Method, device and equipment for detecting environmental adaptability of wavelength conversion system and storage medium

Abstract

The invention discloses an environmental adaptability detection method and device of a wavelength conversion system, equipment and a storage medium. The method comprises the following steps: acquiring a wavelength conversion system to be measured, and determining a dynamic coupling stress loading spectrum based on preset power application scene data matched with the wavelength conversion system to be measured; performing multi-stress synchronous loading on the to-be-tested wavelength conversion system in a pre-constructed test environment based on the dynamic coupling stress loading spectrum, and collecting system-level performance data and component-level performance data of the to-be-tested wavelength conversion system in a stress loading process; determining a detection result based on the system-level performance data, the component-level performance data and a pre-established association model; wherein the detection result comprises a potential failure component and a failure mode. The beneficial effects of improving the detection pertinence and the failure traceability accuracy and providing effective support for the power scene application reliability of the wavelength conversion system are achieved.

Description

Technical Field

[0001] This invention relates to the technical field of wavelength conversion, and more particularly to a method, apparatus, device, and storage medium for detecting the environmental adaptability of a wavelength conversion system. Background Technology

[0002] With the expansion and increasing complexity of power systems, higher demands are placed on the communication performance of relay protection devices. Fiber optic communication, with its high bandwidth, low loss, and strong anti-interference capabilities, has gradually become the preferred communication method for relay protection. As a core component, the wavelength conversion system's environmental adaptability directly affects the reliability of signal transmission. Power system applications operate in complex and harsh environments, including extreme temperatures, strong electromagnetic interference, mechanical vibration, high humidity, salt spray, and dust, among other stresses. These factors often couple together, accelerating system performance degradation. For example, the combined effects of high temperature and high humidity can exacerbate the aging of optical materials, and the combination of short-circuit overvoltage and vibration can lead to displacement or even discharge of optical components. Several cases of communication interruptions due to environmental stress coupling have already occurred in practice.

[0003] Existing environmental adaptability testing methods for wavelength conversion systems often employ single-stress step-by-step testing, such as conducting separate tests for temperature, humidity, and vibration. This fails to realistically simulate the actual operating conditions of multi-stress synchronous coupling in power field environments, resulting in the inability to effectively identify many potential failure modes. Furthermore, current methods do not adequately incorporate specific power-related disturbances (such as power frequency electromagnetic fields and rapid transient overvoltages) and lack the ability to monitor the performance and locate failures of critical components, leading to a significant discrepancy between laboratory test results and actual operational performance. Statistics show that modules that pass traditional testing can have a failure rate more than three times higher in real-world coupling environments than in the laboratory, making it difficult to meet the high reliability requirements of power systems for protection and communication. Summary of the Invention

[0004] This invention provides a method, apparatus, device, and storage medium for detecting the environmental adaptability of a wavelength conversion system, so as to achieve accurate detection of the environmental adaptability of the wavelength conversion system in a power scenario.

[0005] According to one aspect of the present invention, an environmental adaptability detection method for a wavelength conversion system is provided, the method comprising:

[0006] The wavelength conversion system under test is acquired, and the dynamic coupling stress loading spectrum is determined based on the preset power application scenario data that matches the wavelength conversion system under test.

[0007] Based on the dynamic coupled stress loading spectrum, the wavelength conversion system under test is subjected to multi-stress synchronous loading in a pre-constructed test environment, and system-level performance data and component-level performance data of the wavelength conversion system under test are collected during the stress loading process.

[0008] The detection results are determined based on the system-level performance data, the component-level performance data, and the pre-established correlation model; wherein, the detection results include potential failure components and failure modes.

[0009] According to another aspect of the present invention, an environmental adaptability detection device for a wavelength conversion system is provided, the device comprising:

[0010] The stress loading spectrum determination module is used to acquire the wavelength conversion system under test and determine the dynamic coupling stress loading spectrum based on the preset power application scenario data that matches the wavelength conversion system under test.

[0011] The data acquisition module is used to perform multi-stress synchronous loading on the wavelength conversion system under test in a pre-constructed test environment based on the dynamic coupled stress loading spectrum, and to acquire system-level performance data and component-level performance data of the wavelength conversion system under test during the stress loading process.

[0012] The detection result determination module is used to determine the detection result based on the system-level performance data, the component-level performance data, and the pre-established correlation model; wherein, the detection result includes potential failure components and failure modes.

[0013] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0014] At least one processor;

[0015] and memory that is communicatively connected to at least one processor;

[0016] The memory stores a computer program that can be executed by at least one processor, which enables the at least one processor to perform the environmental adaptability detection method of the wavelength conversion system according to any embodiment of the present invention.

[0017] According to another aspect of the present invention, a computer-readable storage medium is provided, which stores computer instructions for causing a processor to execute and implement the environmental adaptability detection method of a wavelength conversion system according to any embodiment of the present invention.

[0018] The technical solution of this invention involves acquiring the wavelength conversion system under test (WRTS), determining a dynamic coupling stress loading spectrum based on preset power application scenario data matching the WRTS, performing multi-stress synchronous loading on the WRTS in a pre-constructed test environment based on the dynamic coupling stress loading spectrum, and collecting system-level and component-level performance data of the WRTS during the stress loading process. The detection results are determined based on the system-level performance data, the component-level performance data, and a pre-established correlation model. The detection results include potential failed components and failure modes. This solves the technical problems of traditional detection methods, which cannot specifically simulate multi-stress coupling conditions in power scenarios and are difficult to locate the root cause of failure. It achieves the technical effect of improving the targeting of detection and the accuracy of failure tracing, and providing effective support for the reliability of wavelength conversion systems in power scenario applications.

[0019] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 A flowchart illustrating an environmental adaptability testing method for a wavelength conversion system provided in an embodiment of the present invention;

[0022] Figure 2a A flowchart illustrating another method for detecting the environmental adaptability of a wavelength conversion system provided in an embodiment of the present invention;

[0023] Figure 2b A flowchart illustrating an optional example of an environmental adaptability detection method for a wavelength conversion system provided in an embodiment of the present invention;

[0024] Figure 3 This is a schematic diagram of the structure of an environmental adaptability detection device for a wavelength conversion system provided in an embodiment of the present invention;

[0025] Figure 4 A schematic diagram of the structure of an electronic device for implementing an environmental adaptability detection method for a wavelength conversion system according to an embodiment of the present invention. Detailed Implementation

[0026] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0027] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0028] Figure 1 This is a flowchart illustrating an environmental adaptability testing method for a wavelength conversion system according to an embodiment of the present invention. This embodiment is applicable to the environmental adaptability testing of wavelength conversion systems. The method can be executed by an environmental adaptability testing device for the wavelength conversion system. This device can be implemented in hardware and / or software and can be configured in an electronic device. Figure 1 As shown, the method specifically includes the following steps:

[0029] S110. Obtain the wavelength conversion system under test, and determine the dynamic coupling stress loading spectrum based on the preset power application scenario data that matches the wavelength conversion system under test.

[0030] The wavelength conversion system under test (WRT) can be understood as a device or apparatus for wavelength conversion that requires environmental adaptability testing. Preset power application scenario data can be understood as pre-collected environmental and fault-related data consistent with the power scenario in which the WRT will be deployed. The dynamic coupled stress loading spectrum can be understood as defining the loading sequence of various environmental stresses (such as temperature, humidity, and vibration) at different times, and a loading program that can adjust the stress intensity according to the environment or the state of system components.

[0031] Specifically, the wavelength conversion system to be tested is obtained, and preset test data that matches the power scenario in which the system is deployed is found. Based on the preset test data, the loading sequence and intensity adjustment rules of various environmental stresses are determined to form a dynamic coupled stress loading spectrum.

[0032] Preferably, when acquiring data for a preset power application scenario, newer data that is less than the current detection time interval can be selected first to avoid the problem of data aging causing deviation between the loaded spectrum and the actual scenario.

[0033] In one optional embodiment, historical fault data and operating environment data of the predetermined power application scenario are analyzed to extract typical multi-stress coupling events. Based on the extracted multi-stress coupling events, a dynamic coupled stress loading spectrum is generated. This dynamic coupled stress loading spectrum is a dynamic stress loading program that defines the loading sequence of various environmental stresses over time and dynamically adjusts the intensity control logic based on the environmental stress state or system component state. The extracted multi-stress coupling events are input into a pre-set loading spectrum generation model, which outputs the corresponding dynamic coupled stress loading spectrum. This loading spectrum generation model is a machine learning model, obtained through iterative training using historical data. The historical data includes mapping data between verified multi-stress coupling events from different power application scenarios and experimentally verified effective dynamic stress loading programs.

[0034] For example, the intensity control logic includes: if the detected conversion medium temperature is ≥60℃, then the ambient humidity is reduced from the current value by 10%RH~15%RH, and the multi-parameter linkage controller is controlled to reduce the excitation source input power by 5%~8% until the conversion medium temperature is ≤55℃; if the detected mechanical vibration acceleration is ≥8g, then the vibration frequency is reduced from the current value by 20%~30%.

[0035] Optionally, the environmental stress corresponding to the multi-stress coupling event includes power system-specific interference stress; the power system-specific interference stress includes at least one of fast transient overvoltage or lightning impulse voltage.

[0036] Optionally, determining the dynamic coupling stress loading spectrum based on preset power application scenario data matching the wavelength conversion system under test includes: determining preset power application scenario data matching the wavelength conversion system under test from a power scenario database based on test requirements; analyzing historical fault data and operating environment data of the preset power application scenario data matching the wavelength conversion system under test to extract multi-stress coupling events; generating the dynamic coupling stress loading spectrum based on the multi-stress coupling events; wherein, the dynamic coupling stress loading spectrum defines the time loading sequence of various environmental stresses and a stress loading program that dynamically adjusts the intensity based on the environmental or system component status.

[0037] Among these, test requirements can be understood as specific requirements proposed based on the testing objectives of the system under test. The power scenario database can be understood as a database storing environmental and fault data from different power scenarios. Historical fault data can be understood as historical system fault-related data (such as fault time and cause) recorded under the power scenario matched to the system under test. Operating environment data can be understood as parameter data of the operating environment (such as temperature, humidity, and vibration frequency). Multi-stress coupling events can be understood as events that cause system anomalies or faults due to the simultaneous action of multiple environmental stresses.

[0038] For example, the multi-stress coupling events include electromagnetic shock and mechanical vibration accompanying short-circuit faults, high temperature and humidity and electromagnetic interference during thunderstorms, temperature and humidity cyclic fluctuations and salt spray corrosion in outdoor substations in coastal areas, high humidity environment and dust accumulation and superposition of power frequency electromagnetic fields in underground cable trenches, and icing low temperature environment and superposition of mechanical vibration in fiber optic composite overhead ground wires, etc.

[0039] Specifically, based on the specific requirements of the system under test, such as testing its adaptability in a coastal substation scenario, preset data matching the future deployment scenario of the system under test is selected from a database storing power scenario data. Then, historical fault data (such as past system faults caused by high temperature and humidity in this scenario) and operating environment data (such as the temperature and humidity variation range in this scenario) in these preset data are analyzed to extract multi-stress coupling events that cause system anomalies due to the simultaneous action of multiple stresses. Finally, based on these multi-stress coupling events, the loading sequence and intensity adjustment rules of various environmental stresses are formulated to generate a dynamic coupled stress loading spectrum.

[0040] Preferably, when extracting multi-stress coupling events, events with a high frequency of occurrence (such as those that occur more than a preset number of times within a preset time period) can be selected first.

[0041] S120. Based on the dynamic coupled stress loading spectrum, perform multi-stress synchronous loading on the wavelength conversion system under test in a pre-constructed test environment, and collect system-level performance data and component-level performance data of the wavelength conversion system under test during the stress loading process.

[0042] The pre-built test environment can be understood as an experimental space that simulates various environmental stresses (such as high temperature, high humidity, and vibration) in a power scenario. Multi-stress synchronous loading can be understood as simultaneously applying multiple preset environmental stresses to the system under test, simulating the simultaneous action of multiple stresses in a real-world scenario. System-level performance data can be understood as parameter data reflecting the overall performance of the system under test. Component-level performance data can be understood as parameter data reflecting the working status of core components (such as excitation sources and conversion media) within the system under test.

[0043] Specifically, according to the determined dynamic coupling stress loading spectrum, multiple environmental stresses are simultaneously applied to the system under test in a specially constructed test environment. During the stress application process, system-level data reflecting the overall performance of the system and component-level data reflecting the state of internal components are continuously collected at preset acquisition time intervals. The preset acquisition time interval can be pre-set based on experience or adjusted during testing; this embodiment does not impose specific limitations on it.

[0044] Optionally, the step of performing multi-stress synchronous loading on the wavelength conversion system under test in a pre-constructed test environment based on the dynamic coupled stress loading spectrum includes: importing the dynamic coupled stress loading spectrum through the control terminal of the test environment, wherein the control terminal automatically triggers each environmental module to synchronously apply stress according to the timing and intensity set by the loading spectrum.

[0045] The control terminal of the test environment can be understood as a device used to control the operation of various environmental modules within the test environment. An environmental module can be understood as a device in the test environment that can generate specific environmental stresses (e.g., a temperature module to adjust temperature, a vibration module to generate vibration).

[0046] Specifically, the dynamic coupled stress loading spectrum is imported into the control terminal of each module in the test environment. The control terminal automatically activates various environmental modules in the test environment, such as temperature, vibration, and humidity, and applies multiple environmental stresses to the system under test simultaneously according to the stress loading time sequence and intensity set in the loading spectrum. For example, the environmental stresses include temperature, humidity, vibration frequency and amplitude, and power frequency electromagnetic field intensity.

[0047] Preferably, stress deviation warnings can be set on the control terminal during the loading process to adjust the environmental module in a timely manner and ensure loading accuracy.

[0048] Optionally, the system-level performance data includes at least one of conversion efficiency, output optical power, center wavelength drift, and bit error rate; the component-level performance data includes at least one of the operating current and junction temperature of the excitation source, the absorption efficiency and fluorescence spectral characteristics of the conversion medium, and the insertion loss and return loss of the optical unit.

[0049] In this context, conversion efficiency can be understood as the efficiency with which the system under test (SUT) converts an input optical signal into a target wavelength optical signal. Output optical power can be understood as the power of the optical signal output by the SUT. Center wavelength drift can be understood as the deviation between the center wavelength of the output optical signal and the standard center wavelength. Bit error rate (BER) can be understood as the probability of an erroneous signal occurring during the transmission of the optical signal by the SUT. Excitation source can be understood as the core component in the SUT that provides the initial optical signal. Operating current can be understood as the current flowing through the excitation source. Junction temperature can be understood as the temperature of the semiconductor PN junction inside the excitation source. Conversion medium can be understood as the core material in the SUT that enables wavelength conversion of the optical signal. Absorption efficiency can be understood as the efficiency with which the conversion medium absorbs the input optical signal. Fluorescence spectral characteristics can be understood as the spectral characteristics (such as peak wavelength and full width at half maximum) of the fluorescence emitted after light absorption by the conversion medium. Optical unit can be understood as the component in the SUT used to transmit and reflect optical signals (such as a lens or optical fiber). Insertion loss can be understood as the power loss of the optical signal as it passes through the optical unit. Return loss can be understood as the power loss caused by reflection of the optical signal at the interface of the optical unit.

[0050] Optionally, the collected data can be processed using a low-pass filter to remove transient fluctuations caused by thermal noise and linear interpolation to complete the data, thus obtaining system-level and component-level performance data. System-level data input compares the raw data with a preset failure threshold. If no anomaly is triggered, it is used only for lifetime prediction; if an anomaly is triggered, component-level data is immediately input synchronously for failure tracing. Component-level data is input in the order of excitation source-conversion medium-optical unit, and the model automatically matches the associated parameters based on the quantitative mapping relationship, requiring no manual intervention.

[0051] S130. Determine the detection results based on the system-level performance data, the component-level performance data, and the pre-established correlation model; wherein, the detection results include potential failure components and failure modes.

[0052] The pre-established correlation model can be understood as a pre-constructed model that reflects the correspondence between system-level performance data and component-level performance data. The detection result can be understood as the result obtained through detection regarding whether the system under test has potential faults. Potentially failing components can be understood as core components in the system under test that may fail and cause abnormal system performance. Failure modes can be understood as the specific type or cause of failure of potentially failing components, such as high temperature causing component aging.

[0053] Specifically, the collected system-level performance data and component-level performance data are input into a pre-built correlation model. The model analyzes the correspondence between system performance data and component performance data to determine whether there are any anomalies in the system. If there are anomalies, potential failure components that may fail are identified, and the specific type of component failure, i.e., the failure mode, is determined.

[0054] Preferably, after determining the test results, multiple sets of performance data collected at different time periods can be repeatedly substituted into the data for verification to improve the accuracy of the test results.

[0055] Optionally, before determining the detection result based on the system-level performance data, the component-level performance data, and the pre-established correlation model, the method further includes: acquiring sample data obtained from experiments using equipment of the same model as the wavelength conversion system under test under different stress combinations; analyzing the correlation patterns between the sample system-level data and the sample component-level data in the sample data according to a data correlation algorithm to obtain a quantitative mapping relationship; and constructing a correlation model based on the quantitative mapping relationship, a preset system-level performance failure threshold, and a preset component-level performance anomaly threshold.

[0056] In this context, "equipment of the same model" can be understood as a wavelength conversion system with the same model, structure, and performance as the wavelength conversion system under test. "Different stress combinations" can be understood as combinations of various environmental stresses in different ways, such as high temperature and high humidity, or low temperature and vibration. "Sample data" can be understood as system-level and component-level performance data obtained from experiments with the same model of equipment under different stress combinations. "Sample system-level data" can be understood as parameter data reflecting the overall performance of the same model of equipment in the sample data. "Sample component-level data" can be understood as parameter data reflecting the internal component states of the same model of equipment in the sample data. "Data association algorithm" can be understood as an algorithm used to analyze the correspondence between sample system-level data and sample component-level data (e.g., linear regression algorithm). "Quantitative mapping relationship" can be understood as a clear numerical correspondence between sample system-level data and sample component-level data obtained through the data association algorithm. "Preset system-level performance failure threshold" can be understood as a pre-set numerical standard for judging whether system-level performance is abnormal; it can be pre-set based on experience, and this embodiment does not impose specific restrictions on it. The preset component-level performance anomaly threshold can be understood as a pre-set numerical standard for judging whether the component-level performance is abnormal. It can be preset based on experience, and this embodiment does not impose specific restrictions on it.

[0057] Specifically, the preferred data association algorithm is linear regression, random forest, or support vector machine. Linear regression is suitable for scenarios where component-level and system-level data are linearly correlated, while random forest is suitable for complex scenarios with multiple component coupling effects. Before using the association model to determine the detection results, an equipment of the same model as the system under test is acquired and tested under different stress combinations to collect sample data. The sample data covers at least eight typical multi-stress coupling scenarios (such as high temperature and high humidity, vibration and electromagnetic interference, low temperature and salt spray, etc.). The sample data is preprocessed, including outlier removal and normalization. The preprocessed sample data is then divided into training and validation sets. System-level performance parameters are used as dependent variables (e.g., conversion efficiency Y), and component-level performance parameters are used as independent variables (e.g., excitation source junction temperature). Conversion medium absorption efficiency Optical unit insertion loss A quantitative mapping relationship is obtained through data association algorithms. Finally, an association model is constructed by combining pre-set system-level performance failure thresholds and component-level performance anomaly thresholds.

[0058] Preferably, the mean absolute percentage error (MAPE) is used to evaluate the prediction accuracy of the association model. It is acceptable to require that the MAPE of the validation set be ≤5% to ensure the model's prediction accuracy.

[0059] For example, the linear mapping relationship can be expressed by the following formula:

[0060]

[0061] in, , , and These are the weighting coefficients, obtained by fitting the training set.

[0062] For example, the nonlinear mapping relationship can be expressed by the following formula:

[0063]

[0064] in, , , and The fitting coefficients are denoted as .

[0065] For example, a data association algorithm, such as the least squares method, is selected. Taking the same type of wavelength conversion system as the object, at least 50 sets of valid data with different stress combinations are collected, such as sample data under high temperature and high humidity, vibration and electromagnetic interference. After outlier removal and standardization preprocessing, the algorithm is used to mine the correlation between system-level performance data (such as conversion efficiency and output optical power) and component-level performance data (such as excitation source junction temperature and conversion medium absorption efficiency). Outlier removal and data standardization preprocessing are performed on the collected valid data to obtain sample system-level data and sample component data. An exemplary expression of the quantitative mapping relationship can be system-level performance parameter change = Σ (component-level performance parameter change × weight coefficient), for example, "conversion efficiency decrease = 0.8 × excitation source junction temperature exceedance + 0.3 × conversion medium absorption efficiency decrease". The weight coefficient is set based on the importance of the component, such as excitation source weight 0.3, conversion medium weight 0.4, and optical unit weight 0.3. This embodiment does not impose specific limitations on it.

[0066] Specifically, the input to the correlation model consists of time-series data including parameter names, real-time values, and acquisition time. System-level data is input first, followed by component-level data corresponding to the time series. The data sampling interval is consistent with the time step size of the stress loading spectrum, defaulting to 10 seconds / sample. If the duration of a stress segment in the loading spectrum is ≤1 minute, the sampling interval is adjusted to 2 seconds / sample to ensure the capture of performance mutations under short-term stress. The timestamp error between system-level and component-level performance data acquisition is ≤10ms, achieved through timing alignment via the synchronization clock module of the control terminal. The weighting coefficient for core component parameters (such as conversion medium absorption efficiency) is 1.2, and the weighting coefficient for non-core component parameters (such as optical unit return loss) is 1.0. Based on DL / T1573-2016 "Requirements for Immunity of Communication Equipment in Power Systems," 80% of the system functional failure threshold is taken (with a 20% safety redundancy). Based on the component physical failure mechanism, 50%-60% of the component failure threshold is taken (early warning to avoid irreversible damage). For example, system-level data is compared with preset failure thresholds. If the conversion efficiency is less than the system-level performance failure threshold or the bit error rate is greater than the preset component-level performance anomaly threshold, the system is determined to be abnormal, and failure tracing is initiated. The correlation model filters component-level parameters that are strongly correlated with abnormal system-level data based on quantitative mapping relationships (such as the conversion medium absorption efficiency corresponding to the decrease in conversion efficiency and the excitation source junction temperature). The filtered component-level parameters are compared with the anomaly thresholds. If the conversion medium absorption efficiency is less than 90% of the initial value, the component is determined to be a potentially failed component. Combined with the current applied stress (such as high temperature + high humidity), the preset failure mode library is queried, and the failure mode "thermal oxidation leads to absorption efficiency decay" is output.

[0067] Optionally, determining the detection result based on the system-level performance data, the component-level performance data, and the pre-established correlation model includes: inputting the real-time collected system-level performance data into the correlation model, comparing it with a preset system-level performance failure threshold, and determining that the system has an anomaly and initiating failure tracing if the system-level performance data is lower than the preset system-level performance failure threshold; the correlation model filters correlation parameters from the collected component-level data according to a quantitative mapping relationship, compares the correlation parameters with a preset component-level performance anomaly threshold, and identifies the components corresponding to the correlation parameters that are higher than the preset component-level performance anomaly threshold as potential failure components; determining the component anomaly type and current loaded stress of the potential failure components, and determining the failure mode based on the current loaded stress, the component anomaly type, and a preset failure mode library.

[0068] Specifically, firstly, the real-time collected system-level performance data is input into the correlation model and compared with a pre-set system-level performance failure threshold. If the system-level performance data is lower than the threshold, it indicates that the system is abnormal, and failure tracing is initiated. Next, the correlation model, based on a pre-built quantitative mapping relationship, filters out correlation parameters related to the abnormal system-level data from the collected component-level data. The correlation parameters are compared with a preset component-level performance anomaly threshold, and the components corresponding to the correlation parameters with values ​​greater than the threshold are identified as potentially failing components. Finally, the anomaly type of the potentially failing component and the currently applied environmental stress are determined. Combined with a pre-established preset failure mode library, such as high temperature corresponding to component aging, the failure mode is determined.

[0069] Preferably, while keeping the stress loading parameters of the test environment unchanged, the potentially failed components of the wavelength conversion system under test are replaced with qualified components of the same model; the wavelength conversion system under test after the replacement components are retested, and system-level performance data is collected. If the recollected system-level data is not lower than the preset system-level performance failure threshold, then the determination of the potentially failed components and failure modes is confirmed to be accurate.

[0070] Preferably, the preset failure mode library can be updated regularly to add newly discovered failure types, thereby improving the comprehensiveness of failure mode determination.

[0071] The technical solution of this invention involves acquiring the wavelength conversion system under test (WRTS), determining a dynamic coupling stress loading spectrum based on preset power application scenario data matching the WRTS, performing multi-stress synchronous loading on the WRTS in a pre-constructed test environment based on the dynamic coupling stress loading spectrum, and collecting system-level and component-level performance data of the WRTS during the stress loading process. The detection results are determined based on the system-level performance data, the component-level performance data, and a pre-established correlation model. The detection results include potential failed components and failure modes. This solves the technical problems of traditional detection methods, which cannot specifically simulate multi-stress coupling conditions in power scenarios and are difficult to locate the root cause of failure. It achieves the technical effect of improving the targeting of detection and the accuracy of failure tracing, and providing effective support for the reliability of wavelength conversion systems in power scenario applications.

[0072] Figure 2a This is a flowchart of another environmental adaptability detection method for a wavelength conversion system provided by an embodiment of the present invention. Based on the above embodiments, this embodiment is a further optimization of the above embodiments, and its specific implementation can be found in the technical solution of this embodiment. Technical terms that are the same as or corresponding to those in the above embodiments will not be repeated here. Figure 2a As shown, the method specifically includes the following steps:

[0073] S210. Obtain the wavelength conversion system under test, and determine the dynamic coupling stress loading spectrum based on the preset power application scenario data that matches the wavelength conversion system under test.

[0074] S220. Based on the dynamic coupled stress loading spectrum, perform multi-stress synchronous loading on the wavelength conversion system under test in a pre-constructed test environment, and collect system-level performance data and component-level performance data of the wavelength conversion system under test during the stress loading process.

[0075] S230. Determine the detection results based on the system-level performance data, the component-level performance data, and the pre-established correlation model; wherein, the detection results include potential failure components and failure modes.

[0076] S240. Based on the correlation model, the system-level performance data, the component-level performance data, and the pre-built damage accumulation model, predict the service life of the wavelength conversion system under test; wherein, the damage accumulation model is the Miner linear cumulative damage model.

[0077] The pre-built damage accumulation model can be understood as a model constructed in advance to calculate the cumulative damage of components under stress and predict the system's service life. The Miner linear cumulative damage model can be understood as a commonly used damage accumulation model that assumes that the damage to components under different stresses is independent and linearly superimposed, predicting the lifespan of components and the system by calculating the degree of damage accumulation. Service life can be understood as the estimated time for the wavelength conversion system under test to operate normally in a real-world application scenario.

[0078] Specifically, after determining the test results, the system can combine the previously constructed correlation model, the collected system-level performance data, component-level performance data, and the pre-constructed damage accumulation model to calculate the damage accumulation of the internal components of the system under stress, and then predict the time (service life) when the wavelength conversion system under test can work normally in the actual application scenario.

[0079] Specifically, when applying the damage accumulation model, the failure time Ti of the component under the corresponding stress is obtained through experimental testing, which is the duration of the component's degradation from its initial state to the failure threshold. For each core component (excitation source, conversion medium, optical unit), accelerated aging tests are conducted under single stress and multi-stress coupling scenarios (for example, single high-temperature stress: temperature gradient of 40℃, 60℃, 80℃, 100℃, with each temperature point continuously tested for 1000 hours; single high-humidity stress: humidity gradient of 65%RH, 85%RH, 95%RH, with the temperature fixed at 40℃, with each humidity point continuously tested for 800 hours; high-temperature + high-humidity coupling stress: temperature 60℃ + humidity 85% RH, continuously tested for 1200 hours; temperature 80℃ + humidity 95% RH, continuously tested for 800 hours). When the component performance parameters reach the failure threshold, such as when the absorption efficiency of the conversion medium drops to 80% of the initial value, the test is stopped, and the cumulative test time at this time is recorded as the failure time under that stress. When two or more stresses are coupled, Correct according to the following formula:

[0080]

[0081] Where K is the coupling correction coefficient, which can be determined experimentally. For example, in high temperature + high humidity coupling, K is 0.7. =1000×0.7=700 hours. If the performance degradation rate of key components exceeds the preset mutation threshold, such as a decrease of ≥5% in the absorption efficiency of the conversion medium within 1 hour, the service life will be recalculated after multiplying the accumulated damage by a correction factor of 1.2-1.5.

[0082] For example, based on the temporal sequence of the dynamic coupled stress loading spectrum, the duration of each stress combination, i.e., the actual loading time, is statistically analyzed segment by segment. Based on the time sequence of the dynamic coupled stress loading spectrum, the duration of each stress combination is statistically analyzed segment by segment. For example, if the stress is loaded for 30 hours at a temperature of 60℃ and a humidity of 85%RH, then... =30 hours.

[0083] For example, from the component-level performance data, the time-series data of excitation source output optical power, conversion medium absorption efficiency, and optical unit insertion loss are extracted. Using the least squares method, with loading time as the horizontal axis (t) and performance parameters as the vertical axis (P), a degradation curve is obtained:

[0084] ;

[0085] Where k is the decay rate and b is the initial parameter value.

[0086] Furthermore, the cumulative damage is calculated using the following formula:

[0087]

[0088] Where n is the number of stress combinations and D is the cumulative damage degree.

[0089] For example, when D≥1, the component is considered to have failed, and the system service life is taken as the shortest predicted life among all core components; the calculation formula is as follows:

[0090]

[0091] Among them, 8760 represents the annual operating hours of the power equipment. The value is 1.

[0092] For example, conversion medium For 700 hours, If the time is 30 hours and D is 0.043, then the predicted lifespan is... Approximately 23.3 years.

[0093] Preferably, if the performance degradation rate of a critical component exceeds a preset mutation threshold per unit time (e.g., a decrease of ≥5% in the absorption efficiency of the conversion medium within 1 hour, far exceeding the normal degradation rate of 0.02% / hour), the cumulative damage of the current component is multiplied by a preset correction factor, and the lifespan is recalculated. The preset correction factor can be preset based on experience, and this embodiment does not impose specific limitations on it.

[0094] For example, if the current D of the conversion medium is 0.043, after triggering the mutation threshold, a preset correction factor of 1.4 is applied, and the correction is then applied. Predicting lifespan Approximately 18.4 years.

[0095] Preferably, when predicting service life, the prediction results can be corrected by combining the stress frequency of the actual application scenarios of the system under test in the future, so that the predicted service life is more in line with reality.

[0096] In an optional embodiment, if the performance degradation rate of any critical component is detected to exceed a preset mutation threshold within a unit time, the correction mechanism of the damage accumulation model is activated. The accumulated damage of the component is multiplied by a correction factor of 1.2 to 1.5, and the service life is recalculated. The preset mutation threshold can be preset based on experience, and this embodiment does not impose specific restrictions on it.

[0097] The technical solution of this invention predicts the service life of the wavelength conversion system under test based on the correlation model, system-level performance data, component-level performance data, and a pre-built damage accumulation model; wherein the damage accumulation model is the Miner linear cumulative damage model.

[0098] Preferably, time-series data of performance parameters of the excitation source, conversion medium, and optical unit of the wavelength conversion system under test are extracted from the collected component-level performance data. With loading time as the horizontal axis, the vertical axis corresponding to the excitation source is set as output optical power or junction temperature, the vertical axis corresponding to the conversion medium is set as absorption efficiency or fluorescence intensity, and the vertical axis corresponding to the optical unit is set as insertion loss. The performance degradation curves of the three types of components are generated by data fitting algorithm (such as least squares method) to intuitively reflect the decay trend of the performance of each component with loading time.

[0099] Preferably, referring to the failure standards of wavelength conversion system components in the power industry and the limit parameters provided by component manufacturers, the failure judgment standards of each core component are clarified. Among them, the failure threshold of the conversion medium is a 20% decrease in absorption efficiency compared with the initial value, the failure threshold of the excitation source is a 30% decrease in output power compared with the initial value, and the failure threshold of the optical unit is a 1dB increase in insertion loss compared with the initial value.

[0100] Preferably, the cumulative damage and extrapolated life are calculated based on Miner's rule, using the actual loading time at each time point during the stress loading process. The component failure time, calculated from the performance degradation curve under the corresponding stress conditions. (That is, the time required for the component to decay from its initial state to the failure threshold) Substitute into the formula The cumulative damage degree D of each component is calculated; when the cumulative damage degree D of any component is not less than 1, the component is determined to have reached a failure state; combined with the expected annual operating time of 8760 hours under the power conditions of the wavelength conversion system under test, the component failure time under the test environment is converted into the time under the actual operating conditions, and then extrapolated to obtain the service life of the entire wavelength conversion system under test.

[0101] The technical solution of this invention predicts the service life of the wavelength conversion system under test based on the correlation model, system-level performance data, component-level performance data, and a pre-built damage accumulation model; wherein the damage accumulation model is a Miner linear cumulative damage model. This solves the problem that traditional detection methods can only locate faults but cannot predict the effective operating time of the wavelength conversion system in actual power scenarios. It achieves the function of calculating service life by combining system and component performance data and the Miner linear cumulative damage model, thus providing accurate lifespan data for wavelength conversion system operation and maintenance planning and replacement cycle determination, and improving the reliability of applications in power scenarios.

[0102] Figure 2b A flowchart illustrating an optional example of a method for detecting the environmental adaptability of a wavelength conversion system is provided. For example... Figure 2b As shown, the method includes:

[0103] S1. Constructing the test environment and loading spectrum: Construct a multi-factor coupled environment test environment, place the wavelength conversion system under test in it and make it work; determine the dynamic coupled stress loading spectrum according to the preset power application scenario data to simulate the coordinated change relationship of various environmental stresses under actual working conditions, and execute the dynamic coupled stress loading spectrum through the control terminal of the test environment to achieve synchronous loading of multiple stresses.

[0104] S2. Layered monitoring and data acquisition: During the stress loading process, system-level performance data and component-level performance data of the wavelength conversion system under test are acquired simultaneously;

[0105] S3. Failure tracing and life prediction: Based on the collected system-level performance data and component-level performance data, establish a correlation model between system performance degradation and component state changes to locate potential failure components and failure modes; and based on the correlation model, system-level performance data and component-level performance data, use a damage accumulation model to predict the service life of the wavelength conversion system under test.

[0106] S4. Pass / Fail Judgment: Based on the predicted service life and / or the attenuation of system-level performance data during stress loading, and comparing with a predetermined coupling pass / fail threshold, output a conclusion on whether the wavelength conversion system has passed the environmental adaptability test. The coupling pass / fail threshold is set according to the actual situation of the wavelength conversion system under test, including one or more of the following: conversion efficiency attenuation, output optical power, center wavelength drift value, bit error rate, and service life. Of course, additional pass / fail thresholds can also be set according to special needs. When all these core performance parameters meet the preset coupling failure threshold, the wavelength conversion system under test is determined to have passed the environmental adaptability test. By introducing a closed-loop testing process integrating stress loading, layered monitoring, failure tracing, and life prediction, a leap from traditional single-stress step-by-step testing to multi-stress coupling simulation under real complex working conditions has been successfully achieved. This not only effectively simulates the synergistic effects of temperature, humidity, vibration, and electromagnetic stress in the special power environment, but also enables component-level failure location and service life prediction.

[0107] The technical solution of this invention addresses the industry pain points of missed detection of potential failure modes, difficulty in fault location, and lack of long-term reliability assessment in the prior art, significantly improving the accuracy of detection results and engineering guidance value, and effectively improving the environmental adaptability detection efficiency and detection accuracy of wavelength conversion systems.

[0108] In some optional embodiments, setting the dynamic coupling stress loading spectrum in step S1 above specifically includes the following steps:

[0109] S11. Extract typical multi-stress coupling events: Based on the test requirements, determine the preset power application scenario data that matches the wavelength conversion system under test from the power scenario database, and analyze the historical fault data and operating environment data in the preset power application scenario data to extract typical multi-stress coupling events.

[0110] S12. Generating a Dynamic Coupled Stress Loading Spectrum: Based on the extracted multi-stress coupling events, a dynamic coupled stress loading spectrum is generated. This spectrum is a dynamic stress loading program that defines the loading sequence of various environmental stresses over time and dynamically adjusts the intensity control logic based on the environmental stress state or system component state. By generating the dynamic stress loading program based on historical power scenario data, a shift from traditional static and isolated stress loading methods to a dynamically coupled and intelligently collaborative testing mode is achieved. This effectively solves the problem of operational distortion caused by single-stress step-by-step testing, providing a more accurate testing basis for the reliability assessment of wavelength conversion systems in real and complex power environments.

[0111] For example, typical multi-stress coupling events in a target power application scenario can be extracted based on the following criteria:

[0112] Based on the annual power communication system fault reports published by the power grid platform, coupling events that account for more than 10% of wavelength conversion system failures were selected; the special environmental protection requirements for power systems stipulated in GB / T14285-2022 "Technical Specification for Relay Protection and Safety Automatic Devices" were also considered; and the expert group determined the relevant events based on its predetermined power application scenarios. With the iteration of standards, and referring to other publicly available documents or authoritative reports, any typical multi-stress coupling events that can be screened for the target power application scenarios should be within the scope of protection of this application.

[0113] In some optional embodiments, generating the dynamic coupled stress loading spectrum based on the multi-stress coupling events specifically includes: inputting the extracted multi-stress coupling events into a pre-set loading spectrum generation model, and outputting the corresponding dynamic coupled stress loading spectrum. The loading spectrum generation model is a machine learning model, obtained through iterative training using historical data. The training process involves at least 1000 iterations and a cross-validation loss function of no more than 0.05. It should be noted that the specific training steps of the machine learning model are existing technologies and will not be elaborated here. The historical data includes mapping data between verified multi-stress coupling events from different power application scenarios and experimentally verified effective dynamic stress loading procedures. The multi-stress coupling events include: electromagnetic shock and mechanical vibration accompanying short-circuit faults; high temperature and humidity and electromagnetic interference during thunderstorms; temperature and humidity fluctuations and salt spray corrosion in coastal substations; high humidity environment and dust accumulation and superposition of power frequency electromagnetic fields in underground cable trenches; and icing and low temperature environment and superposition of mechanical vibration in fiber optic composite overhead ground wires. The environmental stress includes at least temperature, humidity, vibration frequency and amplitude, and power frequency electromagnetic field intensity. This embodiment employs a high-precision machine learning model trained on massive amounts of power scenario data to automatically transform empirical multi-stress coupling events into executable dynamic loading programs. This achieves a high degree of consistency between the test conditions and the real operating environment, effectively solving problems such as incomplete condition coverage and coupling effect distortion caused by manually designed loading spectra in traditional detection methods. This lays a solid foundation for subsequent accurate monitoring and reliability assessment.

[0114] In some optional embodiments, in step S2 above, the system-level performance data includes conversion efficiency, output optical power, center wavelength drift value, and bit error rate; wherein, the conversion efficiency is monitored by an integrating sphere, the output optical power is monitored by an optical power meter, the center wavelength drift value is monitored by a spectrometer, and the bit error rate is monitored by a bit error rate meter.

[0115] In some optional embodiments, the component-level performance data includes: the operating current and junction temperature of the excitation source, where the excitation source is a laser diode (LD) or a light-emitting diode (LED), and the junction temperature is monitored by a junction temperature sensor; the fluorescence spectral characteristics of the conversion medium, where the conversion medium is a quantum dot or phosphor, and the absorption efficiency is monitored by a miniature optical absorption probe; and the insertion loss and return loss of the optical unit, where the optical unit is a lens or a mirror, and the insertion loss and return loss are monitored by an optical time-domain reflectometer. By establishing a synchronous acquisition system for system-level and component-level performance data, a leap from traditional black-box testing to transparent and traceable testing is achieved. The hierarchical monitoring mechanism of this embodiment can not only capture the degradation trend of the overall system performance in real time, but also accurately track the state changes of key components such as the excitation source, conversion medium, and optical unit, providing sufficient data support for subsequent accurate failure location and lifetime prediction. This effectively solves the technical blind spot of traditional testing where only the result is seen but the cause is unknown, achieving the effect of effectively improving the environmental adaptability testing efficiency and accuracy of wavelength conversion systems.

[0116] In some optional embodiments, the damage accumulation model described in step S3 above is the Miner linear cumulative damage model, and its specific logic for predicting the service life of the wavelength conversion system includes:

[0117] S31. Fitting performance degradation curves: Based on the performance data during the stress loading process, fit the performance degradation curves of the excitation source, conversion medium, and optical unit respectively; where the excitation source is plotted on the output optical power or junction temperature as the vertical axis, the conversion medium on the absorption efficiency or fluorescence intensity as the vertical axis, and the optical unit on the insertion loss as the vertical axis, and the loading time as the horizontal axis.

[0118] S32. Set failure thresholds for each component, including a 20% decrease in absorption efficiency of the conversion medium, a 30% decrease in excitation source power attenuation, and a 1dB increase in insertion loss of the optical unit. A 20% decrease in absorption efficiency is an industry-recognized critical failure point—at which point the wavelength conversion efficiency will simultaneously decrease by more than 15%, failing to meet the requirement of a power relay protection signal transmission bit error rate ≤1E-12. For the excitation source (LD / LED): a 30% power attenuation corresponds to the actual failure state where the output optical power cannot drive the fiber optic communication module in a power scenario; a 1dB increase in insertion loss will cause signal transmission attenuation to exceed 20%, breaking the relay protection channel attenuation limit. These thresholds can also be modified; the thresholds provided in this implementation are only recommended based on current technology and are not limited to these specific thresholds.

[0119] S33. Expected Service Life: The cumulative damage degree D is calculated based on Miner's rule. The calculation formula is: D = Σ (actual loading time ti / component failure time Ti under corresponding stress). When D is not less than 1, the component is considered to have failed. The service life of the system under expected power conditions is extrapolated, where the actual loading time ti under expected conditions is converted to an annual operating time of 8760 hours, which is the industry standard time for uninterrupted operation of power equipment throughout the year. By innovatively applying the Miner linear cumulative damage model to the life prediction of wavelength conversion systems and combining it with the performance failure threshold unique to the power industry, a leap from traditional experience-based judgment to quantitative life assessment based on physical failure mechanisms is achieved. This embodiment accurately correlates dynamic test data with the actual failure standard of components, effectively solving the long-standing industry problems of insufficient life prediction accuracy and disconnection from power field conditions in existing technologies, and realizing accurate reliability assessment of power communication equipment.

[0120] In some optional embodiments, the application of the damage accumulation model to predict service life further includes a dynamic correction step: during the stress loading process, if the performance degradation rate of any of the key components is detected to exceed a preset abrupt change threshold within a unit time, the correction mechanism of the damage accumulation model is activated. The accumulated damage of the component is multiplied by a correction factor of 1.2 to 1.5, and the service life is recalculated. By introducing a dynamic correction mechanism into the service life prediction model, an adaptive response to sudden performance degradation of components is achieved. When an abnormal abrupt change in the performance degradation rate is detected, the calculation weight of the accumulated damage is automatically increased, effectively overcoming the defect of traditional linear prediction models that lag in response to nonlinear accelerated aging phenomena, and significantly improving the accuracy and reliability of service life prediction.

[0121] It is worth noting that the aforementioned correction coefficients of 1.2 to 1.5 are a quantitative mapping of the physical law that performance mutations predict accelerated aging risks. This is based on the damage evolution characteristics of materials science, statistical data on power equipment failures, and verification results from accelerated testing. However, the actual values ​​need to be dynamically adjusted based on the component type, the criticality of the mutation parameters, and the severity of the application scenario. Specifically: A sudden change in the performance degradation rate of critical components (such as conversion media and excitation sources) within a unit of time (e.g., a 5% decrease in the absorption efficiency of the conversion media within 1 hour, far exceeding the normal 0.5% / h) essentially indicates an irreversible microscopic damage upgrade in the material or structure.

[0122] For conversion media (quantum dots / phosphors): it may be that the ligand detachment on the surface of quantum dots is intensified under high temperature and high humidity, and the phosphor particles are sintered, which causes the subsequent absorption efficiency decay rate to change from a linear and slow decrease to a nonlinear and accelerated decrease.

[0123] For the excitation source (LD): the sudden increase in junction temperature may lead to the accumulation of thermal stress in the PN junction, causing the expansion of lattice defects in the chip, and the subsequent power decay rate will be 20%-50% higher than before the sudden change;

[0124] For optical units (lenses): Vibration may cause microcracks in the lens surface, which will significantly accelerate the rate of increase in subsequent insertion loss.

[0125] In some alternative embodiments, the method for determining the value of the correction coefficient can be differentiated by component type (high value for core components), by the criticality of the mutation parameter (high value for core parameters), or by the severity of the power application scenario (high value for high-stress scenarios). This embodiment of the invention does not impose specific limitations on this method.

[0126] In some alternative embodiments, examples are given by component type: Conversion medium (quantum dot / phosphor): As the core functional component for wavelength conversion, its performance mutations (such as a sharp drop in absorption efficiency or a shift in fluorescence spectral peaks) have the greatest impact on the overall system performance, and the microscopic damage is the most irreversible; a correction factor of 1.4-1.5 is used. Excitation source (LD / LED): Its power attenuation directly affects the output light signal intensity; mutations can easily lead to an increase in the bit error rate, but this can be partially mitigated through heat dissipation optimization; a correction factor of 1.3-1.4 is used. Optical unit (lens / mirror): Insertion loss mutations are mostly caused by mechanical displacement or microcracks; damage progresses relatively slowly; a correction factor of 1.2-1.3 is used.

[0127] The above-mentioned dynamic coupling stress loading spectrum is executed by a multi-parameter linkage controller to realize the stress loading of environmental stress; wherein the stress loading includes temporal coordination and intensity coordination; intensity coordination is intensity control logic, which is used to dynamically adjust the environmental stress based on the environmental stress state or the system component state.

[0128] For example, if the temperature of the conversion medium is detected to be ≥60℃, the ambient humidity will be reduced from the current value by 10%RH~15%RH, and the multi-parameter linkage controller will be controlled to reduce the input power of the excitation source by 5%~8% until the temperature of the conversion medium is ≤55℃. The conversion medium (quantum dot / phosphor) is the core of wavelength conversion. Its temperature exceeding 60℃ will accelerate thermal oxidation and degradation (background technology pain point). Therefore, the temperature of the conversion medium is used as a trigger condition to directly target the failure risk of this component.

[0129] For example, if a mechanical vibration acceleration ≥8g is detected, the vibration frequency will be reduced by 20%~30% from the current value to prevent the optical unit displacement from exceeding 0.1mm. Mechanical vibration acceleration: short circuit faults in the power system and icing vibrations can cause displacement of the optical unit (lens / reflector) (displacement exceeding 0.1mm will significantly increase light loss). Therefore, controlling the vibration frequency to avoid displacement is necessary to address the failure risk of the optical unit.

[0130] It's worth noting that this isn't about indiscriminately accelerating component aging to quickly obtain failure data, but rather about accurately simulating the multi-stress coupling effects under actual power system operating conditions to objectively assess the environmental adaptability and long-term reliability of the wavelength conversion system. These intensity control logics, while seemingly protective mechanisms, are in fact simulations of real-world power system operating conditions. The existing protection logic works as follows: When the temperature of a core component (such as a quantum dot conversion medium) exceeds a safety threshold (typically 60°C, set by the component manufacturer based on the long lifespan requirements of the power equipment), the system automatically triggers protective measures—for example, reducing ambient humidity through a dehumidification module (reducing the synergistic oxidation of quantum dots by high temperature and humidity) and reducing the excitation source power by adjusting the drive circuit (reducing heat input). This prevents irreversible damage to the component due to the extreme superposition of high temperature and high humidity. Therefore, if humidity is forcibly increased when the temperature is ≥60°C during testing, although it can accelerate component aging, it simulates an extreme damage scenario that will not occur in the power system (which has been avoided in actual operation and maintenance through protection logic). This results in the detected failure mode (such as instantaneous burnout of quantum dots) being completely inconsistent with the actual failure mode on site (such as slow thermal oxidation under long-term high temperature), rendering the test results meaningless and violating the purpose of this application to assess environmental adaptability. The core of dynamic coupling loading is to simulate the dynamic adjustment relationship between stresses, rather than simply superimposing stress to accelerate aging. Performance data collected under this logic (such as temperature 60℃ → humidity reduction → medium absorption efficiency slowly decreasing by 3%) can reflect the system's adaptability after stress adjustment in actual operation, rather than destructive data under unprotected conditions.

[0131] It's worth noting that there are simulated emergency situations, such as timing coordination: when a temperature shock (-40℃→70℃, conversion time ≤5min) is applied, mechanical vibration of 100Hz~200Hz (acceleration 5g~10g) is immediately triggered to simulate the superimposed effect of sudden temperature change and vibration under short-circuit fault. Additional intensity coordination can also be set: when the temperature of the conversion medium is detected to be ≥60℃, the relative humidity is automatically increased from the initial value to 85%RH~95%RH to simulate the oxidation effect of high temperature accelerating humidity on the medium. This tests the system's performance under extreme conditions caused by various factors (maintenance errors, automatic adjustment failures, etc.).

[0132] This application's embodiments achieve accurate replication of actual power system operating conditions by establishing a dynamic strength coordination mechanism based on component state feedback. In the example above, the automatic dehumidification and power reduction control logic when the conversion medium temperature approaches the safety threshold is not a simple equipment protection measure, but a realistic simulation of the actual scenario in power system operation and maintenance where environmental control and load management are used to maintain long-term reliable equipment operation. It effectively distinguishes between "recoverable stress-coupled response" and "irreversible component damage," ensuring that the test results truly reflect the system's environmental adaptability under expected operating conditions, rather than recording failure data under abnormal destructive conditions. Simultaneously, by setting different levels of coordination logic (such as conventional control and extreme condition testing), it satisfies the reliability assessment requirements under standard operating conditions while also providing the flexibility to assess the system's tolerance under abnormal stress superposition. This provides a more scientific and realistic testing method for the reliability assessment of wavelength conversion systems in special power application scenarios, effectively improving the efficiency and accuracy of environmental adaptability testing for wavelength conversion systems.

[0133] For example, the specific stress parameter range corresponding to the multi-stress coupling event includes:

[0134] Short circuit fault: The power frequency electromagnetic field strength of electromagnetic shock is to The duration is 0.1 to 0.5 seconds, the frequency of mechanical vibration is 50 Hz to 200 Hz, the acceleration is 5 g to 8 g, and the duration is 5 to 10 seconds;

[0135] Thunderstorm weather: Temperatures of high temperature and high humidity are to Humidity is to The duration is 2 to 4 hours, and the instantaneous field strength of the electromagnetic interference is... to The duration is from 10 microseconds to 100 microseconds;

[0136] Outdoor coastal substation: Temperature and humidity circulation temperature is to The temperature difference between day and night is not less than The humidity is to The salt spray system cycles every 12 hours. Solution, sedimentation volume is to The duration is 24 hours;

[0137] Underground cable trenches: high humidity to The dust concentration is to Particle size not greater than The field strength of the power frequency electromagnetic field is to Continuous loading;

[0138] Fiber optic composite overhead ground wire: The temperature at low temperatures is to The duration ranges from 12 to 24 hours, with icing vibration frequencies ranging from 1 Hz to 10 Hz, accelerations from 3 g to 5 g, each lasting 30 minutes, and intervals of 2 hours. These parameter ranges, optimized based on actual operational data, include electromagnetic-vibration coupling during short-circuit faults, high temperature and humidity combined with electromagnetic interference during thunderstorms, and temperature and humidity cycling combined with salt spray corrosion in coastal environments. This effectively solves the problem of operational distortion caused by single stress parameters and fixed loading sequences in traditional testing, ensuring a high degree of consistency between the test environment and the actual operating conditions of the power system, and establishing a reliable test benchmark for the environmental adaptability assessment of wavelength conversion systems.

[0139] In some optional embodiments, the environmental stress corresponding to the multi-stress coupling event includes power system-specific interference stress; the power system-specific interference stress includes at least one of fast transient overvoltage or lightning impulse voltage.

[0140] For example, in response to electromagnetic shock and mechanical vibration events accompanying short-circuit faults, a fast transient overvoltage VFTO is applied synchronously, with a peak value of 10kV to 58kV, a rise time of 20ns to 200ns, synchronized with the electromagnetic shock triggering time, and a duration of 50ns to 500ns.

[0141] For example, in response to high temperature and humidity and electromagnetic interference events during thunderstorms, superimposed with lightning impulse voltage, the following approach is adopted: Standard waveform, peak value 100kV to 200kV, triggered 0.5 to 1 second after electromagnetic interference is applied, duration is... to By incorporating two types of power system-specific interference stresses—rapid transient overvoltages and lightning impulse voltages—into multi-stress coupled events, the problem of missed detection of potential failure modes due to the lack of power-specific interferences in traditional detection methods is solved. This enables the detection environment to fully cover the entire power interference spectrum, from substation operational overvoltages to lightning overvoltages, ensuring that the reliability of the wavelength conversion system is fully verified under extreme conditions involving multiple stress couplings of electro-magnetism-thermal-mechanical factors. This provides a more comprehensive safety guarantee for the "zero-interruption" operation of relay protection channels.

[0142] In some optional embodiments, the step of establishing a correlation model between system performance degradation and component state changes based on the collected system-level performance data and component-level performance data to locate potentially failing components and failure modes specifically includes the following steps:

[0143] S10a. Establish a quantitative mapping relationship: Through a data association algorithm, establish a quantitative mapping relationship between the component-level performance data and the system-level performance data, and generate an association model that maps system performance degradation and component state changes.

[0144] S10b, Set thresholds: Set the failure threshold for system-level performance data and the abnormal threshold for component-level performance data;

[0145] S10c, Locating Potentially Failed Components: When system-level performance data exceeds its failure threshold, based on the quantitative mapping relationship, the component-level performance data that first exceeds its abnormal threshold is identified, and the component corresponding to this parameter is located as a potentially failed component. The failure mode is then determined by combining the type of parameter exceeding the limit. By constructing a two-layer judgment system and quantitative mapping relationship of "system-level failure threshold - component-level abnormal threshold," rapid and accurate tracing from system performance anomalies to the root cause of component failure is achieved. When system-level parameters exceed limits, the component parameter that first exceeds the threshold can be automatically identified, directly locking down the weak link and its failure mode that leads to system performance degradation. This solves the industry problem of "only seeing system performance degradation but not knowing the root cause of component failure" in traditional testing, providing a clear direction for product improvement and reliability enhancement.

[0146] For example, the specific rules and reference values ​​for setting the failure threshold for system-level performance data and the abnormal threshold for component-level performance data are as follows:

[0147] Failure threshold for system-level performance data: Based on the hard requirements of power system relay protection fiber optic communication (such as DL / T1573-2016 "Requirements for Immunity of Power System Communication Equipment"), with ensuring signal transmission reliability as the core, 80% of the system functional failure threshold is taken (with a 20% safety redundancy reserved); Abnormal threshold for component-level performance data: Based on the physical characteristics and failure mechanism of the component, with the early warning point before irreversible damage to the component as the core, 50%-60% of the component failure threshold is taken (early warning, different from final failure), to effectively improve the environmental adaptability detection efficiency and detection accuracy of the wavelength conversion system.

[0148] For example, the following is a preferred example of threshold setting in this implementation, wherein the failure threshold for system-level performance data is: conversion efficiency degradation. (40% of the aforementioned 20% failure threshold for the conversion medium, with safety redundancy reserved), output optical power fluctuation. The center wavelength drift value is ≤5nm (meeting the wavelength stability requirements of ITU-T G.652 for optical fibers), and the bit error rate is ≤1×10-12 (meeting the zero-maloperation requirements of relay protection). Abnormal thresholds for component-level performance data: Excitation source: junction temperature ≤90℃ (75% of the LD chip's temperature resistance limit of 120℃), operating current fluctuation ≤±10%, output power attenuation ≤15% (50% of the above 30% failure threshold); Conversion medium: absorption efficiency decrease ≤10% (50% of the 20% failure threshold), fluorescence spectrum peak shift ≤5nm; Optical unit: insertion loss increase ≤0.5dB (50% of the 1dB failure threshold), return loss decrease ≤5dB.

[0149] The core logic of determining the failure mode by combining the above-mentioned parameter exceedance type is that the exceedance type of the component-level parameter (i.e. the specific change characteristics of the parameter, such as a sudden increase in junction temperature, a cliff-like drop in absorption efficiency, and a continuous increase in insertion loss) has a one-to-one correspondence with the physical / chemical failure mechanism of the component. By analyzing the change characteristics (amplitude, rate, and accompanying phenomena) when the parameter exceeds the limit, the failure mode behind it (i.e. the root cause of component failure) can be accurately located.

[0150] For example, determining failures related to the conversion medium:

[0151] Parameter combination: Absorption efficiency decrease ≥10% + local temperature ;

[0152] Failure mode: Rate fluctuation value ≥ ±5%;

[0153] Failure mode: Displacement deviation caused by vibration;

[0154] Judgment criteria: Optical components such as lenses / mirrors undergo micro-displacement under mechanical vibration, which changes the collimation of the optical path and thus causes abnormal fluctuations in output power;

[0155] Excitation source-related failure determination:

[0156] Parameter combination: Junction temperature ≥ +Operating current fluctuation ≥±15%;

[0157] Failure mode: Thermally induced performance degradation;

[0158] Judgment criteria: Laser diodes / light-emitting diodes exhibit carrier leakage and decreased quantum efficiency at excessively high junction temperatures, manifested as abnormal fluctuations in drive current;

[0159] Failure determination related to sealing structure:

[0160] Parameter combination: Humidity sensor reading ≥ 85%RH + dielectric surface insulation resistance decrease ≥ 50%;

[0161] Failure mode: Moisture intrusion due to seal failure;

[0162] Judgment criteria: Deterioration in the sealing performance allows ambient moisture to seep into the system, causing deterioration in electrical performance.

[0163] The technical solution of this invention, by introducing a closed-loop detection process integrating stress loading, layered monitoring, failure tracing, and service life prediction, successfully achieves a leap from traditional single-stress step-by-step testing to multi-stress coupling simulation under real complex working conditions. It not only effectively simulates the synergistic effects of temperature, humidity, vibration, and electromagnetic stress in the special environment of power, but also systematically solves the industry pain points of missed detection of potential failure modes, difficulty in fault location, and lack of long-term reliability assessment in the prior art through component-level failure location and service life prediction. It significantly improves the accuracy of the detection results and the engineering guidance value, and effectively improves the environmental adaptability detection efficiency and detection accuracy of wavelength conversion systems.

[0164] By innovatively applying the Miner linear cumulative damage model to the lifetime prediction of wavelength conversion systems and combining it with the performance failure thresholds specific to the power industry, this application achieves a leap from traditional experience-based judgment to quantitative lifetime assessment based on physical failure mechanisms. This application precisely correlates dynamic test data with actual component failure standards, effectively solving long-standing industry problems in existing technologies such as insufficient lifetime prediction accuracy and disconnect from actual power field conditions, thus enabling accurate reliability assessment of power communication equipment. By establishing a synchronous acquisition system for system-level and component-level performance parameters, it achieves a leap from traditional black-box testing to transparent and traceable testing. This hierarchical monitoring mechanism not only captures the overall performance degradation trend of the system in real time but also accurately tracks the state changes of key components such as the excitation source, conversion medium, and optical units, providing ample data support for subsequent accurate failure location and lifetime prediction. This effectively solves the technical blind spot of traditional testing, which "only sees the result, not the cause," and effectively improves the efficiency and accuracy of environmental adaptability testing for wavelength conversion systems.

[0165] Figure 3 This is a schematic diagram of the structure of an environmental adaptability detection device for a wavelength conversion system provided in an embodiment of the present invention. Figure 3 As shown, the device includes: a stress loading spectrum determination module 310, a data acquisition module 320, and a detection result determination module 330.

[0166] The system includes a stress loading spectrum determination module 310, which acquires data about the wavelength conversion system under test and determines a dynamic coupled stress loading spectrum based on preset power application scenario data matching the wavelength conversion system under test; a data acquisition module 320, which performs multi-stress synchronous loading on the wavelength conversion system under test in a pre-constructed test environment based on the dynamic coupled stress loading spectrum and acquires system-level performance data and component-level performance data of the wavelength conversion system under test during the stress loading process; and a detection result determination module 330, which determines the detection result based on the system-level performance data, the component-level performance data, and a pre-established correlation model; wherein the detection result includes potential failure components and failure modes.

[0167] The technical solution of this invention involves acquiring the wavelength conversion system under test (WRTS), determining a dynamic coupling stress loading spectrum based on preset power application scenario data matching the WRTS, performing multi-stress synchronous loading on the WRTS in a pre-constructed test environment based on the dynamic coupling stress loading spectrum, and collecting system-level and component-level performance data of the WRTS during the stress loading process. The detection results are determined based on the system-level performance data, the component-level performance data, and a pre-established correlation model. The detection results include potential failed components and failure modes. This solves the technical problems of traditional detection methods, which cannot specifically simulate multi-stress coupling conditions in power scenarios and are difficult to locate the root cause of failure. It achieves the technical effect of improving the targeting of detection and the accuracy of failure tracing, and providing effective support for the reliability of wavelength conversion systems in power scenario applications.

[0168] In some optional embodiments, the stress loading spectrum determination module includes:

[0169] The application scenario data determination unit is used to determine preset power application scenario data that matches the wavelength conversion system under test from the power scenario database based on test requirements;

[0170] The coupling event extraction unit is used to analyze historical fault data and operating environment data of a preset power application scenario that matches the wavelength conversion system under test, in order to extract multi-stress coupling events.

[0171] The stress loading spectrum generation unit is used to generate the dynamic coupled stress loading spectrum based on the multi-stress coupling events; wherein, the dynamic coupled stress loading spectrum is a stress loading program that defines the time loading sequence of various environmental stresses and dynamically adjusts the intensity based on the environmental or system component state.

[0172] In some alternative embodiments, the system-level performance data includes at least one of conversion efficiency, output optical power, center wavelength drift, and bit error rate; the component-level performance data includes at least one of the operating current and junction temperature of the excitation source, the absorption efficiency and fluorescence spectral characteristics of the conversion medium, and the insertion loss and return loss of the optical unit.

[0173] In some alternative embodiments, the data acquisition module is specifically used for:

[0174] The dynamic coupling stress loading spectrum is imported through the control terminal of the test environment. The control terminal automatically triggers each environmental module to apply stress synchronously according to the timing and intensity set by the loading spectrum.

[0175] In some alternative embodiments, the apparatus further includes:

[0176] The sample data acquisition module is used to acquire sample data obtained from experiments of the same model of the wavelength conversion system under test under different stress combinations before the test results are determined based on the system-level performance data, the component-level performance data and the pre-established correlation model.

[0177] The mapping relationship acquisition module is used to analyze the correlation patterns between sample system-level data and sample component-level data in the sample data according to the data association algorithm, so as to obtain a quantitative mapping relationship;

[0178] The association model construction module is used to construct an association model based on the quantitative mapping relationship, the preset system-level performance failure threshold, and the preset component-level performance anomaly threshold.

[0179] In some optional embodiments, the detection result determination module includes:

[0180] The failure tracing unit is used to input the real-time collected system-level performance data into the association model, compare it with the preset system-level performance failure threshold, and determine that there is an anomaly in the system when the system-level performance data is lower than the preset system-level performance failure threshold, and initiate failure tracing.

[0181] The potential failure component determination unit is used to filter out correlation parameters from the collected component-level data according to the correlation model based on the quantitative mapping relationship, compare the correlation parameters with a preset component-level performance anomaly threshold, and determine the components corresponding to the correlation parameters that are greater than the preset component-level performance anomaly threshold as potential failure components.

[0182] The failure mode determination unit is used to determine the component anomaly type and current applied stress of the potentially failing component, and to determine the failure mode based on the current applied stress, the component anomaly type and a preset failure mode library.

[0183] In some alternative embodiments, the apparatus further includes:

[0184] The lifetime prediction module is used to predict the service lifetime of the wavelength conversion system under test based on the correlation model, the system-level performance data, the component-level performance data, and the pre-built damage accumulation model; wherein the damage accumulation model is the Miner linear cumulative damage model.

[0185] The environmental adaptability testing device for the wavelength conversion system provided in this embodiment of the invention can execute the environmental adaptability testing method for the wavelength conversion system provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method.

[0186] Figure 4 This is a schematic diagram of an electronic device for implementing the environmental adaptability detection method of the wavelength conversion system according to embodiments of the present invention. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0187] like Figure 4 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 can also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0188] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0189] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as environmental adaptability detection in a wavelength conversion system.

[0190] In some embodiments, the environmental adaptability detection of the method wavelength conversion system can be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the environmental adaptability detection of the method wavelength conversion system described above can be performed. Alternatively, in other embodiments, processor 11 can be configured to perform the environmental adaptability detection of the method wavelength conversion system by any other suitable means (e.g., by means of firmware).

[0191] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations may include: implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0192] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0193] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0194] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0195] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0196] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0197] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0198] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for detecting the environmental adaptability of a wavelength conversion system, characterized in that, include: The wavelength conversion system under test is acquired, and the dynamic coupling stress loading spectrum is determined based on the preset power application scenario data that matches the wavelength conversion system under test. Based on the dynamic coupled stress loading spectrum, the wavelength conversion system under test is subjected to multi-stress synchronous loading in a pre-constructed test environment, and system-level performance data and component-level performance data of the wavelength conversion system under test are collected during the stress loading process. The detection results are determined based on the system-level performance data, the component-level performance data, and the pre-established correlation model; wherein, the detection results include potential failure components and failure modes.

2. The method according to claim 1, characterized in that, The determination of the dynamic coupling stress loading spectrum based on preset power application scenario data matching the wavelength conversion system under test includes: Based on the testing requirements, preset power application scenario data matching the wavelength conversion system under test are determined from the power scenario database; Historical fault data and operating environment data of preset power application scenarios matched with the wavelength conversion system under test are analyzed to extract multi-stress coupling events; The dynamic coupled stress loading spectrum is generated based on the multi-stress coupling events; wherein, the dynamic coupled stress loading spectrum is a stress loading program that defines the time loading sequence of various environmental stresses and dynamically adjusts the intensity based on the state of the environment or system components.

3. The method according to claim 1, characterized in that, The system-level performance data includes at least one of conversion efficiency, output optical power, center wavelength drift, and bit error rate; the component-level performance data includes at least one of the following: operating current and junction temperature of the excitation source, absorption efficiency and fluorescence spectral characteristics of the conversion medium, and insertion loss and return loss of the optical unit.

4. The method according to claim 1, characterized in that, The process of performing multi-stress synchronous loading on the wavelength conversion system under test in a pre-built test environment based on the dynamic coupled stress loading spectrum includes: The dynamic coupling stress loading spectrum is imported through the control terminal of the test environment. The control terminal automatically triggers each environmental module to apply stress synchronously according to the timing and intensity set by the loading spectrum.

5. The method according to claim 1, characterized in that, Before determining the detection result based on the system-level performance data, the component-level performance data, and the pre-established correlation model, the method further includes: Obtain sample data from experiments conducted on a device of the same model as the wavelength conversion system under test under different stress combinations; The correlation between system-level data and component-level data in the sample data is analyzed using a data association algorithm to obtain a quantitative mapping relationship. A correlation model is constructed based on the quantitative mapping relationship, the preset system-level performance failure threshold, and the preset component-level performance anomaly threshold.

6. The method according to claim 1, characterized in that, The determination of the detection result based on the system-level performance data, the component-level performance data, and the pre-established correlation model includes: The real-time collected system-level performance data is input into the association model and compared with the preset system-level performance failure threshold. If the system-level performance data is lower than the preset system-level performance failure threshold, it is determined that there is an anomaly in the system and failure tracing is initiated. The correlation model filters correlation parameters from the collected component-level data based on quantitative mapping relationships, compares the correlation parameters with a preset component-level performance anomaly threshold, and identifies the components corresponding to correlation parameters that are greater than the preset component-level performance anomaly threshold as potentially failed components. Determine the component anomaly type and current applied stress of the potentially failing component, and determine the failure mode based on the current applied stress, the component anomaly type, and a preset failure mode library.

7. The method according to claim 1, characterized in that, Also includes: The service life of the wavelength conversion system under test is predicted based on the correlation model, the system-level performance data, the component-level performance data, and the pre-built damage accumulation model; wherein, the damage accumulation model is the Miner linear cumulative damage model.

8. An environmental adaptability testing device for a wavelength conversion system, characterized in that, include: The stress loading spectrum determination module is used to acquire the wavelength conversion system under test and determine the dynamic coupling stress loading spectrum based on the preset power application scenario data that matches the wavelength conversion system under test. The data acquisition module is used to perform multi-stress synchronous loading on the wavelength conversion system under test in a pre-constructed test environment based on the dynamic coupled stress loading spectrum, and to acquire system-level performance data and component-level performance data of the wavelength conversion system under test during the stress loading process. The detection result determination module is used to determine the detection result based on the system-level performance data, the component-level performance data, and the pre-established correlation model; wherein, the detection result includes potential failure components and failure modes.

9. An electronic device, characterized in that, The electronic device includes: At least one processor; and a memory communicatively connected to the at least one processor; The memory stores a computer program that can be executed by the at least one processor, which is then executed by the at least one processor to enable the at least one processor to perform the environmental adaptability detection method for the wavelength conversion system according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that, when executed by a processor, implement the environmental adaptability detection method for the wavelength conversion system according to any one of claims 1-7.

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