An artificial intelligence-based high and low temperature test chamber operation management system

Abstract

The present invention discloses an artificial intelligence-based high and low temperature test chamber operation and management system, relates to the technical field of test chamber operation and management, and solves the technical problem in the prior art that it is impossible to perform performance tests on the high and low temperature test chamber and simultaneously it is impossible to perform exclusive analysis during the test process. Specifically, a performance test analysis unit performs performance tests on the high and low temperature test chamber, marks the high and low temperature test chamber as a detection end, obtains a performance test analysis coefficient when the detection end simulates the operation environment of the test product, and judges whether the performance test is qualified based on a comparison of the performance test analysis coefficient threshold; after the performance test is qualified, an exclusive analysis unit performs exclusive analysis on the detection end, obtains the test increase parameter type and the usage increase parameter type based on the time period combination analysis, and performs exclusive detection based on the management and control of the test increase parameter type and the usage increase parameter type during the real-time test process.

Description

Technical Field

[0001] The present invention relates to the technical field of test chamber operation management, and in particular to a high and low temperature test chamber operation management system based on artificial intelligence. Background Art

[0002] High and low temperature test chamber is suitable for high temperature and low temperature reliability test of industrial products; it can test various performance indicators of parts and materials of electronic and electrical, automobile and motorcycle, aerospace, ship weapons, colleges and universities, scientific research institutions and other related products under high temperature and low temperature (alternating) cycle changes.

[0003] However, in the prior art, it is impossible to perform performance tests on high and low temperature test chambers, and it is impossible to perform exclusive analysis during the test process, resulting in low test efficiency of the test chamber and inability to guarantee the test accuracy of the test chamber. In addition, it is impossible to perform environmental parameter type analysis on the test product in real time in the test chamber, resulting in the inability to reasonably control the test intensity.

[0004] In view of the above technical defects, a solution is now proposed. Summary of the invention

[0005] The purpose of the present invention is to solve the above-mentioned problems and to propose a high and low temperature test chamber operation management system based on artificial intelligence.

[0006] The purpose of the present invention can be achieved through the following technical solutions:

[0007] An artificial intelligence-based high and low temperature test chamber operation management system, comprising an operation management platform, wherein the operation management platform is communicatively connected with a performance test analysis unit, an exclusive analysis unit and an operation quality assessment unit;

[0008] The performance test analysis unit performs a performance test on the high and low temperature test chamber, marks the high and low temperature test chamber as the detection end, obtains the value ratio information, data deviation information and data interval information of the detection end when the detection end simulates the operating environment of the test product, and obtains the performance test analysis coefficient of the detection end based on the information analysis, and determines whether the performance test is qualified based on the comparison of the performance test analysis coefficient threshold;

[0009] After the performance test is qualified, the exclusive analysis unit performs exclusive analysis on the detection end, sets the product of the real-time test as the exclusion object, obtains the operating environment parameters set during the historical test of the exclusion object according to the historical test of the same type of product of the exclusion object, and obtains the operating parameters generated by the detection end during the historical test, and collects the effective test period and the test reference period. According to the analysis of the period combination, the test increase parameter type and the usage increase parameter type are obtained, and the exclusive detection is performed according to the management and control of the test increase parameter type and the usage increase parameter type during the real-time test process;

[0010] After the exclusive test is qualified, the operation quality assessment unit evaluates the test operation quality of the detection end to determine whether the operation quality of the product test currently conducted by the detection end is qualified.

[0011] As a preferred embodiment of the present invention, the numerical ratio information, data deviation information and data interval information are respectively the deviation value between the simulated numerical values ​​of the operating environment parameters of different test products and the actual numerical values ​​of the operating environment parameters and the numerical ratio of the corresponding actual numerical values ​​of the operating environment parameters, the corresponding average deviation value of the simulated numerical values ​​of the environmental parameters that meet the actual operating environment parameters when the operating environment is simulated for different batches of the same type of test products, and the interval value between the peak value of the parameter value for reciprocating regulation of the simulated numerical values ​​of the environmental parameters according to the actual operating environment parameters and the peak value of the actual operating environment parameters;

[0012] If the performance test analysis coefficient of the detection end exceeds the performance test analysis coefficient threshold, a performance test abnormality signal is generated and the performance test abnormality signal is sent to the operation management platform; if the performance test analysis coefficient of the detection end does not exceed the performance test analysis coefficient threshold, a performance test normal signal is generated and the performance test normal signal is sent to the operation management platform.

[0013] As a preferred embodiment of the present invention, the collection process of the effective test period and the test reference period is as follows:

[0014] The failure rate of completed tests and the failure rate of unfinished tests of the exclusion objects of historical tests are obtained. If the numerical deviation of the two failure rates exceeds the set threshold, the current historical test is marked as a valid test; and the product test period of the completed test exclusion object is marked as a valid test period, and the product use period of the unfinished test exclusion object is marked as a test reference period.

[0015] As a preferred embodiment of the present invention, the time period combined analysis process is as follows:

[0016] According to the effective test period and the test parameter period, the operating environment parameter type of the corresponding period sorting object is obtained, and the non-intersection type of the operating environment parameter type corresponding to the effective test period and the test parameter period is set as the test increase parameter type. Combined with the running performance analysis of the detection end, if the running parameter type of the detection end is consistent with the test increase parameter type, the corresponding parameter type is set to the test increase parameter type. Conversely, if the running parameter type of the detection end is inconsistent with the test increase parameter type, the corresponding parameter type is set to the usage increase parameter type.

[0017] As a preferred embodiment of the present invention, the exclusive detection process is as follows:

[0018] Obtain the floating frequency of the same time and trend as the parameter value of the simulated parameter value of the running environment of the elimination object when the detection end conducts a real-time elimination object test, as well as the number of values ​​in the non-intersecting interval of the parameter value simulation control interval of the additional parameter type used in the real-time elimination object test and the floating interval of the same type of parameter values ​​during the use of the historical test elimination object, and compare them with the floating frequency threshold and the value quantity threshold respectively.

[0019] As a preferred implementation mode of the present invention, if the detection end performs a real-time elimination object test and the corresponding parameter value of the test-added parameter type is at the same time as the parameter value of the elimination object operation environment simulation parameter value and the same trend floating frequency exceeds the floating frequency threshold, or the detection end performs a real-time elimination object test and the number of values ​​in the non-intersecting interval of the parameter value simulation control interval of the additional parameter type and the floating interval of the same type of parameter values ​​during the use of the historical test elimination object exceeds the value quantity threshold, then an exclusive exception signal is generated and the exclusive exception signal is sent to the operation management platform;

[0020] If the detection end performs a real-time elimination object test and the corresponding parameter value of the test added parameter type is simultaneous with the parameter value of the elimination object operating environment simulation and the floating frequency of the same trend does not exceed the floating frequency threshold, and the number of values ​​in the non-intersecting interval of the parameter value simulation control interval of the added parameter type used by the detection end for the real-time elimination object test and the floating interval of the same type of parameter values ​​during the use of the historical test elimination object does not exceed the value quantity threshold, then an exclusive normal signal is generated and sent to the operation management platform.

[0021] As a preferred embodiment of the present invention, the operation quality assessment process is as follows:

[0022] The maximum deviation of the internal temperature uniformity when the device is unloaded and the internal temperature uniformity when the device is loaded during the real-time test of the detection end is obtained, and the peak continuous growth span value of the internal temperature fluctuation value of the device during the test of the detection end is obtained; the maximum deviation of the internal temperature uniformity when the device is unloaded and the internal temperature uniformity when the device is loaded during the real-time test of the detection end, and the peak continuous growth span value of the internal temperature fluctuation value of the device during the test of the detection end are compared with the uniformity maximum deviation threshold and the fluctuation value peak continuous growth span threshold respectively:

[0023] If the maximum deviation between the internal temperature uniformity of the device when it is unloaded and the internal temperature uniformity of the device when it is loaded during the real-time test at the detection end exceeds the uniformity maximum deviation threshold, or the peak value of the continuous growth span of the internal temperature fluctuation value of the device during the test at the detection end exceeds the peak value of the continuous growth span value threshold, an operation quality assessment abnormal signal is generated and sent to the operation management platform;

[0024] If the maximum deviation between the internal temperature uniformity when the equipment is unloaded and the internal temperature uniformity when the equipment is loaded during the real-time test at the detection end does not exceed the maximum uniformity deviation threshold, and the peak continuous growth span value of the internal temperature fluctuation value of the equipment during the test at the detection end does not exceed the peak continuous growth span value threshold of the fluctuation value, a normal operation quality assessment signal is generated and sent to the operation management platform.

[0025] Compared with the prior art, the present invention has the following beneficial effects:

[0026] 1. In the present invention, a performance test is performed on the high and low temperature test chamber, and whether the equipment performance of the current high and low temperature test chamber meets the requirements is inferred based on the performance test of the high and low temperature test chamber, so as to ensure that the high and low temperature test chamber can accurately simulate the environment, so as to improve the test accuracy of the test product, improve the quality inspection efficiency of the test product, and reduce the failure rate in use; an exclusive analysis is performed on the detection end to determine that the detection end is affected by factors other than the simulated environmental parameters when conducting the test product test, resulting in a decrease in the environmental simulation test efficiency of the detection end, and the test efficiency of the detection end cannot be guaranteed, and when the test is abnormal, the cause of the abnormality cannot be accurately inferred, and targeted data management and control cannot be performed.

[0027] 2. In the present invention, the test operation quality of the detection end is evaluated to determine whether the operation quality of the product test currently performed by the detection end is qualified, thereby avoiding abnormal operation of the detection end causing a decrease in the accuracy of the simulation of the operating environment parameters, and failing to accurately infer the quality of the test product, resulting in a decrease in the test failure rate of low-quality test products, and an increase in the risk of test failures of high-quality test products.

[0028] 3. In the present invention, by analyzing whether the type of parameters in the current simulated operating environment is prone to floating, the impact risk can also be analyzed. At the same time, the parameters that are not prone to floating can be selectively simulated to reduce the operating intensity of the detection end. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to facilitate understanding by those skilled in the art, the present invention is further described below with reference to the accompanying drawings.

[0030] Figure 1 This is a principle block diagram of Embodiment 1 of the present invention;

[0031] Figure 2 This is a principle block diagram of embodiment 2 of the present invention. DETAILED DESCRIPTION

[0032] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0033] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0034] Example 1

[0035] See also Figure 1 As shown, an artificial intelligence-based high and low temperature test chamber operation management system includes an operation management platform, wherein the operation management platform is communicatively connected to a performance test analysis unit, an exclusive analysis unit, and an operation quality evaluation unit;

[0036] In this system, when the high and low temperature test chamber is actually used, the operation management platform generates a performance test analysis signal and sends the performance test analysis signal to the performance test analysis unit. After receiving the performance test analysis signal, the performance test analysis unit performs a performance test on the high and low temperature test chamber, and infers whether the equipment performance of the current high and low temperature test chamber meets the requirements based on the performance test of the high and low temperature test chamber, thereby ensuring that the high and low temperature test chamber can accurately simulate the environment, so as to improve the test accuracy of the test product, improve the quality inspection efficiency of the test product, and reduce the use failure rate;

[0037] The high and low temperature test chamber is marked as the detection end. When the detection end simulates the operating environment of the test product, the deviation value between the simulated values ​​of the operating environment parameters of different test products and the actual values ​​of the operating environment parameters and the numerical ratio of the corresponding actual values ​​of the operating environment parameters are obtained, and the deviation value between the simulated values ​​of the operating environment parameters of different test products and the actual values ​​of the operating environment parameters and the numerical ratio of the corresponding actual values ​​of the operating environment parameters are marked as SZB; the average deviation value of the corresponding values ​​of the simulated values ​​of the environmental parameters that meet the actual operating environment parameters when the operating environment simulation is performed on different batches of the same type of test products is obtained, and the average deviation value of the corresponding values ​​of the simulated values ​​of the environmental parameters that meet the actual operating environment parameters when the operating environment simulation is performed on different batches of the same type of test products is marked as PCZ;

[0038] After the simulation of the operating environment parameters is completed when the detection end is used to simulate the operating environment of the test product, the interval value between the peak value of the parameter value that is adjusted back and forth according to the actual operating environment parameter value and the peak value of the actual operating environment parameter value is obtained, and the interval value between the peak value of the parameter value that is adjusted back and forth according to the actual operating environment parameter value and the peak value of the actual operating environment parameter value is marked as JGZ; the environmental parameters are environmental parameters such as temperature and humidity, and equipment parameters such as voltage and current when the test product is running;

[0039] By formula The performance test analysis coefficient G of the detection end is obtained, where β1, β2 and β3 are preset proportional coefficients of the numerical ratio, the numerical average deviation value and the numerical peak interval value respectively, and α is used as an error correction factor with a value of 1.1, and the above collected data are marked as numerical ratio information, data deviation information and data interval information respectively;

[0040] Compare the performance test analysis coefficient G of the detection end with the performance test analysis coefficient threshold:

[0041] If the performance test analysis coefficient G of the detection end exceeds the performance test analysis coefficient threshold, the performance test analysis of the detection end is judged to be unqualified, a performance test abnormality signal is generated and sent to the operation management platform. After receiving the performance test abnormality signal, the operation management platform suspends the test detection of the test product at the current detection end, and performs performance control on the detection end. After completing the performance control, the test product is tested;

[0042] If the performance test analysis coefficient G of the detection end does not exceed the performance test analysis coefficient threshold, the detection end performance test analysis is determined to be qualified, a performance test normal signal is generated and the performance test normal signal is sent to the operation management platform; at the same time, an exclusive analysis signal is generated and the exclusive analysis signal is sent to the exclusive analysis unit;

[0043] After receiving the exclusive analysis signal, the exclusive analysis unit performs exclusive analysis on the detection end, and determines that the detection end is affected by factors other than the simulated environmental parameters when conducting the test product test, resulting in reduced efficiency of the environmental simulation test at the detection end, and the test efficiency of the detection end cannot be guaranteed. In addition, when the test is abnormal, the cause of the abnormality cannot be accurately inferred, and targeted data control cannot be performed;

[0044] The product of the real-time test is set as the elimination object, and the label i is set, where i is a natural number greater than 1. According to the historical tests of the same type of products of the elimination object, the operating environment parameters set during the historical tests of the elimination object are obtained, and the operating parameters generated by the operation of the detection end during the historical tests are obtained. The detection end operating parameters are represented by the parameters existing in the test environment when the detection end is running, such as voltage, electric field strength and other parameters; the failure rate of the completed test and the failure rate of the uncompleted test of the elimination object of the historical test are obtained. If the numerical deviation of the two failure rates exceeds the set threshold, the current historical test is marked as a valid test; and the product test period of the elimination object that has completed the test is marked as a valid test period, and the product use period of the elimination object that has not completed the test is marked as a test reference period;

[0045] According to the effective test period and the test parameter period, the operating environment parameter type of the corresponding period sorting object is obtained, and the non-intersection type of the operating environment parameter type corresponding to the effective test period and the test parameter period is set as the test increase parameter type. Combined with the running performance analysis of the detection end, if the detection end running parameter type is consistent with the test increase parameter type, the corresponding parameter type is set as the test increase parameter type. Conversely, if the detection end running parameter type is inconsistent with the test increase parameter type, the corresponding parameter type is set as the usage increase parameter type.

[0046] Obtain the floating frequency of the same moment and trend when the parameter value corresponding to the test-added parameter type and the simulated parameter value of the running environment of the elimination object are at the same time when the detection end performs the real-time elimination object test, as well as the number of values ​​in the non-intersecting interval of the parameter value simulation control interval of the additional parameter type used when the detection end performs the real-time elimination object test and the floating frequency of the same moment and trend when the detection end performs the real-time elimination object test and the simulated parameter value of the running environment of the elimination object are at the same time, as well as the number of values ​​in the non-intersecting interval of the parameter value simulation control interval of the additional parameter type used when the detection end performs the real-time elimination object test and the floating frequency of the same moment and trend when the detection end performs the real-time elimination object test and the number of values ​​in the non-intersecting interval of the parameter value simulation control interval of the additional parameter type used when the detection end performs the real-time elimination object test and the floating frequency threshold and the value quantity threshold, respectively: wherein, the value in the non-intersecting interval is based on the parameter detection unit, and the parameter value detection unit corresponding to the non-intersecting interval is 1, and when the non-intersecting interval is 1-5, the corresponding number of values ​​is 5;

[0047] If the value of the corresponding parameter of the test-added parameter type when the detection end conducts a real-time elimination object test and the value of the simulated parameter of the running environment of the elimination object are at the same time and the floating frequency of the same trend exceeds the floating frequency threshold, or the number of values ​​in the non-intersecting interval of the parameter value simulation control interval of the additional parameter type used when the detection end conducts a real-time elimination object test and the floating interval of the same type of parameter values ​​during the use of the historical test elimination object exceeds the value quantity threshold, then the exclusive analysis of the detection end is determined to be abnormal, and an exclusive abnormality signal is generated and sent to the operation management platform. After receiving the exclusive abnormality signal, the operation management platform reorganizes the actual test process of the detection end, simulates the running parameters, and performs numerical control on the test-added parameters, and fits the control interval of the additional parameter used according to the actual use, so as to improve the test efficiency of the test product;

[0048] If the corresponding parameter value of the test-added parameter type when the detection end performs a real-time elimination object test is at the same time as the simulated parameter value of the elimination object operation environment and the floating frequency of the same trend does not exceed the floating frequency threshold, and the number of values ​​in the non-intersecting interval of the parameter value simulation control interval of the additional parameter type used by the detection end when performing the real-time elimination object test and the floating interval of the same type of parameter values ​​during the use of the historical test elimination object does not exceed the value quantity threshold, then it is determined that the exclusive analysis of the detection end is normal, an exclusive normal signal is generated, and the exclusive normal signal is sent to the operation management platform;

[0049] After the operation management platform receives the exclusive normal signal, it generates an operation quality assessment signal and sends the operation quality assessment signal to the operation quality assessment unit. After receiving the operation quality assessment signal, the operation quality assessment unit evaluates the test operation quality of the detection end, and determines whether the operation quality of the product test currently being conducted by the detection end is qualified, thereby avoiding the reduction in the accuracy of the simulation of the operating environment parameters due to abnormal operation of the detection end, and the inability to accurately infer the quality of the test product, resulting in a reduction in the test failure rate of low-quality test products, and an increase in the risk of test failures of high-quality test products; in this application, the high and low temperature test cabinets are mainly based on temperature data, but in the prior art, data types other than temperature values ​​are also simulated;

[0050] Obtain the maximum deviation between the internal temperature uniformity of the device when it is unloaded and the internal temperature uniformity of the device when it is loaded during the real-time test at the detection end. The temperature uniformity refers to the difference between the maximum and minimum values ​​of the measured average temperature of each test point in the working space of the temperature test equipment within the specified time after the temperature test equipment is stabilized;

[0051] At the same time, the continuous growth span value of the peak value of the internal temperature fluctuation value of the equipment during the test at the detection end is obtained. The temperature fluctuation refers to the difference between the maximum and minimum values ​​of the measured value at any point in the working space of the equipment within a specified time interval when the temperature test equipment is in a stable state;

[0052] The maximum deviation of the internal temperature uniformity of the device when it is unloaded and when it is loaded during the real-time test at the detection end, and the continuous growth span value of the peak value of the internal temperature fluctuation value of the device during the test at the detection end are compared with the uniformity maximum deviation threshold and the fluctuation value peak continuous growth span threshold respectively:

[0053] If the maximum deviation between the internal temperature uniformity of the device when it is unloaded and the internal temperature uniformity of the device when it is loaded during the real-time test of the detection end exceeds the uniformity maximum deviation threshold, or the continuous growth span value of the peak value of the internal temperature fluctuation value of the device during the test of the detection end exceeds the continuous growth span value threshold of the peak value of the fluctuation value, then the operation quality analysis of the detection end is determined to be abnormal, and an operation quality assessment abnormality signal is generated and sent to the operation management platform. After receiving the operation quality assessment abnormality signal, the operation management platform adjusts the operation of the detection end;

[0054] If the maximum deviation between the internal temperature uniformity of the device when it is unloaded and the internal temperature uniformity of the device when it is loaded during the real-time test of the detection end does not exceed the uniformity maximum deviation threshold, and the peak value of the continuous growth span of the internal temperature fluctuation value of the device during the test of the detection end does not exceed the peak value of the continuous growth span of the fluctuation value, then it is determined that the operation quality analysis of the detection end is normal, and a normal operation quality assessment signal is generated and sent to the operation management platform;

[0055] Example 2

[0056] In the previous embodiment, the detection end is operated and managed. Based on the previous embodiment, this embodiment adds analysis of the type of simulated operation environment parameters, so as to analyze the impact risk by determining whether the type of the current simulated operation environment parameters is prone to fluctuation. At the same time, the parameters that are not prone to fluctuation can be selectively simulated to reduce the operation intensity of the detection end.

[0057] After the operation quality assessment unit completes the assessment and the detection end is assessed as qualified, an impact risk analysis signal is generated and sent to the impact risk analysis unit;

[0058] The impact risk analysis unit receives the impact risk analysis signal and performs an impact analysis on the type of simulated operating environment parameters of the current test product at the detection end;

[0059] The simulated operating environment parameter type of the real-time test product is obtained and set as the simulated parameter type. Combined with the historical operating scenario of the test product, the frequency quotient of the floating frequency of the non-single type parameter value and the floating frequency of the single type parameter value of the current simulated parameter type in the historical operating scenario is obtained. The non-single type indicates that the environmental parameters in the operating scenario change in real time. At the current moment, only the simulated parameter type changes. Otherwise, more than one simulated parameter type changes.

[0060] Get the quantity quotient of the number of parameter types that are earlier than the simulated parameter type value floating in the same period when the non-single type parameter value of the historical running scenario is floating and the total number of floating parameter types, and compare the quantity quotient value of the number of parameter types that are earlier than the simulated parameter type value floating in the same period when the non-single type parameter value of the historical running scenario is floating and the total number of floating parameter types, and the frequency quotient value of the floating frequency of the non-single type parameter value of the current simulated parameter type in the historical running scenario and the floating frequency of the single type parameter value with the quantity quotient value threshold and the frequency quotient value threshold respectively:

[0061] If the quantity quotient of the number of parameter types that are earlier than the simulation parameter type value floating in the same period of the historical operation scenario non-single type parameter value floating time and the total floating parameter type number exceeds the quantity quotient value threshold, or the frequency quotient of the current simulation parameter type non-single type parameter value floating frequency and the single type parameter value floating frequency in the historical operation scenario exceeds the frequency quotient value threshold, then the current simulation parameter type is determined to be a non-volatile type, and the current simulation parameter type is set with a non-volatile floating label, and forwarded to the operation management platform. After receiving the operation management platform, the corresponding type will be subject to regular simulation control in the actual laboratory, rather than control at any time;

[0062] If the quantity quotient of the number of parameter types that are earlier than the simulation parameter type value floating in the same period of the historical operation scenario non-single type parameter value floating time and the total floating parameter type number does not exceed the quantity quotient value threshold, and the frequency quotient of the current simulation parameter type non-single type parameter value floating frequency and the single type parameter value floating frequency in the historical operation scenario does not exceed the frequency quotient value threshold, then the current simulation parameter type is determined to be an easy-to-change type, and the current simulation parameter type is set with an easy-to-float label, and forwarded to the operation management platform. After receiving it, the operation management platform will control the corresponding type at any time in the actual laboratory instead of regular simulation control;

[0063] The above formulas are obtained by collecting a large amount of data and performing software simulation, and a formula close to the actual value is selected. The coefficients in the formula are set by technicians in this field according to actual conditions;

[0064] When the present invention is in use, the performance test analysis unit performs a performance test on the high and low temperature test box, marks the high and low temperature test box as a detection end, and when the detection end simulates the operating environment of the test product, obtains the value ratio information, data deviation information and data interval information of the detection end, and obtains the performance test analysis coefficient of the detection end based on the information analysis, and judges whether the performance test is qualified based on the comparison of the performance test analysis coefficient threshold; after the performance test is qualified, the exclusive analysis unit performs an exclusive analysis on the detection end, sets the product of the real-time test as the exclusion object, and obtains the operating environment parameters set during the historical test of the exclusion object based on the historical test of the same type of products of the exclusion object, and obtains the operating parameters generated by the operation of the detection end during the historical test, and collects the effective test time period and the test reference time period, and obtains the test increase parameter type and the usage increase parameter type based on the time period combination analysis, and performs exclusive detection based on the management and control of the test increase parameter type and the usage increase parameter type during the real-time test.

[0065] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A high and low temperature test chamber operation management system based on artificial intelligence, characterized in that: It includes an operation management platform, wherein the operation management platform is communicatively connected with a performance test analysis unit, an exclusive analysis unit and an operation quality assessment unit; The performance test analysis unit performs a performance test on the high and low temperature test chamber, marks the high and low temperature test chamber as a detection end, and obtains the value ratio information, data deviation information and data interval information of the detection end when simulating the operating environment of the test product at the detection end, and obtains the performance test analysis coefficient of the detection end based on the information analysis, and compares the performance test analysis coefficient threshold to determine whether the performance test is qualified; the value ratio information, data deviation information and data interval information are respectively the deviation value between the simulated values ​​of the operating environment parameters of different test products and the actual values ​​of the operating environment parameters and the value ratio of the corresponding actual values ​​of the operating environment parameters, the corresponding average deviation value of the simulated values ​​of the environmental parameters that meet the actual operating environment parameters when the operating environment is simulated for different batches of the same type of test products, and the interval value between the peak value of the parameter values ​​for reciprocating regulation of the simulated values ​​of the environmental parameters based on the actual operating environment parameters and the peak value of the actual operating environment parameters; If the performance test analysis coefficient of the detection end exceeds the performance test analysis coefficient threshold, a performance test abnormality signal is generated and sent to the operation management platform; If the performance test analysis coefficient of the detection end does not exceed the performance test analysis coefficient threshold, a performance test normal signal is generated and sent to the operation management platform; After the performance test is qualified, the exclusive analysis unit performs exclusive analysis on the detection end, sets the product of the real-time test as the exclusion object, obtains the operating environment parameters set during the historical test of the exclusion object according to the historical test of the same type of product of the exclusion object, and obtains the operating parameters generated by the detection end during the historical test, and collects the effective test period and the test reference period. According to the analysis of the period combination, the test increase parameter type and the usage increase parameter type are obtained, and exclusive detection is performed according to the control of the test increase parameter type and the usage increase parameter type during the real-time test process; the collection process of the effective test period and the test reference period is as follows: Obtain the failure rate of completed tests and the failure rate of uncompleted tests of the exclusion object of the historical test. If the numerical deviation of the two failure rates exceeds the set threshold, mark the current historical test as a valid test; and mark the product test period of the completed test exclusion object as a valid test period, and mark the product use period of the uncompleted test exclusion object as a test reference period; The time period combined analysis process is as follows: According to the effective test period and the test parameter period, the operating environment parameter type of the corresponding period sorting object is obtained, and the non-intersection type of the operating environment parameter type corresponding to the effective test period and the test parameter period is set as the test increase parameter type. Combined with the running performance analysis of the detection end, if the detection end running parameter type is consistent with the test increase parameter type, the corresponding parameter type is set as the test increase parameter type. Conversely, if the detection end running parameter type is inconsistent with the test increase parameter type, the corresponding parameter type is set as the usage increase parameter type. After the exclusive test is qualified, the operation quality assessment unit evaluates the test operation quality of the detection end to determine whether the operation quality of the product test currently conducted by the detection end is qualified.

2. According to the artificial intelligence-based high and low temperature test chamber operation management system of claim 1, it is characterized in that: The exclusiveness test process is as follows: Obtain the floating frequency of the same time and trend as the parameter value of the simulated parameter value of the running environment of the elimination object when the detection end conducts a real-time elimination object test, as well as the number of values ​​in the non-intersecting interval of the parameter value simulation control interval of the additional parameter type used in the real-time elimination object test and the floating interval of the same type of parameter values ​​during the use of the historical test elimination object, and compare them with the floating frequency threshold and the value quantity threshold respectively.

3. According to the artificial intelligence-based high and low temperature test chamber operation management system of claim 2, it is characterized in that: If the detection end performs a real-time elimination object test and the corresponding parameter value of the test-added parameter type is at the same time as the simulation parameter value of the elimination object operation environment and the floating frequency of the same trend exceeds the floating frequency threshold, or the detection end performs a real-time elimination object test and the number of values ​​in the non-intersecting interval of the parameter value simulation control interval of the additional parameter type and the floating interval of the same type of parameter value during the use of the historical test elimination object exceeds the value quantity threshold, then an exclusive abnormality signal is generated and sent to the operation management platform; If the detection end performs a real-time elimination object test and the corresponding parameter value of the test added parameter type is simultaneous with the parameter value of the elimination object operating environment simulation and the floating frequency of the same trend does not exceed the floating frequency threshold, and the number of values ​​in the non-intersecting interval of the parameter value simulation control interval of the added parameter type used by the detection end for the real-time elimination object test and the floating interval of the same type of parameter values ​​during the use of the historical test elimination object does not exceed the value quantity threshold, then an exclusive normal signal is generated and sent to the operation management platform.

4. According to the artificial intelligence-based high and low temperature test chamber operation management system of claim 1, it is characterized in that: The operation quality assessment process is as follows: The maximum deviation of the internal temperature uniformity when the device is unloaded and the internal temperature uniformity when the device is loaded during the real-time test of the detection end is obtained, and the peak continuous growth span value of the internal temperature fluctuation value of the device during the test of the detection end is obtained; the maximum deviation of the internal temperature uniformity when the device is unloaded and the internal temperature uniformity when the device is loaded during the real-time test of the detection end, and the peak continuous growth span value of the internal temperature fluctuation value of the device during the test of the detection end are compared with the uniformity maximum deviation threshold and the fluctuation value peak continuous growth span threshold respectively: If the maximum deviation between the internal temperature uniformity of the device when it is unloaded and the internal temperature uniformity of the device when it is loaded during the real-time test at the detection end exceeds the uniformity maximum deviation threshold, or the peak value of the continuous growth span of the internal temperature fluctuation value of the device during the test at the detection end exceeds the peak value of the continuous growth span value threshold, an operation quality assessment abnormal signal is generated and sent to the operation management platform; If the maximum deviation between the internal temperature uniformity when the equipment is unloaded and the internal temperature uniformity when the equipment is loaded during the real-time test at the detection end does not exceed the maximum uniformity deviation threshold, and the peak continuous growth span value of the internal temperature fluctuation value of the equipment during the test at the detection end does not exceed the peak continuous growth span value threshold of the fluctuation value, a normal operation quality assessment signal is generated and sent to the operation management platform.

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