An optical module and a digital diagnosis method thereof

Abstract

The present invention is applicable to the field of optical module diagnosis technology, and provides an optical module and a digital diagnosis method thereof. The method is applied to an optical module digital diagnosis control system, and the system includes an information acquisition module, a model construction module, a model training module, a parameter determination module, a control application module, a wireless communication module, a wireless communication module, a memory, an alarm, a processing center, and an intelligent control terminal; the information acquisition module, the model construction module, the model training module, the parameter determination module, the control application module, the wireless communication module, the wireless communication module, the memory, and the alarm are respectively connected to the processing center; the intelligent control terminal is respectively connected to the wireless communication module wireless network within the range of a wireless network or the Internet; and a digital diagnosis control device for an optical module is also provided. The present invention can automatically realize optical module production detection and automatic analysis, and improve the detection accuracy.

Description

Technical Field

[0001] The present invention relates to the technical field of optical module digital diagnosis, and in particular to an optical module and a digital diagnosis method thereof. Background Art

[0002] With the rapid development of technologies such as cloud computing, big data, and artificial intelligence, and with the rapid development of the Internet and 5G communications, network capacity has grown rapidly, and the demand for optical modules has also increased dramatically. A large number of optical modules are used in major computer rooms in Internet data centers and base stations in various regions of operators. In these practical application scenarios, whether the optical modules can work properly is crucial. In order to ensure that the optical modules can work properly, strict module testing must be carried out before the optical modules leave the factory. After leaving the factory, the optical modules will be used on switches and other equipment. During use, the communication link may also be abnormal due to optical module failure. At this time, since the user of the optical module is not equipped with a complete set of test systems, or the on-site environment cannot configure a complete set of test systems, the optical modules with abnormalities must be returned to the after-sales service center or the original factory for detailed testing, which takes a long time and affects the communication link. Therefore, the production quality of the optical modules must be strictly tested before leaving the factory to reduce subsequent frequent abnormal problems. Summary of the invention

[0003] In order to solve the deficiencies in the prior art, the purpose of the present invention is to provide an optical module and a digital diagnosis method thereof, which adopts an automated digital rapid diagnosis system to check each function of the optical module one by one, so as to quickly obtain various operating parameters of the optical module, determine the corresponding fault and its location point, improve the diagnosis accuracy and efficiency, ensure the normal operation of the optical module communication link, and avoid various abnormal problems of the optical module such as "abnormal receiving power, degradation of optical power performance, poor eye diagram, unstable communication, severe heat generation, blocked optical link, contaminated port, missing conductive metal of gold finger, and optical signal being restricted due to loose insertion".

[0004] In order to achieve the above purpose, the technical solution adopted by the present invention is:

[0005] A method for digital diagnosis of optical modules is applied to a digital diagnosis control system for optical modules, the system comprising an information acquisition module, a model construction module, a model training module, a parameter determination module, a control application module, a wireless communication module, a memory, an alarm, a processing center, and an intelligent control terminal; the information acquisition module, the model construction module, the model training module, the parameter determination module, the control application module, the wireless communication module, the memory, and the alarm are respectively connected to the processing center; the intelligent control terminal is respectively connected to the wireless communication module wireless network within the range of a wireless network or the Internet;

[0006] The wireless communication module is provided with a wireless network unit, which is responsible for sending and receiving wireless signals and automatically networking with the intelligent control terminal within an effective network range;

[0007] The alarm device compares the actual evaluation score of the model test with the optical module control category model evaluation score standard stored in the memory, and automatically sounds an alarm and notifies to continue training if the standard is not met; and compares the optical module operation test data with the optical module operation quality standard stored in the memory, and automatically sounds an alarm and notifies to adjust the parameters for rework; and compares the optical module operation test data with the optical module operation quality standard stored in the memory, and automatically sounds an alarm and notifies to rework or repair if the standard is not met;

[0008] The memory is responsible for the information storage of the information acquisition module, the model construction module, the model training module, the parameter determination module, the control application module, the wireless communication module, and the alarm, as well as the storage of the optical module control category model evaluation sub-standard and the optical module operation quality standard;

[0009] The processing center is responsible for the information transmission of each functional module, alarm, and memory, and is the hub center of the system. It compares the actual evaluation score of the model test with the optical module control category model evaluation score standard stored in the memory: if it meets the standard, it is the predetermined optical module production detection parameter; if it does not meet the standard, it is passed to the alarm and notified to continue training; and compares the optical module operation test data with the optical module operation quality standard stored in the memory: if it meets the standard, it is set as the formal optical module production detection parameter; if it does not meet the standard, it is passed to the alarm and notified to adjust the parameters for rework; and compares the optical module operation test data with the optical module operation quality standard stored in the memory: if it meets the standard, it is notified that it can be shipped; if it does not meet the standard, it is passed to the alarm and notified to rework or repair;

[0010] The information acquisition module includes a data acquisition unit, a data cleaning unit, a data calibration unit, a normalization standard unit, a filtering and denoising unit, and a data annotation unit, which is responsible for acquiring and preprocessing the normal historical data of the optical module operation and the historical data of various abnormal situations, and passing them to the model building module;

[0011] The model building module includes a feature extraction unit, a data division unit, a forward propagation unit, a loss function unit, a back propagation unit, and an activation function unit, which performs feature extraction and data division on the processed data and inputs the data into the model for data training, and then transmits the data to the model training module;

[0012] The model training module includes a normalized input unit, a convolution output unit, a pooling processing unit, a gradient optimization unit, a learning training unit, and a model evaluation unit, which is responsible for training the model according to the collected data and adjusting the model parameters to optimize the model performance to predetermine the optical module control parameters and pass them to the parameter determination module;

[0013] The parameter determination module includes a transmission rate unit, an optical fiber attenuation unit, a transmission distance unit, an optical fiber channel unit, a trial debugging unit, and an effect confirmation unit, which is responsible for performing control tests under different conditions according to the control parameters predicted by the trained model to obtain the optimal production detection parameters of the optical module and pass them to the control application module;

[0014] The control application module includes a patch production unit, an appearance detection unit, an electrical detection unit, an optical detection unit, an adaptation detection unit, and an aging detection unit, which are responsible for producing and performing various tests on the optical modules according to the program instructions of the optical module production and detection equipment according to the set production and detection parameters, and transmitting them to the processing center.

[0015] The present invention provides a method for digital diagnosis of optical modules, comprising the following steps:

[0016] S10, before detection, the information acquisition module obtains and pre-processes the normal historical data of the optical module operation and the historical data of various abnormal conditions for subsequent model construction, and passes them to the model construction module;

[0017] S20, the model building module extracts features and divides the processed data and inputs the data into the model for data training to ensure the accuracy of model recognition and pass it to the model training module;

[0018] S30, the model training module trains the model according to the collected data and adjusts the model parameters to optimize the model performance to predetermine the optical module control parameters, ensure the stability of the optical module operation, and pass them to the parameter determination module;

[0019] S40, the parameter determination module performs control tests under different conditions according to the control parameters predicted by the trained model to obtain the optimal production and detection parameters of the optical module to ensure the subsequent operation quality, and transmits them to the control application module;

[0020] S50, the control application module performs production and various tests on the optical module according to the set production and testing parameters on the optical module production and testing equipment according to the program instructions to ensure that the optical module meets the quality requirements and transmits it to the processing center;

[0021] S60, the processing center compares the optical module operation test data with the optical module operation quality standard stored in the memory: if it meets the standard, it will be notified that it can be shipped; if it does not meet the standard, it will be transmitted to the alarm and notified to rework or repair.

[0022] Further, the step S10 comprises the following steps:

[0023] S11. The data acquisition unit obtains the historical data of various electrical, optical, and environmental adaptability performances and production test parameters of similar optical modules by networking with the industry database, and transmits it to the data cleaning unit;

[0024] S12. The data cleaning unit cleans the collected historical data to remove abnormal values, duplicate values ​​or missing values ​​in the data to improve the quality and accuracy of the data, and transmits the data to the data calibration unit;

[0025] S13, the data calibration unit obtains the standardized optical module operation data according to the data standardization processing formula "z = (x-μ) / σ, z is the standardized data, x is the original data, μ is the mean of the data, σ is the standard deviation of the data" to improve the stability of the model, and passes it to the normalization standard unit;

[0026] S14, the normalization standard unit obtains the normalized optical module operation data according to the normalization calculation formula "x'=(x-min(x)) / (max(x)-min(x)), x' is the normalized data, x is the original data, min(x) and max(x) are the minimum and maximum values ​​of the data respectively" to improve the model performance, and passes it to the filtering and denoising unit;

[0027] S15, filtering and denoising unit is calculated according to the Gaussian filtering method formula G(x,y) is the two-dimensional Gaussian function pixel, (x,y) is the pixel coordinate, and σ is the standard deviation. The denoised image is obtained for color space conversion and passed to the data annotation unit;

[0028] S16. The data annotation unit automatically labels the preprocessed data for classification, target detection, and semantic segmentation to facilitate the extraction of key features in the subsequent data, which is more conducive to model training, testing, and verification.

[0029] Further, the step S20 comprises the following steps:

[0030] S21, the feature extraction unit converts the labeled data into a feature vector useful for model training and extracts key features of transmission rate, wavelength, sensitivity, optical power, extinction ratio, and packaging form, and transmits them to the data division unit;

[0031] S22, the data division unit divides the data set with key feature vectors after extraction into a training set, a validation set and a test set and establishes a convolutional neural network structure for subsequent model training, and passes it to the forward propagation unit;

[0032] S23, forward propagation unit according to the convolutional layer forward propagation calculation formula a′ is the output of the lth layer, σ is the activation function, Wlm is the convolution kernel of the lth layer, Xm is the input feature map, and bl is the bias term. The convolution layer output is obtained and passed to the loss function unit;

[0033] S24, the loss function unit obtains the loss function value according to the loss function calculation formula "L = max(0, m + y * (f (x) - b)", L is the loss function value, m is the hyperparameter, y is the sample label (0 or 1), f (x) is the predicted value of the model, and b is the classification boundary" for subsequent back propagation and passes it to the back propagation unit;

[0034] S25, the back propagation unit obtains the output of the convolution layer and the fully connected layer according to the back propagation calculation formula "dz[l] = da[l] * g[l]′(z[l]), da[l] is the input data, da[1] is the output data, and g[l]′ is the sigmoid derivative of the activation function", and passes it to the activation function unit;

[0035] S26. The activation function unit obtains the output of the Tanh function according to the ReLU function calculation formula "f(x)=max(0,x), f(x) is the output of the ReLU function, and x is the input of the ReLU function".

[0036] Further, the step S30 comprises the following steps:

[0037] S31, the normalized input unit calculates the formula "h = φ (B N (Wx+b)), h is the output of the fully connected layer, φ is the activation function, B N is the batch normalization operator, W is the weight parameter, x is the input of the fully connected layer, and b is the bias parameter. The output of Batch Norm is obtained and passed to the convolution output unit;

[0038] S32, the convolution output unit is calculated according to the convolution output formula

[0039] O(n,k,ox,oy) convolution output data, C is the convolution channel, R is the convolution kernel height, S is the convolution kernel width, I(n,c,ix,iy), W(k,c,r,s) are the convolution input data" to obtain the convolution output data and pass it to the pooling processing unit;

[0040] S33, the pooling processing unit calculates the maximum pooling formula

[0041] A(i*s+u,j*s+v), M(I,j) is the (I,j)th element of the output feature map, u and v are indexes that vary in the range [0,f-1], and s is the step size. The spatial dimension of the feature map is obtained and passed to the gradient optimization unit;

[0042] S34, the gradient optimization unit calculates the formula according to the gradient descent method is the parameter value of the nth iteration, α is the learning rate or step size, Get the gradient descent data for the gradient of function f at Xn and pass it to the learning training unit;

[0043] S35, the learning training unit determines the training model according to the gradient descent data, and inputs the data in the training set into the model for simulation training under different environmental conditions to ensure the accuracy of model recognition, and transmits it to the model evaluation unit;

[0044] S36, the model evaluation unit obtains the comprehensive evaluation score F according to the model evaluation score calculation formula "F = 2*(Ac*Re) / (Ac+Re), F is the training model evaluation score, Ac is the precision, and Re is the recall rate", and transmits it to the processing center;

[0045] S37. The processing center compares the actual evaluation score of the model with the evaluation score standard of the optical module control category model stored in the memory: if it meets the standard, it is the predetermined optical module production detection parameter; if it does not meet the standard, it is passed to the alarm and notified to continue training.

[0046] Further, the step S40 comprises the following steps:

[0047] S41. The transmission rate unit obtains the transmission rate of the optical module according to the optical module transmission rate calculation formula "Vs = (P / P0) * P / f, Vs is the transmission rate (bps), P is the input signal power (dm), P0 is the noise power (dm), and f is the input signal frequency (Hz)" for subsequent debugging and transfer to the optical fiber attenuation unit;

[0048] S42, the optical fiber attenuation unit obtains the optical module optical fiber attenuation according to the optical module optical fiber attenuation calculation formula "A'=α*L+N1*p1+N2*p2, A' is the optical fiber attenuation (dB), α is the maximum optical fiber attenuation coefficient, L is the optical fiber length, N1 is the number of connector pairs, p1 is the connector loss, N2 is the number of fusion splices, and p2 is the fusion splice loss" and transmits it to the transmission distance unit;

[0049] S43, the transmission distance unit is calculated according to the optical module transmission distance formula "L = [S-(P 出 -A′-S)] / α, L is the transmission distance of the optical module (km), S is the light receiving sensitivity (dm), P is 出is the transmitter output optical power (dm), A′ is the fiber attenuation (dB), and α is the fiber attenuation coefficient (d / km)” to obtain the transmission distance of the optical module and pass it to the fiber channel unit;

[0050] S44, fiber channel unit according to the optical module fiber channel calculation formula "M1 = π * (φ1 / λ1) 2 / 2,M2=π*(n*sinθ / λ2) 2 / 2, M1 and M2 are the number of multi-mode and single-mode fiber channels respectively, φ1 is the multi-mode fiber mode field diameter (nm), λ1 and λ2 are the wavelengths of multi-mode and single-mode fibers respectively (nm), n is the refractive index of the multi-mode fiber core layer, and θ is the maximum receiving angle of the multi-mode fiber. The number of fiber channels of the optical module is obtained and transmitted to the trial debugging unit;

[0051] S45. The trial debugging unit debugs the transmission rate, wavelength, sensitivity, extinction ratio, and transmission power of the optical module under different conditions according to the predetermined production test parameters to ensure the quality requirements of the optical module, and transmits the results to the effect confirmation unit;

[0052] S46, the effect confirmation unit obtains the test data of the optical module's transmitted optical power, sensitivity, eye diagram, extinction ratio, bit error rate, and compatibility through the optical module diagnostic equipment, and transmits it to the processing center;

[0053] S47, the processing center compares the optical module operation test data with the optical module operation quality standard stored in the memory: if it meets the standard, it is set as the formal optical module production inspection parameter; if it does not meet the standard, it is transmitted to the alarm and notified to adjust the parameters for rework.

[0054] Further, the step S50 comprises the following steps:

[0055] S51, the patch production unit performs patching, welding, assembly and packaging through the SFP optical module automatic production machine according to the production order to complete the production of the optical module, and transmits it to the appearance inspection unit;

[0056] S52, the appearance inspection unit completes the appearance inspection of the optical module through a high-definition camera and a three-dimensional coordinate measuring instrument to obtain the appearance size quality information of the optical module, and transmits it to the electrical inspection unit;

[0057] S53, the electrical detection unit detects various electrical performance indicators of the transmitter, receiver, and compatibility of the optical module through the optical module electrical performance detection equipment according to the predetermined operating parameters and instructions, and transmits them to the optical detection unit;

[0058] S54, the optical detection unit detects the emission light power, optical fiber wavelength, optical loss, and optical modulation amplitude of the optical module through the optical module optical performance detection device according to the operation instruction and the operation parameters, and transmits them to the adaptation detection unit;

[0059] S55, the adaptability detection unit detects the environmental adaptability indicators of the temperature cycle, humidity, vibration, impact, and corrosion resistance of the optical module through the optical module environmental adaptability detection equipment, and transmits them to the aging detection unit;

[0060] S56. The aging detection unit detects the performance difference of the optical module when it runs for a long time at high and low temperatures through the optical module aging test equipment to ensure the service life of the optical module.

[0061] An optical module digital diagnosis control system provided by the present invention also includes a computer-readable storage medium containing a memory; the memory stores a computer program, and when the functional modules execute the computer program, the steps of the optical module digital diagnosis method described in any one of the above items are implemented; the computer-readable storage medium stores a computer program, and when the computer program is executed by the functional modules, the steps of the optical module digital diagnosis method described in any one of the above items are implemented.

[0062] The present invention also provides an optical module digital diagnosis control device, which is implemented by the optical module digital diagnosis method described above.

[0063] The beneficial effects of the present invention compared with the prior art are as follows:

[0064] By setting up various functional modules such as information acquisition module, model construction module, model training module, parameter determination module, control application module, etc., and using an automated digital rapid diagnosis system to check the functions of the produced optical modules and the optical modules returned to the factory for inspection one by one, the various operating parameters of the optical modules can be quickly obtained to determine the corresponding faults and their locations, thereby improving diagnostic accuracy and efficiency, reducing diagnostic costs, improving user satisfaction, and ensuring the normal operation of the optical module communication link. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments or exemplary technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0066] Figure 1 It is a schematic diagram of the system module of the present invention;

[0067] Figure 2 This is a schematic diagram of an information acquisition module of the present invention;

[0068] Figure 3 It is a schematic diagram of the model construction module of the present invention;

[0069] Figure 4 This is a schematic diagram of a model training module of the present invention;

[0070] Figure 5 It is a schematic diagram of a parameter determination module of the present invention;

[0071] Figure 6 It is a schematic diagram of the control application module of the present invention;

[0072] Figure 7 It is a schematic diagram of the method flow control program of the present invention;

[0073] Figure 8 This is a schematic diagram of step S10 in the method flow of the present invention;

[0074] Fig. 9 This is a schematic diagram of step S20 in the method flow of the present invention;

[0075] Fig.10 This is a schematic diagram of step S30 in the method flow of the present invention;

[0076] Fig.11 This is a schematic diagram of step S40 in the method flow of the present invention;

[0077] Fig.12 It is a program schematic diagram of step S50 in the method flow of the present invention. DETAILED DESCRIPTION

[0078] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0079] The specific implementation of the present invention is described in detail below in conjunction with specific embodiments:

[0080] In order to make the technical problems, technical solutions and beneficial effects to be solved by the present application clearer, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application. It should be noted that when a module is referred to as "set on" another module, it can be directly on the other module or indirectly on the other module. When a module is referred to as "connected to" another module, it can be directly connected to the other module or indirectly connected to the other module. In the description of the present application, "multiple" means two or more, unless otherwise clearly and specifically defined. "Several" means one or more, unless otherwise clearly and specifically defined.

[0081] In the description of this application, it should be noted that, unless otherwise clearly specified and limited, the terms "connected" and "connection" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements. For ordinary technicians in this field, the specific meanings of the above terms in this application can be understood according to specific circumstances. The terms "include", "comprise", "have" and their variations all mean "including but not limited to", unless otherwise specifically emphasized.

[0082] See also Figure 1 As shown, the present invention provides an optical module digital diagnosis method, which is applied to an optical module digital diagnosis control system, wherein the system comprises an information acquisition module, a model construction module, a model training module, a parameter determination module, a control application module, a wireless communication module, a memory, an alarm, a processing center, and an intelligent control terminal; the information acquisition module, the model construction module, the model training module, the parameter determination module, the control application module, the wireless communication module, the memory, and the alarm are respectively connected to the processing center; the intelligent control terminal comprises a smart phone, a tablet computer, and a smart remote control, which are respectively connected to the wireless communication module wireless network within the range of a wireless network or the Internet.

[0083] The wireless communication module is provided with a wireless network unit, which is responsible for sending and receiving wireless signals and automatically networking with the intelligent control terminal within an effective network range. The wireless signals include eight types of Internet of Things signals, including MQTT, CoAP, HTTP, REST API, Zigbee, LoRaWAN, NB-IoT, and Bluetooth.

[0084] The alarm compares the actual evaluation score of the model test with the optical module control category model evaluation score standard stored in the memory, and automatically sounds an alarm and notifies to continue training if the standard is not met; and compares the optical module operation test data with the optical module operation quality standard stored in the memory, and automatically sounds an alarm and notifies to adjust parameters for rework if the standard is not met; and compares the optical module operation test data with the optical module operation quality standard stored in the memory, and automatically sounds an alarm and notifies to rework or repair if the standard is not met.

[0085] The memory is responsible for storing information of the information acquisition module, the model construction module, the model training module, the parameter determination module, the control application module, the wireless communication module, and the alarm, as well as storing the optical module control category model evaluation sub-standard and the optical module operation quality standard.

[0086] The processing center is responsible for information transmission among various functional modules, alarms, and memories, and is the hub center of the system. The processing center compares the actual evaluation score of the model test with the optical module control category model evaluation score standard stored in the memory: if the standard is met, the optical module production detection parameter is set; if the standard is not met, it is passed to the alarm and notified to continue training; and the optical module operation test data is compared with the optical module operation quality standard stored in the memory: if the standard is met, it is set as the formal optical module production detection parameter; if the standard is not met, it is passed to the alarm and notified to adjust the parameters for rework; and the optical module operation test data is compared with the optical module operation quality standard stored in the memory: if the standard is met, it is notified that it can be shipped; if the standard is not met, it is passed to the alarm and notified to rework or repair.

[0087] See also Figure 2 As shown, the information acquisition module includes a data acquisition unit, a data cleaning unit, a data calibration unit, a normalization standard unit, a filtering and denoising unit, and a data labeling unit, which are responsible for acquiring and preprocessing the normal historical data of the optical module operation and the historical data of various abnormal situations, and passing them to the model building module.

[0088] Furthermore, the data acquisition unit obtains historical data of various electrical, optical, and environmental adaptability performances and production detection parameters of similar optical modules by networking with the industry database, and passes it to the data cleaning unit; the data cleaning unit cleans the collected historical data to remove abnormal values, duplicate values, or missing values ​​in the data, and passes it to the data calibration unit; the data calibration unit obtains standardized data according to the data standardization processing formula "z = (x-μ) / σ", and passes it to the normalization standard unit; the normalization standard unit obtains normalized data according to the normalization calculation formula "x' = (x-min(x)) / (max(x)-min(x))", and passes it to the filtering and denoising unit; the filtering and denoising unit calculates the data according to the filtering method formula The denoised image is obtained and passed to a data annotation unit; the data annotation unit automatically labels the preprocessed data for classification, target detection, and semantic segmentation to facilitate key feature extraction in subsequent data, which is more conducive to model training, testing, and verification.

[0089] See also Figure 3 As shown, the model building module includes a feature extraction unit, a data division unit, a forward propagation unit, a loss function unit, a back propagation unit, and an activation function unit, which performs feature extraction and data division on the processed data and inputs the data into the model for data training, and then passes it to the model training module.

[0090] Furthermore, the feature extraction unit converts the labeled data into feature vectors useful for model training and extracts key features of transmission rate, wavelength, sensitivity, optical power, extinction ratio, and packaging form, and transmits them to the data division unit; the data division unit divides the data set with key feature vectors after extraction into training set, verification set, and test set and establishes a convolutional neural network structure, and transmits it to the forward propagation unit; the forward propagation unit calculates the forward propagation of the convolution layer according to the forward propagation calculation formula The output of the convolution layer is obtained and passed to the loss function unit; the loss function unit obtains the loss function value according to the loss function calculation formula "L = max(0, m + y * (f (x) - b))" and passes it to the back propagation unit; the back propagation unit obtains the output of the convolution layer and the fully connected layer according to the back propagation calculation formula "dz [l] = da [l] * g [l] ′ (z [l])" and passes it to the activation function unit; the activation function unit obtains the output of the Tanh function according to the ReLU function calculation formula "f (x) = max (0, x), f (x) is the output of the ReLU function, x is the input of the ReLU function".

[0091] See also Figure 4 As shown, the model training module includes a normalized input unit, a convolution output unit, a pooling processing unit, a gradient optimization unit, a learning training unit, and a model evaluation unit, which are responsible for training the model according to the collected data and adjusting the model parameters to optimize the model performance to predetermine the optical module control parameters and pass them to the parameter determination module.

[0092] Furthermore, the normalized input unit is calculated according to the Batch Norm layer formula “h=φ(B N (Wx+b))” obtains the output of Batch Norm and passes it to the convolution output unit; the convolution output unit calculates the convolution output according to the convolution output formula Obtain the convolution output data and pass it to the pooling processing unit; the pooling processing unit calculates the maximum pooling formula The spatial dimension of the feature map is obtained and passed to the gradient optimization unit; the gradient optimization unit calculates the formula according to the gradient descent method Gradient descent data is obtained and passed to a learning and training unit; the learning and training unit determines a training model according to the gradient descent data, inputs the data in the training set into the model for simulation training under different environmental conditions, and passes the data to a model evaluation unit; the model evaluation unit obtains a comprehensive evaluation score F according to a model evaluation score calculation formula "F = 2*(Ac*Re) / (Ac+Re)", and passes the score to a processing center.

[0093] See also Figure 5As shown, the parameter determination module includes a transmission rate unit, a fiber attenuation unit, a transmission distance unit, a fiber channel unit, a trial debugging unit, and an effect confirmation unit, which is responsible for performing control tests under different conditions based on the control parameters predicted by the trained model to obtain the optimal production detection parameters of the optical module and pass them to the control application module.

[0094] Further, the transmission rate unit obtains the optical module transmission rate according to the optical module transmission rate calculation formula "Vs = (P / P0) * P / f", and transmits it to the optical fiber attenuation unit; the optical fiber attenuation unit obtains the optical module optical fiber attenuation according to the optical module optical fiber attenuation calculation formula "A' = α * L + N1 * p1 + N2 * p2", and transmits it to the transmission distance unit; the transmission distance unit obtains the optical module optical fiber attenuation according to the optical module transmission distance calculation formula "L = [S-(P 出 -A′-S)] / α” to obtain the transmission distance of the optical module and pass it to the optical fiber channel unit; the optical fiber channel unit calculates the optical module optical fiber channel according to the formula “M1=π*(φ1 / λ1) 2 / 2,M2=π*(n*sinθ / λ2) 2 / 2" to obtain the number of optical fiber channels of the optical module and pass it to the trial debugging unit; the trial debugging unit debugs the transmission rate, wavelength, sensitivity, extinction ratio and emission power of the optical module under different conditions according to the predetermined production detection parameters, and passes them to the effect confirmation unit; the effect confirmation unit obtains the test data of the emission optical power, sensitivity, eye diagram, extinction ratio, bit error rate and compatibility of the optical module through the optical module diagnostic equipment, and passes them to the processing center.

[0095] See also Figure 5 As shown, the control application module includes a patch production unit, an appearance detection unit, an electrical detection unit, an optical detection unit, an adaptation detection unit, and an aging detection unit, which are responsible for producing and testing the optical modules according to the program instructions of the optical module production and testing equipment according to the set production and testing parameters, and transmit them to the processing center.

[0096] Furthermore, the patch production unit performs patching and welding, assembly and packaging through the SFP optical module automatic production machine according to the production command to complete the production of the optical module, and transmits it to the appearance detection unit; the appearance detection unit completes the appearance detection of the optical module through a high-definition camera and a three-dimensional coordinate measuring instrument to obtain the appearance size quality information of the optical module, and transmits it to the electrical detection unit; the electrical detection unit detects the transmitter performance index of the optical module, as well as the receiver performance index, compatibility and other electrical performance indexes through the optical module electrical performance detection equipment according to the predetermined operating parameters and instructions, and transmits them to the optical detection unit; the optical detection unit detects the optical performance indexes of the optical module such as the emission light power, optical fiber wavelength, optical loss, and optical modulation amplitude through the optical module optical performance detection equipment according to the operating instructions and operating parameters, and transmits them to the adaptation detection unit; the adaptation detection unit detects the environmental adaptability indexes of the optical module such as temperature cycle, humidity, vibration, impact, corrosion resistance, etc. through the optical module environmental adaptability detection equipment, and transmits them to the aging detection unit; the aging detection unit detects the performance difference of the optical module running for a long time at high and low temperatures through the optical module aging test equipment to ensure the service life of the optical module.

[0097] System operation working principle:

[0098] Before detection, the information acquisition module obtains the normal historical data of the optical module operation and the historical data of various abnormal situations and pre-processes them for subsequent model construction, and passes them to the model construction module; then the model construction module extracts features and divides the processed data and inputs them into the model for data training to ensure the accuracy of model recognition, and passes them to the model training module; the model training module trains the model according to the collected data and adjusts the model parameters to optimize the model performance to predetermine the control parameters of the optical module to ensure the stability of the operation of the optical module, and passes them to the parameter determination module; then the parameter determination module performs control tests under different conditions according to the control parameters predicted by the trained model to obtain the optimal production and detection parameters of the optical module to ensure the subsequent operation quality, and passes them to the control application module; through the control application module, the optical module production and detection equipment produces and detects the optical module according to the program instructions according to the set production and detection parameters, so as to ensure that the optical module meets the quality requirements, and passes them to the processing center; then the processing center compares the test data of the optical module operation with the optical module operation quality standard stored in the memory: if it meets the standard, it will be notified that it can be shipped; if it does not meet the standard, it will be passed to the alarm and notified to rework or repair.

[0099] When diagnostic personnel or managers are outdoors or away from home, they can use the intelligent control terminal to automatically connect to the wireless network or the Internet through the wireless communication module, thereby combining the intelligent control terminal, the Internet and the Internet of Things technology. The digital diagnosis of the optical module can be remotely monitored in real time through the mobile phone APP, which improves the diagnostic efficiency and reduces the diagnostic cost.

[0100] See also Figure 6 As shown, the present invention provides an optical module digital diagnosis method, comprising the following steps:

[0101] S10, before detection, the information acquisition module obtains and pre-processes the normal historical data of the optical module operation and the historical data of various abnormal conditions for subsequent model construction, and passes them to the model construction module;

[0102] See also Figure 8 As shown, the step S10 includes the following steps:

[0103] S11. The data acquisition unit obtains the historical data of various electrical, optical, and environmental adaptability performances and production test parameters of similar optical modules by networking with the industry database, and transmits it to the data cleaning unit;

[0104] It is further explained that various historical parameter data sets of similar specification optical modules are collected from the industry database through data collection software; the industry database includes historical data of similar optical module manufacturers, and also includes historical data of similar specification optical modules obtained from third-party sharing platforms such as the Internet, social media, or public data sets, so as to obtain high-quality, diverse and rich normal and abnormal historical data of similar specification optical modules, including transmission rate, wavelength, sensitivity, emission power, extinction ratio and its operating temperature, voltage and other historical data as well as historical data of operating parameters of optical module production and testing equipment.

[0105] S12. The data cleaning unit cleans the collected historical data to remove abnormal values, duplicate values ​​or missing values ​​in the data to improve the quality and accuracy of the data, and transmits the data to the data calibration unit;

[0106] It is further explained that due to sensor failure, recording errors or system anomalies in the operation of the optical module, outliers appear, which affect the accuracy of subsequent analysis; missing value processing is performed on data with missing parts; each feature in the data set is detected by writing code to determine the missing values, and the missing value pattern is identified. According to the number and impact of the missing values, the code is written to select to fill or delete samples or variables with missing values, and forward filling or backward filling is used to fill the missing values ​​for time series data; the processing effect is verified by comparing indicators such as data quality before and after processing, model accuracy and reliability. If the processing effect is not good, the processing method is reselected or the data is re-cleaned until the best effect is achieved, so as to remove duplicate, erroneous, and invalid data to effectively process missing values ​​for subsequent model construction and analysis.

[0107] S13, the data calibration unit obtains standardized data according to the data standardization processing formula "z = (x-μ) / σ, z is standardized data, x is original data, μ is the mean of the data, σ is the standard deviation of the data" to improve the stability of the model, and passes it to the normalization standard unit;

[0108] It is further explained that the collected raw data is calibrated, including time synchronization, unit conversion, range adjustment, etc., such as ensuring that all timestamps are in a unified format and synchronized with the system clock; converting data collected by different sensors to the same unit to ensure data accuracy; using the written code to use the above data standardization processing formula "z = (x-μ) / σ" to convert dates, numbers, texts, etc. into a standard normal distribution with a mean of 0 and a standard deviation of 1, subtracting the mean and dividing by the standard deviation to make it a unified standard format to ensure data consistency and comparability for subsequent processing; encoding unstructured data and converting it into structured data, or converting text data into numerical representation for model processing and analysis, thereby processing the raw data into a form that meets specific standards for subsequent model training and analysis.

[0109] S14, the normalization standard unit obtains the normalized data according to the normalization calculation formula "x'=(x-min(x)) / (max(x)-min(x)), x' is the normalized data, x is the original data, min(x) and max(x) are the minimum and maximum values ​​of the data respectively" to improve the model performance, and passes it to the filtering and denoising unit;

[0110] It is further explained that the above normalization calculation formula "x'=(x-min(x)) / (max(x)-min(x))" is used by the written code to proportionally map the cleaned data to the specified range of [0,1] to eliminate the total amount difference between different samples or features and the dimensional influence between the data, make the distribution of the data consistent, and confirm whether the data has been normalized to the specified range or distribution as expected, and verify the normalization effect through statistical descriptions such as maximum value and minimum value; the data includes multiple types such as text, pictures, audio, video, etc., to eliminate the dimensional differences between different data, thereby improving the accuracy and efficiency of data analysis.

[0111] S15, filtering and denoising unit is calculated according to the Gaussian filtering method formula G(x,y) is the two-dimensional Gaussian function pixel, (x,y) is the pixel coordinate, and σ is the standard deviation. The denoised image is obtained for color space conversion and passed to the data annotation unit;

[0112] It is further explained that the Gaussian filter output pixel value obtained according to the Gaussian filter calculation formula is the weighted average of the input pixel values ​​in its neighborhood, and the weight is given by the Gaussian function, where the standard deviation σ determines the width of the Gaussian function, and the Gaussian function is discretized, that is, the weight is calculated within a certain window size, and then these weights are applied to the corresponding neighborhood pixel values ​​of the input image to obtain the output pixel value, and the pixels are arranged from small to large according to the grayscale value and the median is taken as the new grayscale value; the sampling value in the input signal is checked to determine whether it represents the signal itself, and the values ​​in the window are sorted by using an observation window composed of an odd number of samples, the middle value is taken as the output, the earliest value is discarded, a new sample is obtained, and the above calculation process is repeated to reduce the random noise in the image and make the image smoother.

[0113] S16. The data annotation unit automatically labels the preprocessed data for classification, target detection, and semantic segmentation to facilitate the extraction of key features in the subsequent data, which is more conducive to model training, testing, and verification.

[0114] It is further explained that the standardized data is classified through the written code, and then the classified data is automatically labeled, and accurate labels or annotations are added to the data, which are used as target variables or features in subsequent model training, and as training sets and validation sets. It ensures that the model has accurate data references during the training process, so that it can learn how to generate corresponding labels based on data characteristics to improve labeling efficiency and accuracy; for some data, users are required to enter the system through their mobile phones to remotely add new labels or modify existing labels manually to ensure that they accurately reflect the essential characteristics of the data, and deploy the labeled data to the corresponding system platform for subsequent applications or analysis to improve the accuracy and reliability of the labels.

[0115] S20, the model building module extracts features and divides the processed data and inputs the data into the model for data training to ensure the accuracy of model recognition and pass it to the model training module;

[0116] See also Fig. 9 As shown, the step S20 includes the following steps:

[0117] S21, the feature extraction unit converts the labeled data into a feature vector useful for model training and extracts key features of transmission rate, wavelength, sensitivity, optical power, extinction ratio, and packaging form, and transmits them to the data division unit;

[0118] It is further explained that the most representative features that can reflect the essential content of the data and have the degree of discrimination and description are selected from the preprocessed data to reduce the dimension of the features, reduce the computational complexity, and at the same time improve the performance and generalization ability of the model; after feature extraction, principal component analysis is used to perform feature dimensionality reduction to obtain a large amount of feature information in order to reduce the dimension of the features and reduce the computational complexity; after feature dimensionality reduction, the features are binary encoded to improve the interpretability of the features and the performance of the model; since some original data information may be lost after feature extraction and dimensionality reduction, feature reconstruction such as principal component reconstruction or least squares reconstruction is performed to restore the data information as much as possible; evaluation is performed through cross-validation, and the feature extraction method and parameters are adjusted according to the evaluation results to evaluate the quality and effect of the extracted features.

[0119] S22, the data division unit divides the data set with key feature vectors after extraction into a training set, a validation set and a test set and establishes a convolutional neural network structure for subsequent model training, and passes it to the forward propagation unit;

[0120] It is further explained that the ratio of training set, validation set and test set is determined according to the pre-selected segmentation strategy, and the data distribution between each subset is ensured to be as consistent as possible to avoid introducing bias; in some application scenarios, since the data has time series characteristics, the data set is divided into a training set for training the model, a validation set for adjusting model parameters and selecting the best model, and a test set for evaluating the final performance of the model according to the written code to ensure the temporal consistency of the training and test sets; the data set is passed through the convolutional neural network deep learning in a 9:1 relationship to establish the training set and test set required for training the temperature pattern prediction model under different environments, thereby establishing a deep convolutional neural network structure: 3 convolutional layers, 3 maximum pooling layers, 2 fully connected layers, 1 softmax layer, and 1 output layer.

[0121] S23, forward propagation unit according to the convolutional layer forward propagation calculation formula a′ is the output of the lth layer, σ is the activation function, Wlm is the convolution kernel of the first layer, X m is the input feature map, b l Get the convolutional layer output for the "bias term" and pass it to the loss function unit;

[0122] Further explanation: the forward pass calculation formula of the convolution layer is Through "z 2 =a 2 *W 2 +b 2 Substitute "a 2 =σ(z 2 )”, where the superscript 2 is the number of layers 2, the asterisk * is convolution, b is bias, and σ is activation function. In the convolutional neural network, forward propagation includes: the data of the input layer is convolved through the convolution kernel to generate a feature map; the output of the convolution layer is nonlinearly transformed through the activation function; the output of the activation layer is downsampled through the pooling operation to reduce the number of parameters and the amount of calculation; the output of the pooling layer is flattened and input to the fully connected layer to finally get the output result.

[0123] S24, the loss function unit obtains the loss function value according to the loss function calculation formula "L = max(0, m + y * (f (x) - b)", L is the loss function value, m is the hyperparameter, y is the sample label (0 or 1), f (x) is the predicted value of the model, and b is the classification boundary" for subsequent back propagation and passes it to the back propagation unit;

[0124] To further explain, in the loss function calculation formula "L=max(0,m+y*(f(x)-b))", when f(x)≤b, then L=m+y(f(x)-b), when f(x)>b, then L=0; in machine learning or optimization problems, f(x) is an optimization target, b is a threshold, m and y are parameters; when the target value f(x) is less than or equal to the threshold b, the value of L is equal to m+y(f(x)-b); when the target value f(x) is greater than the threshold b, the value of L is 0; use the training set data to train the model, adjust the model parameters to minimize the loss function, so that the predicted value of the positive sample is greater than a certain threshold, and the predicted value of the negative sample is less than a certain threshold.

[0125] S25, the back propagation unit obtains the output of the convolution layer and the fully connected layer according to the back propagation calculation formula "dz[l] = da[l] * g[l]′(z[l]), da[l] is the input data, da[1] is the output data, and g[l]′ is the sigmoid derivative of the activation function", and passes it to the activation function unit;

[0126] It is further explained that the output is dw[l]=dz[l]*a[l-1], db[l]=dz[l], da[l-1]=W[l]T*dz[l]; the back propagation of the convolution layer transfers the error term da[l]da[l] to the previous layer through a convolution operation, flips the convolution kernel and applies it to the error map, calculates the local gradient through the derivative of the activation function, and implements it through matrix operations. The input and the convolution kernel are deformed into matrices and matrix multiplication is performed, that is, the numbers in the convolution window are pulled into a row to form a column vector and matrix multiplication is performed; the back propagation of the fully connected layer calculates the gradient of the weight and the gradient of the bias through the chain rule. For each node, the error term is transferred to the node of the previous layer through the weight matrix, and the local gradient is calculated through the derivative of the activation function. It is implemented through matrix operations. The error term is multiplied by the output of the current layer and multiplied by the input of the previous layer to update it in the direction of minimizing the loss function.

[0127] S26. The activation function unit obtains the output of the Tanh function according to the ReLU function calculation formula "f(x)=max(0,x), f(x) is the output of the ReLU function, and x is the input of the ReLU function".

[0128] It is further explained that before data activation, linear transformation output is performed through the linear transformation calculation formula "y=W*X+b", where X is input data, y is output data, W is weight matrix, and b is bias vector; then function activation is performed through the ReLU function calculation formula, where the input x of the ReLU function is the output of the convolution layer or other layers, which is a nonlinear transformation activation function; when the ReLU function is in forward propagation, the gradient is 1 when the input is greater than 0, otherwise it is 0. Since the calculation is simple, the problem of gradient disappearance will not occur, so it performs better in deep networks and maintains the stability of the gradient during propagation. For each element in the input x, if it is greater than 0, the element itself is output; after function activation, the neural network TDropout output is obtained through the Dropout layer calculation formula "y=f(W*X+b)", where y is the neural network output, f is the activation function, W is the weight matrix, X is the input data, and b is the bias vector, which can effectively prevent overfitting.

[0129] S30, the model training module trains the model according to the collected data and adjusts the model parameters to optimize the model performance to predetermine the optical module control parameters, ensure the stability of the optical module operation, and pass them to the parameter determination module;

[0130] See also Fig.10 As shown, the step S30 includes the following steps:

[0131] S31, the normalized input unit calculates the formula "h = φ (BN (Wx+b)), h is the output of the fully connected layer, φ is the activation function, B N is the batch normalization operator, W is the weight parameter, x is the input of the fully connected layer, and b is the bias parameter. The output of Batch Norm is obtained and passed to the convolution output unit;

[0132] Further explanation, the "h=φ(B N (Wx+b))” through “B N (x) = γ⊙[(x-μ B ) / σ B ]+β”, where μ B is the mean, σ B is the standard deviation, γ is the stretch parameter, β is the offset parameter, (x-μ B ) / σ B To standardize the normal distribution; the Batch Norm layer (referred to as "BN layer") is placed between the affine transformation and the activation function in the fully connected layer, and the output value of each layer in the network is standardized to make it more obedient to the normal distribution, which can accelerate the training speed of the neural network and help prevent gradient disappearance and gradient explosion; the BN layer reduces the correlation between input data by standardizing the data of each mini-batch, so that the mean of each sample is 0 and the variance is 1, thereby reducing the problem of internal covariate shift, helping to accelerate the training process, improve the stability and generalization ability of the model, and avoid reducing the problem of reducing the representation ability of the neural network.

[0133] S32, the convolution output unit is calculated according to the convolution output formula *W(k,c,r,s),O(n,k,ox,oy) is the convolution output data, C is the convolution channel, R is the convolution kernel height, S is the convolution kernel width, I(n,c,ix,iy), W(k,c,r,s) is the convolution input data" to obtain the convolution output data and pass it to the pooling processing unit;

[0134] It is further explained that the number of convolution channels C is obtained according to the design; the convolution kernel height R and the convolution kernel width S are calculated by the formulas "R = (R1-F + 2P) / L + 1" and "S = (S1-F + 2P) / L + 1", respectively, where R1 and R are the heights of the kernel before and after the convolution, F is the convolution kernel size, P is the image plus zero thickness, L is the distance of each movement, S1 and S are the widths of the kernel before and after the convolution, respectively; each input channel corresponds to a convolution template, which is used to generate a feature map, and the elements of the feature maps corresponding to all input channels at the same position are added to form an output channel, and the number of output channels is equal to the number of convolution kernels.

[0135] S33, the pooling processing unit calculates the maximum pooling formula M(I,j) is the (I,j)th element of the output feature map, u and v are indexes that vary in the range [0,f-1], and s is the step size. The spatial dimension of the feature map is obtained and passed to the gradient optimization unit;

[0136] To further explain, select an appropriate window size according to needs, such as 2*2, 3*3, etc., determine the step size of each movement, usually the step size is consistent with the window size, and for the data in each window, calculate the maximum value or average value, and use the calculation result as the output, and each output position corresponds to an area in the input; the maximum pooling algorithm uses an overlapping 2*2 window for the pooling layer to reduce the size of the feature map, thereby reducing the amount of calculation, while retaining the most significant features, and reducing the spatial dimension of the feature map by taking the maximum value in the local area, but does not change the number of channels, and selects the largest value in a local window of the input feature map as the output of the window. Its parameters include: pooling kernel size, step size, padding, expansion, and whether to round up. It can reduce the spatial dimension of the feature map and reduce the size of the feature map, thereby reducing the amount of calculation and the risk of overfitting.

[0137] S34, the gradient optimization unit calculates the formula according to the gradient descent method Xn is the parameter value of the nth iteration, α is the learning rate or step size, Get the gradient descent data for the gradient of function f at Xn and pass it to the learning training unit;

[0138] Further explanation: the gradient descent method can minimize the differentiable function f(x) to obtain the minimum value x by iteratively updating the parameters and gradually approaching the optimal solution. In the formula, the learning rate or step size α is used to control the step size of each iteration, and the gradient of the function f at Xn is That is, the derivative, starting from any initial point, determines the next position according to the gradient direction of the current position (that is, the direction in which the function decreases fastest) and the learning rate, and iterates until the stopping condition is met, including: starting from any initial point, calculating the gradient of the current point Update Parameters Check the stopping condition, if it is satisfied, end the iteration, otherwise recalculate the gradient.

[0139] S35, the learning training unit determines the training model according to the gradient descent data, and inputs the data in the training set into the model for simulation training under different environmental conditions to ensure the accuracy of model recognition, and transmits it to the model evaluation unit;

[0140] It is further explained that the loss function is minimized and the prediction accuracy of the model is improved by continuously adjusting the model parameters to ensure that the model can accurately identify the adaptability of the temperature control of the optical module in different environments; the model is trained through the data set in the training set to fit the data analysis rules, that is, to determine the various learning parameters such as the model weight and bias; and the model parameters and hyperparameters are adjusted during the model training process through the data set in the validation set to optimize the model performance and avoid overfitting. The model is selected, but it does not participate in the determination of the learning parameters, and the model parameters and hyperparameters with smaller model errors are selected; then the generalization ability of the model on unknown data is evaluated through the data set in the test set after the model training is completed, and it is used once after training to evaluate the effect of the final model, and it does not participate in the learning parameter process or the hyperparameter selection process.

[0141] S36, the model evaluation unit obtains the comprehensive evaluation score F according to the model evaluation score calculation formula "F = 2*(Ac*Re) / (Ac+Re), F is the training model evaluation score, Ac is the precision, and Re is the recall rate", and transmits it to the processing center;

[0142] Further explanation, the Ac and Re are respectively expressed by "Ac=(T P +T N ) / (T P +F P +F N +T N ), Re=TP / (T P +F N )”, where T P is the number of true positive samples, F P is the actual number of false positive samples, F N is the number of false negative samples, T N is the number of true negative samples; after the model training is completed, a test is performed to verify the accuracy and reliability of the model. During the test, problems with the model may be found and optimized and adjusted; the training model evaluation score, namely the F value, is the harmonic mean of precision and recall, which can evaluate the performance of the classification model. The value range is 0 to 1, where 1 is the best performance and 0 is the worst performance. The higher the F value, the better the prediction effect of the model, and vice versa. Therefore, if the F value is close to 1, it indicates that the model performs well while maintaining a balance between precision and recall; adjust the hyperparameters according to the model's learning rate, regularization coefficient and other performance to optimize the model performance.

[0143] S37. The processing center compares the actual evaluation score of the model test with the optical module control category model evaluation score standard stored in the memory: if it meets the standard, it is the predetermined optical module production detection parameter; if it does not meet the standard, it is transmitted to the alarm and notified to continue training until it meets the standard.

[0144] It is further explained that the optical module control category identification training model test evaluation score standard is best when it is greater than 0.8, which is specifically determined according to the optical module type, transmitted optical power and receiving sensitivity, bias current, whether the optical port is contaminated and damaged, whether the ESD is damaged, temperature and humidity, vibration, aging, wear and other factors of the optical module. For example, the control category training model evaluation score standard of the optical module with high grade, strong transmitted optical power and receiving sensitivity, reasonable bias current, no pollution and damage to the optical port, no ESD damage, moderate temperature and humidity, no vibration, no aging, and no wear is 1 or close to 1; while the control model evaluation score standard of the optical module with resistance, weak transmitted optical power and receiving sensitivity, unreasonable bias current, pollution and damage to the optical port, ESD damage, unreasonable temperature and humidity, vibration, fast aging, and wear is relatively low, so the optical module must be selected with strong receiving sensitivity, reasonable bias current, no ESD damage, no vibration, no wear, etc., so as to ensure the normal operation of the optical module; the optical module production detection parameters include the production detection process of the optical module, the operating parameters of the production detection equipment, the operating parameters of the optical module, etc.

[0145] S40, the parameter determination module performs control tests under different conditions according to the control parameters predicted by the trained model to obtain the optimal production and detection parameters of the optical module to ensure the subsequent operation quality, and transmits them to the control application module;

[0146] See also Fig.11 As shown, the step S40 includes the following steps:

[0147] S41. The transmission rate unit obtains the transmission rate of the optical module according to the optical module transmission rate calculation formula "Vs = (P / P0) * P / f, Vs is the transmission rate (bps), P is the input signal power (dm), P0 is the noise power (dm), and f is the input signal frequency (Hz)" for subsequent debugging and transfer to the optical fiber attenuation unit;

[0148] Further explanation: the optical module transmission rate calculation formula "Vs = (P / P0) * P / f" is obtained by substituting "Sr = P / P0" into "Vs = Sr * P / f", where Sr is the signal-to-noise ratio (dB); the input signal power P, noise power P0, and input signal frequency f are all obtained through intelligent sensors; the transmission rate Vs is the amount of data transmitted by the optical module per unit time, which determines the smoothness of the network; the signal-to-noise ratio Sr is the ratio of signal power to noise power, which is proportional to the transmission rate; the input signal power P is the speed of the optical module input signal, and the greater the power, the higher the transmission rate may be; the input signal frequency Vs is the frequency range of the optical module input signal, which is inversely proportional to the transmission rate.

[0149] S42. The optical fiber attenuation unit obtains the optical module optical fiber attenuation according to the optical module optical fiber attenuation calculation formula "A'=α*L+N1*p1+N2*p2, A' is the optical fiber attenuation (dB), α is the maximum optical fiber attenuation coefficient, L is the optical fiber length, N1 is the number of connector pairs, p1 is the connector loss, N2 is the number of fusion splices, and p2 is the fusion splice loss" for subsequent debugging and transmission to the transmission distance unit;

[0150] It is further explained that the optical fiber attenuation calculation formula "A'=α*L+N1*p1+N2*p2" is obtained by substituting "A1=α*L, A2=N1*p1, A3=N2*p2" into "A'=A1+A2+A3", and only numerical values ​​are calculated during calculation, where A1 is the optical cable attenuation (dB), A2 is the connector attenuation (dB), and A3 is the fusion attenuation (dB); the maximum optical fiber attenuation coefficient α is obtained through the database; the optical fiber length L (km), the number of connector pairs N1, and the number of fusion splices N2 are all design parameters; the connector loss p1 (dB) and the fusion loss p2 (dB) are both calculated through historical accumulated data.

[0151] S43, the transmission distance unit is calculated according to the optical module transmission distance formula "L = [S-(P 出 -A′-S)] / α, L is the transmission distance of the optical module (km), S is the light receiving sensitivity (dm), P is 出 is the transmitter output optical power (dm), A′ is the fiber attenuation (dB), and α is the fiber attenuation coefficient (d / km)” to obtain the transmission distance of the optical module for subsequent debugging and transfer to the fiber channel unit;

[0152] Further explanation: the optical module transmission distance calculation formula "L = [S - (P 出 -A′-S)] / α” through “P 发 =P 出 -A′-S” into “L=(SP 发 ) / α”, where P 发 is the optical power emitted by the optical module (dm); the optical receiving sensitivity S is obtained by detecting with a spectrometer and is the minimum optical power that the receiver can detect; the emitted optical power P 发 , Transmitter output optical power P 出 All are obtained through detection by a spectrum tester; the optical fiber attenuation A′ is obtained by calculation in the above step S42; the optical fiber attenuation coefficient α is obtained by querying a database; the optical fiber attenuation A′ is also called the optical path loss, that is, the loss of the optical signal caused by various factors during the transmission process.

[0153] S44, fiber channel unit according to the optical module fiber channel calculation formula "M1 = π * (φ1 / λ1) 2 / 2,M2=π*(n*sinθ / λ2) 2 / 2, M1 and M2 are the number of multi-mode and single-mode fiber channels respectively, φ1 is the multi-mode fiber mode field diameter (nm), λ1 and λ2 are the wavelengths of multi-mode and single-mode fibers respectively (nm), n is the refractive index of the multi-mode fiber core layer, and θ is the maximum receiving angle of the multi-mode fiber. The number of fiber channels of the optical module is obtained for subsequent debugging and is passed to the trial debugging unit;

[0154] Further explanation, the multimode optical fiber channel calculation formula "M2 = π*(n*sinθ / λ2) 2 / 2" is substituted into "M2=π*(φ2 / λ2) by "φ1=n*sinθ" 2 / 2”, where φ2 is the single-mode fiber mode field diameter (nm); the multi-mode and single-mode fiber mode field diameters φ1 and φ2 are measured by a fiber mode field diameter tester using a far-field scanning method to accurately measure the mode field diameters of different types of optical fibers such as single-mode and multi-mode; the multi-mode and single-mode fiber wavelengths λ1 and λ2 are measured and obtained by a spectrometer; the “V=φ / λ”; the maximum receiving angle θ is the maximum angle that light can enter the optical fiber and form with the optical fiber axis, and it can be known from the optical fiber design parameters that for optical fibers in the air; the multi-mode optical fiber core layer refractive index n is measured and obtained by a refractive index sensor, and when n is approximately 1, the single-mode fiber mode field diameter φ2 calculation formula can be simplified to φ2=sinθ.

[0155] S45. The trial debugging unit debugs the transmission rate, wavelength, sensitivity, extinction ratio, and transmission power of the optical module under different conditions according to the predetermined production test parameters to ensure the quality requirements of the optical module, and transmits the results to the effect confirmation unit;

[0156] It is further explained that after the optical module is manufactured, the parameters calculated by referring to the above steps S41-S44 are used to control the optical module under various conditions such as operating temperature, power supply voltage, packaging form, optical fiber type, optical interface type, etc., and the optical module is controlled at close range or remotely through the APP software on the smart mobile terminal or the remote control software on the computer to perform various tests under these conditions to ensure that the optical module can operate normally to meet the user's quality requirements. Through the system, managers can remotely control and monitor all optical modules, view daily operating conditions, data changes, equipment activity logs, etc., and remotely connect and control optical modules by prefabricating deployment codes or adding deployment codes. The operation of remote optical modules can be viewed through smart mobile terminals or computers.

[0157] S46, the effect confirmation unit obtains the test data of the optical module's transmitted optical power, sensitivity, eye diagram, extinction ratio, bit error rate, and compatibility through the optical module diagnostic equipment, and transmits it to the processing center;

[0158] It is further explained that according to the predetermined process parameters, the optical power emitted by the optical module is detected by an optical power meter, and the receiving sensitivity of the optical module is detected by the bit error meter according to the operation command; then the eye diagram of the optical module is detected by an eye diagram analyzer to obtain the Q factor, eye diagram margin, jitter, rise time, and fall time; when the extinction ratio tester receives the command, the output power of the optical module is detected and the extinction ratio of the optical module is obtained; according to the predetermined operation parameters, the bit error rate of the optical module is detected by the bit error tester to ensure the transmission quality of the optical module; then the compatibility and performance of the optical module are detected by effectively connecting with the optical module through a preset switch to ensure stable and efficient operation in various network environments to meet the quality standards of the optical module and customer requirements.

[0159] S47, the processing center compares the optical module operation test data with the optical module operation quality standard stored in the memory: if it meets the standard, it is set as the formal optical module production inspection parameter; if it does not meet the standard, it is transmitted to the alarm and notified to adjust the parameters for rework.

[0160] It is further explained that the optical module operation quality standards include: the input voltage, output voltage, current, transmission power, transmission rate, bit error rate, receiving sensitivity and other electrical performance indicators of the optical module meet the standards; the optical performance indicators of the optical module such as the transmitted light power, optical fiber wavelength, optical loss, optical modulation amplitude meet the standards; the environmental adaptability indicators of the optical module such as temperature cycle, humidity, vibration, impact, long-term high-temperature operation meet the standards; the optical module functions normally, the protocol compatibility meets the standards, etc.; the optical module meets the accelerated aging standards in a high temperature and high humidity simulated environment; the length, width (or height), flatness, straightness and other dimensions of the optical module meet the requirements; the surface should be free of abrasions and scratches, and the appearance is complete and correct; the optical module control parameters include various production equipment such as SFP optical module end face cleaning equipment, SFP optical module automatic loading machine, SFP optical module automatic unloading machine, as well as automatic operation parameters of various detection equipment such as high-definition cameras, three-dimensional coordinate measuring instruments, and optical module automatic testing equipment.

[0161] S50, the control application module performs production and various tests on the optical module according to the set production and testing parameters on the optical module production and testing equipment according to the program instructions to ensure that the optical module meets the quality requirements and transmits it to the processing center;

[0162] See also Fig.12 As shown, the step S50 includes the following steps:

[0163] S51, the patch production unit performs patching, welding, assembly and packaging through the SFP optical module automatic production machine according to the production order to complete the production of the optical module, and transmits it to the appearance inspection unit;

[0164] To further explain, optoelectronic chips, optical components, integrated circuits (ICs), components and electronic raw materials are selected according to design requirements to ensure the quality of incoming materials; optoelectronic chips, optical components, integrated circuits (ICs), components and other semiconductor bare chip devices are attached to carriers such as PCBs, Kovar alloys, ceramic substrates, etc. through the SFP optical module automatic production machine according to predetermined process parameters and control commands, and then the optical devices are automatically welded; the PCBA with the welded devices is placed into the shell base, small accessories such as springs and unlocking keys are installed, the upper pressure cover is placed and screwed to complete the assembly of the optical module hardware part, and then the module is packaged to protect the internal components; finally, foreign matter on the end face and appearance surface is cleaned to ensure that the surface appearance of the optical module is neat.

[0165] S52, the appearance inspection unit completes the appearance inspection of the optical module through a high-definition camera and a three-dimensional coordinate measuring instrument to obtain the appearance size quality information of the optical module, and transmits it to the electrical inspection unit;

[0166] It is further explained that according to the set process parameters, the high-definition camera is controlled to perform 360-degree shooting, and images of various positions of the optical module are captured from multiple different angles. The different positions of the optical module are identified at different angles, so that the positions of the optical module and its shape can be more accurately and comprehensively identified, or based on multiple images of the optical module captured from different angles, the optical module is identified by multi-angle matching, so as to determine whether the appearance of the optical module is complete, whether the outer shell surface is damaged, deformed, cracked, abraded, scratched and other defects, whether the interface is intact, and whether the label is clearly visible; then, according to the control instructions, the corresponding operating parameters are obtained by the three-dimensional coordinate measuring instrument according to the training model to automatically detect the length, width (or height), flatness, straightness and other dimensional information of the optical module; finally, the optical fiber end face is detected by the optical fiber end face detector to keep the port clean and ensure the quality of the optical module.

[0167] S53, the electrical detection unit detects the operating voltage and current, transmission power, output waveform transmitter performance indicators of the optical module, and the transmission rate, bit error rate, receiving sensitivity receiver performance indicators, compatibility and other electrical performance indicators of the optical module through the optical module electrical performance detection equipment according to the predetermined operating parameters and instructions, and transmits them to the optical detection unit;

[0168] It is further explained that according to the predetermined operating parameters, the optical power at the transmitting end of the optical module is measured by an optical power meter to ensure the signal strength and stability of the optical module during the transmission process; the eye diagram of the optical module is measured by an eye diagram analyzer to obtain the Q factor, eye diagram margin, jitter, rise time, and fall time to evaluate the quality of its output waveform, reflecting the overall performance of the transmitter signal such as jitter, noise and distortion; the operating voltage, operating current and other electrical parameters of the transmitter are obtained by an intelligent sensor to ensure that it meets the design requirements; the transmission rate and receiving sensitivity of the optical module at different optical powers are tested by a bit error meter to determine the thresholds of the minimum received optical power and the maximum received optical power, which is helpful to evaluate the receiving ability of the optical module in harsh environments, and simulate the actual transmission environment to measure the bit error rate of the optical module under specific conditions to ensure higher transmission quality and reliability; the optical module is installed on different switches or devices to perform various protocol compatibility tests, and it is run for a predetermined time to determine whether the optical module can communicate normally with devices of different brands and models to ensure that it has good compatibility.

[0169] S54, the optical detection unit detects the optical performance indicators of the optical module, such as the emission light power, optical fiber wavelength, optical loss, and optical modulation amplitude, through the optical module optical performance detection device according to the operation instruction and the operation parameters, and transmits them to the adaptive detection unit;

[0170] It is further explained that the optical power emitted by the optical module is measured by an optical power meter according to predetermined operating parameters to ensure that its value is within a specified range; then the optical attenuation is adjusted by an optical attenuator to simulate optical signals of different intensities to detect whether the optical module can normally receive signals under what optical power; the digital signal of the optical module is collected multiple times by an oscilloscope and superimposed to form an eye-like graphic to reflect whether the measured signal is lost and the quality of signal transmission; the extinction ratio of the optical module is measured by an oscilloscope to reflect the contrast of the optical signal in the "on" and "off" states; the optical module is connected to a standard optical fiber for a long-term copy test, and the bit error rate is observed and recorded to ensure that it is lower than the specified threshold.

[0171] S55, the adaptability detection unit detects the temperature cycle, humidity, vibration, impact, corrosion resistance and other environmental adaptability indicators of the optical module through the optical module environmental adaptability detection equipment, and transmits them to the aging detection unit;

[0172] It is further explained that the optical module is tested in a high and low temperature environment in a cycle to evaluate its performance stability under different temperature conditions to ensure that the optical module can still maintain normal operation under extreme temperature environments; the optical module is tested in a high humidity environment to evaluate its moisture resistance to ensure that the optical module can maintain reliability and stability in humid environments such as oceans or rainforests; the seismic performance of the optical module is evaluated through a vibration test to ensure that the optical module will not be damaged by vibration during transportation and use; the corrosion resistance of the optical module is tested in a salt spray environment such as the ocean or other corrosive environments to ensure the service life of the optical module.

[0173] S56. The aging detection unit detects the performance difference of the optical module when it runs for a long time at high and low temperatures through the optical module aging test equipment to ensure the service life of the optical module.

[0174] Further explanation: put the optical module into the low-temperature aging test equipment, set the temperature to a specific low temperature value such as -50℃, and maintain it for the set time (such as 4 hours). During this period, monitor the temperature regularly to ensure uniform distribution. During the low-temperature maintenance period, the optical module should be in a power-off state. After the low-temperature treatment is completed, power on again and perform performance tests, including key parameters such as optical power, receiving sensitivity, and extinction ratio, and compare them with the performance at room temperature; then transfer the optical module from the low-temperature environment to the high-temperature aging test equipment, set the temperature to a specific high temperature value such as +90℃ or 75℃, and maintain it for a period of time. During the high-temperature test, the optical module should remain powered on. After the high-temperature maintenance is completed, repeat the performance test steps to ensure that the performance indicators of the optical module in the high-temperature environment are still up to standard; compare the parameter differences before and after aging, as well as the performance differences at room temperature and high and low temperatures, to evaluate the aging effect of the optical module.

[0175] S60, the processing center compares the test data of the optical module operation with the optical module operation quality standard stored in the memory: if it meets the standard, it will notify that it can be shipped; if it does not meet the standard, it will be transmitted to the alarm and notified to rework or repair.

[0176] It is further explained that in addition to the optical module trial debugging standards described in step S47 above, the optical module operation quality standards also include: the output optical power of the optical module meets the standards; the stable wavelength of the optical module is 20-50ppm; the bandwidth test data of the optical module meets the standards; the optical module complies with the MSA multi-source agreement and has passed CE, ROHS, FCC, ISO9001 and other certifications, and meets international standards and safety requirements; the optical module has a DDM function, and the real-time data such as the operating temperature, operating voltage, operating current, transmitted optical power and received optical power in the optical module meet the standards; the appearance inspection of the optical module is complete, without any signs of damage, the gold finger has no scratches, and the serial number is clearly marked; the operating temperature of the optical module is 0-70℃, the operating voltage is around 3.3V, and the allowable fluctuation range is 5%; the transmitted optical power, extinction ratio, center wavelength and other indicators of the transmitting end meet the standards, and the received optical power, overload optical power, receiving sensitivity and other indicators of the receiving end meet the standards.

[0177] It is further explained that the above steps are displayed in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows, unless clearly stated in this document. There is no strict order restriction for the execution of these steps, and these steps can also be executed according to other orders, and some steps can include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed and completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.

[0178] Further explanation, the present invention is described according to the content implemented by the software program of the optical module digital diagnosis control system. Each optical module digital diagnosis method is divided into several modules or units to implement the software program instructions generated by each step. The software program instructions include the optical module digital diagnosis method described above.

[0179] An optical module digital diagnosis control system provided by the present invention also includes a computer-readable storage medium containing a memory; the memory stores a computer program, and when the functional modules execute the computer program, the steps of the optical module digital diagnosis method described in any one of the above items are implemented; the computer-readable storage medium stores a computer program, and when the computer program is executed by the functional modules, the steps of the optical module digital diagnosis method described in any one of the above items are implemented.

[0180] It is further explained that the computer program is divided into multiple modules / units, and the multiple modules / units are stored in the memory and executed by each module / unit to complete the present invention; the multiple modules / units can be a series of computer program instructions that can complete specific functions, and the instructions are used to describe the execution process of the computer program in each module / unit.

[0181] The present invention also provides an optical module digital diagnosis control device, which is implemented by the optical module digital diagnosis method described above.

[0182] It is further explained that the control device is composed of the above-mentioned associated detection equipment or devices, and can be made into a complete set of optical module automatic production and detection equipment together with the optical module production and detection equipment according to the needs of the optical module manufacturer; it can also be made into a unit device of each functional module belonging to each step or the entire control device, and each control device is connected to each optical module production and detection equipment through a wireless connection during installation; the structures of these devices are not described in detail here; the control device can also be used in professional optical module detection institutions to improve detection efficiency.

[0183] It is obvious to those skilled in the art that the present application is not limited to the details of the above exemplary embodiments, and that the present application can be implemented in other specific forms without departing from the spirit or basic features of the present application. Therefore, no matter from which point of view, the embodiments should be regarded as exemplary and non-restrictive, and the scope of the present application is defined by the attached claims rather than the above description, and therefore it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims are included in the present application.

[0184] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present application and are not intended to limit it. Although the present application has been described in detail with reference to the preferred embodiments, a person of ordinary skill in the art should understand that the technical solution of the present application can be modified or replaced by equivalents, and all of these should be included in the scope of protection of the present application.

Claims

1. A digital diagnosis method for an optical module, characterized in that: The method is applied to an optical module digital diagnosis control system, the system comprising an information acquisition module, a model construction module, a model training module, a parameter determination module, a control application module, a wireless communication module, a memory, an alarm, a processing center, and an intelligent control terminal; the information acquisition module, the model construction module, the model training module, the parameter determination module, the control application module, the wireless communication module, the memory, and the alarm are respectively connected to the processing center; the intelligent control terminal is respectively connected to the wireless communication module wireless network within the wireless network or the Internet; the method comprises the following steps: S10, before detection, the information acquisition module obtains and pre-processes the normal historical data of the optical module operation and the historical data of various abnormal conditions for subsequent model construction, and passes them to the model construction module; S20, the model building module extracts features and divides the processed data and inputs the data into the model for data training to ensure the accuracy of model recognition and pass it to the model training module; S30, the model training module trains the model according to the collected data and adjusts the model parameters to optimize the model performance to predetermine the optical module control parameters, ensure the stability of the optical module operation, and pass them to the parameter determination module, including the following steps: S31, the normalized input unit is calculated according to the Batch Norm layer formula "h=φ(B N (Wx+b)), h is the output of the fully connected layer, φ is the activation function, B N is the batch normalization operator, W is the weight parameter, x is the input of the fully connected layer, and b is the bias parameter. The output of Batch Norm is obtained and passed to the convolution output unit; S32, convolution output unit according to the convolution output calculation formula" Convolution output data, C is the convolution channel, R is the convolution kernel height, S is the convolution kernel width, I (n, c, ix, iy), W (k, c, r, s) is the convolution input data "obtain the convolution output data and pass it to the pooling processing unit; S33, the pooling processing unit is calculated according to the maximum pooling formula" M(I,j) is the (I,j)th element of the output feature map, u and v are indexes that vary in the range [0,f-1], and s is the step size. The spatial dimension of the feature map is passed to the gradient optimization unit; S34, gradient optimization unit calculates the formula according to the gradient descent method" Xn is the parameter value of the nth iteration, α is the learning rate or step size, Get the gradient descent data for the gradient of function f at Xn and pass it to the learning training unit; S35, the learning training unit determines the training model according to the gradient descent data, and inputs the data in the training set into the model for simulation training under different environmental conditions to ensure the accuracy of model recognition, and transmits it to the model evaluation unit; S36, the model evaluation unit obtains the comprehensive evaluation score F according to the model evaluation score calculation formula "F = 2*(Ac*Re) / (Ac+Re), F is the training model evaluation score, Ac is the precision, and Re is the recall rate", and transmits it to the processing center; S37, the processing center compares the actual evaluation score of the model with the evaluation score standard of the optical module control category model stored in the memory: if it meets the standard, it is the predetermined optical module production detection parameter; if it does not meet the standard, it is transmitted to the alarm and notified to continue training; S40, the parameter determination module performs control tests under different conditions according to the control parameters predicted by the trained model to obtain the optimal production and detection parameters of the optical module to ensure the subsequent operation quality, and transmits them to the control application module; S50, the control application module performs production and various tests on the optical module according to the set production and testing parameters on the optical module production and testing equipment according to the program instructions to ensure that the optical module meets the quality requirements and transmits it to the processing center; S60, the processing center compares the test data of the optical module operation with the optical module operation quality standard stored in the memory: if it meets the standard, it will notify that it can be shipped; if it does not meet the standard, it will be transmitted to the alarm and notified to rework or repair.

2. According to claim 1, a method for digital diagnosis of optical modules is characterized in that: The system further comprises: The wireless communication module is provided with a wireless network unit, which is responsible for sending and receiving wireless signals and automatically networking with the intelligent control terminal within an effective network range; The alarm device compares the actual evaluation score of the model test with the optical module control category model evaluation score standard stored in the memory, and automatically sounds an alarm and notifies to continue training if the standard is not met; and compares the optical module operation test data with the optical module operation quality standard stored in the memory, and automatically sounds an alarm and notifies to adjust the parameters for rework; and compares the optical module operation test data with the optical module operation quality standard stored in the memory, and automatically sounds an alarm and notifies to rework or repair if the standard is not met; The memory is responsible for the information storage of the information acquisition module, the model construction module, the model training module, the parameter determination module, the control application module, the wireless communication module, and the alarm, as well as the storage of the optical module control category model evaluation sub-standard and the optical module operation quality standard; The processing center is responsible for the information transmission of each functional module, alarm, and memory, and is the hub center of the system. It compares the actual evaluation score of the model test with the optical module control category model evaluation score standard stored in the memory: if it meets the standard, it is the predetermined optical module production detection parameter; if it does not meet the standard, it is passed to the alarm and notified to continue training; and compares the optical module operation test data with the optical module operation quality standard stored in the memory: if it meets the standard, it is set as the formal optical module production detection parameter; if it does not meet the standard, it is passed to the alarm and notified to adjust the parameters for rework; and compares the optical module operation test data with the optical module operation quality standard stored in the memory: if it meets the standard, it is notified that it can be shipped; if it does not meet the standard, it is passed to the alarm and notified to rework or repair; The information acquisition module includes a data acquisition unit, a data cleaning unit, a data calibration unit, a normalization standard unit, a filtering and denoising unit, and a data annotation unit, which is responsible for acquiring and preprocessing the normal historical data of the optical module operation and the historical data of various abnormal situations, and passing them to the model building module; The model building module includes a feature extraction unit, a data division unit, a forward propagation unit, a loss function unit, a back propagation unit, and an activation function unit, which performs feature extraction and data division on the processed data and inputs the data into the model for data training, and then transmits the data to the model training module; The model training module includes a normalized input unit, a convolution output unit, a pooling processing unit, a gradient optimization unit, a learning training unit, and a model evaluation unit, which is responsible for training the model according to the collected data and adjusting the model parameters to optimize the model performance to predetermine the optical module control parameters and pass them to the parameter determination module; The parameter determination module includes a transmission rate unit, an optical fiber attenuation unit, a transmission distance unit, an optical fiber channel unit, a trial debugging unit, and an effect confirmation unit, which is responsible for performing control tests under different conditions according to the control parameters predicted by the trained model to obtain the optimal production detection parameters of the optical module and pass them to the control application module; The control application module includes a patch production unit, an appearance detection unit, an electrical detection unit, an optical detection unit, an adaptation detection unit, and an aging detection unit, which are responsible for producing and performing various tests on the optical modules according to the program instructions of the optical module production and detection equipment according to the set production and detection parameters, and transmitting them to the processing center.

3. The optical module digital diagnosis method according to claim 1, characterized in that: The step S40 comprises the following steps: S41. The transmission rate unit obtains the transmission rate of the optical module according to the optical module transmission rate calculation formula "Vs = (P / P0) * P / f, Vs is the transmission rate (bps), P is the input signal power (dm), P0 is the noise power (dm), and f is the input signal frequency (Hz)" for subsequent debugging and transfer to the optical fiber attenuation unit; S42, the optical fiber attenuation unit obtains the optical module optical fiber attenuation according to the optical module optical fiber attenuation calculation formula "A'=α*L+N1*p1+N2*p2, A' is the optical fiber attenuation (dB), α is the maximum optical fiber attenuation coefficient, L is the optical fiber length, N1 is the number of connector pairs, p1 is the connector loss, N2 is the number of fusion splices, and p2 is the fusion splice loss" and transmits it to the transmission distance unit; S43, the transmission distance unit is calculated according to the optical module transmission distance formula "L = [S-(P 出 -A′-S)] / α, L is the transmission distance of the optical module (km), S is the light receiving sensitivity (dm), P is 出 is the transmitter output optical power (dm), A′ is the fiber attenuation (dB), and α is the fiber attenuation coefficient (d / km)” to obtain the transmission distance of the optical module and pass it to the fiber channel unit; S44, Fiber channel unit according to the optical module fiber channel calculation formula "M1 = π * (φ1 / λ1) 2 / 2,M2=π*(n*sinθ / λ2) 2 / 2, M1 and M2 are the number of multi-mode and single-mode fiber channels respectively, φ1 is the multi-mode fiber mode field diameter (nm), λ1 and λ2 are the wavelengths of multi-mode and single-mode fibers respectively (nm), n is the refractive index of the multi-mode fiber core layer, and θ is the maximum receiving angle of the multi-mode fiber. The number of fiber channels of the optical module is obtained and transmitted to the trial debugging unit; S45. The trial debugging unit debugs the transmission rate, wavelength, sensitivity, extinction ratio, and transmission power of the optical module under different conditions according to the predetermined production test parameters to ensure the quality requirements of the optical module, and transmits the results to the effect confirmation unit; S46, the effect confirmation unit obtains the test data of the optical module's transmitted optical power, sensitivity, eye diagram, extinction ratio, bit error rate, and compatibility through the optical module diagnostic equipment, and transmits it to the processing center; S47, the processing center compares the optical module operation test data with the optical module operation quality standard stored in the memory: if it meets the standard, it is set as the formal optical module production inspection parameter; if it does not meet the standard, it is transmitted to the alarm and notified to adjust the parameters for rework.

4. The optical module digital diagnosis method according to claim 1, characterized in that: The step S50 comprises the following steps: S51, the patch production unit performs patching, welding, assembly and packaging through the SFP optical module automatic production machine according to the production order to complete the production of the optical module, and transmits it to the appearance inspection unit; S52, the appearance inspection unit completes the appearance inspection of the optical module through a high-definition camera and a three-dimensional coordinate measuring instrument to obtain the appearance size quality information of the optical module, and transmits it to the electrical inspection unit; S53, the electrical detection unit detects various electrical performance indicators of the transmitter, receiver, and compatibility of the optical module through the optical module electrical performance detection equipment according to the predetermined operating parameters and instructions, and transmits them to the optical detection unit; S54, the optical detection unit detects the emission light power, optical fiber wavelength, optical loss, and optical modulation amplitude of the optical module through the optical module optical performance detection device according to the operation instruction and the operation parameters, and transmits them to the adaptation detection unit; S55, the adaptability detection unit detects the temperature cycle, humidity, vibration, impact, and corrosion resistance of the optical module through the optical module environment adaptability detection equipment, and transmits the results to the aging detection unit; S56. The aging detection unit detects the performance difference of the optical module when it runs for a long time at high and low temperatures through the optical module aging test equipment to ensure the service life of the optical module.

5. A method for digital diagnosis of optical modules according to claims 1-4, characterized in that: The system also includes a computer-readable storage medium containing a memory; the memory stores a computer program, and when the functional modules execute the computer program, the steps of the optical module digital diagnosis method described in any one of claims 1 to 4 are implemented; the computer-readable storage medium stores a computer program, and when the computer program is executed by the functional modules, the steps of the optical module digital diagnosis method described in any one of claims 1 to 4 are implemented; and also includes an optical module digital diagnosis control device, which is implemented by the optical module digital diagnosis method described in any one of claims 1 to 4.

6. An optical module digital diagnosis control device, characterized in that: The method is implemented by using a digital diagnosis method for an optical module as described in any one of claims 1 to 4 above.

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