CN115257891A - CBTC scene testing method based on key position extraction and random position fusion - Google Patents
CBTC scene testing method based on key position extraction and random position fusion Download PDFInfo
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Abstract
The invention discloses a CBTC scene testing method based on key position extraction and random position fusion, which comprises the following steps: all factors X for controlling speed of participating CBTC systemiRespectively setting corresponding weight values thetai(ii) a The frequency and the extent influencing the speed control of the CBTC system are taken as an evaluation function S participating in the control of the CBTC system; calculating the numerical value of an evaluation function S, and combining the weight values of all the line position points to form a multi-dimensional curve graph; matching the peak point of the multidimensional curve graph with the arrangement attributes of the train track line plane graph to obtain a curve of the control parameters and the position of the CBTC system; expanding the position set D by adopting a kennard-Stone algorithm, and taking the value position points as random scene injection opportunities; training the test cases by adopting a heuristic search algorithm to obtain an optimal combined test case set; when the train runs to the value position point, the concurrent scene test case is automatically loaded to carry out the scene test on the CBTC system. The scheme remarkably improves the test efficiency of the CBTC system.
Description
Technical Field
The invention relates to the technical field of railway system operation detection test, in particular to a CBTC scene testing method based on key position extraction and random position fusion.
Background
Most of the existing CBTC system test case automatic generation algorithms are a code layer-oriented white box test method and a model-based black box test method. The white-box testing method mainly finds code errors of CBTC system software through an automatic testing technology based on symbolic execution and a core technology through program instrumentation, path exploration, constraint propagation and constraint solving. The black box testing method based on the model mainly abstracts the requirement specification into the model, then generates a corresponding test case through path search of the abstracted model, executes testing in a specific tool according to different modeling languages, and compares an operation result with an expected result.
The automatic test technology based on symbolic execution is mainly used for static test, dynamic test and regression test which can be obtained by codes in the development stage of the CBTC system. The automatic test case generation technology based on the model is mainly used for functional verification of a CBTC system in the early development stage, a special test tool is needed, testers need to be familiar with the system, the test cases are realized by searching paths when the functions are complex, the conditions of path unreachability or state space explosion are often generated, and the test cases cannot be applied to an engineering test system with complex test functions, so that a great amount of tests for engineering acceptance are written by manpower, and meanwhile, the test cases cannot be operated according to the accurate train position due to randomness in the test case execution process.
Disclosure of Invention
The CBTC scene testing method based on the fusion of the key position extraction and the random position is applied to the engineering test of the CBTC system, the control key features of the CBTC system are extracted, the test cases matched with the specific positions are generated by combining the random characteristics of the scene test, the optimal random scene injection time is searched, the quantity of unnecessary test cases can be greatly reduced, and the test efficiency of the CBTC system is obviously improved.
In order to achieve the technical purpose, the invention provides a technical scheme that a CBTC scene testing method based on the fusion of key position extraction and random position comprises the following steps:
all factors X for controlling speed of participating CBTC systemiRespectively setting corresponding weight values thetai；
The frequency and the extent influencing the speed control of the CBTC system are taken as an evaluation function S participating in the control of the CBTC system;
extracting composition factors of the train track to construct a track database, calculating an evaluation function S numerical value according to weight value information contained in each kilometer sign track line and the weight value of a train control trigger factor in the distance L of each kilometer sign, and combining the weight values of each line position point to form a multidimensional curve graph;
dividing peak points of the multi-dimensional curve graph into a plurality of position types, expanding the position set D by adopting a random sampling algorithm to obtain a random position class, and taking the random position class as the injection opportunity of a train control test scene;
training the test cases by adopting a heuristic search algorithm to obtain an optimal combined test case set, wherein the optimal combined test case is used as a concurrent scene test case;
in the automatic train lap test process, when the train runs to a value-taking position point, the concurrent scene test case pair is automatically loaded to carry out scene test on the CBTC system.
Preferably, the L distance is internally related to the control factor XiThe weighted value M is used as an evaluation function S participating in the speed control of the CBTC system, and the formula is as follows:
wherein M = theta1X1+θ2X2+…+θiXi+…+θ8X8。
Preferably, a three-dimensional curve in the multi-dimensional graph represents a real driving route of the train, an X axis represents a direction from west to east, a Y axis represents a direction from south to north, a Z coordinate is height, and the height of a curve line represents a gradient.
Preferably, the location types include, but are not limited to: a turnout side strand area position type, a straight-through high-speed point position type, an inbound point position type, an outbound point position type, a platform parking point position type, a ZC cross-connection area position type, a turn-back point position type and a curve area position type.
Preferably, the random sampling algorithm comprises one of a kennard-Stone algorithm, a hierarchical sampling method, and a monte carlo random sampling method.
Preferably, the method for expanding the position set D by using the kennard-Stone algorithm comprises the following steps:
according to key control points extracted by a target control curve, carrying out equivalent classification on all-line key control points according to position types by using an equivalent classification method, and after class classification, the effect of the representative data of each class in the test is equivalent to other values in the class;
taking a plurality of position category data extracted from all key control positions as a position complete set U, and taking a set A of coupling control position elements as an initial training sample set;
the set C is a complementary set C = C of the set D in the complete set UUD, selecting the maximum distance from the minimum distances of all data in the distance A set from the set C as extended data of the set D, and continuously extending the set D to obtain a set D0; synchronously expanding the set A to obtain a set A0; and the value position points of the set A0 are used as random scene injection opportunities.
Preferably, the step of dividing the peak point of the multidimensional curve graph into a plurality of position types, extending the position set D by using a random sampling algorithm to obtain a random position class, and using the random position class as the injection timing of the train control test scene includes the following steps:
matching the peak point of the multidimensional curve graph with the arrangement attributes of the train track circuit plane graph, and dividing the peak point of the track data participating in control into a plurality of position types according to an equivalent classification method to obtain a curve of the control parameters and the positions of the CBTC system; and expanding the position set D by adopting a random sampling algorithm, acquiring a position value set V0 of different position sets D0 where the train runs, taking the value position points as a random position class, and taking the random position class as the injection time of the train control test scene.
Preferably, training the test cases by adopting a heuristic search algorithm to obtain an optimal combined test case set, wherein the optimal combined test case set comprises the following steps:
inputting a parameter value set T and a coverage intensity T of a test scene of the CBTC system;
according to the coverage intensity t, carrying out combined pairing on the parameter values to generate a combined table to be covered;
generating a test case by using a heuristic algorithm;
according to whether the table to be covered is completely covered, generating a use case with the largest number of covering combinations each time as an optimal use case corresponding to the position point;
and continuously searching by utilizing a particle swarm algorithm to obtain a test case set, wherein the test case set is used as an optimal combined test case set.
Preferably, the heuristic search algorithm comprises one of a genetic algorithm, an ant colony algorithm, simulated annealing, a tabu algorithm and a particle swarm algorithm.
The invention has the beneficial effects that: the CBTC scene testing method based on the fusion of the key position extraction and the random position is applied to the engineering test of the CBTC system, the control key features of the CBTC system are extracted, the test cases matched with the specific positions are generated by combining the random characteristics of the scene test, the factors participating in the train control are considered, the optimal random scene injection time is found, the quantity of unnecessary test cases can be greatly reduced, and the testing efficiency of the CBTC system is remarkably improved.
Drawings
FIG. 1 is a flow chart of a CBTC scene testing method based on key location extraction and random location fusion according to the present invention.
Fig. 2 is a data construction structure diagram of the track database.
Fig. 3 is a multi-dimensional graph corresponding to the weight values of the upper travel position points of the travel route.
Fig. 4 is a multi-dimensional graph corresponding to the weight value of the upper travel position point of the travel route.
Fig. 5 is a train lap test chart.
FIG. 6 is a block diagram of a test case algorithm generated by a particle swarm algorithm.
FIG. 7 load test case flow diagram.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the present invention more apparent, the following detailed description of the present invention is given with reference to the accompanying drawings and examples, it is to be understood that the specific embodiment described herein is only a preferred embodiment of the present invention, and is only used for explaining the present invention, and does not limit the scope of the present invention, and all other embodiments obtained by a person of ordinary skill in the art without making creative efforts belong to the scope of the present invention.
Example (b): as shown in fig. 1, a flow chart of a CBTC scene testing method based on key location extraction and random location fusion includes the following steps:
will participate in the speed control of the CBTC system by various factors XiRespectively setting corresponding weight values thetaiWeight value θiAnd performing linear interpolation, secondary interpolation and cubic spline curve interpolation according to the value range of the line factors.
CBTC systems are highly security critical systems, often ensuring high quality delivery of CBTC systems through extensive and complete testing. However, in the process of performing automatic testing on the CBTC system, neither automatic testing nor manual testing can accurately find the optimal testing time. In fact, the process of train running on the track line is the process of tracking expected speed movement by periodically acquiring movement authorization and periodically calculating a speed-distance curve, and meanwhile, the operation scene control of a remote ATS (automatic train supervision) system and the fault scene protection are randomly received, so that the system is ensured to run safely according to the ATS set train schedule. In order to find the optimal random scene injection time, the factors participating in train control need to be analyzed in detail, and the train operation controlled by the CBTC system is influenced by the following eight factors besides the train control algorithm of the CBTC system, which is shown in Table 1.
Table 1 is a train operation influence factor table.
The frequency and the extent influencing the speed control of the CBTC system are taken as an evaluation function S participating in the control of the CBTC system; the larger the S number is, the more software functions the CBTC system calls at the same time in a specific time are, the more sufficient the CBTC software is tested, and the more easily problems are found.
The method comprises the steps of extracting composition factors of train tracks to construct a track database, calculating the L distance of each kilometer post according to weight value information contained in each kilometer post track line, calculating the numerical value of an evaluation function S, and combining the weight values of each line position point to form a multi-dimensional curve graph.
In the running process of the train, the beacon moment of each period participates in the operation of the speed of the train, and the trackside axle counting, the turnout and the signal machine participate in the speed calculation of the train in each period in a mobile authorization mode, so that the beacon, the axle counting, the turnout and the signal machine are used as a sampling sequence of the scene test. Defining theta as the weight value of each parameter participating in signal system control in the process of train advancing, taking the error of certain time delay and position precision of communication into consideration, and taking the weight value M of the participating control factor in a certain distance L as an evaluation function S participating in CBTC system speed control, wherein L is a configurable item:
M＝θ1X1+θ2X2+…+θiXi+…+θ8X8
the train track is complicated, and for the convenience of train control, a track database is usually adopted to express that the train track consists of factors such as turnout areas, curve curvatures, basic data, speed limits, gradients, stopping points and the like.
Fig. 2 shows a data structure diagram of the track database, wherein the turnout area includes a device name, a positioning passing speed limit, a reverse passing speed limit, and a driving direction; the curve curvature includes: starting the work milestone, finishing the work milestone, the curvature radius and the driving direction; the basic data includes: name, line number name, work milestone and type; the speed limit comprises the following steps: starting the work milestone, finishing the work milestone, limiting the speed value and the line number name; the slope includes: starting the work milestone, finishing the work milestone, the slope value (0.1/1000 m) and the line number name; the parking spot comprises a device name, a line number name, a reference device and a distance.
And generating a multi-dimensional curve chart of the track line and the control weight of the train control system, as shown in fig. 3 and 4, wherein a three-dimensional curve in the multi-dimensional curve chart represents a real driving route of the train, an X axis represents a west-east direction, a Y axis represents a south-north direction, and a Z coordinate is height, namely, the height change of the curve line represents the gradient. The control weight value of circuit is distinguished with the colour, can adopt standard colour or RGB colour to set for, sets for certain colour for the lower limit of control weight, and certain colour is the upper limit that control weight arrived, and the centre is excessive colour, is convenient for the tester to observe the frequency and the width that a certain travel point of train participated in the control through the sign of colour scale to carry out artifical decision-making.
Matching the peak point of the multi-dimensional curve graph with the arrangement attributes of the train track circuit plane graph, and dividing the factor peak point of the track data participating in control into eight position types according to an equivalent classification method, wherein the eight position types comprise a turnout side stock area position type, a straight-through high-speed point position type, an incoming point position type, an outgoing point position type, a platform parking point position type, a ZC cross area position type, a turning point position type and a curve area position type. When a certain position point belongs to a plurality of position types, determining which type the certain position point belongs to according to the decision priority judgment standard of an expert.
Specifically, as shown in fig. 5, assuming that the departure point of the train is located at the turning point a, the train travels to the point B of the turnout side strand area through the switch machine according to the topological relation of traveling in the up-down direction and the down-down direction; the train adjusts the driving speed to continue to drive forwards according to the speed limit of the corresponding section and the curvature information of the curve; according to the instruction of a signal machine at the point C in front of the platform, the station passes through the station entering area, the platform parking area and the station exiting area; the train continuously runs to a turning-back position D point, runs into a turning-back rail, changes the running direction, runs into a downlink to a point E through a point switch, runs out to a point G of a turnout according to the indication of a signal machine at a point F in front of the platform, passes through a station entering area, a station stopping area and a station leaving area, runs to a point G of the turnout, runs to a point H along a side strand, and changes a running route to run back to a turning-back point A through the point switch. Therefore, the topological graph of the ascending is ABCD, the topological relation of the descending is DEFGHA, and a multidimensional graph of the driving route and the train control weight can be generated according to the data of the ascending and descending.
Matching the peak points of the multidimensional curve graph with the arrangement attributes of the train track circuit plane graph, and dividing the factor peak points of the track data participating in control into eight position types according to an equivalent classification method to obtain curves of control parameters and positions of the CBTC system;
and (3) expanding the position set D by adopting a kennard-Stone algorithm, acquiring a position value set V0 of different position sets D0 driven by the train, and taking a value position point as a random scene injection opportunity.
Specifically, a curve of a control parameter and a position of the CBTC system is obtained according to the train control weight value, the peak point represents that the frequency and the breadth of the position participating in the control of the CBTC signal system are the highest, and potential problems of the system can be found more easily by taking the peak point as a time point of random scene injection. The multi-dimensional curve is combined with a train track map and found in a classified mode, and because peak points are located at a turnout side strand position, a high-speed area, an inbound area, a platform parking point, an outbound area, a ZC handover area, a turn-back area and a curve area, when a train runs to the turnout side strand position, particularly a key communication point for reading beacon information of the train or a communication point for automatically opening an access road of the train, if a train issues a remote train control command and CBTC system equipment fails, various factors are mutually restricted, and potential problems of the CBTC system are easily found. Because the exhaustive test cannot be realized, a part of representative data can be selected from a large amount of possible data to be used as a test case, and key control is extracted according to a target control curveAnd point making, namely performing equivalent classification on the key control points of the whole line according to eight classes of positions by using an equivalent classification method, and after class division, the action of the representative data of each class in the test is equivalent to other values in the class. Let the set of positions be D = { D =1,d2,d3,…,dk,…,dnN =8, wherein the k-th factor has m values, and the set of position values is V = { V =1,v2,v3,…,vk,…,vm}。
According to the real working condition of the train in the running process, the ATS operation, the equipment fault scene and the protection scene are sporadic, and in order to reflect the randomness, random factors need to be introduced into the scene test case generation, so that the random fault of the CBTC test software can be found conveniently. The random position selection of the train fully considers the difference degree with the position set D, and the higher the difference degree with the position of the train on a track map, the more representative the random position selection of the train is. For this purpose, a kennard-Stone algorithm is adopted to form a random position sample set, a plurality of position category data extracted from all key control positions are used as a position complete set U (the invention comprises eight position categories), and A is taken as a set A = { d } of coupling control position elements11,d21,d31,…,dk1,…,dn1-n =8 as an initial training sample set; the set C is a complementary set C = C of the set D in the complete set UUD, selecting the maximum distance from the minimum distances of the data in the distance A set from the C set as the extended data D of the set D9The specific calculation method comprises the following steps:
Δ(K)＝min{Lkd11,Lkd21,…,Lkdk1,…,Lkdn1},K∈C
d91＝{max{Δ(K)}}
D0＝{d1,d2,d3,…,dk,…,dn},n＝9
A0＝{d11,d21,d31,…,dk1,…,dn1,d12,d22,d32,…,dk2,…,dn2},n＝9
sequentially calculate d92And continuously expanding the sets D and DA, until d is completed8Value set V = { V =1,v2,v3,…,vk,…,vm}。
Thus, different position sets D0 of the train running are obtained, and position value sets V respectively representing turnout reverse position points, high-speed points, station entering points, platform parking points, station leaving points, turning points and random position points are respectively V1,V2,…,V9Replaced by a number from 1-9, respectively, the location point serves as a random scene injection opportunity. Therefore, the CBTC system scene test injection time with the fusion of the control characteristics and the random characteristics is obtained. It is worth mentioning that: the selection of the random operation scene can adopt a hierarchical sampling method for equivalent substitution besides a kennard-Stone algorithm, divide the line into sections and randomly extract distance data from each section category, or equally divide and sample the line data, or adopt a Monte Carlo random sampling method to randomly sample according to appointed weight.
As many test scenes occur in the running process of the train, the test scene table is shown in table 2; the method for testing the combination of the concurrent scenes is adopted, the testing problem of the CBTC system can be found more easily than the single scene test, and the main method for testing the operation scenes of the CBTC system is a generation method of a combined coverage table.
Table 2 is a test scenario table.
According to the test requirements of a specific scene, a single-factor test coverage table can be adopted to generate a test case or a multi-factor test coverage table can be adopted to generate the test case.
The specific definition is: suppose A is an n × m matrix with the jth column representing the jth parameter and its elements taken from the finite coincident set Tj(j =1,2, \8230;, m), i.e. { a }ij|i＝1,2,…,n}∈TjIf any l column of A (2. Ltoreq. L.ltoreq.m) the i-th column and the j-th column satisfy: ith1Column, i th2Column, \8230;, ithlAll columns satisfy, Ti1Symbol of (a), Ti2Symbol of (1) \ 8230;, TilAll combinations of symbols of (1) are in the ith1Column, i th2Column, 8230lThe column forms l-element ordered pairs, and A is called l-element coverage matrix, also called l-dimension combination coverage test case matrix. When l =2, it is referred to as a 2-way combinational test table, also called a pair covering test table (pair covering array). Kuhn DR and Reilly MJ have indicated in their studies that 70% of software defects can be triggered by a combination of two parameters, more than 90% of software errors can be detected by 3 parameter interactions, and if 100% of software design defects are to be detected, a combination of 6 parameters is required.
And training the test cases by adopting a heuristic search algorithm to obtain an optimal combined test case set, wherein the optimal combined test case is used as a concurrent scene test case.
The generation method of the combined test case can adopt a mathematical construction method, a greedy algorithm, a heuristic search algorithm and the like; the invention adopted in the text adopts a heuristic search algorithm, which is obtained according to the evolution law in nature and the experience of solving the problem. The heuristic search algorithm is to conduct guide transformation on the test cases to obtain a better combined test case set on the basis of the existing test case set.
The heuristic algorithms currently used for generating combined test cases include the following: genetic algorithm, ant colony algorithm, simulated annealing, tabu algorithm and particle swarm algorithm. The invention is illustrated by taking a particle swarm algorithm as an example, and a test case algorithm framework diagram is generated by taking the particle swarm algorithm as shown in FIG. 6.
The method comprises the following specific steps:
inputting a parameter value set T and a coverage intensity T of a CBTC system test scene;
according to the coverage intensity t, combining and pairing the parameter values to generate a combination table to be covered;
generating a test case by using a heuristic algorithm;
generating a case with the most covering combination number each time as an optimal case corresponding to the position point according to whether the table to be covered is completely covered or not;
and (4) continuously searching by using the particle swarm algorithm to obtain a test case set (the search position updating strategy of the particle swarm algorithm is used as a known technology of a person skilled in the art and is not described repeatedly), and the test case set is used as an optimal combined test case set.
The test case generated according to the combined test can be automatically loaded in the automatic train lap test process when the train runs to the corresponding position, so that the test problem of the CBTC system software can be found more effectively.
The position information obtained by the algorithm based on the key position extraction and random position fusion represents the control frequency, the breadth and the randomness of the train control system, the position can be used as a key position point for injecting the train test case, the selection of the test case is not limited to a concurrent combined test case, and the method is also suitable for manually compiling the test case and extracting the test case from a demand book by a manual method; besides the combined test method, the invention can also adopt an artificial intelligence method to extract and generate the test case of the CBTC system by converting the requirements into natural language, and the execution time of the test case determines the effectiveness of the test case regardless of the generation method of the test case. According to the invention, the test cases associated with the position types are automatically matched according to the running positions of the trains through the automatic lap test of the trains, and the test cases are executed according to the priority sequence, and the termination condition of the automatic test is that the execution of the required test cases is finished.
As shown in FIG. 7, the flow chart for loading test cases includes the following steps:
traversing test is carried out on a real train or a simulated train along a set test line, and the train updates the running position in real time;
the test system automatically judges the position category attribute information corresponding to the train;
screening the classes of executable test cases according to the position class attribute information;
selecting a test case with the highest priority with the position attribute as a priority test case through a heuristic algorithm;
and when all the test cases are executed, stopping the test.
The above embodiments are preferred embodiments of the CBTC scenario testing method based on the combination of the key location extraction and the random location, and the scope of the invention is not limited thereto, and the equivalent changes in the shape and structure according to the invention are within the protection scope of the invention.
Claims (10)
1. The CBTC scene testing method based on the fusion of key position extraction and random position is characterized by comprising the following steps:
will participate in the speed control of the CBTC system by various factors XiRespectively setting corresponding weight values thetai；
Extracting composition factors of the train track to construct a track database, calculating the numerical value of an evaluation function S according to weight value information contained in each kilometer sign track line and the weight value M of the train control trigger factor in the distance L of each kilometer sign, and combining the weight values of each line position point to form a multidimensional curve graph; taking a peak point of the multi-dimensional curve graph as a key position class, and taking the key position class as the injection opportunity of the train control test scene;
training the test cases by adopting a heuristic search algorithm to obtain an optimal combined test case set, wherein the optimal combined test case is used as a concurrent scene test case;
in the automatic train lap test process, when a train runs to a value-taking position point, the automatic loading concurrent scene test case pair carries out scene test on the CBTC system.
2. The CBTC scene testing method based on key location extraction and random location fusion according to claim 1,
further comprising: and dividing peak points of the multi-dimensional curve graph into a plurality of position types, expanding the position set D by adopting a random sampling algorithm to obtain a random position class, and using the random position class as the injection opportunity of the train control test scene.
3. The CBTC scenario testing method based on key location extraction and random location fusion of claim 1,
participating in controlling factor X within L distanceiThe weighted value M is used as an evaluation function S participating in the speed control of the CBTC system, and the formula is as follows:
wherein M = theta1X1+θ2X2+…+θiXi+…+θ8X8。
4. The CBTC scenario testing method based on key location extraction and random location fusion of claim 1,
a three-dimensional curve in the multi-dimensional curve graph represents a real running route of a train, an X axis represents a direction from west to east, a Y axis represents a direction from south to north, a Z coordinate is height, and the height of a curve line represents a gradient.
5. The CBTC scene testing method based on key location extraction and random location fusion according to claim 1,
location types include, but are not limited to: a turnout side strand area position type, a straight-through high-speed point position type, an inbound point position type, an outbound point position type, a platform parking point position type, a ZC cross-connection area position type, a turn-back point position type and a curve area position type.
6. The CBTC scene testing method based on key location extraction and random location fusion according to claim 2,
the random sampling algorithm comprises one of a kennard-Stone algorithm, a hierarchical sampling method and a Monte Carlo random sampling method.
7. The CBTC scenario testing method based on key location extraction and random location fusion of claim 6,
the method for expanding the position set D by adopting the kennard-Stone algorithm comprises the following steps:
according to key control points extracted from a target control curve, carrying out equivalence classification on all-line key control points according to position types by using an equivalence classification method, and after classification, the effect of the representative data of each class in the test is equivalent to other values in the class;
taking a plurality of position category data extracted from all key control positions as a position complete set U, and taking a set A of coupling control position elements as an initial training sample set;
the set C is a complementary set C = C of the set D in the complete set UUD, selecting the maximum distance from the minimum distances of all data in the distance A set from the set C as extended data of the set D, and continuously extending the set D to obtain a set D0; synchronously expanding the set A to obtain a set A0; and the value position points of the set A0 are used as random scene injection opportunities.
8. The CBTC scene testing method based on key location extraction and random location fusion according to claim 1,
the method comprises the following steps of dividing peak points of a multi-dimensional curve chart into a plurality of position types, expanding a position set D by adopting a random sampling algorithm to obtain a random position class, and using the random position class as the injection opportunity of a train control test scene:
matching the peak points of the multidimensional curve graph with the arrangement attributes of the train track circuit plan, and dividing the peak points of the track data participating in control into a plurality of position types according to an equivalent classification method to obtain curves of control parameters and positions of the CBTC system;
and expanding the position set D by adopting a random sampling algorithm, acquiring a position value set V0 of different position sets D0 where the train runs, taking the value position points as a random position class, and taking the random position class as the injection time of the train control test scene.
9. The CBTC scenario testing method based on key location extraction and random location fusion of claim 1,
training the test cases by adopting a heuristic search algorithm to obtain an optimal combined test case set, comprising the following steps of:
inputting a parameter value set T and a coverage intensity T of a CBTC system test scene;
according to the coverage intensity t, combining and pairing the parameter values to generate a combination table to be covered;
generating a test case by utilizing a heuristic algorithm;
according to whether the table to be covered is completely covered, generating a use case with the largest number of covering combinations each time as an optimal use case corresponding to the position point;
and continuously searching by using a particle swarm algorithm to obtain a test case set, wherein the test case set is used as an optimal combined test case set.
10. The CBTC scene testing method based on key location extraction and random location fusion according to claim 9,
the heuristic search algorithm comprises one of a genetic algorithm, an ant colony algorithm, simulated annealing, a tabu algorithm and a particle swarm algorithm.
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