CN114896166A

CN114896166A - Scene library construction method and device, electronic equipment and storage medium

Info

Publication number: CN114896166A
Application number: CN202210643964.2A
Authority: CN
Inventors: 吴振昕; 杨渊泽; 赵朋刚; 刘涛; 彭亮; 张正龙; 迟霆; 赵思佳; 周忠贺; 赵悦岑
Original assignee: FAW Group Corp
Current assignee: FAW Group Corp
Priority date: 2022-06-08
Filing date: 2022-06-08
Publication date: 2022-08-12

Abstract

The invention discloses a scene library construction method, a scene library construction device, electronic equipment and a storage medium, wherein the method comprises the following steps: acquiring at least one test scene category associated with at least one test task, and determining the category ratio of each test scene category; aiming at each test task, obtaining the number of test scenes corresponding to the test task, and determining a recommended test set corresponding to the test task based on the ratio of the number of the test scenes to the category; and constructing at least one scene library based on the recommended test set corresponding to each test task, and testing each scene library based on each test task to determine a target scene library based on the test result. According to the technical scheme of the embodiment of the invention, the technical effects of improving the effectiveness of the test scene and the accuracy of the subsequent test are realized by analyzing the category distribution of the test scene categories and the optimal scale of the scene library.

Description

Scene library construction method and device, electronic equipment and storage medium

Technical Field

The invention relates to the technical field of automatic driving, in particular to a scene library construction method and device, electronic equipment and a storage medium.

Background

With the automobile coming to be the third generation mobile intelligent terminal, the requirements of intellectualization and networking are higher and higher. In the process of developing the automatic driving function of the automobile, the automatic driving function of the automobile needs to be realized based on a certain test scene, test cases need to be manufactured according to a specific driving scene, partial danger and a limit scene during test work, so that a test case set with corresponding functions can be obtained, and the development of a comprehensive scene library to cover test scene libraries with different functions and different purposes is the target of the development work of all scenes at the present stage.

At present, in order to make the test scene library cover different types of test scenes as much as possible, the test scene library is usually implemented by increasing the number of test scenes and a manual experience screening and supplementing manner, but as the number of scenes in the scene library is increased, the scale of the scene library is also increased, so that the storage and management costs are also increased.

Disclosure of Invention

The invention provides a method and a device for constructing a scene library, electronic equipment and a storage medium, which are used for improving the coverage rate of test scene types in the scene library and the test efficiency.

According to an aspect of the present invention, there is provided a method for constructing a scene library, the method including: acquiring at least one test scene category associated with at least one test task, and determining the category ratio of each test scene category;

aiming at each test task, obtaining the number of test scenes corresponding to the test task, and determining a recommended test set corresponding to the test task based on the ratio of the number of the test scenes to the category;

and constructing at least one scene library based on the recommended test set corresponding to each test task, and testing each scene library based on each test task to determine a target scene library based on the test result.

According to another aspect of the present invention, there is provided a scene library construction apparatus including:

the test scene type acquisition module is used for acquiring at least one test scene type associated with at least one test task and determining the type ratio of each test scene type;

the recommended test set determining module is used for acquiring the number of test scenes corresponding to the test tasks aiming at each test task and determining the recommended test set corresponding to the test tasks based on the ratio of the number of the test scenes to the category;

and the target scene library determining module is used for constructing at least one scene library based on the recommended test set corresponding to each test task, testing each scene library based on each test task and determining the target scene library based on the test result.

According to another aspect of the present invention, there is provided an electronic apparatus including:

at least one processor; and

a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,

the memory stores a computer program executable by the at least one processor, the computer program being executable by the at least one processor to enable the at least one processor to perform the scene library construction method according to any of the embodiments of the present invention.

According to another aspect of the present invention, there is provided a computer-readable storage medium storing computer instructions for causing a processor to implement the scene library construction method according to any one of the embodiments of the present invention when the computer instructions are executed.

The technical scheme of the embodiment of the invention firstly obtains at least one test scene category associated with at least one test task, and determining the class ratio of each test scene class, further, aiming at each test task, obtaining the number of test scenes corresponding to the test task, and determining a recommended test set corresponding to the test task based on the number of test scenarios and the category ratio, and finally, constructing at least one scene library based on the recommended test set corresponding to each test task, testing each scene library based on each test task, the target scene library is determined based on the test result, the problems that the test scenes in the scene library are excessively accumulated and the coverage degree of the test scene categories does not meet the test requirement in the prior art are solved, by analyzing the category distribution of the test scene categories and the optimal scale of the scene library, the technical effects of improving the effectiveness of the test scene and the accuracy of subsequent tests are achieved.

It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present invention, nor do they necessarily limit the scope of the invention. Other features of the present invention will become apparent from the following description.

Drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced 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 based on these drawings without creative efforts.

Fig. 1 is a flowchart of a scene library construction method according to an embodiment of the present invention;

fig. 2 is a schematic structural diagram of a scene library construction device according to a second embodiment of the present invention;

fig. 3 is a schematic structural diagram of an electronic device implementing the scene library construction method according to the embodiment of the present invention.

Detailed Description

In order to make the technical solutions of the present invention better understood, 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 derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.

Example one

Fig. 1 is a flowchart of a method for constructing a scene library according to an embodiment of the present invention, where the method is applicable to a situation where a test scene included in a test scene library does not satisfy a requirement of a test task when a vehicle function is tested, the method may be executed by a scene library constructing apparatus, the scene library constructing apparatus may be implemented in a form of hardware and/or software, and the scene library constructing apparatus may be configured in a terminal and/or a server. As shown in fig. 1, the method includes:

s110, at least one test scene category associated with at least one test task is obtained, and category proportion of each test scene category is determined.

The test task may be a task for testing different functions of the vehicle. For example, when the automatic driving function of the vehicle needs to be tested, the corresponding test tasks may include, but are not limited to, an adaptive cruise test, an automatic emergency braking test, a congestion driving test, and the like. It should be noted that the test task may include a test task in an actual situation, or may also include a test task expected to be implemented in the future, and this embodiment does not specifically limit this. The test scenario categories may be functional requirements that need to be met for performing the test task. For example, when the test task is an adaptive cruise test, its associated test scenario categories may include, but are not limited to, performing appropriate vehicle launch, performing appropriate route-taking, and identifying and responding to temporary static obstacles, etc.; when the test task is an automatic emergency braking test, the associated test scenario categories may include, but are not limited to, identifying and responding to a forward stopping vehicle, identifying and responding to a temporary static obstacle, identifying and responding to a forward lane change cut-in vehicle, and the like; when the test task is a congested road condition ride test, its associated test scenario categories may include, but are not limited to, performing appropriate vehicle following, performing appropriate lane changes, and identifying and responding to adjacent stopped vehicles, among others. The category fraction may be a percentage of the target test scenario category among all test scenario categories associated with all test tasks.

In practical application, when a certain function of a vehicle needs to be subjected to performance test, a corresponding test task can be determined for the function of the vehicle to be tested, and then, a test scenario type associated with each test task can be determined. Optionally, the obtaining at least one test scenario category associated with at least one test task includes: and determining each test scene type associated with each test task according to the pre-established corresponding relationship between each test task and each test scene type.

Specifically, when determining the test scenario category associated with the test task, a correspondence table between each test task and each test scenario category may be established in advance, so as to determine each test scenario category associated with each test task according to the correspondence table.

Further, in order to determine the importance degree of each test scenario category, statistical analysis may be performed on the determined test scenario categories to determine the number of times each test scenario type appears, and further, the category ratio of each test scenario category may be determined according to the frequency of appearance, so as to analyze the importance degree of each test scenario category based on the category ratio. Optionally, determining the category duty ratio of each test scenario category includes: overlapping the types of the test scenes of the test tasks, and determining the frequency information of the test scenes; and determining the class ratio of each test scene class based on the frequency information.

The frequency information may be the occurrence frequency or the occurrence frequency of each test scenario category.

In specific implementation, after determining all test scenario categories associated with each test task, all test scenario categories of the test tasks may be subjected to superposition processing to obtain a total test scenario category library corresponding to a vehicle function to be tested, and further, the number of times of occurrence of each test scenario category in the total test scenario category library is statistically analyzed to determine a category fraction based on the number of times of each test scenario category. Illustratively, when the automatic driving function of a vehicle needs to be tested, the number of corresponding test tasks may be 27, each test task corresponds to a plurality of test scenario categories, and after all the test scenario categories of the 27 test tasks are subjected to superposition processing, a total test scenario category library including 500 test scenario categories is obtained, wherein when the test scenario category is 50 times that a vehicle is properly started in the total test scenario category library, the corresponding category duty ratio is 10%; if the number of times that the test scenario category appears in the total test scenario category library for performing appropriate route driving is 20, the category occupancy rate is 4%.

S120, aiming at each test task, obtaining the number of test scenes corresponding to the test task, and determining a recommended test set corresponding to the test task based on the ratio of the number of the test scenes to the category.

The number of test scenarios may be the number of test scenarios required by the current test task. For example, the number of test scenarios may be 500, 1000, 1500, or the like. The recommended test set may be a set that contains preferred test scenario categories. The preferred test scenario category may be a test scenario category that has a clear test meaning for testing vehicle functions.

Optionally, for each test task, obtaining a number of test scenarios corresponding to the test task, and determining a recommended test set corresponding to the test task based on a ratio of the number of test scenarios to the category, includes: determining the number of test scenes corresponding to the test tasks based on the current test requirements of the test tasks; determining the scene number of each test scene type based on the ratio of the test scene number to the test scene type; and constructing a recommended test set corresponding to the test task according to a preset effective scene library and the number of scenes.

The current test requirement can be a test requirement of a test developer for a vehicle function to be tested at the current moment. For example, according to the current test requirement, the number of corresponding test scenarios may be 500, 1000, 1500, or the like. The number of scenes is the number of categories of each test scene. The effective scene library can be a pre-established scene library used for storing effective scenes obtained by analyzing scene effectiveness based on vehicle function test requirements. The effective scene can be a scene having practical test significance in the vehicle function test, namely a scene useful for the vehicle function test process.

In specific implementation, when a recommended test set of each test task is determined, the current test requirements of each test task can be acquired to obtain the number of test scenes corresponding to each test task, further, the number of each test scene type under the current test scene number is determined according to the number of the test scenes of each test task and the analog proportion of each test scene type in each test task, the test scene types of the target scene number are called from a pre-established effective scene library based on the determined scene number of each test scene type, and the recommended test set corresponding to each test task is constructed based on the called test scene types.

It should be noted that the advantage of constructing the recommended test set is: the method can test the functions of the target vehicle based on the recommended test set in the early vehicle function development process so as to debug the functions of the target vehicle according to the test result.

S130, constructing at least one scene library based on the recommended test set corresponding to each test task, and testing each scene library based on each test task to determine a target scene library based on the test result.

The scenario library may be a set including all test scenarios required for testing the vehicle function. The test results may include a test pass, a test fail, and a test exception.

In practical applications, when the scene libraries with different orders of magnitude are subjected to simulation tests of different test tasks, the obtained test effects are different, and therefore, the scene library including a proper number of test scenes needs to be determined so as to realize the optimal test effect based on the scene library. Optionally, constructing at least one scenario library based on the recommended test set corresponding to each test task includes: determining at least one preset proportion aiming at each test task, and performing expansion processing on the recommended test set based on the preset proportion to obtain at least one functional test set; and overlapping the functional test sets of the test tasks to obtain at least one scene library.

In this embodiment, the preset ratio may be a preset expansion ratio of the test set. Illustratively, the preset ratio may be 1:10, 1:20, or 1:50, etc. For example, the preset ratio may be set to 1:20, the number of the test scenario categories in the recommended test set is 500, and after the recommended test set is expanded based on the preset ratio, the number of the test scenario categories in the functional test set is 10000.

In specific implementation, after determining the recommended test set corresponding to each test task, in order to determine the test effect of the scene libraries of different orders of magnitude, a plurality of preset ratios may be determined, and the recommended test set of each test task is expanded according to the preset ratios, so as to obtain a plurality of functional test sets corresponding to each test task, and further, the functional test sets of each test task are respectively overlapped, so as to obtain a plurality of scene libraries, where each scene library corresponds to each preset ratio.

Further, the determined scene libraries are tested based on the test tasks respectively, so that test results are obtained, and the target scene library meeting requirements can be determined based on the test results of each scene library.

Optionally, determining the target scene library based on the test result includes: determining the test passing rate, the test failure information and the test abnormal reason of each scene library according to the test result; and determining the target scene library in each scene library based on the test passing rate, the test failure information and the test abnormal reason.

In this embodiment, the test passing rate may be a ratio of the number of test scenario categories that pass the test in each test task to the total number of test scenario categories. The test failure information may be specific conditions of the test scenario category failing the test in each test task. The reason for the test exception may be a reason for the test exception occurring in the test scenario category in each test task. For example, the test exception cause may include, but is not limited to, a problem occurring in a scene file, a missing key parameter, and the like.

In specific implementation, after the test results of each scene library are obtained, when each scene library corresponds to the same test task, the specific condition of the test scene class failing the test can be analyzed according to the ratio of the number of the test scene classes of the test to the number of the total scene test classes, and the error reporting reason of the abnormal test scene class can be analyzed, so that the target scene library with reasonable scene number and the test result meeting the requirements can be determined by analyzing the test passing rate, the test failure information and the test abnormal reason of each scene library.

It should be noted that the target scene library corresponds to a vehicle function to be tested, and after the target scene library is determined, when other testing tasks of the vehicle function are subsequently tested, the testing scene can be directly called from the target scene library.

It should be further noted that, as time changes, the test scenario in the target scenario library may not meet the current actual situation, or the test scenario in the target scenario library does not meet the test task requirement due to the change of the newly added test task, and based on this, after the target scenario library is determined, the target scenario library also needs to be dynamically updated, so that the target scenario library always meets the test task requirement and the actual application scenario requirement.

On the basis of the technical scheme, the method further comprises the following steps: when a newly added test task is detected, updating the target scene library based on the newly added test task; and acquiring scene library management rules to update the target scene library based on the scene library management rules.

In practical application, when the same function of a vehicle is tested, test developers may develop other test tasks different from the existing test tasks along with the increase of time, and the newly developed test tasks can be used as new test tasks.

In this embodiment, the scene library management rule may be a rule according to which subsequent maintenance of the built scene library is required. By way of example, the scenario library management rules may include, but are not limited to, storage space, update frequency, scenario date requirements, and scenario utilization statistics. For example, for a storage space, the target scene library has a fixed storage space, and the storage space of the target scene library needs to be kept unchanged all the time when a test scene is added or removed later; for the updating frequency, an updating period can be preset, so that the target scene library can be periodically updated based on the preset updating period; for the scene date requirement, as time increases, test scenes in the target scene library which are far away from the current time may not meet the current actual situation, and therefore, the test scenes can be removed, so that the test scenes at the latest time can be supplemented in the vacant storage space.

The technical scheme of the embodiment of the invention includes the steps of firstly obtaining at least one test scene category associated with at least one test task, determining category proportion of each test scene category, further obtaining the number of test scenes corresponding to the test task aiming at each test task, determining a recommended test set corresponding to the test task based on the number of the test scenes and the category proportion, finally constructing at least one scene library based on the recommended test set corresponding to each test task, testing each scene library based on each test task, determining a target scene library based on a test result, solving the problems that in the prior art, the test scenes are excessively stacked in the scene library, and the coverage degree of the test scene categories cannot meet the test requirement, and improving the coverage rate of the test scenes by analyzing the category distribution of the test scene categories and the optimal scale of the scene library, The effectiveness of the test scene and the accuracy of the subsequent test result.

Example two

Fig. 2 is a schematic structural diagram of a scene library constructing apparatus according to a second embodiment of the present invention. As shown in fig. 2, the apparatus includes: a test scenario category obtaining module 210, a recommended test set determining module 220, and a target scenario library determining module 230.

The test scenario category obtaining module 210 is configured to obtain at least one test scenario category associated with at least one test task, and determine a category ratio of each test scenario category;

a recommended test set determining module 220, configured to obtain, for each test task, the number of test scenarios corresponding to the test task, and determine, based on the ratio of the number of test scenarios to the category, a recommended test set corresponding to the test task;

the target scene library determining module 230 is configured to construct at least one scene library based on the recommended test set corresponding to each test task, and test each scene library based on each test task to determine the target scene library based on the test result.

Optionally, the test scenario category obtaining module 210 includes a test scenario category determining unit, configured to determine each test scenario category associated with each test task according to a pre-established correspondence between each test task and each test scenario category.

Optionally, the test scenario category obtaining module 210 further includes a category proportion determining unit, configured to perform superposition processing on each test scenario category of each test task, and determine frequency information of each test scenario; and determining the class ratio of each test scene class based on the frequency information.

Optionally, the recommended test set determining module 220 includes a test scenario number determining unit, a scenario number determining unit, and a recommended test set constructing unit.

The test scene number determining unit is used for determining the number of test scenes corresponding to the test tasks based on the current test requirements of the test tasks;

the scene number determining unit is used for determining the scene number of each test scene type based on the ratio of the test scene number to the test scene type;

and the recommended test set constructing unit is used for constructing a recommended test set corresponding to the test task according to the preset effective scene library and the number of scenes.

Optionally, the target scene library determining module 230 includes a scene library constructing unit, configured to determine at least one preset ratio for each test task, so as to perform expansion processing on the recommended test set based on the preset ratio to obtain at least one functional test set; and overlapping the functional test sets of the test tasks to obtain at least one scene library.

Optionally, the target scene library determining module 230 further includes a target scene library determining unit, configured to determine, according to the test result, a test passing rate, test failure information, and a test exception reason of each scene library; and determining a target scene library in each scene library based on the test passing rate, the test failure information and the test abnormal reason.

Optionally, the apparatus further comprises: the target scene library updating module is used for updating the target scene library based on the newly increased test task when the newly increased test task is detected; and acquiring a scene library management rule to update the target scene library based on the scene library management rule.

The scene library construction device provided by the embodiment of the invention can execute the scene library construction method provided by any embodiment of the invention, and has corresponding functional modules and beneficial effects of the execution method.

EXAMPLE III

FIG. 3 illustrates a schematic diagram of an electronic device 10 that may be used to implement an embodiment of the present invention. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smart phones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.

As shown in fig. 3, the electronic device 10 includes at least one processor 11, and a memory communicatively connected to the at least one processor 11, such as a Read Only Memory (ROM)12, a Random Access Memory (RAM)13, and the like, wherein the memory stores a computer program executable by the at least one processor, and the processor 11 can perform various suitable actions and processes according to the computer program stored in the Read Only Memory (ROM)12 or the computer program loaded from a storage unit 18 into the Random Access Memory (RAM) 13. In the RAM 13, various programs and data necessary for the operation of the electronic apparatus 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to bus 14.

A number of components in the electronic device 10 are connected to the I/O interface 15, including: an input unit 16 such as a keyboard, a mouse, or the like; an output unit 17 such as various types of displays, speakers, and the like; a storage unit 18 such as a magnetic disk, an optical disk, or the like; and a communication unit 19 such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information/data with other devices via a computer network such as the internet and/or various telecommunication networks.

The processor 11 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, or the like. The processor 11 performs the various methods and processes described above, such as the scene library construction method.

In some embodiments, the scene library construction method may be implemented as a computer program tangibly embodied in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 10 via the ROM 12 and/or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the scene library construction method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the scene library construction method by any other suitable means (e.g., by means of firmware).

Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, Field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), system on a chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.

A computer program for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be performed. A computer program can execute entirely on a machine, partly on a machine, as a stand-alone software package partly on a machine and partly on a remote machine or entirely on a remote machine or server.

In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. A computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.

The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), Wide Area Networks (WANs), blockchain networks, and the internet.

The computing system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical host and VPS service are overcome.

It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present invention may be executed in parallel, sequentially, or in different orders, and are not limited herein as long as the desired results of the technical solution of the present invention can be achieved.

The above-described embodiments should not be construed as limiting the scope of the invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A scene library construction method is characterized by comprising the following steps:

acquiring at least one test scene category associated with at least one test task, and determining the category ratio of each test scene category;

and constructing at least one scene library based on the recommended test set corresponding to each test task, and testing each scene library based on each test task to determine a target scene library based on a test result.

2. The method of claim 1, wherein obtaining at least one test scenario category associated with at least one test task comprises:

and determining each test scene category associated with each test task according to the pre-established corresponding relationship between each test task and each test scene category.

3. The method of claim 1, wherein determining the class ratio for each of the test scenario classes comprises:

overlapping the types of the test scenes of the test tasks, and determining the frequency information of the test scenes;

and determining the class ratio of each test scene class based on the frequency information.

4. The method of claim 1, wherein the obtaining, for each of the test tasks, a number of test scenarios corresponding to the test task and determining a recommended test set corresponding to the test task based on the number of test scenarios versus the category comprises:

determining the number of test scenes corresponding to the test tasks based on the current test requirements of each test task;

determining the scene quantity of each test scene category based on the class ratio of the test scene quantity to the test scene category;

and constructing a recommended test set corresponding to the test task according to a preset effective scene library and the number of scenes.

5. The method of claim 1, wherein constructing at least one scenario library based on the recommended test set corresponding to each of the test tasks comprises:

determining at least one preset proportion aiming at each test task, and performing expansion processing on the recommended test set based on the preset proportion to obtain at least one functional test set;

and overlapping the functional test sets of the test tasks to obtain at least one scene library.

6. The method of claim 1, wherein determining a library of target scenarios based on the test results comprises:

determining the test passing rate, the test failure information and the test abnormal reason of each scene library according to the test result;

and determining the target scene library in each scene library based on the test passing rate, the test failure information and the test abnormal reason.

7. The method of claim 1, further comprising:

when a newly added test task is detected, updating the target scene library based on the newly added test task;

and the number of the first and second groups,

and acquiring scene library management rules to update the target scene library based on the scene library management rules.

8. A scene library building apparatus, comprising:

a recommended test set determining module, configured to obtain, for each test task, a number of test scenarios corresponding to the test task, and determine, based on a ratio between the number of test scenarios and the category, a recommended test set corresponding to the test task;

9. An electronic device, characterized in that the electronic device comprises:

at least one processor; and

the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the scene library construction method of any one of claims 1-7.

10. A computer-readable storage medium, wherein the computer-readable storage medium stores computer instructions for causing a processor to implement the scene library construction method according to any one of claims 1 to 7 when executed.