identification matrix and the current second row identification matrix; updating the current second row identification matrix and returning to the step of determining norm values after XOR operation is carried out on the current first row identification matrix and the current second row identification matrix until all row identification matrices except the current first row identification matrix are traversed to obtain a plurality of norm values corresponding to the current first row identification matrix as the current norm groups; performing a continuous product operation on elements in the current norm group to obtain an isolation parameter of a fault corresponding to the first row of the identification matrix; updating the current first row identification matrix, and returning to the step of determining any row identification matrix except the current first row identification matrix as the current second row identification matrix until all row identification matrices are traversed to obtain the isolation parameters of the faults corresponding to each row identification matrix; determining that the fault corresponding to the row identification matrix with the isolation parameter larger than 1 is an isolation fault; determining the sum of the fault rates of all the isolation faults as a third correlation quantity; and determining the ratio of the third correlation quantity to the second correlation quantity as the first fault isolation rate.

Step 104, including:

step 1041: determining an automatic cost matrix when the equipment is automatically tested based on the equipment testability model; the automatic cost matrix is used for expressing the cost of each test when the equipment is automatically tested.

Step 1042: determining any proper subset of the column vector array as a pending automatic column vector group according to the built-in fault-test correlation matrix; the built-in fault-test correlation matrix is a proper subset of the set of pending automatic column vectors.

Step 1043: determining an identification vector of the undetermined automatic column vector group as a second identification vector; the second identification vector is a 1 xnth order 0-1 matrix.

Step 1044: and determining the fault detection rate of the to-be-determined automatic column vector group as a second fault detection rate according to the row vector array and the second identification vector.

Step 1045: and determining the fault isolation rate of the to-be-determined automatic column vector group as a second fault isolation rate according to the row vector array and the second identification vector.

Step 1046: using a formula according to the second identification vector and the automatic cost matrix

Determining an optimization coefficient of the undetermined automatic column vector group as a second optimization coefficient; wherein B is a second optimization coefficient;

the ith element in the second identification vector;

is the ith element in the second cost matrix.

Step 1047: and updating the undetermined automatic column vector groups, and returning to the step of determining that the identification vectors of the undetermined automatic column vector groups are second identification vectors until all proper subsets of the column vector array are traversed to obtain a second optimization coefficient, a second fault detection rate and a second fault isolation rate of each undetermined automatic column vector group.

Step 1048: a second constraint is constructed.

Step 1049: and determining the undetermined automatic column vector group with the minimum second optimization coefficient as an optimal undetermined automatic column vector group based on the second constraint condition, the second fault detection rate and the second fault isolation rate.

Step 10410: and converting the optimal undetermined automatic column vector group into a matrix form to obtain an automatic fault-test related undetermined matrix.

Step 10411: and deleting the row vectors of which all elements are 0 in the automatic fault-test correlation undetermined matrix to obtain the automatic fault-test correlation matrix.

Step 105, comprising:

step 1051: determining an artificial cost matrix when the equipment is manually tested based on the equipment testability model; the manual cost matrix is used for expressing the cost of each test when the equipment is manually tested.

Step 1052: determining any proper subset of the column vector array as a pending artificial column vector group according to the automatic fault-test correlation matrix; the automatic fault-test correlation matrix is a proper subset of the set of pending artificial column vectors.

Step 1053: determining an identification vector of the to-be-determined artificial column vector group as a third identification vector; the third identification vector is a 1 xn order 0-1 matrix.

Step 1054: and determining the fault detection rate of the to-be-determined artificial column vector group as a third fault detection rate according to the row vector array and the third identification vector.

Step 1055: and determining the fault isolation rate of the to-be-determined artificial column vector group as a third fault isolation rate according to the row vector array and the third identification vector.

Step 1056: according to the third identification vector and the artificial cost matrix, using a formula

Determining an optimization coefficient of the undetermined artificial column vector group as a third optimization coefficient; wherein D is a third optimization coefficient;

identifying the ith element in the vector for the third;

is the ith element in the third price matrix.

Step 1057: and updating the undetermined artificial column vector groups, and returning to the step of determining that the identification vectors of the undetermined artificial column vector groups are the third identification vectors until all proper subsets of the column vector array are traversed to obtain a third optimization coefficient, a third fault detection rate and a third fault isolation rate of each undetermined artificial column vector group.

Step 1058: a third constraint is constructed.

Step 1059: and determining the undetermined artificial column vector group with the minimum third optimization coefficient as an optimal undetermined artificial column vector group based on the third constraint condition, the third fault detection rate and the third fault isolation rate.

Step 10510: and converting the optimal undetermined manual column vector group into a matrix form to obtain a manual fault-test related undetermined matrix.

Step 10511: and deleting the row vectors of which the elements in the artificial fault-test correlation undetermined matrix are all 0 to obtain the artificial fault-test correlation matrix.

In particular, the method comprises the following steps of,

the first constraint is:

wherein, w _B Represents the sum of all elements in the test weight matrix; r is _B Representing the sum of all elements in the reliability matrix;

a first failure detection rate is indicated that indicates a first failure detection rate,

a first fault isolation rate is indicated and,

represents the lower weight limit of the test specimen;

represents a lower reliability limit;

representing a first lower fault detection rate limit;

representing a first lower fault isolation rate limit;

the second constraint is:

wherein,

a second failure detection rate is indicated that is,

a second fault isolation rate is indicated and,

representing a second lower fault detection rate limit;

representing a second lower fault isolation rate limit;

the third constraint is:

wherein,

a third failure detection rate is indicated and,

a third fault isolation rate is indicated and,

represents a third lower fault detection rate limit,

representing a third lower fault isolation rate limit.

Step 106, specifically comprising:

step 1061: and determining the test corresponding to each column vector in the fault-test correlation completion matrix to obtain a test set.

Step 1062: and determining the fault corresponding to each row vector in the fault-test correlation completion matrix to obtain a fault set.

Example two

As shown in fig. 2, the method for determining a test set of hierarchical testability optimization provided by this embodiment includes the following steps:

step1: and performing testability modeling and testability analysis on the equipment to generate a D matrix.

And performing equipment testability modeling by using modeling methods such as a fault tree, a signal flow diagram, a Bayesian network and the like, and generating a D matrix by testability analysis, wherein the D matrix comprises all fault modes and test items. The D matrix reflects the failure mode-test relationship, which is defined as follows:

under ideal conditions, the existence of fault propagation is not consideredAnd if the factors of false alarm, missing detection and the like exist in the determinacy and the test, a certain correlation exists between the fault mode and the available test. If there is a fault f _m And test t _n And associating, then: failure mode f _m Will result in test t _n The detection result is failure; if test t _n If the detection result is passed, then the failure mode f can be determined _m No occurrence occurred. Whether the detection result of the test passes the relationship between whether the failure mode which can be detected by the test occurs or not can be determined and can be deduced, and the relationship between the failure and the test is called correlation. By mathematically describing the graphic model, all failure modes F = { F in the graphic model are represented in the form of a boolean matrix ₁ ，f ₂ ，…f _m And available test T = { T } ₁ ，t ₂ ，…t _n Correlation between them. The mathematical model of a multiple signal flow graph is generally described by a fault-test correlation Matrix (Dependency Matrix), also called D Matrix, and is denoted as:

wherein: row i of the matrix D:

the generation result of the testability model of the equipment is an F-T matrix:

F _i ＝[d _i1 d _i2 … d _in ]

F _i detection information indicating that the ith failure mode can be responded to by each test.

Column j of the D matrix: t is _j ＝[d _1j d _2j … d _mj ] ^T ；

T _j Information representing the respective failure modes measurable by the jth test.

Ideally, the fault-test correlation matrix of the system is a binary matrix, i.e., the element d in the matrix _ij Only two values of 0 or 1 are available, which represents the test t _j For fault signal f _i Pass or fail detection of (a). When d is _ij Description of test t =1 _j A detection result of (2) is fail and also indicates a test t _j Can detect a fault f _i Whether or not this occurs, i.e. test t _j And fault f _i Correlation; when d is _ij Description of test t at =0 _j The result of detection of (2) is a pass, which also indicates the test t _j Failure to detect fault f _i Whether or not this occurs, i.e. test t _j And fault f _i Unrelated, as shown in equations 2-4:

at this time, the testability modeling and analyzing work is basically completed, and the subsequent related work can be carried out according to the built model, such as the expected testability level of the system: fault Detection Rate (FDR), fault Isolation Rate (FIR).

And 2, step: BIT test project optimization

As shown in fig. 3, the purpose of this step is to obtain an F-BIT optimal matrix through steps of testability prediction, test optimization, and the like, with the BIT-level detection rate and isolation rate index as constraint conditions, so as to obtain an optimal BIT test failure mode set and test set.

Let T ^BIT For the subset to be solved of T, using the test set to identify the vector

To represent T ^BIT Is involved with T if T is tested _j In the selection of the one or more of the plurality of cells,

otherwise

1. Failure detection rate, isolation rate prediction

The failure detection rate is defined as the percentage of the total number of failures that can be correctly detected to the total number of failures actually occurred in the Unit Under Test (UUT) within a specified time. The unit to be tested can be the whole system or a unit system of any layer.

On the premise of this assumption, the fault F _i Set T tested by BIT ^BIT The detection conditions are as follows:

if FD ^BIT Is T ^BIT The set of detectable faults, namely:

the failure detection rate can therefore be expressed as (m) ₁ Number of all failure modes):

when failure rate data is taken into account, it can be further rewritten as

In the formula, λ _i Is the failure rate of the ith failure.

The fault isolation rate is defined as the ratio of the number of faults which can be correctly isolated within a specified time and is not more than the specified number of replaceable units to the number of faults detected within the same time; it is assumed that,

wherein f is _i Is the ith row of the D matrix and,

the result is the same as the result of the multiplication of the corresponding elements of the two same-dimensional matrixes. Then the fault F _i And fault F _j Can be in test set T ^BIT The conditions in (1) are:

wherein the symbols

Representing exclusive-or operation, | · | non-counting ₁ Representing the vector 1 norm. If FI ^BIT Is T ^BIT The set of isolatable fault constituents, namely:

the fault isolation rate can be expressed as:

wherein λ is _i Is failure mode F _i Fault probability of

2. Optimization algorithm

The test item optimization is to select a test set with the minimum test cost on the premise of meeting the BIT fault detection rate and the fault isolation rate. For the BIT test, whether the weight of the BIT equipment meets the bearing capacity of the equipment or not and the influence of the BIT on the reliability of the equipment are also important to consider, and the reliability requirement cannot be exceeded.

Corresponding to each test (column of matrix), assuming all tests are implemented by BIT, defining a test cost set

For each test (column of matrix), a set of test weights is defined, assuming that all tests are performed with BITs

For each test (column of the matrix), a test equipment reliability set is defined assuming that all tests are implemented with BITs

The optimized mathematical model is as follows:

wherein:

total weight:

representing the total weight of BIT corresponding to the test selected for optimization;

reliability:

representing the total reliability of BIT corresponding to the optimized and selected test;

and the lower limit of the fault detection rate and the fault isolation rate of the BIT level required to be reached by the system. And (3) optimizing a model resolving algorithm, and rewriting a mathematical optimization model:

further, the optimization problem with constraints is rewritten into an unconstrained optimization problem:

in the formula, ρ is an applied penalty term, and a larger positive real number can be taken according to practical problems.

Aiming at the above unconstrained optimization problem, to obtain

A resolving method based on particle swarm is adopted, and the flow of the method is as follows:

step1: initialization

And determining the number M of particles in the particle swarm and the iteration number. Initializing inertial weight w, learning factor c ₁ ，c ₂ . According to the dimension n of the above-described solution problem, each particle has a velocity vector and a position vector. Initializing the initial velocity vector of the particle

And position vector

Wherein, each component of the position vector only consists of 0 or 1, and the position vector and the test set identification vector in the original problem

Correspondingly, through multiple iterations, the optimal test set identification vector is obtained.

Step2: calculating fitness

Of each particle

And (3) sequentially bringing the optimal values into an optimization target of an unconstrained optimization problem to obtain a unique calculated value, taking the calculated value as the fitness of the particle, and using the fitness to measure the degree of goodness and badness of the position of the particle. Meanwhile, aiming at setting a global optimal position G for recording the most positions of all the particles, a particle optimal position is set for each particleP _i The optimal position of each particle is recorded.

Step3: velocity of renewed particles

Velocity vector for each particle

Updating is carried out, and the particle velocity updating formula is

In the formula

Representing the d-dimensional velocity component of the ith particle in the t-th iteration,

G ^d ，

meaning similar thereto, w is the inertial weight, c ₁ ，c ₂ For the learning factor, rand () is a random number.

Step4: updating particle positions

Updating the position vector of the particle according to the new velocity vector of the particle, wherein the updating formula is as follows:

in the formula,

representing the d-dimensional position component of the ith particle in the t iteration.

Step5: repeated iterations

And repeating Step2 to Step4 until the specified number of iterations is reached.

Step6: draw a conclusion

And after repeated iteration is completed, the optimal position G of the particle is the optimal solution.

3. Input-output of this step:

performing BIT-level optimization on the D matrix of the complete machine to obtain D ^BIT ，D ^BIT The failure modes (rows in the matrix) in (d) are the BIT level optimal failure mode set, and the tests (columns in the matrix) in (DBIT) the BIT level optimal test set.

And step3: ATE test project optimization

As shown in FIG. 4, let T ^ATE For the subset to be solved of T, identifying the vector by the test set

To represent T ^ATE Inclusion relationship with T if T is tested _j In the selection of the one or more of the plurality of the objects,

otherwise

Note that in actual testing, the ATE test set must contain the BIT test set, i.e., T ^BIT ∈T ^ATE Therefore, if

Then

And this value must not be changed in subsequent optimizations.

1. Fault detection rate, isolation rate prediction

The fault detection rate is as follows:

failure fault F _i Is tested by ATE test set T ^ATE The detection conditions are as follows:

if FD ^ATE Is T ^ATE The set of detectable faults, namely:

the failure detection rate can thus be expressed as

Also, for fault isolation rate:

if FI ^ATE Is T ^ATE The set of isolatable faults, namely:

the fault isolation rate can be expressed as:

2. optimization algorithm

For ATE testing, the weight of the testing equipment is not considered, and the testing equipment has no influence on the reliability of the equipment, so that how to achieve the minimum testing cost is minimum under the condition of considering whether the requirements of detection rate and isolation rate can be met.

Also, for each test (column of the matrix), a set of test costs is defined

Also, if a test has been selected into the BIT test set, i.e. if the test has already been selected into the BIT test set

Then the process of the first step is carried out,

the optimized mathematical model is as follows:

wherein:

and the lower limit of the fault detection rate and the fault isolation rate of the ATE level is reached by the system requirement.

An optimization model calculation algorithm: and (3) rewriting the optimization problem containing the constraint into an unconstrained optimization problem:

3. input-output of this step:

optimizing the D matrix of the complete machine at ATE level to obtain D ^ATE ，D ^ATE The failure mode (row in matrix) in (A) is the ATE level optimal failure mode set, D ^ATE The test (columns in the matrix) in (a) is an ATE-level optimal test set, and the ATE level includes a BIT-level failure mode set, a test set.

And 4, step4: MT test item optimization

As shown in fig. 5, the mt test item optimization achieves the minimum test cost under the requirements of the detection rate and the isolation rate of the whole machine. Let T ^MT For the subset to be solved of T, identifying the vector by the test set

To represent T ^MT Inclusion relationship with T if T is tested _j In the selection of the one or more of the plurality of the objects,

otherwise

Because in actual testing, MT test set must include BIT and ATE test sets, i.e., T ^BIT ∈T ^ATE ∈T ^MT Therefore, if

Then

And this value must not be changed in subsequent optimizations.

1. Fault detection rate, isolation rate prediction

The fault detection rate is as follows:

failure fault F _i Test set T by MT ^MT The detection conditions are as follows:

if FD ^MT Is T ^MT The set of detectable faults, namely:

the fault detection rate can therefore be expressed as:

also, for fault isolation rate:

if FI ^MT Is T ^MT The set of isolatable faults, namely:

the fault isolation rate can be expressed as:

2. optimization algorithm

For MT test, the weight of test equipment is not considered, and the reliability of equipment is not influenced by the test equipment, so that the minimum test cost is minimum under the condition that the requirements of the detection rate and the isolation rate of the whole machine can be met.

Also, for each test (column of the matrix), a set of test costs is defined

Similarly, if a test has been selected into the ATE test set, i.e., the test is not available

Then the user can use the device to make a visual display,

the optimized mathematical model is as follows:

wherein:

the lower limit of the fault detection rate and the fault isolation rate of the whole machine which are required by the system is met;

the optimization model calculation algorithm contains a constrained optimization problem and is rewritten into an unconstrained optimization problem:

3. input-output of this step:

to complete machineOptimizing the D matrix at the complete machine level to obtain D ^MT ，D ^MT The failure mode in (the row in the matrix) is the optimal failure mode set of the whole equipment, D ^MT The test in (the column in the matrix) is equipped with a complete machine optimal test set, and the complete machine level comprises a fault mode set and a test set at an ATE \ BIT level.

EXAMPLE III

The embodiment provides a test set determination system for hierarchical testability optimization, which includes:

the device testability model building module is used for carrying out testability modeling on the device to obtain a device testability model;

the initial fault-test correlation matrix determining module is used for carrying out testability analysis on the equipment based on the equipment testability model to obtain an initial fault-test correlation matrix; the initial fault-test correlation matrix is used for describing the correlation between the fault mode of the equipment and the test;

the internal fault-test correlation matrix determining module is used for carrying out internal test level optimization on the initial fault-test correlation matrix to obtain an internal fault-test correlation matrix;

the automatic fault-test correlation matrix determining module is used for carrying out automatic test level optimization on the initial fault-test correlation matrix based on the built-in fault-test correlation matrix to obtain an automatic fault-test correlation matrix;

the artificial fault-test correlation matrix determining module is used for carrying out artificial test level optimization on the initial fault-test correlation matrix based on the automatic fault-test correlation matrix to obtain an artificial fault-test correlation matrix;

the test set and fault set determining module is used for determining a test set and a fault set according to the fault-test related completion matrix; the test set and the fault set are used for corresponding tests of the fault-test related finalization matrix; the fault-test correlation completion matrix includes a built-in fault-test correlation matrix, and a built-in fault-test correlation matrix.

The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. For the system disclosed by the embodiment, the description is relatively simple because the system corresponds to the method disclosed by the embodiment, and the relevant points can be referred to the description of the method part.

The principles and embodiments of the present invention have been described herein using specific examples, which are provided only to help understand the method and the core concept of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the above, the present disclosure should not be construed as limiting the invention.

Claims (10)

1. A method for determining a test set of hierarchical testability optimization, comprising:

performing testability modeling on the equipment to obtain an equipment testability model;

performing testability analysis on the equipment based on the equipment testability model to obtain an initial fault-test correlation matrix; the initial fault-test correlation matrix is used for describing the correlation between the fault mode of the equipment and the test;

performing built-in test level optimization on the initial fault-test correlation matrix to obtain a built-in fault-test correlation matrix;

performing automatic test-level optimization on the initial fault-test correlation matrix based on the built-in fault-test correlation matrix to obtain an automatic fault-test correlation matrix;

performing manual test-level optimization on the initial fault-test correlation matrix based on the automatic fault-test correlation matrix to obtain a manual fault-test correlation matrix;

determining a test set and a fault set according to the fault-test correlation finalization matrix; the test set and the fault set are used for corresponding tests of the fault-test related finalization matrix; the fault-test correlation conclusion matrix includes a built-in fault-test correlation matrix, and a built-in fault-test correlation matrix.

2. The method of claim 1, wherein the initial fault-test correlation matrix is an mxn order 0-1 matrix; wherein m represents the total number of failure modes of the device; n represents the total number of test types of the equipment;

when the element of the ith row and the jth column in the initial fault-test correlation matrix is 1, the correlation between the ith fault mode and the jth test is represented; i =1,2,. M; j =1,2,. N; and when the element of the ith row and the jth column in the initial fault-test correlation matrix is 0, the ith fault mode is irrelevant to the jth test.

3. The method of claim 2, wherein the performing the test-level optimization on the initial fault-test correlation matrix to obtain a built-in fault-test correlation matrix comprises:

determining a built-in cost matrix, a test weight matrix and a reliability matrix when the device is tested in the machine based on the device testability model; the built-in cost matrix is used for expressing the cost of each test when the device is tested in the built-in test; the test weight matrix is used for expressing the weight of each test device when the device is tested in the machine; the reliability matrix is used for expressing the reliability of each test device when the device is tested in the machine;

respectively determining a row vector array and a column vector array of the initial fault-test correlation matrix;

determining any proper subset of the column vector array as a set of column vectors in the to-be-determined machine;

determining an identification vector of the to-be-determined inner column vector group as a first identification vector; the first identification vector is a 1 Xn-order 0-1 matrix; when the jth element in the first identification vector is 1, the jth element in the first identification vector indicates that the jth column vector in the column vector array is included in the built-in column vector group; when the jth element in the first identification vector is 0, the jth element in the first identification vector indicates that the jth column vector in the column vector array is not included in the built-in column vector group;

determining the fault detection rate of the column vector group in the machine to be determined as a first fault detection rate according to the row vector array and the first identification vector;

determining the fault isolation rate of the column vector group in the undetermined machine as a first fault isolation rate according to the row vector array and the first identification vector;

according to the first identification vector and the built-in cost matrix, a formula is utilized

Determining an optimization coefficient of a column vector group in the undetermined machine as a first optimization coefficient; wherein A is a first optimization coefficient;

is the ith element in the first identification vector;

is the ith element in the first price matrix;

updating the to-be-determined internal column vector group, and returning to the step of determining that the identification vector of the to-be-determined internal column vector group is a first identification vector until all proper subsets of the column vector array are traversed to obtain a first optimization coefficient, a first fault detection rate and a first fault isolation rate of each to-be-determined internal column vector group;

constructing a first constraint condition;

determining the undetermined internal column vector group with the minimum first optimization coefficient as an optimal undetermined internal column vector group based on the first constraint condition, the first fault detection rate, the first fault isolation rate, the test weight matrix and the reliability matrix;

converting the optimal undetermined internal column vector group into a matrix form to obtain an internal fault-test related undetermined matrix;

and deleting the row vectors of which all elements are 0 in the internal fault-test correlation undetermined matrix to obtain the internal fault-test correlation matrix.

4. The method of claim 3, wherein the determining the failure detection rate of the column vector group to be determined as a first failure detection rate according to the row vector array and the first identification vector comprises:

determining a row vector which meets the detection condition of a column vector group to be determined in the row vector array as a machine row vector;

acquiring the fault rates of faults corresponding to all the row vectors in the row vector array;

determining the sum of the failure rates of the faults corresponding to all the built-in row vectors as a first related quantity;

determining the sum of the failure rates of the failures corresponding to all the row vectors in the row vector array as a second correlation quantity;

and determining the ratio of the first correlation quantity to the second correlation quantity as a first fault detection rate.

5. The method of claim 4, wherein the determining the fault isolation rate of the column vector group to be determined as a first fault isolation rate according to the row vector array and the first identification vector comprises:

determining any row vector in the row vector array as a current row vector;

multiplying the current row vector by the corresponding element of the first identification vector to obtain a current row identification matrix;

traversing all row vectors in the row vector array to obtain a plurality of row identification matrixes;

determining any row identification matrix as a current first row identification matrix;

determining any row identification matrix except the current first row identification matrix as a current second row identification matrix;

determining a norm value after the exclusive or operation is carried out on the current first row identification matrix and the current second row identification matrix;

updating the current second row identifier matrix and returning to the step of determining norm values after XOR operation is carried out on the current first row identifier matrix and the current second row identifier matrix until all row identifier matrices except the current first row identifier matrix are traversed to obtain a plurality of norm values corresponding to the current first row identifier matrix as the current norm matrix;

performing a continuous product operation on the elements in the current norm group to obtain an isolation parameter of the fault corresponding to the first row of the identification matrix;

updating the current first row identification matrix, and returning to the step of determining that any row identification matrix except the current first row identification matrix is the current second row identification matrix until all row identification matrices are traversed to obtain the isolation parameters of the faults corresponding to each row identification matrix;

determining the fault corresponding to the row identification matrix with the isolation parameter larger than 1 as an isolation fault;

determining the sum of the fault rates of all the isolation faults as a third correlation quantity;

and determining the ratio of the third correlation quantity to the second correlation quantity as a first fault isolation rate.

6. The method of claim 5, wherein the step of performing an automatic test-level optimization on the initial failure-test correlation matrix based on the built-in failure-test correlation matrix to obtain an automatic failure-test correlation matrix comprises:

determining an automatic cost matrix when the equipment is automatically tested based on the equipment testability model; the automatic cost matrix is used for expressing the cost of each test when the equipment is automatically tested;

determining any proper subset of the column vector array as a pending automatic column vector group according to the built-in fault-test correlation matrix; the built-in fault-test correlation matrix is a proper subset of the set of undetermined automatic column vectors;

determining an identification vector of the undetermined automatic column vector group as a second identification vector; the second identification vector is a 1 Xn-order 0-1 matrix;

determining the fault detection rate of the to-be-determined automatic column vector group as a second fault detection rate according to the row vector array and the second identification vector;

determining the fault isolation rate of the to-be-determined automatic column vector group as a second fault isolation rate according to the row vector array and the second identification vector;

using a formula according to the second identification vector and the automatic cost matrix

Determining an optimization coefficient of the undetermined automatic column vector group as a second optimization coefficient; wherein B is a second optimization coefficient;

identifying the ith element in the vector for the second label;

is the ith element in the second cost matrix;

updating the undetermined automatic column vector groups, and returning to the step of determining that the identification vectors of the undetermined automatic column vector groups are second identification vectors until all proper subsets of the column vector array are traversed to obtain a second optimization coefficient, a second fault detection rate and a second fault isolation rate of each undetermined automatic column vector group;

constructing a second constraint condition;

determining the undetermined automatic column vector group with the minimum second optimization coefficient as an optimal undetermined automatic column vector group based on the second constraint condition, the second fault detection rate and the second fault isolation rate;

converting the optimal undetermined automatic column vector group into a matrix form to obtain an automatic fault-test related undetermined matrix;

and deleting the row vectors of which all elements are 0 in the automatic fault-test correlation undetermined matrix to obtain an automatic fault-test correlation matrix.

7. The method according to claim 6, wherein the initial fault-test correlation matrix is optimized manually at test level based on the automatic fault-test correlation matrix to obtain a manual fault-test correlation matrix;

determining an artificial cost matrix when the equipment is artificially tested based on the equipment testability model; the manual cost matrix is used for expressing the cost of each test when the equipment is manually tested;

determining any proper subset of the column vector array as a group of undetermined manual column vectors according to the automatic fault-test correlation matrix; the automatic fault-test correlation matrix is a proper subset of the set of undetermined artificial column vectors;

determining an identification vector of the undetermined artificial column vector group as a third identification vector; the third identification vector is a 1 Xn-order 0-1 matrix;

determining the fault detection rate of the to-be-determined artificial column vector group as a third fault detection rate according to the row vector array and the third identification vector;

determining the fault isolation rate of the to-be-determined artificial column vector group as a third fault isolation rate according to the row vector array and the third identification vector;

according to the third identification vector and the artificial cost matrix, a formula is utilized

Determining an optimization coefficient of the undetermined artificial column vector group as a third optimization coefficient; wherein D is a third optimization coefficient;

identifying the ith element in the vector for the third;

is the ith element in the third price matrix;

updating the undetermined artificial column vector group, and returning to the step of determining that the identification vector of the undetermined artificial column vector group is a third identification vector until all proper subsets of the column vector array are traversed to obtain a third optimization coefficient, a third fault detection rate and a third fault isolation rate of each undetermined artificial column vector group;

constructing a third constraint condition;

determining a pending artificial column vector group with the minimum third optimization coefficient as an optimal pending artificial column vector group based on the third constraint condition, the third fault detection rate and the third fault isolation rate;

converting the optimal undetermined manual column vector group into a matrix form to obtain a manual fault-test related undetermined matrix;

and deleting the row vectors of which the elements are all 0 in the artificial fault-test correlation undetermined matrix to obtain the artificial fault-test correlation matrix.

8. The method of claim 7, wherein the step of determining the test set comprises,

the first constraint condition is as follows:

wherein, w _B Represents the sum of all elements in the test weight matrix; r is _B Representing the sum of all elements in the reliability matrix;

a first failure detection rate is indicated that indicates a first failure detection rate,

a first fault isolation rate is indicated and,

represents the lower weight limit of the test specimen;

represents a lower reliability limit;

representing a first lower fault detection rate limit;

representing a first lower fault isolation rate limit;

the second constraint condition is as follows:

wherein,

a second failure detection rate is indicated and,

a second fault isolation rate is indicated and,

representing a second lower fault detection rate limit;

representing a second lower fault isolation rate limit;

the third constraint condition is as follows:

wherein,

a third failure detection rate is indicated and,

a third fault isolation rate is indicated and,

represents a third lower fault detection rate limit,

representing a third lower fault isolation rate limit.

9. The method according to claim 1, wherein the determining the test set and the fault set according to the fault-test correlation completion matrix specifically includes:

determining a test corresponding to each column vector in the fault-test correlation completion matrix to obtain the test set;

and determining the fault corresponding to each row vector in the fault-test related finalization matrix to obtain the fault set.

10. A hierarchical testability-optimized test set determination system, comprising:

the device testability model building module is used for carrying out testability modeling on the device to obtain a device testability model;

the initial fault-test correlation matrix determining module is used for carrying out testability analysis on the equipment based on the equipment testability model to obtain an initial fault-test correlation matrix; the initial fault-test correlation matrix is used for describing the correlation between the fault mode of the equipment and the test;

the internal fault-test correlation matrix determining module is used for carrying out internal test level optimization on the initial fault-test correlation matrix to obtain an internal fault-test correlation matrix;

an automatic fault-test correlation matrix determination module, configured to perform automatic test-level optimization on the initial fault-test correlation matrix based on the in-machine fault-test correlation matrix to obtain an automatic fault-test correlation matrix;

a manual failure-test correlation matrix determination module, configured to perform manual test-level optimization on the initial failure-test correlation matrix based on the automatic failure-test correlation matrix to obtain a manual failure-test correlation matrix;

a test set and fault set determination module for determining a test set and a fault set according to the fault-test correlation finalization matrix; the test set and the fault set are used for corresponding tests of the fault-test related finalization matrix; the fault-test correlation conclusion matrix includes a built-in fault-test correlation matrix, and a built-in fault-test correlation matrix.

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