CN117472671A - System testing method, device, equipment and medium based on image recognition result - Google Patents
System testing method, device, equipment and medium based on image recognition result Download PDFInfo
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Abstract
The embodiment of the invention discloses a system testing method, a device, equipment and a medium based on an image recognition result, which comprise the following steps: acquiring an image recognition result of a target information output interface image in a target system and a test sequence for matching the target information output interface image; determining a plurality of first target identification parameter arrays according to the image identification result of the target information output interface image; generating an identification parameter mapping curve of the second target identification parameter and the first target identification parameter according to an image identification result of the target information output interface image; correcting the first target identification parameter array according to the identification parameter mapping curve to obtain a corrected target identification parameter array; generating a test model according to the corrected target identification parameter array and the image identification result of the target information output interface image; and testing the test model according to the test sequence. The technical scheme of the embodiment of the invention can improve the accuracy and the degree of automation of the system test based on the image recognition result.
Description
Technical Field
The embodiment of the invention relates to the technical field of system testing, in particular to a system testing method and device based on an image recognition result, electronic equipment and a storage medium.
Background
Currently, most equipment systems are configured with an information output interface for outputting important information related to the display equipment system. In the testing stage of the equipment system, the accuracy of data configuration in the equipment system and/or the overall usability of the system often need to be tested by utilizing the information output interface of the equipment system to output the displayed information in real time.
Accordingly, when performing a system test according to information displayed by the device system information output interface in real time, the prior art generally includes a manual test and an automatic test based on an image recognition result. The manual testing mode is to actively observe an equipment system information output interface by a tester to acquire information displayed by the equipment system information output interface, and perform data correction with a test case pre-configured by the equipment system to test the equipment system. The automatic test mode based on the image recognition result is to automatically recognize and acquire information by adopting an image recognition mode for the information output and displayed by the equipment system information output interface in real time, and to perform data check according to the automatically acquired information and the test case pre-configured by the equipment system so as to test the equipment system.
The inventors have found that the following drawbacks exist in the prior art in the process of implementing the present invention: the manual test mode has the problem of lower test efficiency and accuracy. In an automatic test mode based on an image recognition result, the image recognition effect is often poor due to severe restriction of image acquisition conditions, for example, the placement position of a camera relative to an information output interface of equipment is unreasonable or the problem of recognizing a field light source is solved, and the image recognition effect is poor. Meanwhile, when the comparison data stored in the database for comparing the image recognition results is imperfect, the image recognition effect is often also poor. When the image recognition effect is poor, the image recognition result is extremely easy to include a fuzzy recognition result. In view of a certain error rate of image recognition, even if the image recognition effect is ideal, a certain fuzzy recognition result is generated in the image recognition result. Therefore, when the image recognition result has a fuzzy recognition result, a certain difficulty is often brought to the subsequent automatic test result judgment.
Disclosure of Invention
The embodiment of the invention provides a system testing method and device based on an image recognition result, electronic equipment and a storage medium, which can improve the accuracy and the automation degree of system testing based on the image recognition result.
According to an aspect of the present invention, there is provided a system testing method based on an image recognition result, including:
acquiring an image recognition result of a target information output interface image in a target system and a test sequence matched with the target information output interface image;
determining a plurality of first target identification parameter arrays according to the image identification result of the target information output interface image;
generating an identification parameter mapping curve of a second target identification parameter and the first target identification parameter according to an image identification result of the target information output interface image;
correcting the first target identification parameter array according to the identification parameter mapping curve to obtain a corrected target identification parameter array;
generating a test model of the target system according to the corrected target identification parameter array and the image identification result of the target information output interface image;
and testing the test model according to the test sequence.
According to another aspect of the present invention, there is provided a system testing apparatus based on an image recognition result, including:
the identification result test sequence acquisition module is used for acquiring an image identification result of a target information output interface image in a target system and a test sequence matched with the target information output interface image;
The first target identification parameter array determining module is used for determining a plurality of first target identification parameter arrays according to the image identification result of the target information output interface image;
the identification parameter mapping curve generation module is used for generating an identification parameter mapping curve of a second target identification parameter and the first target identification parameter according to an image identification result of the target information output interface image;
the corrected target identification parameter array generation module is used for correcting the first target identification parameter array according to the identification parameter mapping curve to obtain a corrected target identification parameter array;
the test model generation module is used for generating a test model of the target system according to the corrected target identification parameter array and the image identification result of the target information output interface image;
and the test model test module is used for testing the test model according to the test sequence.
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 memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the image recognition result-based system test method according to any one 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 system testing method based on image recognition results according to any one of the embodiments of the present invention when executed.
According to the embodiment of the invention, after the image recognition result of the target information output interface image in the target system and the test sequence matched with the target information output interface image are obtained, a plurality of first target recognition parameter arrays are determined according to the image recognition result of the target information output interface image, and recognition parameter mapping curves of second target recognition parameters and the first target recognition parameters are generated according to the image recognition result of the target information output interface image, so that the first target recognition parameter arrays are corrected according to the recognition parameter mapping curves. After the corrected target recognition parameter array is obtained, a test model of the target system is generated by utilizing the corrected target recognition parameter array and the image recognition result of the target information output interface image, and finally the generated test model is tested by utilizing the test sequence, so that the problem of lower system test accuracy caused by inaccurate image recognition result when the system test is performed according to the image recognition result of the target information output interface image in the target system is solved, and the accuracy and the automation degree of the system test based on the image recognition result can be improved.
It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the invention or to delineate the scope of the invention. Other features of the present invention will become apparent from the description that follows.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of a system testing method based on image recognition results according to a first embodiment of the present invention;
fig. 2 is a flowchart of a system testing method based on image recognition results according to a second embodiment of the present invention;
fig. 3 is a schematic flow chart of determining a kilometer scale group according to a second embodiment of the present invention;
fig. 4 is a schematic diagram of a driving model construction flow based on multidimensional data according to a second embodiment of the present invention;
fig. 5 is a schematic flow chart of a test sequence result matching analysis test based on a real-time train state according to a second embodiment of the present invention;
Fig. 6 is a schematic diagram of a system testing device based on an image recognition result according to a third embodiment of the present invention;
fig. 7 is a schematic structural diagram of an electronic device according to a fourth embodiment of the present invention.
Detailed Description
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present invention and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise 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 1
Fig. 1 is a flowchart of a system testing method based on an image recognition result, which is provided in an embodiment of the present invention, and the embodiment is applicable to a case of testing a target system according to a corrected image recognition result of a target information output interface image in the target system, where the method may be performed by a system testing device based on the image recognition result, and the device may be implemented by software and/or hardware, and may be generally integrated in an electronic device, and the electronic device may be a terminal device or a server device, so long as the target system can be tested by using the image recognition result of the target information output interface image in the target system and a matched test sequence. Accordingly, as shown in fig. 1, the method includes the following operations:
s110, acquiring an image recognition result of a target information output interface image in a target system and a test sequence of matching the target information output interface image.
The target system may be any type of system with an information output interface. The target information output interface may be one or more interfaces in the target system for outputting and displaying the related information. Optionally, the information output by the target information output interface may be related information of the target system, such as system parameters including a system processor utilization rate, a memory utilization rate, or a fan rotation speed, or a result of system processing output. Optionally, the information output by the target information output interface may also be related information detected and output by the target system, such as a position detected by the target system, a wind speed, an obstacle, and the like. The target information output interface image may then be an image of the target information output interface. The test sequence may be reference data for testing data output by the target system through the target information output interface.
In a specific example, taking a signal system of a train or other traffic equipment, such as a train control system, etc., as an example of a target system, the target information output interface may be a human-computer interaction interface (Driver Machine Interface, DMI) of the train on-board equipment, which is also called a driver-on-board equipment interface, and is a platform for the driver to communicate with the on-board equipment. The driver can acquire a series of driving information such as the running speed, the alarming speed, the distance from the stripping point and the like through the DMI; on the other hand, the driver also needs to send control commands to ATP (Automatic Train Protection, train autoguard) via DMI. The train operation control system (hereinafter referred to as train control system) plays an irreplaceable role in the process of guaranteeing the safe operation of the high-speed railway. The data test of the train control system is an important test link of the train control system, and a tester writes a test sequence by taking an engineering data table provided by a design unit as an input basis. During testing, real vehicle-mounted equipment is introduced as a testing tool, a train real operation scene is traversed in a testing environment finally, the output information of the DMI is obtained by identifying the image information of the DMI, each item of data in the DMI and the testing sequence are compared and verified one by one, and comprehensive evaluation is given to the accuracy of data configuration in a train control system and the overall usability of the system.
In a specific example, the target information output interface may be a display screen interface of a server device, which is illustrated by taking a device such as a server as a target system. In the process of carrying out fan speed regulation test on the server, the current rotation speed information of the fan, the temperature information of each radiating component of the server and the like can be output through a display screen interface of the server equipment. Correspondingly, before the speed regulation of the server fan is tested, the mapping relation between the fan speed and the theoretical temperature information of each radiating component can be obtained through theoretical data to serve as a test sequence. During testing, current rotation speed information of the fan and current temperature information of each heat dissipation part of the server can be obtained through an image recognition result of a display screen interface of the server equipment, a matched test sequence is searched according to the current rotation speed information of the fan, so that the current temperature information of each heat dissipation part of the server is compared with theoretical temperature information of each heat dissipation part in the test sequence, whether the speed regulation of the fan is reasonable or not is judged, and the like.
In one specific example, a train control system of a train is taken as a target system and a DMI interface is taken as a target information output interface as an example. The train control system generally needs to use a train control engineering data table (hereinafter referred to as engineering data table) as configuration data. Meanwhile, the engineering data table is also an input data source for compiling a test sequence. Engineering data sheets typically have the following 14 types of forms: (1) RBC (Radio Block Center ) information table: describing the distribution of RBCs on a line, including the number of RBCs, the number of each RBC, the jurisdiction, the start-stop kilometer post and the TEL (Telephone in car) number; (2) station information table: describing station distribution conditions on a line, including the number of stations and the serial numbers thereof; (3) switch information table: describing turnout information on a line, including turnout names, belonged stations, turnout kilometer marks and positive side line mark information; (4) excessive information table: describing phase-separated area information on a line, including length and start-stop kilometer posts; (5) large number switch information table: describing large number turnout information on a line, including the name of the large number turnout, the station to which the turnout belongs and the speed of crossing the turnout; (6) bridge tunnel information table: describing the condition of a bridge tunnel on a line, including the names of the bridge and the tunnel and the start-stop kilometer post; (7) Line mileage broken link list (hereinafter referred to as broken link list): describing the broken link condition arranged on a line, including a line pin, a type, a length and a position kilometer sign; (8) line gradient table: describing gradient information on a line, wherein the gradient information comprises a variable slope point kilometer post, a specific gradient value and a gradient region length; (9) a signal data table: describing signal blocking information on a line, including signal points and track section related information; (10) line speed table: describing speed information on a line, wherein the speed information comprises speed change point kilometers, specific speed values and speed region lengths; (11) foreign matter disaster table: describing the conditions of disaster monitoring areas on a line, including line types, disaster monitoring, starting and stopping kilometer posts, corresponding foreign object relays, disaster zone names and stations to which the disaster monitoring devices belong; (12) transponder location table: describing information of the transponder on the line, including the name, the number, the kilometer post, the application and the belonged station of the transponder; (13) coordinate system information table: describing the conversion information of the mileage system on the line, including line numbers, line categories, mileage system names before and after conversion and kilometer posts before and after conversion; (14) train route information: the data arrangement in each station on the descriptive line comprises information such as an initial transponder, a route number, a route starting and ending point, a route speed, a route passing turnout positioning and reversing situation, a route track section composition and the like.
Accordingly, configuration attribute data of the train control system may be generally obtained according to engineering data tables, including but not limited to switches, signal points, line speeds, line gradients, RBC jurisdictions, station jurisdictions, split-phase areas, bridge tunnels, broken-chain areas, foreign disaster areas, transponders, large-number switches, mileage switching points, and the like. The data is distributed at certain specific locations or areas in the line according to its corresponding characteristics, and may be considered as a number of items of attributes attached to the line, referred to as data configuration attributes. Therefore, the actual position on the line, the kilometer post corresponding to the position, and the plurality of configuration attributes distributed under the position can form a corresponding relationship, which may be one-to-one or one-to-many. Based on the corresponding relation between a certain point or a certain area of the line and the attribute characteristics of the certain point or the certain area, the configured points are selected from the points or the areas with the established mapping relation on the line according to the test requirements of different test items, and then the ordered configured point sets or the ordered area sets are the test sequences. The test sequence can also be regarded as an operation state model of the train under the line according to the engineering data table, the data content in the default test sequence is correct, and the DMI outputs the identified data as the actual operation state of the train. That is, the actual running state of the train is compared with the specified running state model in the test, and the deviation between the actual running state and the specified running state within a certain range is allowed.
Optionally, the image recognition result of the target information output interface image may be a result obtained after image recognition based on an image obtained by shooting the target information output interface by an external camera, or may be a result obtained after image recognition of the target information output interface image after the target system automatically acquires the target information output interface image.
S120, determining a plurality of first target identification parameter arrays according to the image identification result of the target information output interface image.
The first target identification parameter may be an identification parameter included in an image identification result of the target information output interface image, and capable of matching the image identification result of the target information output interface image with the test sequence. Accordingly, the first object identification parameter array may be an array constructed from a plurality of first object identification parameters.
It will be appreciated that when the information to be output and displayed via the target information output interface changes relatively rapidly, such as in the order of milliseconds, the speed of rotation of the fan and the speed of rotation of the speed may vary, and that the information output by the target information output interface may not be continuous over the information refresh period due to the hysteresis of the target information output interface information refresh period. For example, the current target information output interface outputs kilometer post 1 or fan rotation speed 1 at the time of 10:00:01, the refresh period of the information output by the target information output interface is 1 second, and the current target information output interface outputs kilometer post 2 or fan rotation speed 2 at the time of 10:00:02. It will be appreciated that there are a number of other kilometers that are actually present between the kilometers 1 and 2 during the simulated travel of the train. During the server fan operation test, there are actually a number of other fan speeds between fan speed 1 and fan speed 2. Therefore, a single first target recognition parameter in the image recognition result of the target information output interface image may not be completely matched with the first target recognition parameter in the test sequence. Therefore, in order to achieve accurate positioning of the test sequence, a plurality of first target recognition parameter arrays may be determined from the image recognition result of the target information output interface image. Each first target identification parameter array can be used for matching with the first target identification parameters of each test sequence, and the test sequence of which the first target identification parameters belong to the first target identification parameter array is used as a test sequence which can be referred to carry out system test.
For example, when the target system is a train control system of a train and the target information output interface is a DMI interface, the first target identification parameter array may be an array formed by two kilometer post parameters. Correspondingly, the kilometer post in the test sequence is matched with the first target identification parameter array constructed by each kilometer post, so that the test sequence of which the kilometer post is positioned in the first target identification parameter array interval can be determined as a test sequence which can be referred to for testing.
For example, when the target system is a server device and the target information output interface is a display screen interface of the server device, the first target identification parameter array may be an array composed of two fan speeds. Correspondingly, the fan rotating speed in the test sequence is matched with the first target identification parameter array constructed by the fan rotating speeds, so that the test sequence with the fan rotating speed in the first target identification parameter array interval can be determined as the test sequence capable of referring to the test.
S130, generating an identification parameter mapping curve of the second target identification parameter and the first target identification parameter according to the image identification result of the target information output interface image.
The second target recognition parameter may be a recognition parameter that is included in the image recognition result of the target information output interface image and has an association relationship with the first target recognition parameter. For example, in a test scenario of the train control system, the second target identification parameter may be time. In a test scenario of the server fan speed, the second target identification parameter may be a current total load of the server device, etc. The identification parameter mapping curve may be a curve constructed according to the first target identification parameter and the second target identification parameter, for example, may be a mapping curve of time-kilometer post, or may be a mapping curve of current total load-fan rotation speed, etc.
Correspondingly, after determining a plurality of first target recognition parameter arrays according to the image recognition result of the target information output interface image, determining second target recognition parameters matched with each first target recognition parameter according to the image recognition result of the target information output interface image, further establishing an association relationship between the first target recognition parameters and the second target recognition parameters, and generating a recognition parameter mapping curve of the second target recognition parameters and the first target recognition parameters.
And S140, correcting the first target identification parameter array according to the identification parameter mapping curve to obtain a corrected target identification parameter array.
The corrected target identification parameter array may be an array obtained by correcting the first target identification parameter array by using an identification parameter mapping curve.
It will be appreciated that the first object recognition parameter is often subject to ambiguous recognition. By way of example, taking a train control system test as an illustration, in the dynamic recognition process of the DMI image, problems such as data adhesion and data packet loss may occur due to the influence of factors such as recognition period, communication delay and train running speed. When dynamically identifying information such as time, kilometer scale, gradient, speed limit, code sequence, RBC connection state, GSMR connection state, split-phase area, bridge tunnel station area, driving mode and grade, and starting line in a DMI image, the DMI image information identification is wrong or missing due to the large identification information quantity, high information change speed and external environment factors under the actual use scene. The fuzzy recognition result under a single DMI image may include several examples: (1) time: the time lacks the number of digits, and a certain number of digits are identified to be wrong; (2) kilometer post: the kilometer post lacks the number of digits, and a certain number of digits are identified to be wrong; (3) gradient: the positive and negative of the gradient can not be identified, the gradient lacks digits, and the digits of a certain digit are identified with errors; (4) speed or speed limit: the speed lacks the number of digits, and a certain number of digits are identified to be wrong; (5) phase separation zone status: the identification result is discontinuous; (6) bridge tunnel station area: the recognition result is discontinuous and the name is wrong.
Therefore, in order to reduce the difficulty of system test and improve the efficiency and the automation degree of system test, the second target recognition parameter in the image recognition result of the target information output interface image can be utilized to correct the recognition result of the first target recognition parameter. Specifically, since the identification parameter mapping curve includes the association relationship between the first target identification parameter and the second target identification parameter, the first target identification parameter array can be corrected based on the identification parameter mapping curve to obtain a corrected target identification parameter array. It can be understood that the situation that the information identification is wrong or missing is no longer existed in each first target identification parameter in the corrected target identification parameter array, and the corrected target identification parameter array can be used for matching the test sequence.
S150, generating a test model of the target system according to the corrected target identification parameter array and the image identification result of the target information output interface image.
The test model may include various configuration attribute data that the target system needs to test.
Correspondingly, after the corrected target recognition parameter array is obtained, various configuration attribute data to be tested can be added to the corrected target recognition parameter array according to the image recognition result of the target information output interface image, so that a test model of the target system is generated.
Illustratively, taking train system testing as an illustration, the content of the test model may include, but is not limited to, kilometer post, time, gradient, speed, RBC connection status, and train control system level, etc., to generate a real-time train status model as the test model to be tested.
Illustratively, taking a fan system test of a server device as an example, the content of the test model may include, but is not limited to, a fan speed, a total load zone of the device, a temperature zone of each heat dissipation component, a temperature early warning condition, and the like.
S160, testing the test model according to the test sequence.
Correspondingly, after the test model corresponding to the target system is generated, the test model can be tested according to the matched test sequence.
By way of example, a train system test is taken as an illustration, and specific driving information such as speed, gradient, distance and the like is determined through a test model during the test and is compared with a matched test sequence item by item. If the data comparison results are consistent, the test is passed; if the data comparison results are inconsistent, the test is not passed.
By way of example, taking a fan system test of a server device as an illustration, during the test, the total device load, the temperatures of all heat dissipation components and the temperature early warning conditions corresponding to the current fan rotation speed are determined through a test model, and are compared with a matched test sequence item by item. If the data comparison results are consistent, if the total load of the current equipment belongs to the range interval of the total load of the test sequence equipment, the temperature of the current part belongs to the range interval of the temperature of the corresponding part in the test sequence, and the like, the test is passed; if the data comparison results are inconsistent, the test is not passed.
According to the embodiment of the invention, after the image recognition result of the target information output interface image in the target system and the test sequence matched with the target information output interface image are obtained, a plurality of first target recognition parameter arrays are determined according to the image recognition result of the target information output interface image, and recognition parameter mapping curves of second target recognition parameters and the first target recognition parameters are generated according to the image recognition result of the target information output interface image, so that the first target recognition parameter arrays are corrected according to the recognition parameter mapping curves. After the corrected target recognition parameter array is obtained, a test model of the target system is generated by utilizing the corrected target recognition parameter array and the image recognition result of the target information output interface image, and finally the generated test model is tested by utilizing the test sequence, so that the problem of lower system test accuracy caused by inaccurate image recognition result when the system test is performed according to the image recognition result of the target information output interface image in the target system is solved, and the accuracy and the automation degree of the system test based on the image recognition result can be improved.
Example two
Fig. 2 is a flowchart of a system testing method based on an image recognition result according to a second embodiment of the present invention, where the present embodiment is implemented based on the foregoing embodiment, and in the present embodiment, various specific alternative implementations of determining a plurality of first target recognition parameter arrays, generating a recognition parameter mapping curve and correcting the target recognition parameter arrays, generating a test model of a target system, and testing the test model are provided. Accordingly, as shown in fig. 2, the method of this embodiment may include:
S210, acquiring an image recognition result of a target information output interface image in a target system and a test sequence of matching the target information output interface image.
S220, determining a plurality of first target recognition parameter arrays according to the image recognition result of the target information output interface image.
In an optional embodiment of the invention, the determining a plurality of first object identification parameter arrays according to the image identification result of the object information output interface image may include: constructing and updating buffer area data in real time according to the image recognition result of the target information output interface image; under the condition that the buffer data meets the data traversing condition, traversing the buffer data according to a first data traversing sequence to acquire a current first target identification parameter array; the current first target identification parameter array comprises latest first target identification data and next-new first target identification data; taking the latest first target identification data in the current first target identification parameter array as a current data traversal starting position, traversing the buffer zone data according to a second data traversal sequence, and searching first target identification data different from the latest first target identification data as first target identification data to be updated; updating the secondary new first target identification data according to the latest first target identification data, and updating the latest first target identification data according to the first target identification data to be updated to obtain each first target identification parameter array; the data in each first target identification parameter array are connected end to end.
The buffer area can be used for buffering the image recognition result of the target information output interface image. Buffer data, that is, partial image recognition result data stored in the buffer. The data traversal condition is a condition for starting traversal of the buffer. Alternatively, the data traversal condition may be, for example, that the amount of buffer data exceeds a set threshold (e.g., 4).
In a specific example, taking a train system test as an illustration, fig. 3 is a schematic flow chart of determining a kilometer post group according to the second embodiment of the present invention, and as shown in fig. 3, an exemplary DMI image recognition result buffer (hereinafter referred to as a buffer) may be constructed according to a continuous and dynamic DMI image recognition result. The buffer area can store the latest DMI image recognition result in real time, delete the oldest DMI image recognition result and ensure that the size of the buffer area is certain. The size of the buffer zone can be adaptively adjusted according to the period of receiving the DMI image recognition result, the period of filling the test record and the like so as to match the processing speed of each step. The information stored in one piece of data in the buffer data (i.e., the recognition result of one DMI image) may include, but is not limited to: DMI time, train speed, current kilometer post, gradient, current speed limit, code sequence, RBC connection state, GSMR connection state, bridge section, tunnel section, station section, split-phase section, temporary speed limit section, train control system grade and the like.
It should be noted that the latest data injection end of the buffer area may be referred to as a back end, the oldest data deletion end may be referred to as a front end, and the searching from back to front may be that the searching from new to old is performed. The buffer Size may be defined as Size1. The Size1 may be determined by various influencing factors such as the recognition cycle and/or recognition speed of the DMI image, the network environment, the computer performance, and the computing resource overhead. For example, when the recognition cycle of the DMI images is 500ms and the recognition doubling speed is 2, size1 may be 16, that is, the buffer may store the recognition results of 16 DMI images.
Correspondingly, after the buffer area is constructed, the stored data is empty. When formally entering the system test flow, each time a recognition result of a DMI image is obtained, the result can be stored into a buffer area to obtain buffer area data. The latest running position of the train can be positioned according to the data of the buffer zone, namely, the latest kilometer post (hereinafter referred to as the latest kilometer post) and the next latest kilometer post (hereinafter referred to as the next latest kilometer post) in the buffer zone are found, and a kilometer post array is constructed according to the latest kilometer post and the next latest kilometer post. Due to factors such as train running speed, DMI refreshing period, identification accuracy, DMI identification data transmission period, quality and the like, repeated, missing or disordered errors of the kilometer post easily occur. It is desirable to process kilometer posts to locate the train position as accurately as possible.
When generating the kilometer scale group, whether the buffer data in the buffer meets the traversing condition of the buffer can be judged. If it is determined that the buffer data satisfies the data traversal condition, such as the number of buffer data exceeding one-fourth of the total number of buffers, the buffer data may be traversed in the first data traversal order. Alternatively, the first data traversal order may be a backward-to-forward traversal order, i.e., a traversal order from the latest data to the oldest data. When traversing the buffer data, firstly, the first kilometer label which is not zero and does not lack the number of bits is used as the latest kilometer label, the traversal is continued according to the data traversal sequence, and the first kilometer label which is different from the latest kilometer label (can be the kilometer label with missing number of bits or error) is used as the next new kilometer label, so that the kilometer label array of the first latest kilometer label and the next new kilometer label is found.
After the first kilometer post group is found, the positioning period of the test sequence is used as a reference, the positioning period of each test sequence (namely, the period of comparing the test result with the period of the test sequence, which can also be called as a recognition period) continues to traverse the buffer data from back to front, the kilometer post which is equal to the latest kilometer post in the last kilometer post group is found, the latest kilometer post in the last kilometer post group is updated to be the next latest kilometer post in the current kilometer post group, the latest kilometer post position in the last kilometer post group is the current data traversal initial position, namely, the new initial traversal position, and the data buffer is traversed according to the second data traversal sequence. Alternatively, the second data traversal order may be a back-and-forth traversal order, i.e., a traversal order from the oldest data to the newest data. Specifically, the buffer data may be traversed according to the second data traversal order to search for a kilometer symbol that is different from the next new kilometer symbol in the current kilometer symbol array as the latest kilometer symbol in the current kilometer symbol array. If each kilometer sign group can be successfully found, the latest kilometer sign of each kilometer sign group is connected with the next new kilometer sign end to end, so that the non-repeatability and the continuity of the running position of the train are ensured. If the intermediate traversal process does not find a matching kilometer post, then the next recognition cycle can be waited to restart searching for a new head-end connected kilometer post array. It will be appreciated that different end-to-end kilometer scale arrays may be used to locate matching test sequences.
It should be noted that, as shown in fig. 3, if the kilometer post equal to the latest kilometer post in the last kilometer post group is not found in the first 1/M of the buffer area data searched to Size1 from the back to the front, it indicates that the capacity of the current buffer area is smaller, and the requirement of the system test is not satisfied. At this time, the Size of the buffer area can be expanded to be Size2, the forefront kilometer post (namely the oldest kilometer post) in the buffer area data is taken as a next new kilometer post, and the buffer area data after the expansion is traversed backwards to search the first kilometer post which is different from the next new kilometer post, namely the latest kilometer post. If not found, the latest kilometer post is equal to the next kilometer post, and the train can be regarded as being in a stationary state.
It is understood that both Size2 and M may be determined by the recognition result, the recognition cycle, the recognition speed, the network environment, the computer performance, the computing resource overhead, and other related factors. For example, when the recognition cycle of the recognition result is 500ms and the recognition double speed is 2, then M may be 4. If the recognition doubling speed is increased to N times 2, then Size2 can be extended to N times Size 1.
Correspondingly, after each first target identification parameter array connected end to end is obtained, the matched test sequence can be positioned for each first target identification parameter array. For example, as shown in fig. 3, the kilometer post of the test sequence can be matched with each kilometer post group, and the range of the kilometer post group to which the kilometer post of the test sequence belongs can be found. For example, if a test sequence a has a km label of k172+044, and one km label group a has a number of K172+040, K172+050], it may be determined that the test sequence a matches the km label group a. Therefore, under the condition that the train is running, namely the latest kilometer post and the next new kilometer post are continuously updated, the test sequence kilometer post is periodically traversed, and the kilometer post in the real-time latest kilometer post and the next new kilometer post are found out from the test sequence. The test sequence is positioned, the matching relation between the actual position of the train in the test and the test items in the test sequence is established, and the position of the current train in the test sequence can be accurately positioned.
It can be seen that, in the above example, the image recognition result of the interface image is output based on the dynamic blurred target information, and the test item detected by the current automatic test can be located in real time according to the first target recognition parameter, so as to fill out the test record or be used for building the test model in the later flow.
Or, because the first target identification parameters in the first target identification parameter arrays which are initially determined may have errors, the matched test sequences may be temporarily not positioned after the first target identification parameter arrays which are connected end to end are obtained, and the matched test sequences may be positioned in the process of testing the test model according to the test sequences after the first target identification parameter arrays are corrected. For example, under the condition that the train is running, that is, the latest kilometer post and the next new kilometer post are continuously updated and corrected, the test sequence kilometer post is periodically traversed, and the kilometer post in the real-time latest kilometer post and the next new kilometer post are found out from the test sequence. Therefore, the test sequence can be positioned more accurately, and the actual position of the train in the test and the test items in the test sequence are established with more accurate matching relation. The embodiment of the invention does not limit the positioning time of the test sequence.
S230, correcting the second target identification parameters to be corrected of the buffer area data according to the image identification result of the target information output interface image, and obtaining corrected buffer area data.
S240, adding the correction buffer data to a system state data area according to a time ascending order.
The second target identification parameter to be corrected may be a second target identification parameter to be corrected, for example, a second target identification parameter having missing item or error item information. The correction buffer data may be data after the second target identification parameters to be corrected are completed.
Wherein the system status data area may be used to store the modified buffer data in a certain order. The system state data area is the same as the buffer area, and the time latest data of the system state data area is called a back end, and the time oldest data is called a front end.
It can be understood that there is a probability that the second target recognition parameters to be corrected exist in each buffer data, so, in order to effectively utilize the effect of the second target recognition parameters on correcting the first target recognition parameters, the second target recognition parameters to be corrected of the buffer data may be corrected in advance according to the image recognition result of the target information output interface image, for example, the second target recognition parameters to be corrected are complemented, so as to obtain corrected buffer data. Optionally, the method for correcting the second target identification parameter to be corrected of the buffer data may include, but is not limited to, correction according to a variation trend or interpolation correction, and the embodiment of the present invention is not limited to a specific correction method for the second target identification parameter to be corrected.
Continuing with the above train system test as an illustration, multiple repetition or deletion of the kilometer post is caused due to the influence of factors such as train traveling speed, DMI refresh period, identification accuracy, transmission period, and the like. Therefore, in the dynamic DMI image recognition process, the multi-dimensional data collaborative analysis can be combined to correct the kilometer post data, and an accurate real-time train state model is built based on the corrected kilometer post data. Specifically, the DMI time identification result in the buffer data may be used as the second target identification parameter, and the second target identification parameter to be corrected may be corrected first. For example, for small number of missing or erroneous time bits, the correction can be based on continuous DMI time trend; for a large number of time bit missing or error problems, interpolation correction can be performed according to the DMI recognition time sequence and the complete DMI time recognition result. Fig. 4 is a schematic diagram of a driving model construction flow based on multidimensional data according to a second embodiment of the present invention, in a specific example, as shown in fig. 4, complete DMI identification data with corrected DMI identification time may be placed in a train status data area (hereinafter referred to as a system status data area) after being arranged in ascending order according to a time sequence.
S250, traversing the system state data area according to a first state data traversing sequence to generate a first identification parameter mapping curve of the second target identification parameter and the first target identification parameter.
The first state data traversal order may be a traversal order of the system state data area from front to back. The first identification parameter map may be a type of identification parameter map calculated after traversing the system state data region in the first state data traversal order.
And S260, traversing the system state data area according to a second state data traversing sequence to generate a second identification parameter mapping curve of the second target identification parameter and the first target identification parameter.
Wherein the second state data traversal order may be a back-to-front traversal order of the system state data region. The second identification parameter map may be another identification parameter map calculated after traversing the system state data region in the second state data traversal order.
Alternatively, the abscissa of the data points in the first identification parameter mapping curve and the second identification parameter mapping curve may be the first target identification parameter, and the ordinate may be the second target identification parameter.
Continuing with the above example, as shown in FIG. 4, the system status data field may first be traversed in a front-to-back order, with the first kilometer post without missing digits as the starting data point. Further, the running distance of the train is calculated according to the known time-speed curve of each data point in the system state data area, and further, the kilometer post of the train at each time is calculated according to the running distance of the train, so that a first time-kilometer post curve is obtained and is used as a first identification parameter mapping curve. And then traversing the system state data area according to the sequence from back to front, and repeating the process to obtain a second time-kilometer scale curve as a second identification parameter mapping curve. Alternatively, the first identification parameter mapping curve and the second identification parameter mapping curve may be obtained by arranging the data points in ascending order according to time.
S270, correcting the first target recognition parameter array according to the recognition parameter mapping curve to obtain a corrected target recognition parameter array.
In an optional embodiment of the present invention, the correcting the first target identification parameter array according to the identification parameter mapping curve to obtain a corrected target identification parameter array may include: calculating a mapping curve comparison matching result between the first identification parameter mapping curve and the second identification parameter mapping curve; and correcting the first target identification parameter array according to the error range of the mapping curve comparison matching result to obtain the corrected target identification parameter array.
The mapping curve comparison and matching result may be a comparison result corresponding to each data point between the first identification parameter mapping curve and the second identification parameter mapping curve.
Specifically, when the first target identification parameter array is corrected according to the identification parameter mapping curve, the difference between the ordinate coordinates of each corresponding data point in the first identification parameter mapping curve and the second identification parameter mapping curve can be calculated as a mapping curve comparison matching result by taking the data point as a unit, and meanwhile, the first target identification parameter array can be corrected according to the error range of the mapping curve comparison matching result to obtain a corrected target identification parameter array.
In an optional embodiment of the present invention, the correcting the first target identification parameter array according to the error range of the mapping curve comparison matching result to obtain the corrected target identification parameter array may include: under the condition that the error range of the mapping curve comparison matching result exceeds a preset error range threshold value, determining an error interval to be corrected according to the first identification parameter mapping curve and the second identification parameter mapping curve; determining an adjacent data interval of the error interval to be corrected, and correcting the first target recognition parameter array according to the associated image recognition data of the adjacent data interval to obtain the corrected target recognition parameter array; and under the condition that the error range of the mapping curve comparison matching result does not exceed a preset error range threshold, carrying out fitting correction on the first identification parameter mapping curve, the second identification parameter mapping curve and a first target identification parameter array in the buffer zone data to obtain the corrected target identification parameter array.
The preset error range threshold may be specifically set according to an actual test requirement, for example, the distance error may be 100m or 150m, the rotation speed error may be 200rpm or 300rpm, etc., and the embodiment of the present invention does not limit a specific value of the preset error range threshold. The error interval to be corrected can be a set interval of data points of which the error of the mapping curve compared with the matching result exceeds a preset error range threshold value. The adjacent data interval may then be a collection interval of data points adjacent to the error interval to be corrected. The associated image recognition data of the adjacent data interval may be reference data in the image recognition data that may be used to correct the data points in the error interval to be corrected.
Continuing with the above example, as shown in fig. 4, when the kilometer scale group is corrected according to the error range of the mapping curve comparison matching result, the two time-kilometer scale curves may be compared. If the comparison result has a larger error range and exceeds a preset error range threshold, a fitting data point or a data point set with larger error can be positioned, namely, an error interval to be corrected is determined. And carrying out interpolation correction on the error interval to be corrected according to the start and stop data points of the error interval to be corrected and the time and speed of the data points of the adjacent data interval outside the interval, and re-fitting the time-speed curve and matching the time-kilometer scale curve. If only one data point has a relatively large error, the previous data point and the next data point of the data point are taken, so that the speed and the numerical value of the kilometer post for correcting the data point are calculated according to the image recognition results such as the time, the speed and the like of the previous data point and the next data point. If the comparison result is within a certain error range and does not exceed the preset error range threshold, the twice time-kilometer scale matching result can be fitted with each kilometer scale in the buffer data, and a fitted and corrected time-kilometer scale curve can be obtained. And in the final fitting corrected time-kilometer sign curve, the accuracy of the kilometer sign data is higher. Correspondingly, the kilometer scale group determined by the preamble can be subjected to fitting correction according to the finally fitted corrected time-kilometer scale curve, so as to obtain a corrected kilometer scale group.
Alternatively, the acceptable preset error range threshold may be determined by factors such as current speed, identification period, network environment, computer performance, and computing resource overhead. For example, the current speed is 350km/h, the recognition period is 500ms, and the acceptable error range is 100m.
It should be noted that, the modified target recognition parameter array may be directly used to locate a matching relationship between the test sequence and the first target recognition parameter array, or may also be used to verify whether the recognition result of the latest first target recognition data and the next latest first target recognition data in the test sequence that has been located and matched is correct.
S280, generating a test model of the target system according to the corrected target identification parameter array and the image identification result of the target information output interface image.
In an optional embodiment of the present invention, the generating the test model of the target system according to the corrected target identification parameter array and the image identification result of the target information output interface image may include: determining the multi-dimensional model parameter information matched with the corrected target identification parameter array according to the image identification result of the target information output interface image; and establishing a mapping relation between the corrected target identification parameter array and the multidimensional model parameter information matched with the corrected target identification parameter array to obtain a test model of the target system.
The multidimensional model parameter information is the image identification information of other various dimensions matched with the corrected target identification parameter array.
Specifically, after the corrected target recognition parameter array is obtained, the image recognition result of the interface image can be output according to the target information to carry out sorting analysis, and other recognition information matched with each corrected first target recognition parameter is searched for so as to establish a mapping relation between each first target recognition parameter in the corrected target recognition parameter array and the matched multidimensional model parameter information, thereby obtaining a test model of the target system.
Continuing to describe the above example, as shown in fig. 4, on the basis of the kilometer post data, a real-time train state model may also be called a driving model, which is established according to the corresponding relationship between time and the kilometer post curve, in combination with other identifying information, such as gradient, speed, RBC connection state, and train control system level. After the model is built, the model is stored in a model data area (hereinafter referred to as a model area) for a specific test comparison process of a subsequent test sequence.
In the above example, modeling is performed on the fuzzy DMI recognition result through multidimensional data collaborative analysis, so that the train running state obtained according to the DMI result can be corrected, a real-time train state model can be accurately constructed, and the matching test sequence can be verified or positioned, so that the matching test sequence can be more accurately matched, and the analysis of the test result is more convenient.
S290, testing the test model according to the test sequence.
In an optional embodiment of the present invention, the testing the test model according to the test sequence may include: determining a target first target identification parameter array matched with the first target identification parameters of the target test sequence; wherein, the first target identification parameter of the target test sequence belongs to the range interval of the target first target identification parameter array; traversing each test model from the set position of the target first target identification parameter array according to the target reference test parameters of the target test sequence to determine a target test model; wherein the target reference test parameters of the target test model are the same as the target reference test parameters of the target test sequence; and testing the target test model according to the target test sequence.
Wherein the target test sequence may be the sequence currently being tested. The target first target identification parameter array is the first target identification parameter array matched with the target test sequence in a positioning way. It can be understood that the first target identification parameter of the target test sequence belongs to the range section of the target first target identification parameter array. The associated test model may include a test model corresponding to each first target identification parameter in the target first target identification parameter array, and may also include a test model corresponding to a first target identification parameter adjacent to each first target identification parameter in the target first target identification parameter array. The target test model may be a test model that is capable of data alignment with the target test sequence for testing. The target reference test parameters may be baseline reference parameters for use as positioning target test models.
Continuing to take the train system test as an example, after the result matching analysis based on the real-time train state, testing the real-time train state extracted through the driving model and the matched test sequence, and evaluating the whole test process to obtain a test result. Specifically, based on a target test sequence to be tested at present, the driving model can provide data support for test record and test comparison. And comparing the real-time train state extracted based on the driving model with a target test sequence, and analyzing the result. After the round of testing is completed, unmatched test items are screened, and the whole testing process is evaluated, so that a testing result is obtained. Fig. 5 is a schematic flow chart of a test sequence result matching analysis test based on a real-time train condition according to a second embodiment of the present invention, and in a specific example, as shown in fig. 5, a specific flow of testing a test model according to a test sequence is as follows:
(1) And positioning the current target test sequence according to the first target identification parameter array or the corrected target identification parameter array, determining the latest kilometer post and the next latest kilometer post in the first target identification parameter array or the corrected target identification parameter array, marking the latest kilometer post as Km_next, and marking the next new kilometer post as Km_pre. Further, the positions of Km_next and Km_pre in the buffer area are judged, and when the positions of Km_next and Km_pre are arranged in the central area of the buffer area, the current buffer area is used as a basis to start to be compared with the information of the target test sequence.
It should be noted that the central area may be considered as the middle third area, or may be adaptively adjusted according to the recognition rate, so as to avoid the influence of the DMI recognition error and ensure the success rate of the test item positioning.
(2) And traversing from the middle position of the buffer zone to the front end and the rear end respectively to find out the associated test model of the target test sequence. Alternatively, the test model corresponding to the plurality of data from the buffer from the intermediate position to the outside of the target kilometer scale group where the target sequence is located may be used as the associated test model, and the target test model matched with the target test sequence may be determined from the associated test models. Specifically, a driving position B of the relevant information of the position A in the target test sequence is determined from each relevant test model, and the relevant test model corresponding to the driving position B is determined as the target test model.
In a specific example, assuming that the kilometer scale of the target test sequence is k172+044, the target kilometer scale group corresponding to the target test sequence is [ k172+040, k172+050], for the gradient test item, if the gradient in the target test sequence is determined to be 1.5, but the gradient corresponding to the kilometer scale k172+040 and the kilometer scale k172+050 is not 1.5, traversing other adjacent kilometer scales of the target kilometer scale group [ k172+040, k172+050] can be continued, and determining that the gradient of the kilometer scale k172+060 is 1.5, and then, each data in the driving model corresponding to the kilometer scale k172+060 can be used as a target test model, and the target test sequence can be subjected to a comparison test. Similarly, for speed limit testing, the traversing target can be information for searching speed limit change points.
(3) And comparing the information of the target test model and the target test sequence according to the test guideline. If the information of the target test model and the target test sequence are consistent, filling the test record, and judging the test result to pass. If the information of the target test model and the target test sequence are not consistent, the driving state in the buffer area is recorded and marked, and the personnel to be tested are checked.
(4) Screening out non-conforming items marked in the step (3) after the whole testing process is finished, rechecking the description of the real driving state in the round of testing and the description of the correct driving state by the testing sequence through the driving model area, and obtaining a final testing result by combining the conforming items of the information after secondary confirmation.
According to the technical scheme, the plurality of first target identification parameter arrays are determined through traversing the constructed buffer zone data, the first target identification parameter arrays are corrected through the plurality of identification parameter mapping curves, and then the test model of the target system is generated according to the corrected target identification parameter arrays and the image identification result of the target information output interface image, so that the test model is tested according to the test sequence, and the accuracy and the automation degree of the system test based on the image identification result can be improved.
It should be noted that any permutation and combination of the technical features in the above embodiments also belong to the protection scope of the present invention.
Example III
Fig. 6 is a schematic diagram of a system testing device based on an image recognition result according to a third embodiment of the present invention, as shown in fig. 6, where the system testing device based on an image recognition result includes: the recognition result test sequence acquisition module 310, the first target recognition parameter array determination module 320, the recognition parameter mapping curve generation module 330, the corrected target recognition parameter array generation module 340, the test model generation module 350 and the test model test module 360, wherein:
the recognition result test sequence acquisition module 310 is configured to acquire an image recognition result of a target information output interface image in a target system and a test sequence matched with the target information output interface image;
a first target recognition parameter array determining module 320, configured to determine a plurality of first target recognition parameter arrays according to an image recognition result of the target information output interface image;
the recognition parameter mapping curve generating module 330 is configured to generate a recognition parameter mapping curve of the first target recognition parameter and a second target recognition parameter according to the image recognition result of the target information output interface image;
The corrected target recognition parameter array generating module 340 is configured to correct the first target recognition parameter array according to the recognition parameter mapping curve, so as to obtain a corrected target recognition parameter array;
the test model generating module 350 is configured to generate a test model of the target system according to the corrected target recognition parameter array and the image recognition result of the target information output interface image;
and a test model test module 360, configured to test the test model according to the test sequence.
According to the embodiment of the invention, after the image recognition result of the target information output interface image in the target system and the test sequence matched with the target information output interface image are obtained, a plurality of first target recognition parameter arrays are determined according to the image recognition result of the target information output interface image, and recognition parameter mapping curves of second target recognition parameters and the first target recognition parameters are generated according to the image recognition result of the target information output interface image, so that the first target recognition parameter arrays are corrected according to the recognition parameter mapping curves. After the corrected target recognition parameter array is obtained, a test model of the target system is generated by utilizing the corrected target recognition parameter array and the image recognition result of the target information output interface image, and finally the generated test model is tested by utilizing the test sequence, so that the problem of lower system test accuracy caused by inaccurate image recognition result when the system test is performed according to the image recognition result of the target information output interface image in the target system is solved, and the accuracy and the automation degree of the system test based on the image recognition result can be improved.
Optionally, the first object identification parameter array determining module 320 is specifically configured to: constructing and updating buffer area data in real time according to the image recognition result of the target information output interface image; under the condition that the buffer data meets the data traversing condition, traversing the buffer data according to a first data traversing sequence to acquire a current first target identification parameter array; the current first target identification parameter array comprises latest first target identification data and next-new first target identification data; taking the latest first target identification data in the current first target identification parameter array as a current data traversal starting position, traversing the buffer zone data according to a second data traversal sequence, and searching first target identification data different from the latest first target identification data as first target identification data to be updated; updating the secondary new first target identification data according to the latest first target identification data, and updating the latest first target identification data according to the first target identification data to be updated to obtain each first target identification parameter array; the data in each first target identification parameter array are connected end to end.
Optionally, the identifying parameter mapping curve generating module 330 is specifically configured to: correcting a second target identification parameter to be corrected of the buffer area data according to an image identification result of the target information output interface image to obtain corrected buffer area data; adding the correction buffer data to a system state data area according to a time ascending order; traversing the system state data area according to a first state data traversing sequence to generate a first identification parameter mapping curve of the second target identification parameter and the first target identification parameter; and traversing the system state data area according to a second state data traversing sequence to calculate and generate a second identification parameter mapping curve of the second target identification parameter and the first target identification parameter.
Optionally, the modified target recognition parameter array generating module 340 is specifically configured to: calculating a mapping curve comparison matching result between the first identification parameter mapping curve and the second identification parameter mapping curve; and correcting the first target identification parameter array according to the error range of the mapping curve comparison matching result to obtain the corrected target identification parameter array.
Optionally, the modified target recognition parameter array generating module 340 is specifically configured to: under the condition that the error range of the mapping curve comparison matching result exceeds a preset error range threshold value, determining an error interval to be corrected according to the first identification parameter mapping curve and the second identification parameter mapping curve; determining an adjacent data interval of the error interval to be corrected, and correcting the first target recognition parameter array according to the associated image recognition data of the adjacent data interval to obtain the corrected target recognition parameter array; and under the condition that the error range of the mapping curve comparison matching result does not exceed the preset error range threshold, carrying out fitting correction on the first identification parameter mapping curve, the second identification parameter mapping curve and the first target identification parameter array in the buffer zone data to obtain the corrected target identification parameter array.
Optionally, the test model generating module 350 is specifically configured to: determining the multi-dimensional model parameter information matched with the corrected target identification parameter array according to the image identification result of the target information output interface image; and establishing a mapping relation between the corrected target identification parameter array and the multidimensional model parameter information matched with the corrected target identification parameter array to obtain a test model of the target system.
Optionally, the test model test module 360 is specifically configured to: determining a target first target identification parameter array matched with the first target identification parameters of the target test sequence; wherein, the first target identification parameter of the target test sequence belongs to the range interval of the target first target identification parameter array; traversing the associated test model of the target first target identification parameter array according to the target reference test parameters of the target test sequence, and determining a target test model; wherein the target reference test parameters of the target test model are the same as the target reference test parameters of the target test sequence; and testing the target test model according to the target test sequence.
The system testing device based on the image recognition result can execute the system testing method based on the image recognition result provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method. Technical details which are not described in detail in the present embodiment may be referred to the system test method based on the image recognition result provided in any embodiment of the present invention.
Since the system testing device based on the image recognition result described above is a device capable of executing the system testing method based on the image recognition result in the embodiment of the present invention, a person skilled in the art will be able to understand the specific implementation of the system testing device based on the image recognition result of the embodiment of the present invention and various modifications thereof, so how the system testing device based on the image recognition result implements the system testing method based on the image recognition result in the embodiment of the present invention will not be described in detail herein. The device adopted by the system testing method based on the image recognition result in the embodiment of the invention belongs to the scope of protection required by the application.
Example IV
Fig. 7 shows a schematic diagram of the structure of an electronic device 10 that may be used to implement an embodiment of the 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. Electronic equipment may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, 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. 7, the electronic device 10 includes at least one processor 11, and a memory, such as a Read Only Memory (ROM) 12, a Random Access Memory (RAM) 13, etc., communicatively connected to the at least one processor 11, in which the memory stores a computer program executable by the at least one processor, and the processor 11 may perform various appropriate actions and processes according to the computer program stored in the Read Only Memory (ROM) 12 or the computer program loaded from the storage unit 18 into the Random Access Memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 may 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.
Various 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, etc.; 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, digital Signal Processors (DSPs), and any suitable processor, controller, microcontroller, etc. The processor 11 performs the respective methods and processes described above, such as a system test method based on the image recognition result.
For example, a system test method based on image recognition results may include the operations of:
acquiring an image recognition result of a target information output interface image in a target system and a test sequence matched with the target information output interface image;
Determining a plurality of first target identification parameter arrays according to the image identification result of the target information output interface image;
generating an identification parameter mapping curve of a second target identification parameter and the first target identification parameter according to an image identification result of the target information output interface image;
correcting the first target identification parameter array according to the identification parameter mapping curve to obtain a corrected target identification parameter array;
generating a test model of the target system according to the corrected target identification parameter array and the image identification result of the target information output interface image;
and testing the test model according to the test sequence.
In some embodiments, the system test method based on the image recognition result may be implemented as a computer program tangibly embodied on a computer-readable storage medium, such as the 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 system test method based on the image recognition result described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the system test method based on the image recognition results in any other suitable manner (e.g., by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On 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, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
A computer program for carrying out 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 implemented. The computer program may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the 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. The 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) through 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 may 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 input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background 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 background, 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. The client and server are typically 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 hosts and VPS service are overcome.
Claims (10)
1. A system testing method based on image recognition results, comprising:
acquiring an image recognition result of a target information output interface image in a target system and a test sequence matched with the target information output interface image;
determining a plurality of first target identification parameter arrays according to the image identification result of the target information output interface image;
generating an identification parameter mapping curve of a second target identification parameter and the first target identification parameter according to an image identification result of the target information output interface image;
correcting the first target identification parameter array according to the identification parameter mapping curve to obtain a corrected target identification parameter array;
generating a test model of the target system according to the corrected target identification parameter array and the image identification result of the target information output interface image;
and testing the test model according to the test sequence.
2. The method of claim 1, wherein determining a plurality of first object recognition parameter arrays from the image recognition result of the object information output interface image comprises:
constructing and updating buffer area data in real time according to the image recognition result of the target information output interface image;
Under the condition that the buffer data meets the data traversing condition, traversing the buffer data according to a first data traversing sequence to acquire a current first target identification parameter array; the current first target identification parameter array comprises latest first target identification data and next-new first target identification data;
taking the latest first target identification data in the current first target identification parameter array as a current data traversal starting position, traversing the buffer zone data according to a second data traversal sequence, and searching first target identification data different from the latest first target identification data as first target identification data to be updated;
updating the secondary new first target identification data according to the latest first target identification data, and updating the latest first target identification data according to the first target identification data to be updated to obtain each first target identification parameter array;
the data in each first target identification parameter array are connected end to end.
3. The method according to claim 2, wherein generating an identification parameter mapping curve of a second target identification parameter and the first target identification parameter according to the image identification result of the target information output interface image comprises:
Correcting a second target identification parameter to be corrected of the buffer area data according to an image identification result of the target information output interface image to obtain corrected buffer area data;
adding the correction buffer data to a system state data area according to a time ascending order;
traversing the system state data area according to a first state data traversing sequence to generate a first identification parameter mapping curve of the second target identification parameter and the first target identification parameter;
and traversing the system state data area according to a second state data traversing sequence to calculate and generate a second identification parameter mapping curve of the second target identification parameter and the first target identification parameter.
4. The method of claim 3, wherein said modifying said first set of target recognition parameters according to said recognition parameter mapping curve to obtain a modified set of target recognition parameters comprises:
calculating a mapping curve comparison matching result between the first identification parameter mapping curve and the second identification parameter mapping curve;
and correcting the first target identification parameter array according to the error range of the mapping curve comparison matching result to obtain the corrected target identification parameter array.
5. The method of claim 4, wherein the correcting the first target recognition parameter array according to the error range of the mapping curve comparison matching result to obtain the corrected target recognition parameter array includes:
under the condition that the error range of the mapping curve comparison matching result exceeds a preset error range threshold value, determining an error interval to be corrected according to the first identification parameter mapping curve and the second identification parameter mapping curve;
determining an adjacent data interval of the error interval to be corrected, and correcting the first target recognition parameter array according to the associated image recognition data of the adjacent data interval to obtain the corrected target recognition parameter array;
and under the condition that the error range of the mapping curve comparison matching result does not exceed the preset error range threshold, carrying out fitting correction on the first identification parameter mapping curve, the second identification parameter mapping curve and the first target identification parameter array in the buffer zone data to obtain the corrected target identification parameter array.
6. The method of claim 1, wherein generating the test model of the target system based on the corrected target recognition parameter array and the image recognition result of the target information output interface image comprises:
Determining the multi-dimensional model parameter information matched with the corrected target identification parameter array according to the image identification result of the target information output interface image;
and establishing a mapping relation between the corrected target identification parameter array and the multidimensional model parameter information matched with the corrected target identification parameter array to obtain a test model of the target system.
7. The method of claim 1, wherein the testing the test model according to the test sequence comprises:
determining a target first target identification parameter array matched with the first target identification parameters of the target test sequence; wherein, the first target identification parameter of the target test sequence belongs to the range interval of the target first target identification parameter array;
traversing the associated test model of the target first target identification parameter array according to the target reference test parameters of the target test sequence, and determining a target test model; wherein the target reference test parameters of the target test model are the same as the target reference test parameters of the target test sequence;
and testing the target test model according to the target test sequence.
8. A system testing device based on image recognition results, comprising:
the identification result test sequence acquisition module is used for acquiring an image identification result of a target information output interface image in a target system and a test sequence matched with the target information output interface image;
the first target identification parameter array determining module is used for determining a plurality of first target identification parameter arrays according to the image identification result of the target information output interface image;
the identification parameter mapping curve generation module is used for generating an identification parameter mapping curve of a second target identification parameter and the first target identification parameter according to an image identification result of the target information output interface image;
the corrected target identification parameter array generation module is used for correcting the first target identification parameter array according to the identification parameter mapping curve to obtain a corrected target identification parameter array;
the test model generation module is used for generating a test model of the target system according to the corrected target identification parameter array and the image identification result of the target information output interface image;
and the test model test module is used for testing the test model according to the test sequence.
9. An electronic device, the electronic device comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the image recognition result-based system test method of any one of claims 1-7.
10. A computer readable storage medium storing computer instructions for causing a processor to perform the image recognition result based system test method of any one of claims 1-7.
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