Running test system and method

Abstract

The embodiment of the invention provides a running test system and method, and relates to the technical field of data processing, and the system comprises a service module which is used for distributing a target identity recognition ID according to target information; the association module is used for generating target association data according to the target identity identification ID and the target image data; and the analysis module is used for generating a target test result according to the target associated data. According to the method and the device, the running test score can be automatically generated without depending on external equipment, so that the applicability to a test scene with a large number of testers is improved, the statistical accuracy of test data is improved, and the conditions of missed counting, wrong counting and the like are avoided.

Description

Technical Field

[0001] Embodiments of the present application relate to the technical field of data processing, and in particular, to a running test system and method. Background Art

[0002] Currently, in related technologies, most running test results are statistically calculated based on devices such as bib numbers, sports shoe cards, and sports bracelets worn by testers. The above methods have poor applicability to test scenarios with a large number of testers, poor statistical accuracy of test data, and are extremely prone to problems such as missed counting and mis-counting.

[0003] Therefore, there is an urgent need for a new technical solution to solve the above technical problems. Summary of the Invention

[0004] Embodiments of the present application provide a running test system and method, which can automatically generate running test results without relying on external devices, which is beneficial to improving the applicability to test scenarios with a large number of testers, improving the statistical accuracy of test data, and avoiding situations such as missed counting and mis-counting.

[0005] In the first aspect of the present application, a running test system is provided, including: A service module for allocating a target identification ID according to target information; An association module for generating target association data according to the target identification ID and target image data; An analysis module for generating a target test result according to the target association data.

[0006] In some feasible embodiments, the above service module includes: An allocation unit for allocating a target identification ID according to target information; And / or, further includes: An execution unit for performing a target push operation according to the target test result.

[0007] In some feasible embodiments, the above association module includes: A collection unit for performing a target collection operation according to the target action to generate target image data; An association unit for performing a target association operation on the target image data and the target identification ID to generate target association data.

[0008] In some feasible embodiments, the above analysis module includes: A parsing unit for generating target test data according to the target association data; A query unit for determining a target determination rule according to the target association data; A generating unit, configured to generate a target test result according to target test data and a target determination rule.

[0009] In some possible implementation manners, the above parsing unit includes: A preprocessing component, configured to generate target preprocessing data according to target associated data; A parsing component, configured to generate target test data according to the target preprocessing data.

[0010] In some possible implementation manners, the above preprocessing component includes: A first preprocessing component, configured to generate first preprocessing data according to target associated data, wherein the difference between the image clarity corresponding to the first preprocessing data and the target clarity is less than or equal to a first preset difference; A second preprocessing component, configured to generate second preprocessing data according to target associated data, wherein the difference between the image angle corresponding to the second preprocessing data and the target angle is less than or equal to a second preset difference; And / or, a third preprocessing component, configured to generate third preprocessing data according to target associated data, wherein the difference between the image occlusion rate corresponding to the third preprocessing data and the target occlusion rate is less than or equal to a third preset difference.

[0011] In some possible implementation manners, the above query unit includes: A query component, configured to determine multiple target parameters according to target associated data; A determination component, configured to determine a target determination rule according to the multiple target parameters, wherein the target determination rule includes: a preset duration threshold, a preset number threshold, and / or a preset distance threshold.

[0012] In some possible implementation manners, the above generating unit includes: A first generating component, configured to generate a test failure result when the test duration is less than the preset duration threshold; A second generating component, configured to generate a test failure result when the test number is less than the preset number threshold; And / or, A third generating component, configured to generate a test failure result when the test distance is less than the preset distance threshold.

[0013] In some possible implementation manners, the above analysis module is provided with multiple target interfaces, and / or supports multiple target protocols.

[0014] In a second aspect of the present application, a running test method is provided, including: Allocating a target identity recognition ID according to target information; Generate target associated data based on the target identity recognition ID and the target image data; Generate a target test result based on the target associated data.

[0015] The running test system and method provided by the embodiments of the present application, wherein the system includes: a service module for allocating a target identity recognition ID according to target information; an association module for generating target associated data according to the target identity recognition ID and the target image data; and an analysis module for generating a target test result according to the target associated data. The present application can realize automatic generation of running test scores without relying on external devices, which is beneficial to improving the applicability to test scenarios with a large number of testers, improving the statistical accuracy of test data, and avoiding situations such as missed counting and mis-counting.

[0016] It should be understood that the content described in the summary of the invention section is not intended to limit the key or important features of the embodiments of the present application, nor is it used to limit the scope of the present application. Other features of the present application will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In combination with the drawings and with reference to the following detailed description, the above and other features, advantages and aspects of the embodiments of the present application will become more obvious. In the drawings, the same or similar reference numerals represent the same or similar elements, where: Figure 1 is a structural schematic diagram of a running test system provided by an embodiment of the present application; Figure 2 is a flowchart schematic diagram of a running test method provided by an embodiment of the present application; Figure 3 is a structural schematic diagram of an electronic device provided by an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0018] To make the objectives, technical solutions and advantages of the embodiments of the present disclosure clearer, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below with reference to the drawings in the embodiments of the present disclosure. Apparently, the described embodiments are some but not all of the embodiments of the present disclosure. All other embodiments obtained by those of ordinary skill in the art without creative efforts based on the embodiments of the present disclosure fall within the scope of the present disclosure.

[0019] In addition, the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B may represent: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in this article generally represents an "or" relationship between the associated objects before and after.

[0020] In the first aspect of the embodiments of the present application, a running test system is proposed. Figure 1 As shown in the structural schematic diagram of a running test system 100 provided by the embodiments of the present application, Figure 1 as shown, the system 100 includes: a service module 110, an association module 120, and an analysis module 130.

[0021] Among them, the service module is used to allocate a target identity recognition ID according to the target information.

[0022] In some feasible implementation manners, the above service module includes: An allocation unit is used to allocate a target identity recognition ID according to the target information.

[0023] It should be noted that the above target information may correspond to the basic personal information of the target user, for example: the name, gender, age, department, code, and / or, face photo image, etc. of the target user.

[0024] Exemplarily, the above allocation unit can be used to allocate a corresponding unique target identity recognition ID for the target user according to the name, gender, age, department, code, and / or, face photo image, etc. of the above target user.

[0025] Specifically, the above allocation unit can be provided with a target management server for individually and / or batch inputting information such as the name, gender, age, department, code, and / or, face image, etc. of the above target user. Among them, the parameters of the above target management server can include: memory ≥ 128GB, hard disk ≥ 4TB, processor ≥ 2.0GHz, etc. The deployment location of the above target management server can be set by itself according to the actual situation.

[0026] And / or, the above service module further includes: An execution unit is used to perform a target push operation according to the target test result.

[0027] It should be noted that the above target push operation can include: a physical education course push operation, and / or, an exercise item push operation, etc.

[0028] Exemplarily, the above target test result can include: a test qualified result, a test unqualified result, and / or, a scoring result of the target test, etc.

[0029] Specifically, the above execution unit can be provided with a target sports management system software for performing a corresponding physical education course push operation and / or an exercise item push operation according to the above test unqualified result and / or the target test scoring result.

[0030] Thus, by configuring the above-mentioned allocation unit, the above service module can achieve accurate and automated allocation of target identity recognition IDs according to target information, and / or, by configuring the execution unit, the above service module can achieve accurate automated execution of corresponding sports course push operations, and / or, exercise item push operations, etc., which is beneficial to accurately identify the physical function exercise needs of testers and improve the user experience.

[0031] Among them, the association module is used to generate target association data according to the target identity recognition ID and the target image data.

[0032] In some feasible implementation manners, the above-mentioned association module includes: The acquisition unit is used to perform a target acquisition operation according to the target action to generate target image data.

[0033] Exemplarily, the above acquisition unit can perform a target acquisition operation to generate target image data when recognizing the target action and / or recognizing the target clothing feature parameters according to the target human body posture recognition algorithm and / or the image text recognition algorithm. Among them, the above target image data can carry the motion parameters of the tester and / or the personnel feature parameters, etc.

[0034] Specifically, the above target image data includes: target video data and / or target picture data, etc. Among them, the above target video data can include: video data corresponding to various running tests, for example: video data corresponding to a 400-meter running test; video data corresponding to an 800-meter running test; video data corresponding to a 1000-meter running test; video data corresponding to a 3000-meter running test; and / or video data corresponding to a 5000-meter running test, etc. The above target picture data can include: picture data corresponding to various running tests. For example: picture data corresponding to a 400-meter running test; picture data corresponding to an 800-meter running test; picture data corresponding to a 1000-meter running test; picture data corresponding to a 3000-meter running test; and / or picture data corresponding to a 5000-meter running test, etc.

[0035] Specifically, the above target clothing feature parameters can include: numbers, etc.

[0036] Specifically, the above target action can be set according to the actual test situation. For example: the above target action can include: the raising hand action used in daily training.

[0037] It should be noted that the above acquisition unit can be set with multiple target modes, among which, the above multiple target modes can include: daily training mode, system assessment mode, manual assessment mode, etc.

[0038] Specifically, for the daily training mode, the above-mentioned acquisition unit can adopt net timing, that is, after recognizing the corresponding tester's raising hand action, and / or, starting to perform the target acquisition operation at the corresponding moment when recognizing that the corresponding tester crosses the target test starting point, and ending the execution of the target acquisition operation at the corresponding moment when the corresponding tester passes the target test end point. It should be noted that the test timing period corresponds to the execution period of the above-mentioned target acquisition operation.

[0039] Specifically, for the system assessment mode, after the tester reaches the designated starting point position of the test track, a voice conversation is carried out with the built-in microphone of the acquisition unit to specify the system assessment mode, so that the acquisition unit broadcasts the target instruction through the built-in speaker at the preparation time set by the tester, such as: "On your marks, get set, go" instruction, and / or, starting gun sound, such as: the imitation sound of "bang" gunshot, so that the acquisition unit starts the above-mentioned target acquisition operation. It should be noted that the test timing period corresponds to the execution period of the above-mentioned target acquisition operation.

[0040] Specifically, for the manual assessment mode, after the tester reaches the designated starting point position of the test track, the target instruction can be given by a manual referee, such as: "On your marks, get set, go" command, and / or, initiate the starting gun sound, so that the acquisition unit starts to execute the above-mentioned target acquisition operation. It should be noted that the test timing period corresponds to the execution period of the above-mentioned target acquisition operation. Specifically, the above-mentioned acquisition unit may include: a target camera, a vertical pole is arranged at the target starting point and the target end point to perform the above-mentioned target acquisition action. Among them, the above-mentioned target camera may include: a horizontally expanded camera, an analog camera, and / or, a network camera, etc. The interior of the above-mentioned target camera may be embedded with an AI chip, a motion optical sensor, a microphone, and / or, a speaker, etc. The parameters corresponding to the above-mentioned AI chip may include: computing power ≥ 2Tops. The parameters corresponding to the above-mentioned motion optical sensor may include: pixel ≥ 4 million, maximum frame rate ≥ 25fps, protection level ≥ IP55, etc.

[0041] The association unit is used to perform a target association operation on the target image data and the target identity recognition ID to generate target association data.

[0042] Exemplarily, after the above-mentioned acquisition unit completes the acquisition operation of the above-mentioned target image data, the above-mentioned association unit can perform target association on the target image data and the target identity recognition ID to generate target association data, and send them to the analysis module together.

[0043] Specifically, the above-mentioned association unit can compare the face image in the target video data with the face image in the target identity recognition ID, and / or compare the target clothing feature parameters in the picture data with the clothing parameters in the target identity recognition ID. When the similarity corresponding to the comparison result is greater than the preset similarity, perform a target association operation on the above-mentioned target image data and the above-mentioned target identity recognition ID to generate target association data. Among them, the above-mentioned preset similarity is positively correlated with the generation accuracy requirement of the target association data, that is, the higher the generation accuracy requirement of the target association data, the higher the above-mentioned preset similarity.

[0044] Thus, by configuring the above-mentioned acquisition unit, the above-mentioned association module can realize the automatic acquisition of the target image data corresponding to the running data of the tester at any time, thereby improving the acquisition efficiency of the test data of the tester; by configuring the above-mentioned association unit, the test data of the tester and the target identity recognition ID corresponding to the tester can be automatically associated to improve the generation accuracy of the test results corresponding to the tester and reduce the probability of incorrect writing of the test results.

[0045] Among them, the analysis module is used to generate a target test result according to the target association data.

[0046] In some feasible implementation manners, the above-mentioned analysis module includes: The parsing unit is used to generate target test data according to the target association data.

[0047] Exemplarily, the above-mentioned parsing unit may include: a target video algorithm box, which is used to parse and generate target test data according to the above-mentioned target association data. Among them, the target test data includes: test items, test duration, test times, and / or test distance, etc. The corresponding parameters of the above-mentioned target video algorithm box may include: real-time face and personnel clothing feature parameter detection and tracking for 20 channels of video, support for comparison of 300,000 face databases and personnel clothing feature parameters, etc.

[0048] In some feasible implementation manners, the above-mentioned parsing unit includes: The preprocessing component is used to generate target preprocessing data according to the target association data.

[0049] In some feasible implementation manners, the above-mentioned preprocessing component includes: The first preprocessing component is used to generate first preprocessing data according to the target association data, where the difference between the image clarity corresponding to the first preprocessing data and the target clarity is less than or equal to the first preset difference.

[0050] Exemplarily, the above-mentioned first preprocessing component can be used to process the image clarity of the target image data in the target associated data, so that the difference between the image clarity of the target image data in the target associated data and the target clarity is less than or equal to a first preset difference. Among them, the above-mentioned target clarity is positively correlated with the generation accuracy requirement of the target test data, that is, the higher the generation accuracy requirement of the target test data, the higher the above-mentioned target clarity. The above-mentioned first preset difference is negatively correlated with the generation accuracy requirement of the target test data, that is, the higher the generation accuracy requirement of the target test data, the smaller the above-mentioned first preset difference.

[0051] It should be noted that the above-mentioned image may include: a face image, and / or, a scene image. The target clarity and the first preset difference corresponding to the face image and the scene image can be set differently.

[0052] The second preprocessing component is used to generate second preprocessing data according to the target associated data, where the difference between the image angle corresponding to the second preprocessing data and the target angle is less than or equal to a second preset difference.

[0053] Exemplarily, the above-mentioned second preprocessing component can be used to process the image angle of the target image data in the target associated data, so that the difference between the image angle of the target image data in the target associated data and the target angle is less than or equal to a second preset difference. Among them, the above-mentioned second preset difference is negatively correlated with the generation accuracy requirement of the target test data, that is, the higher the generation accuracy requirement of the target test data, the smaller the above-mentioned second preset difference.

[0054] It should be noted that the above-mentioned image may include: a face image, and / or, a scene image. The target angle and the second preset difference corresponding to the face image and the scene image can be set differently.

[0055] And / or, the third preprocessing component is used to generate third preprocessing data according to the target associated data, where the difference between the image occlusion rate corresponding to the third preprocessing data and the target occlusion rate is less than or equal to a third preset difference.

[0056] Exemplarily, the above-mentioned third preprocessing component can be used to process the image occlusion rate of the target image data in the target associated data, so that the difference between the image occlusion rate of the target image data in the target associated data and the target occlusion rate is less than or equal to a third preset difference. Among them, the target occlusion rate is negatively correlated with the generation accuracy requirement of the target test data, that is, the higher the generation accuracy requirement of the target test data, the smaller the above-mentioned target occlusion rate. The above-mentioned second preset difference is negatively correlated with the generation accuracy requirement of the target test data, that is, the higher the generation accuracy requirement of the target test data, the smaller the above-mentioned second preset difference.

[0057] It should be noted that the above images may include: face images, and / or, scene images. The target occlusion rate and the third preset difference corresponding to the above face images and scene images can be set differently.

[0058] Thus, by configuring the above first preprocessing component, second preprocessing component, and / or third preprocessing component, the above preprocessing component can process the clarity, angle, and / or occlusion rate of the target image data in the target associated data, so as to improve the clarity of the target preprocessed data, adjust the angle of the target preprocessed data, and / or occlusion rate, thereby improving the parsing accuracy and parsing efficiency of the parsing component for parsing and generating target test data based on the target preprocessed data.

[0059] The parsing component is used to generate target test data according to the target preprocessed data; Exemplarily, the above parsing component can parse and generate the above target test data according to the target preprocessed data whose clarity, angle, and / or occlusion rate have been adjusted by the preprocessing component.

[0060] Thus, by configuring the above preprocessing component and the above parsing component, the above parsing unit can improve the generation accuracy and generation efficiency of the target test data, thereby improving the generation accuracy and generation efficiency of the target test result.

[0061] In some feasible implementation manners, the above analysis module further includes: The query unit is used to determine the target determination rule according to the target associated data.

[0062] Exemplarily, the above target determination rule can be pre-entered or obtained through networking, and no specific limitation is made here. Among them, the above target determination rule may include: movement duration determination rule, movement times determination rule, and / or movement distance determination rule, etc.

[0063] In some feasible implementation manners, the above query unit includes: The query component is used to determine multiple target parameters according to the target associated data.

[0064] Exemplarily, the above query component can determine the above multiple target parameters according to the target identity recognition ID carried in the target associated data. Among them, the above multiple target parameters may include: gender parameter, and / or age parameter, etc.

[0065] Specifically, the above query component can be used to determine the gender parameter and / or age parameter according to the target identity recognition ID carried in the target associated data.

[0066] In some feasible implementation manners, the above query unit further includes: Determination component, configured to determine a target determination rule according to multi-target parameters, where the target determination rule includes: a preset duration threshold, a preset number threshold, and / or a preset distance threshold.

[0067] Exemplarily, the above determination component can be used to determine the preset duration threshold, the preset number threshold, and / or the preset distance threshold of the tester corresponding to the test item according to the above gender parameter and / or age parameter, etc.

[0068] Thus, the above query unit can accurately determine multi-target parameters according to the target association data by configuring the above query component; and can accurately determine the target determination rule according to the above multi-target parameters by configuring the determination component.

[0069] In some feasible embodiments, the above analysis module further includes: Generation unit, configured to generate a target test result according to the target test data and the target determination rule.

[0070] Exemplarily, the above generation unit can be used to generate the above test pass result, test fail result, and / or the scoring result of the target test according to the actual test duration of the tester corresponding to the target test data and the corresponding preset duration threshold, the number of tests and the corresponding preset number threshold, and / or the test distance and the corresponding preset distance threshold.

[0071] In some feasible embodiments, the above generation unit includes: First generation component, configured to generate a test fail result when the test duration is less than the preset duration threshold.

[0072] Exemplarily, the above first generation component can be used to generate a test fail result when the actual test duration of the tester corresponding to the target test data is less than the preset duration threshold; and generate a test pass result when the actual test duration of the tester corresponding to the target test data is greater than or equal to the preset duration threshold.

[0073] In some feasible embodiments, the above generation unit further includes: Second generation component, configured to generate a test fail result when the number of tests is less than the preset number threshold.

[0074] Exemplarily, the above second generation component can be used to generate a test fail result when the actual number of tests of the tester corresponding to the target test data is less than the preset number threshold; and generate a test pass result when the actual number of tests of the tester corresponding to the target test data is greater than or equal to the preset number threshold.

[0075] In some feasible embodiments, the above-mentioned generating unit, and / or, further includes: A third generating component, configured to generate a test failure result when the test distance is less than a preset distance threshold.

[0076] Exemplarily, the above-mentioned third generating component may be configured to generate a test failure result when the actual test distance of the tester corresponding to the target test data is less than the preset distance threshold; and generate a test pass result when the actual test distance of the tester corresponding to the target test data is greater than or equal to the preset distance threshold.

[0077] Thus, by configuring the above-mentioned first generating component, second generating component, and / or third generating component, the above-mentioned generating unit can generate the above-mentioned test pass result and test failure result according to the actual test duration of the tester corresponding to the above-mentioned target test data and the corresponding preset duration threshold, the test times and the corresponding preset times threshold, and / or the test distance and the corresponding preset distance threshold, thereby improving the generation accuracy and generation efficiency of the target test result.

[0078] Based on this, the above-mentioned analysis module, by configuring the above-mentioned parsing unit, can accurately generate target test data such as test items, test duration, test times, and / or test distance according to the target associated data; by configuring the query unit, it can accurately and automatically determine target determination rules such as the movement duration determination rule, movement times determination rule, and / or movement distance determination rule according to the above-mentioned target test data such as test items, test duration, test times, and / or test distance; by configuring the generating unit, it can automatically generate target test results such as test pass results, test failure results, and / or scoring results of the target test according to the above-mentioned target test data such as test items, test duration, test times, and / or test distance and the above-mentioned target determination rules such as the movement duration determination rule, movement times determination rule, and / or movement distance determination rule, which is beneficial to improving the automation generation accuracy and generation efficiency of the target test result.

[0079] In some feasible embodiments, the above-mentioned analysis module is provided with multiple target interfaces, and / or, supports multiple target protocols.

[0080] Exemplarily, the above-mentioned analysis module may be provided with multiple target interfaces for real-time communication with the above-mentioned service module and / or associated module. Among them, the above-mentioned multiple target interfaces may include: network interfaces, USB interfaces, HDMI interfaces, and / or HTTP interfaces.

[0081] Exemplarily, and / or, the above analysis module may support multiple target protocols for supporting the inflow of target-related data and / or background management. Among them, the multiple target protocols include: ONVIF protocol, HTTP interface protocol, and / or RTSP protocol.

[0082] Thus, by setting multiple target interfaces, the above analysis module can improve the interoperability with the service module and / or the physical layer of the associated module, enhancing the flexibility of the system; by supporting multiple target protocols, it can achieve the normalization processing of heterogeneous target-related data.

[0083] In some feasible embodiments, the above system further includes: A display module for displaying target data.

[0084] Exemplarily, the above display module may be communicatively connected to the above analysis module, and is provided with an LED display screen and / or a liquid crystal display screen for displaying target data. Among them, the above target data includes: basic information of the tester, test time, remaining test content, and / or test scores and rankings, etc. Among them, the basic information of the above tester may include: tester name, etc. The above remaining test content may include: remaining laps, etc.

[0085] Thus, the above system can achieve the visual management of the corresponding test data of the tester by configuring the display module.

[0086] Based on this, the running test system provided by the embodiments of the present application includes: a service module for allocating a target identification ID according to target information; an associated module for generating target-associated data according to the target identification ID and target image data; an analysis module for generating a target test result according to the target-associated data. The present application can achieve the automatic generation of running test scores without relying on external devices, which is beneficial to improving the applicability to test scenarios with a large number of testers, improving the statistical accuracy of test data, and avoiding situations such as missed counting and miscounting.

[0087] It should be noted that the number of the service module, the associated module, and the analysis module in the system of the present application can be expanded according to actual needs to further improve the applicability to test scenarios with a large number of testers. The service module, the associated module, and the analysis module in the present application can be connected based on a communication method to achieve data transmission.

[0088] In the second aspect of the present application, a running test method is provided. Figure 2 It is a flowchart of a running test method provided by an embodiment of the present application, as Figure 2 shown, the method 200 includes: Step S1; Allocate a target identity recognition ID according to the target information; Step S2; Generate target associated data according to the target identity recognition ID and the target image data; Step S3; Generate a target test result according to the target associated data.

[0089] Those skilled in the art can clearly understand that for the convenience and conciseness of description, the specific working process of the described method can refer to the corresponding process in the foregoing system embodiment, which will not be elaborated herein.

[0090] Figure 3 It is a schematic structural diagram of an electronic device 300 provided by an embodiment of the present application. As Figure 3 shown, the electronic device 300 includes a central processing unit (CPU) 301, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 302 or the program loaded from the storage section 308 into the random access memory (RAM) 303. In the RAM 303, various programs and data required for the operation of the terminal device or the server are also stored. The CPU 301, the ROM 302, and the RAM 303 are connected to each other through a bus 304. The input / output (I / O) interface 305 is also connected to the bus 304.

[0091] The following components are connected to the I / O interface 305: an input section 306 including a keyboard, a mouse, etc.; an output section 307 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc. and a speaker, etc.; a storage section 308 including a hard disk, etc.; and a communication section 309 including a network interface card such as a LAN card, a modem, etc. The communication section 309 performs communication processing via a network such as the Internet. The drive 310 is also connected to the I / O interface 305 as needed. A removable medium 311, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 310 as needed, so that the computer program read from it can be installed into the storage section 308 as needed.

[0092] Specifically, according to the embodiments of the present application, the above method flow steps can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program product, which includes a computer program carried on a machine-readable medium, and the computer program includes program codes for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication section 309, and / or installed from the removable medium 311. When the computer program is executed by the central processing unit (CPU) 301, the above functions defined in the system of the present application are executed.

[0093] It should be noted that the computer-readable medium described in this application can be a computer-readable signal medium, a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples of a computer-readable storage medium can include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, 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 disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this application, a computer-readable storage medium can be any tangible medium that contains or stores a program, and this program can be used by or in conjunction with an instruction execution system, apparatus, or device. In this application, a computer-readable signal medium can include a data signal propagated in a baseband or as part of a carrier wave, in which computer-readable program code is carried. Such a propagated data signal can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium, and this computer-readable medium can send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on a computer-readable medium can be transmitted using any appropriate medium, including but not limited to: wireless, wire, optical cable, RF, etc., or any suitable combination of the above.

[0094] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram can represent a module, a program segment, or a part of code, and the aforementioned module, program segment, or part of code contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks can occur in a different order than that marked in the accompanying drawings. For example, two consecutive blocks shown can actually be executed substantially in parallel, and they can sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, as well as the combination of blocks in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system for performing the specified functions or operations, or can be implemented by a combination of dedicated hardware and computer instructions.

[0095] The units or modules involved in the embodiments of the present application can be implemented in software or in hardware. The described units or modules can also be provided in a processor. Among them, the names of these units or modules do not, in some cases, constitute a limitation on the units or modules themselves.

[0096] The above description is only a preferred embodiment of the present application and an explanation of the applied technical principles. Those skilled in the art should understand that the scope of the application involved in the present application is not limited to the technical solutions formed by the specific combination of the above technical features, and should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the aforementioned application concept. For example, the technical solutions formed by mutually replacing the above features with the technical features (but not limited to) having similar functions applied in the present application.

Claims

1. A running test system, characterized in that, Including: A service module for allocating a target identity recognition ID according to target information; An association module for generating target association data according to the target identity recognition ID and target image data; An analysis module for generating a target test result according to the target association data; The analysis module includes: A parsing unit for generating target test data according to the target association data; The parsing unit includes: A preprocessing component for generating target preprocessing data according to the target association data; A parsing component for generating the target test data according to the target preprocessing data; Wherein, the preprocessing component includes: A first preprocessing component for generating first preprocessing data according to the target association data, wherein the difference between the image clarity corresponding to the first preprocessing data and the target clarity is less than or equal to a first preset difference; A second preprocessing component for generating second preprocessing data according to the target association data, wherein the difference between the image angle corresponding to the second preprocessing data and the target angle is less than or equal to a second preset difference; And / or, a third preprocessing component for generating third preprocessing data according to the target association data, wherein the difference between the image occlusion rate corresponding to the third preprocessing data and the target occlusion rate is less than or equal to a third preset difference.

2. The running test system according to claim 1, characterized in that, The service module includes: An allocation unit for allocating a target identity recognition ID according to the target information; And / or, further includes: An execution unit for performing a target push operation according to the target test result.

3. The running test system according to claim 1, wherein The association module includes: An acquisition unit for performing a target acquisition operation according to a target action to generate the target image data; An association unit for performing a target association operation on the target image data and the target identity recognition ID to generate the target association data.

4. The running test system according to claim 1, characterized in that, The analysis module further includes: A query unit for determining a target determination rule according to the target association data; A generation unit for generating the target test result according to the target test data and the target determination rule.

5. The running test system according to claim 4, wherein The query unit includes: A query component for determining multiple target parameters according to the target association data; A determination component for determining the target determination rule according to the multiple target parameters, wherein the target determination rule includes: a preset duration threshold, a preset number threshold, and / or a preset distance threshold.

6. The running test system according to claim 5, wherein The generation unit includes: A first generation component for generating a test failure result when the test duration is less than the preset duration threshold; A second generation component for generating a test failure result when the test number is less than the preset number threshold; And / or, A third generation component for generating a test failure result when the test distance is less than the preset distance threshold.

7. The running test system according to any one of claims 1-6, characterized in that, The analysis module is provided with multiple target interfaces, and / or supports multiple target protocols.

8. A running test method, applicable to the running test system as described in claim 1, characterized in that, Including: Allocating a target identity recognition ID according to target information; Generating target association data according to the target identity recognition ID and target image data; Generate target preprocessed data according to the target associated data; Generate target test data according to the target preprocessed data to generate a target test result; Wherein, the target preprocessed data includes: first preprocessed data, second preprocessed data, and / or third preprocessed data; Wherein, the difference between the image clarity corresponding to the first preprocessed data and the target clarity is less than or equal to a first preset difference; The difference between the image angle corresponding to the second preprocessed data and the target angle is less than or equal to a second preset difference; The difference between the image occlusion rate corresponding to the third preprocessed data and the target occlusion rate is less than or equal to a third preset difference.

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