Optical module health state prediction method, device, equipment and medium

Abstract

The invention relates to an optical module health state prediction method and device, equipment and a medium. The method comprises the steps of collecting historical operation data of an optical module; preprocessing the historical operation data to obtain target historical operation data; according to historical data of each index in a plurality of indexes in the target historical operation data, calculating an occurrence probability of an optical module fault type corresponding to the index; calculating the fault probability of the optical module according to the occurrence probability of the optical module fault type corresponding to each index in the plurality of indexes; and predicting the health state of the optical module according to the fault probability of the optical module. Therefore, according to the technical scheme, the fault probability of the optical module is calculated by integrating the multiple indexes of the optical module, and then the health state of the optical module is predicted according to the fault probability of the optical module. Therefore, the health state of the optical module is accurately predicted, a user can find the problem of the optical module in time, the risk of network service interruption is effectively reduced, and the user experience is improved.

Description

Technical Field

[0001] The present application relates to the field of optical modules, and in particular, to a method, device, equipment and medium for predicting the health state of an optical module. Background Art

[0002] With the development of the Internet, optical fibers have become more and more popular. Optical fibers have the advantages of efficiently, rapidly and low-lossly transmitting optical signals, and are commonly used for long-distance transmission of data. An optical module is a main component for data to be transmitted via optical fiber. If the optical module fails, the downstream network service will be interrupted. Therefore, predicting the health state of the optical module is particularly important.

[0003] Currently, in the existing technical solutions, data of specific indicators of the optical module are collected and analyzed to predict the health state of the optical module.

[0004] However, predicting the health state of the optical module through the data of a single indicator of the optical module in the above technical solutions will result in inaccurate prediction of the health state of the optical module, making it impossible for users to discover problems of the optical module in advance, and further causing interruption of the network service and affecting the user experience. Summary of the Invention

[0005] Embodiments of the present application provide a method, device, equipment and medium for predicting the health state of an optical module, aiming to solve the problem that inaccurate prediction of the health state of the optical module will occur when predicting the health state of the optical module through the data of a single indicator of the optical module.

[0006] In a first aspect, embodiments of the present application provide a method for predicting the health state of an optical module, which includes: Collecting historical operation data of the optical module, where the historical operation data includes historical data of multiple indicators in the optical module, and each indicator in the multiple indicators corresponds to an optical module failure type; Preprocessing the historical operation data to obtain target historical operation data; Calculating the occurrence probability of the optical module failure type corresponding to each indicator according to the historical data of each indicator in the target historical operation data; Calculating the probability of the optical module failing according to the occurrence probabilities of the optical module failure types corresponding to each indicator in the multiple indicators; Predicting the health state of the optical module according to the probability of the optical module failing.

[0007] Optionally, the calculating the occurrence probability of the optical module failure type corresponding to each indicator according to the historical data of each indicator in the target historical operation data includes: Obtaining the normal usage duration of the optical module; Obtain the used duration of the optical module; Count the number of abnormal data in the historical data of each metric in the target historical operation data; Calculate the occurrence probability of the optical module failure type corresponding to the metric according to the normal usage duration, the used duration, the total number of historical data of each metric in the target historical operation data, and the number of abnormal data.

[0008] Optionally, the calculation formula for the occurrence probability of the optical module failure type corresponding to each metric among the multiple metrics is:

[0009] Wherein, is the occurrence probability of the optical module failure type corresponding to each metric, is the normal usage duration of the optical module, is the used duration of the optical module, is the number of abnormal data in the historical data of each metric, is the total number of the historical data of each metric.

[0010] Optionally, the multiple metrics include: optical power, temperature, bias current, and bias voltage.

[0011] Optionally, the calculation formula for the probability of the optical module failing is:

[0012] Wherein, is the probability of the optical module failing, is the occurrence probability of the optical module failure type corresponding to the optical power, is the occurrence probability of the optical module failure type corresponding to the temperature, is the occurrence probability of the optical module failure type corresponding to the bias current, is the occurrence probability of the optical module failure type corresponding to the bias voltage.

[0013] Optionally, after calculating the probability of the optical module failing according to the occurrence probability of the optical module failure type corresponding to each metric among the multiple metrics, and before predicting the health state of the optical module according to the probability of the optical module failing, the method further includes: Calculate the remaining usage duration of the optical module according to the probability of the optical module failing, the normal usage duration of the optical module, and the used duration of the optical module; The predicting the health state of the optical module according to the probability of the optical module failing includes: Predict the health status of the optical module according to the probability of the optical module failing and the remaining service life.

[0014] Optionally, the calculation formula for the remaining service life of the optical module is:

[0015] Wherein, is the remaining service life of the optical module, is the normal service life of the optical module, is the used service life of the optical module, is the probability of the optical module failing.

[0016] In a second aspect, an embodiment of the present application further provides an optical module health status prediction device, which includes units for executing the above method.

[0017] In a third aspect, an embodiment of the present application further provides a computer device, which includes a memory and a processor. A computer program is stored on the memory, and when the processor executes the computer program, the above method is implemented.

[0018] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium. The storage medium stores a computer program, and when the computer program is executed by a processor, the above method can be implemented.

[0019] An embodiment of the present application provides a method, device, equipment and medium for predicting the health status of an optical module. Among them, the method includes: collecting historical operation data of the optical module, the historical operation data includes historical data of multiple indicators in the optical module, and each indicator in the multiple indicators corresponds to an optical module failure type; preprocessing the historical operation data to obtain target historical operation data; calculating the occurrence probability of the optical module failure type corresponding to each indicator according to the historical data of each indicator in the target historical operation data; calculating the probability of the optical module failing according to the occurrence probability of the optical module failure type corresponding to each indicator in the multiple indicators; predicting the health status of the optical module according to the probability of the optical module failing. It can be seen that in the technical solution of the present application, the probability of the optical module failing is calculated by integrating multiple indicators of the optical module, and then the health status of the optical module is predicted according to the probability of the optical module failing. Thus, the health status of the optical module can be accurately predicted, enabling users to timely discover problems with the optical module, effectively reducing the risk of network service interruption, and improving the user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The accompanying drawings here are incorporated into the specification and form a part of this specification, showing embodiments consistent with the present application, and are used together with the specification to explain the principles of the present application.

[0021] To more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, for those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0022] One or more embodiments are exemplarily illustrated by the pictures in the corresponding drawings. These exemplary illustrations do not limit the embodiments. Elements with the same reference numerals in the drawings represent similar elements. Unless otherwise stated, the drawings in the figures do not constitute a proportional limitation.

[0023] Figure 1 One of the schematic flowcharts of a method for predicting the health status of an optical module provided by an embodiment of the present application; Figure 2 Another schematic flowchart of a method for predicting the health status of an optical module provided by an embodiment of the present application; Figure 3 Another schematic flowchart of a method for predicting the health status of an optical module provided by an embodiment of the present application; Figure 4 A schematic block diagram of a device for predicting the health status of an optical module provided by an embodiment of the present application; Figure 5 A schematic structural diagram of a computer device provided by an embodiment of the present application. Detailed implementation manners

[0024] To make the objectives, technical solutions, and advantages of the embodiments of the present application clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are some but not all of the embodiments of the present application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without creative efforts belong to the scope of protection of the present application.

[0025] The following disclosure provides many different embodiments or examples for implementing different structures of the present application. To simplify the disclosure of the present application, the components and settings of specific examples are described below. Of course, they are only examples and are not intended to limit the present application. In addition, the present application may repeat reference numerals and / or letters in different examples. This repetition is for the purpose of simplification and clarity and does not itself indicate the relationship between the various embodiments and / or settings discussed.

[0026] It should be understood that when used in this specification and the appended claims, the terms "comprising" and "including" indicate the presence of the described features, wholes, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, wholes, steps, operations, elements, components, and / or their combinations.

[0027] It should also be understood that the terms used in this application specification are for the purpose of describing specific embodiments only and are not intended to limit this application. As used in this application specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an", and "the" are intended to include the plural forms.

[0028] It should be further understood that the term "and / or" used in this application specification and the appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0029] As used in this specification and the appended claims, the term "if" can be interpreted as "when...", "once", "in response to determining", or "in response to detecting" according to the context. Similarly, the phrases "if determined" or "if [the described condition or event] is detected" can be interpreted as meaning "once determined", "in response to determining", "once [the described condition or event] is detected", or "in response to detecting [the described condition or event]" according to the context.

[0030] To solve the technical problem that predicting the health status of an optical module based on the data of a single index of the optical module in the prior art may result in inaccurate prediction of the health status of the optical module, this application provides an optical module health status prediction device that can accurately predict the health status of the optical module.

[0031] Please refer to Figure 1 , Figure 1 a method for predicting the health status of an optical module provided in an embodiment of this application. In one embodiment, the method includes: S1. Collect historical operation data of the optical module.

[0032] The historical operation data includes historical data of multiple indicators in the optical module. Among them, the multiple indicators include: optical power, temperature, bias current, and bias voltage. It should be noted that the optical power includes transmitted optical power and received optical power. The temperature is the operating temperature of the optical module. The collector periodically collects data corresponding to multiple indicators of the optical module to obtain the historical operation data of the optical module. It should be noted that different indicators can correspond to the same collection period or different collection periods. This application does not limit this.

[0033] Each of the multiple metrics corresponds to an optical module fault type. As shown in Table 1, Table 1 is a correspondence table between optical module metrics and optical module fault types. For example, the optical module fault type corresponding to the optical power is abnormal optical power.

[0034] Table 1

[0035] S2. Preprocess the historical operation data to obtain the target historical operation data.

[0036] Among them, to eliminate the influence of noise in the historical operation data, perform cleaning, denoising, and normalization processing operations on the collected historical operation data to obtain the target historical operation data, thereby improving the quality of the target historical operation data and facilitating the accurate prediction of the health status of the optical module. Among them, the target historical operation data includes the historical data of multiple metrics in the optical module.

[0037] S3. Calculate the occurrence probability of the optical module fault type corresponding to each metric in the target historical operation data based on the historical data of each metric.

[0038] For example, the historical data corresponding to the optical power metric in the target historical operation data is dataset A, and calculate the probability of abnormal optical power occurrence of this optical module based on the data in dataset A.

[0039] S4. Calculate the probability of the optical module failing based on the occurrence probability of the optical module fault type corresponding to each metric.

[0040] In one embodiment, calculate the probability of the optical module failing based on the occurrence probability of abnormal optical power of the optical module, the occurrence probability of abnormal temperature of the optical module, the occurrence probability of abnormal bias current of the optical module, and the occurrence probability of abnormal bias voltage of the optical module.

[0041] S5. Predict the health status of the optical module based on the probability of the optical module failing.

[0042] Among them, the greater the probability of the optical module failing, the worse the health status of the optical module, and vice versa.

[0043] Specifically, the correspondence between the probability of a failure occurring in an optical module and the health status of the optical module is set according to empirical values. For example, when the probability of a failure occurring in the optical module does not exceed 10%, it indicates that the health status of the optical module is excellent. When the probability of a failure occurring in the optical module is between 10% and 30% (excluding 30%), it indicates that the health status of the optical module is good. When the probability of a failure occurring in the optical module exceeds 30%, it indicates that the health status of the optical module is poor. Of course, for optical modules from different manufacturers, the correspondence between the probability of a failure occurring in the optical module and the health status of the optical module may vary. For example, when the probability of a failure occurring in the optical module does not exceed 5%, it indicates that the health status of the optical module is excellent. This application does not limit this.

[0044] The embodiment of the present application provides a method for predicting the health status of an optical module, including: collecting historical operation data of the optical module, where the historical operation data includes historical data of multiple indicators in the optical module, and each indicator in the multiple indicators corresponds to an optical module failure type; preprocessing the historical operation data to obtain target historical operation data; calculating the occurrence probability of the optical module failure type corresponding to each indicator according to the historical data of each indicator in the target historical operation data; calculating the probability of a failure occurring in the optical module according to the occurrence probabilities of the optical module failure types corresponding to each indicator in the multiple indicators; and predicting the health status of the optical module according to the probability of a failure occurring in the optical module. It can be seen that in the technical solution of the present application, the probability of a failure occurring in the optical module is calculated by integrating multiple indicators of the optical module, and then the health status of the optical module is predicted according to the probability of a failure occurring in the optical module. Thus, the health status of the optical module can be accurately predicted, enabling the user to timely discover problems with the optical module, effectively reducing the risk of network service interruption, and improving the user experience.

[0045] Please refer to Figure 2 , Figure 2 which is the second schematic flow chart of a method for predicting the health status of an optical module provided by an embodiment of the present application. In one embodiment, the calculating the occurrence probability of the optical module failure type corresponding to each indicator according to the historical data of each indicator in the target historical operation data includes: S30. Obtain the normal usage duration of the optical module.

[0046] The normal usage duration of the optical module is the normal service life of the optical module. The unit of this normal usage duration is years. The normal usage duration of the optical module is determined when the optical module leaves the factory.

[0047] S31. Obtain the used duration of the optical module.

[0048] The used duration of the optical module refers to the duration that the optical module has actually been used after leaving the factory, and the unit of this used duration is years.

[0049] S32. Count the number of abnormal data in the historical data of each metric in the target historical operation data.

[0050] The abnormal data in the historical data of each metric indicates that the value of the abnormal data does not fall within the normal range value corresponding to the metric. The normal range value corresponding to each metric of the optical module has been determined during factory production.

[0051] For example, the optical power when the optical module is operating normally is [-7, +2] dBm. The historical data corresponding to the optical power metric in the target historical operation data is the data set A {-2, -3, -4, -8, 1, 1, 2}. Among them, the number of abnormal data in the data set A is 1, that is, the abnormal data is "-8".

[0052] S33. Calculate the occurrence probability of the optical module failure type corresponding to the metric based on the normal usage duration, the used duration, the total number of historical data of each metric in the target historical operation data, and the number of abnormal data.

[0053] In one embodiment, the calculation formula for the occurrence probability of the optical module failure type corresponding to each metric is:

[0054] Among them, is the occurrence probability of the optical module failure type corresponding to each metric, is the normal usage duration of the optical module, is the used duration of the optical module, is the number of abnormal data in the historical data of each metric, is the total number of historical data of each metric.

[0055] In one embodiment, the calculation formula for the probability of the optical module failing is:

[0056] Among them, is the probability of the optical module failing, is the occurrence probability of the optical module failure type corresponding to the optical power, is the occurrence probability of the optical module failure type corresponding to the temperature, is the occurrence probability of the optical module failure type corresponding to the bias current, is the occurrence probability of the optical module failure type corresponding to the bias voltage.

[0057] Please refer to Figure 3 , Figure 3FIG. 3 is a schematic flow chart of a method for predicting the health state of an optical module provided by an embodiment of the present application. In one embodiment, after calculating the probability of the optical module failing according to the occurrence probabilities of the optical module failure types corresponding to each of the multiple metrics, the method further includes: S6. Calculate the remaining service life of the optical module according to the probability of the optical module failing, the normal service life of the optical module, and the used service life of the optical module.

[0058] In one embodiment, the formula for calculating the remaining service life of the optical module is:

[0059] where, is the remaining service life of the optical module, is the normal service life of the optical module, is the used service life of the optical module, is the probability of the optical module failing.

[0060] The predicting the health state of the optical module according to the probability of the optical module failing includes: S50. Predict the health state of the optical module according to the probability of the optical module failing and the remaining service life.

[0061] The longer the remaining service life of the optical module, the better the health state of the optical module, and vice versa.

[0062] Predict the health state of the optical module through these two dimensions of the probability of the optical module failing and the remaining service life, so as to more accurately predict the health state of the optical module.

[0063] In one embodiment, display the historical operation data of the optical module, the occurrence probabilities of the optical module failure types corresponding to each metric of the optical module, the probability of the optical module failing, and the health state evaluation result of the optical module through a visualization interface, so as to facilitate engineers to view and manage the optical module.

[0064] See Figure 4 , Figure 4 FIG. 4 is a schematic block diagram of an optical module health state prediction device provided by an embodiment of the present application. Corresponding to the above optical module health state prediction method, the present application also provides an optical module health state prediction device. The optical module health state prediction device includes units for executing the above optical module health state prediction method, and the optical module health state prediction device can be configured in terminals such as desktop computers, tablet computers, and laptops. Specifically, the optical module health state prediction device includes: The acquisition module 401 is configured to acquire the historical operation data of the optical module, where the historical operation data includes the historical data of multiple metrics in the optical module, and each metric in the multiple metrics corresponds to an optical module fault type; The preprocessing module 402 is configured to preprocess the historical operation data to obtain target historical operation data; The first calculation unit 403 is configured to calculate the occurrence probability of the optical module fault type corresponding to each metric according to the historical data of each metric in the target historical operation data; The second calculation unit 404 is configured to calculate the probability of the optical module failing according to the occurrence probability of the optical module fault type corresponding to each metric in the multiple metrics; The prediction unit 405 is configured to predict the health status of the optical module according to the probability of the optical module failing.

[0065] In one embodiment, the first calculation unit 403 is specifically configured to obtain the normal usage duration of the optical module; Obtain the used duration of the optical module; Count the number of abnormal data in the historical data of each metric in the target historical operation data; Calculate the occurrence probability of the optical module fault type corresponding to each metric according to the normal usage duration, the used duration, the total number of the historical data of each metric in the target historical operation data, and the number of abnormal data.

[0066] In one embodiment, the calculation formula for the occurrence probability of the optical module fault type corresponding to each metric in the multiple metrics is:

[0067] where, is the occurrence probability of the optical module fault type corresponding to each metric, is the normal usage duration of the optical module, is the used duration of the optical module, is the number of abnormal data in the historical data of each metric, is the total number of the historical data of each metric.

[0068] In one embodiment, the multiple metrics include: optical power, temperature, bias current, and bias voltage.

[0069] In one embodiment, the calculation formula for the probability of the optical module failing is:

[0070] where, is the probability of the optical module failing, is the occurrence probability of the optical module fault type corresponding to the optical power, is the occurrence probability of the optical module fault type corresponding to the temperature, is the occurrence probability of the optical module fault type corresponding to the bias current, is the occurrence probability of the optical module fault type corresponding to the bias voltage.

[0071] In one embodiment, the device further includes: A third calculation unit 406, configured to calculate the remaining service life of the optical module according to the probability of the optical module failing, the normal service life of the optical module, and the used service life of the optical module.

[0072] In one embodiment, the calculation formula for the remaining service life of the optical module is:

[0073] Wherein, is the remaining service life of the optical module, is the normal service life of the optical module, is the used service life of the optical module, is the probability of the optical module failing.

[0074] In one embodiment, the prediction unit 405 is further configured to predict the health status of the optical module according to the probability of the optical module failing and the remaining service life.

[0075] As Figure 5 shown, an embodiment of the present application provides a computer device, including a processor 51, a communication interface 52, a memory 53, and a communication bus 54. Among them, the processor 51, the communication interface 52, and the memory 53 complete mutual communication through the communication bus 54, The memory 53 is used to store a computer program; In an embodiment of the present application, when the processor 51 is used to execute the program stored on the memory 53, it implements a control method for predicting the health status of an optical module provided by any one of the foregoing method embodiments.

[0076] Those of ordinary skill in the art can understand that all or part of the processes of implementing the methods in the above embodiments can be completed by instructing related hardware through a computer program. The computer program can be stored in a storage medium, and the storage medium is a computer-readable storage medium. The computer program is executed by at least one processor in the computer system to implement the process steps of the method embodiments of the above.

[0077] Therefore, the embodiments of the present application also provide a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the optical module health status prediction method provided in any of the foregoing method embodiments are implemented.

[0078] The storage medium is a physical, non-transitory storage medium. For example, it can be various physical storage media such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a magnetic disk, or an optical disc that can store program codes. The computer-readable storage medium can be non-volatile or volatile.

[0079] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, computer software, or a combination of the two. To clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described according to functions in the above description. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present application.

[0080] In several embodiments provided by the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of each unit is only a logical function division, and there can be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed.

[0081] The steps in the method embodiments of the present application can be adjusted, combined, and deleted according to actual needs. The units in the device embodiments of the present application can be combined, divided, and deleted according to actual needs. In addition, the functional units in each embodiment of the present application can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit.

[0082] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a storage medium. Based on such an understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a terminal, or a network device, etc.) to execute all or part of the steps of the methods described in each embodiment of the present application.

[0083] In the above embodiments, the descriptions of the respective embodiments have their own emphases. For parts not described in detail in a certain embodiment, reference may be made to the relevant descriptions of other embodiments.

[0084] Obviously, those skilled in the art can make various changes and modifications to this application without departing from the spirit and scope of this application. Thus, provided that these modifications and variations of this application fall within the scope of the claims of this application and their equivalent technologies, this application is also intended to include these changes and modifications.

[0085] As described above, the above are only the specific implementation manners of this application, but the protection scope of this application is not limited thereto. Any person skilled in the art within the technical scope disclosed by this application can easily think of various equivalent modifications or substitutions, and these modifications or substitutions should all be covered by the protection scope of this application. Therefore, the protection scope of this application shall be subject to the protection scope of the claims.

Claims

1. A method for predicting the health status of an optical module, characterized in that: The method comprises: Collecting historical operation data of the optical module, the historical operation data including historical data of multiple indicators in the optical module, each indicator in the multiple indicators corresponds to a fault type of the optical module; Preprocessing the historical operation data to obtain target historical operation data; Calculate the probability of occurrence of the optical module failure type corresponding to the indicator according to the historical data of each indicator in the target historical operation data; Calculating the probability of failure of the optical module according to the probability of occurrence of the optical module failure type corresponding to each indicator in the multiple indicators; The health status of the optical module is predicted according to the probability of failure of the optical module.

2. The method according to claim 1, characterized in that: The calculating the occurrence probability of the optical module failure type corresponding to the indicator according to the historical data of each indicator in the target historical operation data includes: Obtaining the normal use time of the optical module, where the normal use time is the nominal life value provided by the manufacturer of the optical module; Obtaining the usage time of the optical module, where the usage time is the cumulative time of the optical module from the first operation to the present; Counting the number of abnormal data in the historical data of each indicator in the target historical operation data; The occurrence probability of the optical module failure type corresponding to the indicator is calculated according to the normal use time, the used time, the total number of historical data of each indicator in the target historical operation data, and the number of abnormal data.

3. The method according to claim 2, characterized in that: The calculation formula for the occurrence probability of the optical module failure type corresponding to each indicator in the multiple indicators is: in, The probability of occurrence of the optical module failure type corresponding to each indicator, is the normal use time of the optical module, is the usage time of the optical module. is the number of abnormal data in the historical data of each indicator, The total number of historical data for each indicator.

4. The method according to any one of claims 1 to 3, characterized in that: The multiple indicators include: optical power, temperature, bias current and bias voltage.

5. The method according to claim 4, characterized in that: The calculation formula for the probability of the optical module failing is: in, is the probability of optical module failure, is the probability of occurrence of optical module failure type corresponding to optical power, is the probability of occurrence of optical module failure type corresponding to temperature, is the probability of occurrence of optical module failure type corresponding to the bias current, is the probability of occurrence of the optical module failure type corresponding to the bias voltage.

6. The method according to claim 2 or 3, characterized in that: After calculating the probability of failure of the optical module according to the probability of occurrence of the optical module failure type corresponding to each indicator in the multiple indicators, and before predicting the health state of the optical module according to the probability of failure of the optical module, the method further includes: Calculating the remaining usage time of the optical module according to the probability of failure of the optical module, the normal usage time of the optical module and the usage time of the optical module; The predicting the health status of the optical module according to the probability of failure of the optical module includes: The health status of the optical module is predicted according to the probability of failure of the optical module and the remaining usage time.

7. The method according to claim 6, characterized in that: The calculation formula for the remaining usage time of the optical module is: in, is the remaining usage time of the optical module. is the normal use time of the optical module, is the usage time of the optical module. is the probability of an optical module failure.

8. An optical module health status prediction device, characterized in that: The device comprises: A collection module, used for collecting historical operation data of the optical module, wherein the historical operation data includes historical data of multiple indicators in the optical module, each of the multiple indicators corresponds to a fault type of the optical module; A preprocessing module, used for preprocessing the historical operation data to obtain target historical operation data; A first calculation unit, configured to calculate the probability of occurrence of an optical module fault type corresponding to each indicator in the target historical operation data according to the historical data of each indicator in the plurality of indicators in the target historical operation data; A second calculation unit, configured to calculate a probability of failure of the optical module according to a probability of occurrence of a failure type of the optical module corresponding to each indicator in the multiple indicators; The prediction unit is used to predict the health status of the optical module according to the probability of failure of the optical module.

9. A computer device, characterized in that: The computer device includes a memory and a processor, the memory stores a computer program, and the processor implements the method according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 7 can be implemented.

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