A high-precision testing method for a monitoring housing

Abstract

The present invention relates to the technical field of monitoring devices, and discloses a high-precision testing method for a monitoring housing, including the following steps: Step S01: Collect images of the target object; Step S02: Extract feature points from the denoised target image in Step S01; Step S03: Perform model matching processing on the feature points extracted in Step S02; Step S04: Perform pressure-bearing detection on the target object successfully matched in Step S03; Step S05: Make a pressure-bearing judgment on the target object; Step S06: Perform temperature adaptation detection on the target object successfully matched in Step S03; Step S07: Make a temperature adaptation judgment on the target object; Step S08: Generate a detection report of the target object based on the matching detection results; Step S09: Output the test report of the target object. In summary, a high-precision testing method for a monitoring housing can timely adjust processing parameters and technological processes by monitoring the housing data, thereby optimizing the production process and improving production efficiency.

Description

Technical Field

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

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

[0003] Traditional testing methods often focus on the waterproof and dustproof performance of the housing, and classify the waterproof and dustproof performance of the monitoring housing according to the classification standards. The methods and technical means are relatively simple and limited, and the testing methods are single, ignoring the analysis of the physical factors of the monitoring housing: the housing material should be able to withstand certain impacts and squeezes without deformation or cracking. Ignoring this point will cause the housing to be vulnerable in harsh environments. The change of temperature will also affect the housing material, thereby affecting the operation efficiency and life of the device. Summary of the Invention

[0004] In order to overcome the above-mentioned 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-mentioned background art.

[0005] The present invention provides the following technical solutions: A high-precision testing method for a monitoring housing, comprising the following steps:

[0006] Step S01: Image acquisition of the target object: Use a camera to perform image acquisition on the target object and perform denoising processing on the image;

[0007] Step S02: Feature point extraction of the target image after denoising processing in Step S01: Perform feature point extraction on the target image through an image processing library, identify the key feature points of the target image, and extract the key feature point data of the target image;

[0008] Step S03: Model matching processing of the feature points extracted in Step S02: Perform model matching processing based on the key feature point data of the target image and the preset template data, obtain the matching result according to the matching algorithm, and screen the matching result;

[0009] Step S04: Conduct pressure-bearing detection on the target object that has successfully matched in Step S03: Based on the pressure-bearing detection mathematical model, conduct pressure-bearing detection on the target object that has successfully matched, and calculate the pressure-bearing coefficient of the target object;

[0010] Step S05: Conduct pressure-bearing judgment on the target object: Based on the pressure-bearing coefficient of the target object calculated in Step S04, conduct pressure-bearing judgment on the target object;

[0011] Step S06: Conduct temperature adaptation detection on the target object that has successfully matched in Step S03: Based on the temperature detection mathematical model, conduct temperature adaptation detection on the target object that has successfully matched, and calculate the temperature adaptation coefficient of the target object;

[0012] Step S07: Conduct temperature adaptation judgment on the target object: Based on the temperature adaptation coefficient of the target object calculated in Step S06, conduct temperature adaptation judgment on the target object;

[0013] Step S08: Generate a detection report for the target object based on the matching detection result: Generate a detection report for the target object based on the matching result in Step S03, the judgment result in Step S05, and the judgment result in Step S07;

[0014] Step S09: Output the test report of the target object: Output the test report of the target object to the client.

[0015] Preferably, in Step S01, a camera is used to collect an image of the target object, and the target object is a monitoring housing. The specific content of denoising the image is: Sort the image pixels and perform filtering processing by combining spatial proximity and pixel value similarity.

[0016] Preferably, in Step S02, feature points of the target image are extracted through an image processing library, and the key feature points of the target image are identified. The feature points are the points on the critical region with obvious color depth changes in the image, and the points with color depth changes exceeding a preset threshold in the image are selected as key feature points.

[0017] Preferably, in Step S03, model matching processing is performed based on the key feature point data of the target image and the preset template data, and the matching result is obtained according to the matching algorithm. The specific content of screening the matching result is as follows:

[0018] Step S11: Extract the coordinates of the key feature points of the target image , and 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;

[0019] Step S12: Establish a matching model to calculate the matching index of key feature points. The calculation formula is , where represents the matching index of the key feature point, represents the mean value of the abscissa of the corresponding key feature point in the preset template, represents the mean value of the ordinate of the corresponding key feature point in the preset template;

[0020] Step S13: According to the matching index of the key feature points calculated in Step S12, compare the matching index of the key feature points with the preset matching threshold . If the matching index of the key feature point is greater than or equal to the preset matching threshold , the matching is successful, and the pressure-bearing detection and temperature adaptation detection of the target object are entered; if the matching index of the key feature point is less than the preset matching threshold , the matching is unsuccessful, and it is determined that the detection of the target object is unqualified and cannot enter the pressure-bearing detection and temperature adaptation detection of the target object.

[0021] Preferably, in the said Step S04, according to the pressure-bearing detection mathematical model, the pressure-bearing detection of the successfully matched target object is carried out, and the specific content of calculating the pressure-bearing coefficient of the target object is as follows:

[0022] Step S11: Use a sensor to collect the pressure-bearing parameters of the target object and calculate the maximum pressure-bearing stress of the target object. The calculation formula is: , where represents the maximum pressure-bearing stress of the target object, represents the external pressure of the target object, represents the internal pressure of the target object, represents the outer diameter of the target object, represents the inner diameter of the target object;

[0023] Step S12: Calculate the pressure-bearing coefficient of the target object according to the maximum pressure-bearing stress of the target object. The calculation formula is: , where, represents the pressure-bearing coefficient of the target object, represents the allowable stress of the material of the target object, represents the pressure-bearing correction factor of the target object, represents the material consumption correction factor of the target object, and e represents a constant.

[0024] Preferably, in the said Step S05, the specific content of the pressure-bearing judgment of the target object is: compare the calculated pressure-bearing coefficient of the target object with the preset pressure-bearing threshold Compare. If the pressure-bearing coefficient of the target object is greater than or equal to the preset pressure-bearing threshold , it is determined that the pressure-bearing capacity of the target object is qualified. If the pressure-bearing coefficient of the target object is less than the preset pressure-bearing threshold , it is determined that the pressure-bearing capacity of the target object is unqualified.

[0025] Preferably, in step S06, according to the temperature detection mathematical model, the temperature adaptation detection of the successfully matched target object is carried out, and the specific content of calculating the temperature adaptation coefficient of the target object is as follows:

[0026] 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;

[0027] Step S12: Calculate the relative wavelength change value of the target object caused by the temperature change. The calculation formula is: , where, represents the relative wavelength change value of the target object caused by the temperature change, T represents the preset initial temperature, represents the change amount of the ambient temperature where the target object is located, represents the linear thermal expansion coefficient, represents the change amount of the optical fiber length corresponding to the change amount of the ambient temperature where the target object is located;

[0028] Step S13: Calculate the temperature adaptation coefficient of the target object according to steps S11 and S12. The calculation formula is: , where, represents the temperature adaptation coefficient of the target object, represents the material stiffness of the target object.

[0029] Preferably, in step S07, the specific content of the temperature adaptation judgment of the target object is: Compare the calculated temperature adaptation coefficient of the target object with the preset threshold . If the temperature adaptation coefficient of the target object is greater than or equal to the preset threshold , it is determined that the adaptation ability of the target object is qualified. If the temperature adaptation coefficient of the target object is less than the preset threshold , it is determined that the adaptation ability of the target object is unqualified.

[0030] Preferably, in step S08, mismatch information is generated for the mismatch results in step S03, the mismatch information includes the reasons for mismatch, judgment information is generated for the judgment results in steps S05 and S07, the judgment information includes whether the judgment content is qualified, and a test report of the target object is generated.

[0031] Technical effects and advantages of the present invention:

[0032] The present invention includes step S01: performing image acquisition on a target object, step S02: extracting feature points from the target image after denoising processing 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: making a pressure-bearing judgment on the target object, step S06: performing temperature adaptation detection on the target object successfully matched in step S03, step S07: making a temperature adaptation judgment on the target object, step S08: generating a detection report of the target object based on the matching detection results, step S09: outputting the test report of the target object. In short, a high-precision testing method for a monitoring housing obtains a matching result based on a matching algorithm through model matching processing, screens the matching results, selects the monitoring housing that meets the specifications, monitors the monitoring housing data, generates a test report, can timely adjust the processing parameters and technological processes, thereby optimizing the production process and improving production efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 It is a flowchart of a high-precision testing method for a monitoring housing. DETAILED DESCRIPTION OF THE INVENTION

[0034] Next, the technical solutions in the present invention will be clearly and completely described in conjunction with the drawings in the present invention. In addition, the forms of each structure described in the following embodiments are only examples, and a high-precision testing method for a monitoring housing involved in the present invention is not limited to the structures described in the following embodiments. All other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of the present invention.

[0035] As Figure 1 shown, the present invention provides a high-precision testing method for a monitoring housing, including the following steps: Step S01: performing image acquisition on a target object: using a camera to perform image acquisition on the target object and performing denoising processing on the image;

[0036] Step S02: extracting feature points from the target image after denoising processing in step S01: extracting feature points from the target image through an image processing library, identifying the key feature points of the target image, and extracting the key feature point data of the target image;

[0037] Step S03: Perform model matching on the feature points extracted in step S02: Perform model matching based on the key feature point data of the target image and the preset template data, obtain the matching result according to the matching algorithm, and screen the matching result;

[0038] Step S04: Perform pressure-bearing detection on the target object that has been successfully matched in step S03: Perform pressure-bearing detection on the target object that has been successfully matched according to the pressure-bearing detection mathematical model, and calculate the pressure-bearing coefficient of the target object;

[0039] Step S05: Perform pressure-bearing judgment on the target object: Perform pressure-bearing judgment on the target object according to the pressure-bearing coefficient of the target object calculated in step S04;

[0040] Step S06: Perform temperature adaptation detection on the target object that has been successfully matched in step S03: Perform temperature adaptation detection on the target object that has been successfully matched according to the temperature detection mathematical model, and calculate the temperature adaptation coefficient of the target object;

[0041] Step S07: Perform temperature adaptation judgment on the target object: Perform temperature adaptation judgment on the target object according to the temperature adaptation coefficient of the target object calculated in step S06;

[0042] Step S08: Generate a detection report for the target object based on the matching detection result: Generate a detection report for the target object based on the matching result in step S03, the judgment result in step S05, and the judgment result in step S07;

[0043] Step S09: Output the test report of the target object: Output the test report of the target object to the client.

[0044] In this embodiment, it should be specifically noted that in step S01, a camera is used to collect an image of the target object, and the target object is a monitoring housing. The specific content of denoising the image is: Sort the image pixels and perform filtering processing in combination with spatial proximity and pixel value similarity.

[0045] In this embodiment, it should be specifically noted that in step S02, a feature point extraction is performed on the target image through an image processing library, and the key feature points of the target image are identified. The feature points are the points on the critical region where the color depth change in the image is obvious, and the points where the color depth change in the image exceeds the preset threshold are selected as the key feature points.

[0046] In this embodiment, it should be specifically noted that in step S03, based on the key feature point data of the target image and the preset template data, model matching processing is performed, and the matching result is obtained according to the matching algorithm, and the specific content of screening the matching result is as follows:

[0047] Step S11: Extract the coordinates of the key feature points of the target image , and 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;

[0048] Step S12: Establish a matching model to calculate the matching index of the key feature points. The calculation formula is , where represents the matching index of the key feature points, represents the mean value of the abscissa of the corresponding key feature points in the preset template, represents the mean value of the ordinate of the corresponding key feature points in the preset template;

[0049] Step S13: According to the matching index of the key feature points calculated in step S12, compare the matching index of the key feature points with the preset matching threshold . If the matching index of the key feature points is greater than or equal to the preset matching threshold , the matching is successful, and the pressure bearing detection and temperature adaptation detection of the target object are entered; if the matching index of the key feature points is less than the preset matching threshold , the matching is unsuccessful, and it is determined that the target object detection is unqualified and cannot enter the pressure bearing detection and temperature adaptation detection of the target object.

[0050] In this embodiment, it should be specifically noted that in step S04, according to the pressure bearing detection mathematical model, the pressure bearing detection of the target object with successful matching is performed, and the specific content of calculating the pressure bearing coefficient of the target object is as follows:

[0051] Step S11: Use the sensor to collect the pressure bearing parameters of the target object. The pressure bearing parameters of the target object include: the maximum pressure bearing 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 allowable stress of the material of the target object. Calculate the maximum pressure bearing stress of the target object. The calculation formula is: , where represents the maximum pressure bearing stress of the target object, represents the external pressure of the target object, represents the internal pressure of the target object, represents the outer diameter of the target object, represents the inner diameter of the target object;

[0052] Step S12: Calculate the pressure-bearing coefficient of the target object based on the maximum pressure-bearing stress of the target object. The calculation formula is: , where, represents the pressure-bearing coefficient of the target object, represents the allowable stress of the material of the target object, represents the pressure-bearing correction factor of the target object, represents the material consumption correction factor of the target object, and e represents a constant.

[0053] In this embodiment, it should be specifically noted that in the step S05, the specific content of the pressure-bearing judgment on the target object is: comparing the calculated pressure-bearing coefficient of the target object with the preset pressure-bearing threshold . If the pressure-bearing coefficient of the target object is greater than or equal to the preset pressure-bearing threshold , it is determined that the pressure-bearing capacity of the target object is qualified. If the pressure-bearing coefficient of the target object is less than the preset pressure-bearing threshold , it is determined that the pressure-bearing capacity of the target object is unqualified.

[0054] In this embodiment, it should be specifically noted that in the step S06, the specific content of the temperature adaptation detection of the successfully matched target object according to the temperature detection mathematical model and the calculation of the temperature adaptation coefficient of the target object are as follows:

[0055] Step S11: Collect the temperature parameters of the target object through a sensor. The temperature parameters include: the preset initial temperature, the change in the ambient temperature of the target object, the optical fiber length corresponding to the preset initial temperature, the 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. 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;

[0056] Step S12: Calculate the relative wavelength change value of the target object caused by the temperature change. The calculation formula is: , where, represents the relative wavelength change value of the target object caused by the temperature change, T represents the preset initial temperature, represents the change in the ambient temperature of the target object, represents the linear coefficient of thermal expansion, represents the change in the length of the optical fiber corresponding to the change in the ambient temperature of the target object;

[0057] Step S13: Calculate the temperature adaptation coefficient of the target object according to Step S11 and Step S12. The calculation formula is: , where represents the temperature adaptation coefficient of the target object, represents the material stiffness of the target object.

[0058] In this embodiment, it should be specifically noted that in Step S07, the specific content of the temperature adaptation judgment on the target object is: compare the calculated temperature adaptation coefficient of the target object with the preset threshold . If the temperature adaptation coefficient of the target object is greater than or equal to the preset threshold , it is determined that the adaptation ability of the target object is qualified. If the temperature adaptation coefficient of the target object is less than the preset threshold , it is determined that the adaptation ability of the target object is unqualified.

[0059] In this embodiment, it should be specifically noted that in Step S08, generate mismatch information for the mismatch result in Step S03. The mismatch information includes the reason for the mismatch, generate judgment information for the judgment results in Step S05 and Step S07. The judgment information includes whether the judgment content is qualified, and generate a test report for the target object.

[0060] In this embodiment, it should be specifically noted that in Step S09, the specific content of outputting the test report of the target object to the client is: Test purpose: Briefly describe the main purpose of the test; Test objective: Specifically describe the effects hoped to be achieved through the test, including covering all key functions and discovering potential defects; Test scope: Clearly define the specific scope of the test, including functions; Performance test: Conduct performance tests, record performance indicators, test results, and whether the requirements are met; Defect statistics: Describe the defects found in the test from the aspects of the number of defects, severity, and status; Defect analysis: Classify, count, and analyze the discovered defects to find common problems or high-risk areas; Test conclusion: Summarize the overall situation of the test and judge whether the test objectives and requirements are met; Suggestions: Put forward improvement suggestions based on the test results, including fixing defects and optimizing performance; When outputting the test report to the client, it should be ensured that the format of the report is standardized, the content is accurate, and the logic is clear, so that the client can quickly understand the test results and make corresponding decisions. At the same time, necessary modifications and improvements should be made to the report according to the specific needs and feedback of the client.

[0061] The main difference between this embodiment and the prior art lies in that this embodiment includes the following steps: Step S01: Collect images of the target object; Step S02: Extract feature points from the target image after denoising in Step S01; Step S03: Perform model matching processing on the feature points extracted in Step S02; Step S04: Perform pressure-bearing detection on the target object with successful matching in Step S03; Step S05: Make a pressure-bearing judgment on the target object; Step S06: Perform temperature adaptation detection on the target object with successful matching in Step S03; Step S07: Make a temperature adaptation judgment on the target object; Step S08: Generate a detection report of the target object based on the matching detection results; Step S09: Output the test report of the target object. In short, a high-precision test method for a monitoring housing obtains a matching result according to a matching algorithm through model matching processing, screens the matching results, selects the monitoring housing that meets the specifications, monitors the data of the monitoring housing, generates a test report, and can timely adjust the processing parameters and technological processes, thereby optimizing the production process and improving production efficiency.

[0062] Finally, the above are only the preferred embodiments of the present invention and are not used to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

[0063] As mentioned above, the above is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed in the present application can easily think of changes or replacements, which should all be covered by the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to 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; According to the pressure detection mathematical model, the pressure detection is carried out on the successfully matched target object, and the specific contents of the pressure coefficient of the target object are 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, represents the material consumption correction factor of the target object, and e represents a constant; 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 target object's pressure bearing test and temperature adaptation test are started; if the matching index of the key feature point is 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 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.

6. 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.

7. 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.

8. 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.

Images