High-precision testing method for monitoring shell

Abstract

The invention relates to the technical field of monitoring equipment, and discloses a monitoring shell high-precision test method, which comprises the following steps: S01, carrying out image acquisition on a target object, S02, carrying out feature point extraction on a target image subjected to denoising processing in the step S01, S03, carrying out model matching processing on the feature points extracted in the step S02, and S04, carrying out model matching processing on the feature points extracted in the step S02. S04, carrying out pressure bearing detection on the target object successfully matched in the step S03, S05, carrying out pressure bearing judgment on the target object, S06, carrying out temperature adaptation detection on the target object successfully matched in the step S03, S07, carrying out temperature adaptation judgment on the target object, and S08, generating a detection report of the target object according to a matching detection result. S09, outputting a test report of the target object, and in short, according to the high-precision test method for the monitoring shell, the processing parameters and the technological process can be adjusted in time by monitoring the shell data, so that the production process is optimized, and the production efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence, and more specifically to a high-precision testing method for a monitoring shell. Background Art

[0002] With the continuous development of monitoring technology, the application scenarios of monitoring equipment are becoming more and more extensive, and the requirements for the quality and reliability of the equipment are becoming higher and higher. The monitoring casing is an important component to protect the internal components of the equipment. Its accuracy and quality directly affect the performance and service life of the entire monitoring equipment. The development of computer technology and artificial intelligence has also significantly improved data processing and analysis capabilities. By processing and analyzing test data with data analysis software, problems and deficiencies in the production process can be identified more accurately, providing a basis for subsequent improvements.

[0003] Traditional testing methods often focus on the waterproof and dustproof performance of the casing, and classify the waterproof and dustproof performance of the monitoring casing according to the classification standards. The methods and technical means are relatively simple and limited, and the testing method is single, ignoring the analysis of the physical factors of the monitoring casing: the casing material should be able to withstand a certain impact and extrusion, which will cause deformation or cracking. Ignoring this point will cause the casing to be vulnerable to damage in harsh environments, and temperature changes will also affect the casing material, thereby affecting the operating efficiency and life of the equipment. Summary of the invention

[0004] In order to overcome the above defects of the prior art, the present invention provides a high-precision testing method for a monitoring housing to solve the problems existing in the above background technology.

[0005] The present invention provides the following technical solution: a high-precision testing method for monitoring a housing, comprising the following steps: Step S01: Capturing an image of a target object: using a camera to capture an image of the target object, and performing denoising on the image; Step S02: extracting feature points of the target image after the denoising process in step S01: extracting feature points of the target image through the image processing library, identifying key feature points of the target image, and extracting key feature point data of the target image; Step S03: performing model matching processing on the feature points extracted in step S02: performing model matching processing based on the key feature point data of the target image and the preset template data, obtaining matching results according to the matching algorithm, and screening the matching results; Step S04: Performing a pressure test on the target object successfully matched in step S03: Performing a pressure test on the target object successfully matched according to a pressure test mathematical model, and calculating the pressure coefficient of the target object; Step S05: Performing pressure bearing judgment on the target object: performing pressure bearing judgment on the target object according to the pressure bearing coefficient of the target object calculated in step S04; Step S06: Performing a temperature adaptation test on the target object successfully matched in step S03: Performing a temperature adaptation test on the target object successfully matched according to a temperature detection mathematical model, and calculating a temperature adaptation coefficient of the target object; Step S07: Performing temperature adaptation judgment on the target object: performing temperature adaptation judgment on the target object according to the temperature adaptation coefficient of the target object calculated in step S06; Step S08: Generate a detection report of the target object according to the matching detection result: Generate a detection report of the target object according to the matching result in step S03, the judgment result in step S05 and the judgment result in step S07; Step S09: Output the test report of the target object: output the test report of the target object to the client.

[0006] Preferably, in step S01, a camera is used to capture an image of a target object, the target object being a monitoring housing, and the specific content of the image denoising process is: sorting the image pixels and performing filtering process in combination with spatial proximity and pixel value similarity.

[0007] Preferably, in step S02, feature points of the target image are extracted through an image processing library, and key feature points of the target image are identified, wherein the feature points are points on a critical area where the color depth in the image changes significantly, and points where the color depth in the image changes beyond a preset threshold are selected as key feature points.

[0008] Preferably, in step S03, model matching is performed based on the key feature point data of the target image and the preset template data, a matching result is obtained according to a matching algorithm, and the specific contents of screening the matching result are as follows: Step S11: Extract the coordinates of the key feature points of the target image , the coordinates of the corresponding key feature points in the preset template are , where i represents the number of key feature points of the target image, i=1, 2, 3, ..., n; Step S12: Establish a matching model to calculate the matching index of key feature points. The calculation formula is: ,in Represents the matching index of key feature points, Represents the mean of the horizontal coordinates of the corresponding key feature points in the preset template, Indicates the mean value of the ordinate of the corresponding key feature point in the preset template; Step S13: Based on the matching index of the key feature point calculated in step S12, the matching index of the key feature point is Matches the preset threshold For comparison, if the matching index of key feature points Greater than or equal to the preset matching threshold , then the match is successful, and the pressure detection and temperature adaptation detection of the target object are entered; if the matching index of the key feature point Less than the preset matching threshold , the match is unsuccessful, the target object detection is judged to be unqualified, and the pressure test and temperature adaptation test of the target object cannot be entered.

[0009] Preferably, in step S04, the pressure bearing test is performed on the successfully matched target object according to the pressure bearing test mathematical model, and the specific content of calculating the pressure bearing coefficient of the target object is as follows: Step S11: Use the sensor to collect the pressure parameters of the target object and calculate the maximum pressure stress of the target object. The calculation formula is: ,in represents the maximum compressive stress of the target object, Indicates the external pressure of the target object, represents the internal pressure of the target object, represents the outer diameter of the target object, Indicates the inner diameter of the target object; Step S12: Calculate the pressure coefficient of the target object according to the maximum pressure stress of the target object, and the calculation formula is: ,in, Indicates the pressure coefficient of the target object, represents the material allowable stress of the target object, represents the pressure correction factor of the target object, It represents the material consumption correction factor of the target object, and e represents a constant.

[0010] Preferably, in step S05, the specific content of the pressure bearing judgment of the target object is: the calculated pressure bearing coefficient of the target object With the preset pressure threshold For comparison, if the pressure coefficient of the target object Greater than or equal to the preset pressure threshold , then the target object is judged to have qualified pressure bearing capacity. If the pressure bearing coefficient of the target object is Less than the preset pressure threshold , it is judged that the pressure bearing capacity of the target object is unqualified.

[0011] Preferably, in step S06, the temperature adaptation test is performed on the successfully matched target object according to the temperature detection mathematical model, and the temperature adaptation coefficient of the target object is calculated as follows: Step S11: Calculate the effective refractive index of the target object, the calculation formula is: , where fe represents the effective refractive index of the target object, R represents the optical fiber length corresponding to the preset initial temperature, represents the cross-coupling coefficient of the target object, Represents the self-coupling coefficient of the target object; Step S12: Calculate the relative wavelength change value of the target object caused by temperature change, and the calculation formula is: ,in, Indicates the relative wavelength change of the target object caused by temperature change, T represents the preset initial temperature, Indicates the change in the ambient temperature of the target object. represents the linear thermal expansion coefficient, Indicates the change in the length of the optical fiber corresponding to the change in the ambient temperature of the target object; Step S13: Calculate the temperature adaptation coefficient of the target object according to step S11 and step S12, and the calculation formula is: ,in, Indicates the temperature adaptation coefficient of the target object, Represents the material stiffness of the target object.

[0012] Preferably, in step S07, the specific content of determining the temperature adaptability of the target object is: the temperature adaptability coefficient of the target object calculated is With the preset threshold For comparison, if the temperature adaptation coefficient of the target object Greater than or equal to the preset threshold , then the target object is judged to have qualified adaptability. If the temperature adaptability coefficient of the target object is Greater than or equal to the preset threshold , then it is judged that the target object’s adaptability is unqualified.

[0013] Preferably, the step S08 generates mismatch information for the mismatch result in step S03, the mismatch information includes the mismatch reason, generates judgment information for the judgment results in step S05 and step S07, the judgment information includes whether the judgment content is qualified, and generates a test report for the target object.

[0014] Technical effects and advantages of the present invention: The present invention is provided with step S01: performing image acquisition on the target object, step S02: performing feature point extraction on the target image after denoising in step S01, step S03: performing model matching processing on the feature points extracted in step S02, step S04: performing pressure bearing detection on the target object successfully matched in step S03, step S05: performing pressure bearing judgment on the target object, step S06: performing temperature adaptability detection on the target object successfully matched in step S03, step S07: performing temperature adaptability judgment on the target object, step S08: generating a detection report of the target object according to the matching detection result, and step S09: outputting a test report of the target object. In short, a high-precision testing method for a monitoring shell obtains a matching result according to a matching algorithm through model matching processing, and screens the matching result to screen out a monitoring shell that meets the specifications, monitors the monitoring shell data, generates a test report, and can timely adjust processing parameters and process flow, thereby optimizing the production process and improving production efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 The present invention is a flow chart of a high-precision testing method for monitoring a housing. DETAILED DESCRIPTION

[0016] The technical solution of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the present invention. In addition, the forms of the various structures recorded in the following embodiments are merely illustrative. The high-precision testing method for a monitoring shell involved in the present invention is not limited to the various structures recorded in the following embodiments. All other implementations obtained by ordinary technicians in this field without making creative work belong to the scope of protection of the present invention.

[0017] like Figure 1 As shown, the present invention provides a high-precision testing method for a monitoring housing, comprising the following steps: Step S01: capturing an image of a target object: capturing an image of the target object using a camera, and performing denoising on the image; Step S02: extracting feature points of the target image after the denoising process in step S01: extracting feature points of the target image through the image processing library, identifying key feature points of the target image, and extracting key feature point data of the target image; Step S03: performing model matching processing on the feature points extracted in step S02: performing model matching processing based on the key feature point data of the target image and the preset template data, obtaining matching results according to the matching algorithm, and screening the matching results; Step S04: Performing a pressure test on the target object successfully matched in step S03: Performing a pressure test on the target object successfully matched according to a pressure test mathematical model, and calculating the pressure coefficient of the target object; Step S05: Performing pressure bearing judgment on the target object: performing pressure bearing judgment on the target object according to the pressure bearing coefficient of the target object calculated in step S04; Step S06: Performing a temperature adaptation test on the target object successfully matched in step S03: Performing a temperature adaptation test on the target object successfully matched according to a temperature detection mathematical model, and calculating a temperature adaptation coefficient of the target object; Step S07: Performing temperature adaptation judgment on the target object: performing temperature adaptation judgment on the target object according to the temperature adaptation coefficient of the target object calculated in step S06; Step S08: Generate a detection report of the target object according to the matching detection result: Generate a detection report of the target object according to the matching result in step S03, the judgment result in step S05 and the judgment result in step S07; Step S09: Output the test report of the target object: output the test report of the target object to the client.

[0018] In this embodiment, it should be specifically explained that in step S01, a camera is used to capture an image of a target object, the target object is a monitoring shell, and the specific content of the image denoising process is: sorting the image pixels and filtering them in combination with spatial proximity and pixel value similarity.

[0019] In the present embodiment, it should be specifically explained that in the step S02, feature points of the target image are extracted through the image processing library, and key feature points of the target image are identified. The feature points are points on the critical area where the color depth in the image changes significantly, and the points where the color depth in the image changes beyond a preset threshold are selected as key feature points.

[0020] In this embodiment, it should be specifically explained that in step S03, model matching is performed based on the key feature point data of the target image and the preset template data, matching results are obtained according to the matching algorithm, and the specific contents of screening the matching results are as follows: Step S11: Extract the coordinates of the key feature points of the target image , the coordinates of the corresponding key feature points in the preset template are , where i represents the number of key feature points of the target image, i=1, 2, 3, ..., n; Step S12: Establish a matching model to calculate the matching index of key feature points. The calculation formula is: ,in Represents the matching index of key feature points, Represents the mean of the horizontal coordinates of the corresponding key feature points in the preset template, Indicates the mean value of the ordinate of the corresponding key feature point in the preset template; Step S13: Based on the matching index of the key feature point calculated in step S12, the matching index of the key feature point is Matches the preset threshold For comparison, if the matching index of key feature points Greater than or equal to the preset matching threshold , then the match is successful, and the pressure detection and temperature adaptation detection of the target object are entered; if the matching index of the key feature point Less than the preset matching threshold , the match is unsuccessful, the target object detection is judged to be unqualified, and the pressure test and temperature adaptation test of the target object cannot be entered.

[0021] In this embodiment, it should be specifically explained that in step S04, the pressure bearing test is performed on the successfully matched target object according to the pressure bearing test mathematical model, and the specific content of calculating the pressure bearing coefficient of the target object is as follows: Step S11: using sensors to collect pressure parameters of the target object, the pressure parameters of the target object include: the maximum pressure stress of the target object, the external pressure of the target object, the internal pressure of the target object, the outer diameter of the target object, the inner diameter of the target object and the material allowable stress of the target object, and calculating the maximum pressure stress of the target object, the calculation formula is: ,in represents the maximum compressive stress of the target object, Indicates the external pressure of the target object, represents the internal pressure of the target object, represents the outer diameter of the target object, Indicates the inner diameter of the target object; Step S12: Calculate the pressure coefficient of the target object according to the maximum pressure stress of the target object, and the calculation formula is: ,in, Indicates the pressure coefficient of the target object, represents the material allowable stress of the target object, represents the pressure correction factor of the target object, It represents the material consumption correction factor of the target object, and e represents a constant.

[0022] In this embodiment, it should be specifically explained that in step S05, the specific content of the pressure bearing judgment of the target object is: the calculated pressure bearing coefficient of the target object With the preset pressure threshold For comparison, if the pressure coefficient of the target object Greater than or equal to the preset pressure threshold , then the target object is judged to have qualified pressure bearing capacity. If the pressure bearing coefficient of the target object is Less than the preset pressure threshold , it is judged that the pressure bearing capacity of the target object is unqualified.

[0023] In this embodiment, it should be specifically explained that in step S06, the temperature adaptation detection is performed on the successfully matched target object according to the temperature detection mathematical model, and the specific content of the temperature adaptation coefficient of the target object is calculated as follows: Step S11: The temperature parameters of the target object are collected by a sensor, wherein the temperature parameters include: a preset initial temperature, a change in the ambient temperature of the target object, an optical fiber length corresponding to the preset initial temperature, a change in the optical fiber length corresponding to the change in the ambient temperature of the target object, and the material stiffness of the target object, and the effective refractive index of the target object is calculated using the following formula: , where fe represents the effective refractive index of the target object, R represents the optical fiber length corresponding to the preset initial temperature, represents the cross-coupling coefficient of the target object, Represents the self-coupling coefficient of the target object; Step S12: Calculate the relative wavelength change value of the target object caused by temperature change, and the calculation formula is: ,in, Indicates the relative wavelength change of the target object caused by temperature change, T represents the preset initial temperature, Indicates the change in the ambient temperature of the target object. represents the linear thermal expansion coefficient, Indicates the change in the length of the optical fiber corresponding to the change in the ambient temperature of the target object; Step S13: Calculate the temperature adaptation coefficient of the target object according to step S11 and step S12, and the calculation formula is: ,in, Indicates the temperature adaptation coefficient of the target object, Represents the material stiffness of the target object.

[0024] In this embodiment, it should be specifically explained that in step S07, the specific content of the temperature adaptation judgment of the target object is: the temperature adaptation coefficient of the target object calculated is With the preset threshold For comparison, if the temperature adaptation coefficient of the target object Greater than or equal to the preset threshold , then the target object is judged to have qualified adaptability. If the temperature adaptability coefficient of the target object is Greater than or equal to the preset threshold , then it is judged that the target object’s adaptability is unqualified.

[0025] In this embodiment, it should be specifically explained that step S08 generates mismatch information for the mismatch result in step S03, and the mismatch information includes the reason for the mismatch; generates judgment information for the judgment results in step S05 and step S07, and the judgment information includes whether the judgment content is qualified; and generates a test report for the target object.

[0026] In this embodiment, it should be specifically explained that in the step S09, the specific contents of the test report of the target object output to the client are as follows: Test purpose: briefly describe the main purpose of the test; Test goal: specifically explain the effect to be achieved through the test, including covering all key functions and discovering potential defects; Test scope: clarify the specific scope of the test, including functions; Performance test: perform performance testing, record performance indicators, test results and whether the requirements are met; Defect statistics: describe the defects found in the test in terms of the number of defects, severity and status; Defect analysis: classify, count and analyze the defects found to find common problems or high-risk areas; Test conclusion: summarize the overall situation of the test and determine whether the test objectives and requirements are met; Suggestions: make improvement suggestions based on the test results, including fixing defects and optimizing performance; When outputting the test report to the client, ensure that the format of the report is standardized, the content is accurate, and the structure is clear, so that the client can quickly understand the test results and make corresponding decisions. At the same time, the report should be modified and improved as necessary according to the specific needs and feedback of the client.

[0027] The difference between the present embodiment and the prior art mainly lies in that the present embodiment is provided with step S01: image acquisition of the target object, step S02: feature point extraction of the target image after denoising in step S01, step S03: model matching processing of the feature points extracted in step S02, step S04: pressure detection of the target object successfully matched in step S03, step S05: pressure judgment of the target object, step S06: temperature adaptability detection of the target object successfully matched in step S03, step S07: temperature adaptability judgment of the target object, step S08: generating a detection report of the target object according to the matching detection result, step S09: outputting a test report of the target object. In short, a high-precision testing method for a monitoring shell obtains a matching result according to a matching algorithm through model matching processing, and screens the matching results to screen out monitoring shells that meet the specifications, monitors the monitoring shell data, generates a test report, and can timely adjust processing parameters and process flow, thereby optimizing the production process and improving production efficiency.

[0028] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

[0029] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A high-precision testing method for monitoring housing, characterized in that: The following steps are involved: Step S01: Capturing an image of a target object: using a camera to capture an image of the target object, and performing denoising on the image; Step S02: extracting feature points of the target image after the denoising process in step S01: extracting feature points of the target image through the image processing library, identifying key feature points of the target image, and extracting key feature point data of the target image; Step S03: performing model matching processing on the feature points extracted in step S02: performing model matching processing based on the key feature point data of the target image and the preset template data, obtaining matching results according to the matching algorithm, and screening the matching results; Step S04: Performing a pressure test on the target object successfully matched in step S03: Performing a pressure test on the target object successfully matched according to a pressure test mathematical model, and calculating the pressure coefficient of the target object; Step S05: Performing pressure bearing judgment on the target object: performing pressure bearing judgment on the target object according to the pressure bearing coefficient of the target object calculated in step S04; Step S06: Performing a temperature adaptation test on the target object successfully matched in step S03: Performing a temperature adaptation test on the target object successfully matched according to a temperature detection mathematical model, and calculating a temperature adaptation coefficient of the target object; Step S07: Performing temperature adaptation judgment on the target object: performing temperature adaptation judgment on the target object according to the temperature adaptation coefficient of the target object calculated in step S06; Step S08: Generate a detection report of the target object according to the matching detection result: Generate a detection report of the target object according to the matching result in step S03, the judgment result in step S05 and the judgment result in step S07; Step S09: Output the test report of the target object: output the test report of the target object to the client.

2. A high-precision testing method for a monitoring housing according to claim 1, characterized in that: In step S01, a camera is used to collect images of a target object, which is a monitoring shell. The specific content of the image denoising process is: sorting the image pixels and filtering them in combination with spatial proximity and pixel value similarity.

3. A high-precision testing method for a monitoring housing according to claim 1, characterized in that: In step S02, feature points of the target image are extracted through an image processing library, and key feature points of the target image are identified. The feature points are points on a critical area where the color depth in the image changes significantly, and points where the color depth in the image changes beyond a preset threshold are selected as key feature points.

4. A high-precision testing method for a monitoring housing according to claim 1, characterized in that: In step S03, model matching is performed based on the key feature point data of the target image and the preset template data, a matching result is obtained according to a matching algorithm, and the specific contents of screening the matching result are as follows: Step S11: Extract the coordinates of the key feature points of the target image , the coordinates of the corresponding key feature points in the preset template are , where i represents the number of key feature points of the target image, i=1, 2, 3, ..., n; Step S12: Establish a matching model to calculate the matching index of key feature points. The calculation formula is: ,in Represents the matching index of key feature points, Represents the mean of the horizontal coordinates of the corresponding key feature points in the preset template, Indicates the mean value of the ordinate of the corresponding key feature point in the preset template; Step S13: Based on the matching index of the key feature point calculated in step S12, the matching index of the key feature point is Matches the preset threshold For comparison, if the matching index of key feature points Greater than or equal to the preset matching threshold , then the match is successful, and the pressure detection and temperature adaptation detection of the target object are entered; if the matching index of the key feature point Less than the preset matching threshold , the match is unsuccessful, the target object detection is judged to be unqualified, and the pressure test and temperature adaptation test of the target object cannot be entered.

5. A high-precision testing method for a monitoring housing according to claim 1, characterized in that: In step S04, the pressure bearing test is performed on the successfully matched target object according to the pressure bearing test mathematical model, and the specific content of the pressure bearing coefficient of the target object is calculated as follows: Step S11: Use the sensor to collect the pressure parameters of the target object and calculate the maximum pressure stress of the target object. The calculation formula is: ,in represents the maximum compressive stress of the target object, Indicates the external pressure of the target object, represents the internal pressure of the target object, represents the outer diameter of the target object, Indicates the inner diameter of the target object; Step S12: Calculate the pressure coefficient of the target object according to the maximum pressure stress of the target object, and the calculation formula is: ,in, Indicates the pressure coefficient of the target object, represents the material allowable stress of the target object, represents the pressure correction factor of the target object, It represents the material consumption correction factor of the target object, and e represents a constant.

6. A high-precision testing method for a monitoring housing according to claim 1, characterized in that: In step S05, the specific content of the pressure bearing judgment of the target object is: the pressure bearing coefficient of the target object calculated is With the preset pressure threshold For comparison, if the pressure coefficient of the target object Greater than or equal to the preset pressure threshold , then the target object is judged to have qualified pressure bearing capacity. If the pressure bearing coefficient of the target object is Less than the preset pressure threshold , it is judged that the pressure bearing capacity of the target object is unqualified.

7. A high-precision testing method for a monitoring housing according to claim 1, characterized in that: In step S06, the temperature adaptation test is performed on the successfully matched target object according to the temperature detection mathematical model, and the temperature adaptation coefficient of the target object is calculated as follows: Step S11: Calculate the effective refractive index of the target object, the calculation formula is: , where fe represents the effective refractive index of the target object, R represents the optical fiber length corresponding to the preset initial temperature, represents the cross-coupling coefficient of the target object, Represents the self-coupling coefficient of the target object; Step S12: Calculate the relative wavelength change value of the target object caused by temperature change, and the calculation formula is: ,in, Indicates the relative wavelength change of the target object caused by temperature change, T represents the preset initial temperature, Indicates the change in the ambient temperature of the target object. represents the linear thermal expansion coefficient, Indicates the change in the length of the optical fiber corresponding to the change in the ambient temperature of the target object; Step S13: Calculate the temperature adaptation coefficient of the target object according to step S11 and step S12, and the calculation formula is: ,in, Indicates the temperature adaptation coefficient of the target object, Represents the material stiffness of the target object.

8. A high-precision testing method for a monitoring housing according to claim 1, characterized in that: In step S07, the specific content of determining the temperature adaptability of the target object is: the temperature adaptability coefficient of the target object calculated is With the preset threshold For comparison, if the temperature adaptation coefficient of the target object Greater than or equal to the preset threshold , then the target object is judged to have qualified adaptability. If the temperature adaptability coefficient of the target object is Greater than or equal to the preset threshold , then it is judged that the target object’s adaptability is unqualified.

9. A high-precision testing method for a monitoring housing according to claim 1, characterized in that: The step S08 generates mismatch information for the mismatch result in step S03, the mismatch information includes the mismatch reason, generates judgment information for the judgment results in step S05 and step S07, the judgment information includes whether the judgment content is qualified, and generates a test report for the target object.

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