Rapid self-checking method and device for optical fiber switch

Abstract

The invention discloses a fast self-checking method and device for an optical fiber switch, and belongs to the technical field of switch self-checking, and the self-checking method comprises the following specific steps: I, collecting the operation data of the optical fiber switch, and constructing a switch virtual model; iI, identifying and predicting potential faults of the optical fiber switch and optimizing a self-inspection process; iII, collecting information of external equipment and optimizing workload distribution among the equipment; according to the method, the overall performance of the self-checking process of the optical fiber switch can be ensured to be optimal, the optimization efficiency is greatly improved, the time and resource consumption in the self-checking process is reduced, the possibility of human errors is reduced, the overall self-checking accuracy and efficiency are improved, uncertain factors in the self-checking process can be effectively handled, and the self-checking accuracy and efficiency are improved. A self-checking process with higher robustness is provided, high calculation cost caused by global search is avoided, the self-checking period is shortened, the overall response speed is improved, and the flexibility and adaptability of the self-checking process are enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of switch self-checking, and in particular to a method and device for rapid self-checking of an optical fiber switch. Background Art

[0002] With the rapid development of information technology and communication technology, fiber optic networks play a vital role in modern communication systems. As the central equipment of the communication network, fiber optic switches undertake a large number of data transmission and switching tasks. The reliability of their operation is directly related to the quality and stability of network services. However, with the increasing complexity of equipment and the diversification of application environments, the potential failure risks faced by fiber optic switches are gradually increasing. Since fiber optic switches may be affected by environmental factors, equipment aging, accidental damage and other factors during long-term operation, the risk of failure increases. Traditional fiber optic switch self-test methods usually rely on regular manual inspections or basic automated tests, which have problems such as low detection efficiency and inaccurate fault location, and are difficult to meet the growing network needs. Therefore, timely and accurate fault detection and maintenance are particularly important.

[0003] After searching, Chinese patent number CN201711102368.9 discloses a method and device for rapid self-testing of optical fiber switches. Although this invention saves the company the cost of professional testing equipment, the inspection operation is less professional and can be performed by personnel in any position, and can achieve rapid batch testing, improve work efficiency, and can be applied and promoted in batches, it cannot guarantee the overall optimal performance of the optical fiber switch self-test process. The self-test process consumes a lot of time and resources, and the possibility of human error is high. In addition, the existing method and device for rapid self-testing of optical fiber switches cannot provide a more robust self-test process, the self-test cycle is long, and the overall response speed is reduced. For this reason, we propose a method and device for rapid self-testing of optical fiber switches. Summary of the invention

[0004] The purpose of the present invention is to solve the defects in the prior art and to propose a method and device for rapid self-checking of an optical fiber switch.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] A fast self-check method for an optical fiber switch, the specific steps of the self-check method are as follows:

[0007] Ⅰ. Collect the operation data of the fiber optic switch and build a virtual model of the switch;

[0008] II. Identify and predict potential failures of fiber optic switches and optimize the self-check process;

[0009] III. Collect external device information and optimize workload distribution among devices;

[0010] IV. Real-time analysis of the self-test information of each switch and optimization of the self-test parameters of the optical fiber switch;

[0011] V. Store various data and model parameters during the self-checking process through blockchain;

[0012] VI. Comprehensively analyze all test results and generate a self-test report.

[0013] As a further solution of the present invention, the specific steps of building the switch virtual model in step I are as follows:

[0014] S1.1: The sensors installed in the fiber optic switch collect the operation data of the equipment in real time and the historical data accumulated from the long-term operation of the fiber optic switch. Then, each group of data is processed by removing noise, correcting erroneous data and filling missing values. The outliers in the data are identified and eliminated by the boxplot method, and the data of different dimensions are converted into dimensionless form by normalization or standardization methods.

[0015] S1.2: According to the historical data and the equipment specifications of the optical fiber switch, various parameter information of the optical fiber switch is extracted, and a three-dimensional geometric model of the optical fiber switch is constructed. The three-dimensional geometric model is divided into multiple groups of finite element units through the adaptive meshing technology, and each unit is connected through nodes;

[0016] S1.3: Model the structure, materials, and external forces of the equipment, and simulate the stress and temperature distribution of the equipment under different working conditions. Compare the output of the virtual model with historical data, verify the accuracy of the model, and make real-time adjustments to the model.

[0017] As a further solution of the present invention, the operation data of the device described in S1.1 specifically includes optical signal strength, device temperature, voltage, current and error log information;

[0018] S1.1 The historical data specifically includes the operation status information and fault records of the fiber optic switch at different time points;

[0019] S1.2 The various parameter information of the optical fiber switch specifically includes the optical characteristics, physical dimensions, electrical characteristics, thermal characteristics, mechanical characteristics, information transmission rate and transmission delay of the device.

[0020] As a further solution of the present invention, the specific steps of identifying and predicting potential failures of the optical fiber switch in step II are as follows:

[0021] S2.1: collect the pre-processed operation data and historical fault records of the optical fiber switch, annotate the collected data manually or automatically using an existing fault diagnosis system according to the historical fault records, and divide each group of annotated data into a training set, a validation set, and a test set;

[0022] S2.2: Build a CNN model, design and initialize the model input layer, multiple convolutional layers, pooling layers, fully connected layers, and output layer architecture, input the training set into the CNN model, and the model forward propagates the training set data from the input layer. After that, the output layer outputs the predicted value, and calculates the loss value between the predicted value and the actual value through the cross entropy loss function;

[0023] S2.3: The loss value is back-propagated from the output layer, and the gradient descent method is used to optimize the parameters of each network layer. After each round of training, the performance indicators of the CNN model are evaluated on the validation set, and the hyperparameters of the model are optimized based on the results of the validation set. The CNN model is repeatedly trained and validated until the model performance indicators reach the preset threshold, and then the training is stopped;

[0024] S2.4: Evaluate the final optimized CNN model on the test set, calculate the performance of the model on unseen data, and then collect and preprocess various data in real time during the operation of the fiber optic switch. Use the trained CNN model to infer the preprocessed data and output the fault prediction result. If the prediction result exceeds the preset threshold, it is determined that there is a potential fault.

[0025] As a further solution of the present invention, the specific steps of the optimized self-checking process described in step II are as follows:

[0026] S3.1: Based on the real-time collected fiber optic switch and the prediction results, the state space of the fiber optic switch self-check is constructed, and the actions that can be executed in each state are combined into an action space. Then, the starting state of the self-check process is used as the root node of the search tree to initialize the search tree;

[0027] S3.2: Starting from the root node, select child nodes according to the maximum upper confidence strategy until reaching a node that has not been fully expanded or visited, and then select a set of unexplored actions from the action space to expand the node;

[0028] S3.3: Starting from the newly expanded node, use a random strategy or a heuristic strategy to simulate the self-check process until all the detection steps are completed and then stop the simulation. Record the cumulative reward obtained during the simulation, and then transmit the cumulative reward obtained from the simulation from the newly expanded node to the root node, and update the number of visits and cumulative rewards of each node on the path.

[0029] S3.4: Repeat multiple selections, expansions, simulations, and backpropagation until the preset iteration time is reached, and then stop. Select the node path with the highest reward value under the root node as the optimal decision path in the current state. According to the selected optimal decision path, execute each step of the self-check process, monitor and record the actual self-check process results.

[0030] As a further solution of the present invention, the specific steps of optimizing the self-test parameters of the optical fiber switch in step IV are as follows:

[0031] S4.1: according to the parameter information of the optical fiber switch, the range and value space of the self-test parameters are set, multiple groups of initial sample points are selected in the parameter space by Latin hypercube sampling, and the objective function value of each sample point is evaluated, and a Gaussian process model is constructed based on the selected groups of initial sample points;

[0032] S4.2: Model the objective function through the Gaussian process model and provide a prediction distribution for each unobserved point, maximize the expected improvement function in the parameter space to determine the parameter point for the next objective function evaluation, and evaluate the objective function at the selected parameter point, adding the new data point to the existing data set;

[0033] S4.3: Use the updated data set to retrain the Gaussian process model, correct the predicted distribution of the objective function, and repeat the iterative update until the change in the objective function value is less than the preset threshold. After the stopping condition is met, select the parameter point with the best objective function value among the currently evaluated parameter points as the final optimization result, and then replace the original self-test parameters with the self-test parameters corresponding to the parameter point.

[0034] A fast self-check device for an optical fiber switch comprises a sensor module, a central processing unit, a graphics processing unit, an analog-to-digital converter, a communication module, a storage unit, a power management module and a display alarm module;

[0035] The sensor module specifically includes an optical sensor, a temperature sensor, and a current sensor, which are used to detect the optical signal strength and transmission quality in the optical fiber switch, the temperature inside the optical fiber switch and the surrounding environment, and the current consumption of the device;

[0036] The central processing unit is used to process data from various sensors; the graphics processor is used to provide accelerated computing capabilities when performing complex data analysis; the analog-to-digital converter is used to convert analog signals collected by the sensor into digital signals;

[0037] The communication module is used to communicate with the remote monitoring system and network equipment through wireless and wired network connections; the storage unit is used to store the firmware, detection algorithm and historical self-test data of the self-test device;

[0038] The power management module is used to manage the power distribution of the entire device; the display alarm module is used to display the working status of the device and, when a serious fault or abnormality is detected, issue an alarm to prompt the operation and maintenance personnel to handle it in time.

[0039] Compared with the prior art, the present invention has the following beneficial effects:

[0040] 1. The optical fiber switch fast self-test method sets the range and value space of the self-test parameters according to the parameter information of the optical fiber switch, selects multiple groups of initial sample points in the parameter space by Latin hypercube sampling, and evaluates the objective function value of each sample point, and constructs a Gaussian process model based on the selected groups of initial sample points, models the objective function through the Gaussian process model, and provides a predicted distribution for each unobserved point, maximizes the expected improvement function in the parameter space to determine the parameter point for the next step of objective function evaluation, and evaluates the objective function at the selected parameter point, adds new data points to the existing data set, retrains the Gaussian process model with the updated data set, corrects the predicted distribution of the objective function, repeats the iterative update until the objective function value change value is less than the preset threshold, and after the stop condition is met, selects the parameter point with the best objective function value among the currently evaluated parameter points as the final optimization result, and then replaces the original self-test parameter with the self-test parameter corresponding to the parameter point, which can ensure the overall optimal performance of the optical fiber switch self-test process, greatly improve the optimization efficiency, reduce the time and resource consumption in the self-test process, reduce the possibility of human errors, and improve the accuracy and efficiency of the overall self-test.

[0041] 2. The present invention constructs the state space of the self-check of the optical fiber switch based on the real-time collection of the optical fiber switch and the prediction results, and forms the action space with the actions that can be executed in each state, and then uses the starting state of the self-check process as the root node of the search tree to initialize the search tree, starting from the root node, and selecting child nodes according to the maximum upper confidence strategy until reaching a node that has not been fully expanded or has not been visited, and then selecting a group of actions that have not been explored from the action space to expand the node, starting from the newly expanded node, using a random strategy or a heuristic strategy to simulate the execution of the self-check process until all detection steps are completed and the simulation is stopped, and the cumulative reward obtained during the simulation is recorded, and then the simulation result is calculated. The accumulated reward is transmitted back from the newly expanded node to the root node, and the number of visits and accumulated rewards of each node on the path are updated. The selection, expansion, simulation and backpropagation are repeated many times until the preset iteration time is reached and the node path with the highest reward value under the root node is selected as the optimal decision path in the current state. According to the selected optimal decision path, the various steps of the self-check process are executed, and the actual self-check process results are monitored and recorded. This can effectively deal with the uncertainty factors in the self-check process, provide a more robust self-check process, avoid the high computing cost brought by global search, shorten the self-check cycle, improve the overall response speed, and enhance the flexibility and adaptability of the self-check process. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] The accompanying drawings are used to provide further understanding of the present invention and constitute a part of the specification. They are used to explain the present invention together with the embodiments of the present invention and do not constitute a limitation of the present invention.

[0043] Figure 1 The present invention provides a flowchart of a method for rapid self-checking of an optical fiber switch. DETAILED DESCRIPTION

[0044] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0045] Example 1

[0046] Reference Figure 1 This embodiment discloses a fast self-check method for a fiber optic switch. The specific steps of the self-check method are as follows:

[0047] Collect fiber switch operation data and build a virtual model of the switch.

[0048] Specifically, sensors installed in the fiber optic switch are used to collect the operating data of the equipment in real time, and historical data accumulated from the long-term operation of the fiber optic switch is collected. Each group of data is then processed by removing noise, correcting erroneous data, and filling in missing values. The box-line method is used to identify and eliminate outliers in the data, and normalization or standardization methods are used to convert data of different dimensions into dimensionless forms. According to historical data and equipment specifications of the fiber optic switch, various parameter information of the fiber optic switch is extracted, and a three-dimensional geometric model of the fiber optic switch is constructed. The three-dimensional geometric model is divided into multiple groups of finite element units through adaptive meshing technology. Each unit is connected through nodes. The structure, material, and external force of the equipment are modeled, and the stress and temperature distribution of the equipment under different working conditions are simulated. The output of the virtual model is compared with the historical data to verify the accuracy of the model and adjust the model in real time.

[0049] In this embodiment, the operating data of the device specifically includes optical signal strength, device temperature, voltage, current and error log information; the historical data specifically includes operating status information and fault records of the optical fiber switch at different time points; the various parameter information of the optical fiber switch specifically includes the optical characteristics, physical dimensions, electrical characteristics, thermal characteristics, mechanical characteristics, information transmission rate and transmission delay of the device.

[0050] Identify and predict potential fiber switch failures and optimize the self-diagnosis process.

[0051] Specifically, the pre-processed operation data and historical fault records of the optical fiber switch are collected. According to the historical fault records, the collected data are annotated manually or automatically by using the existing fault diagnosis system, and each group of annotated data is divided into a training set, a validation set and a test set. A CNN model is constructed, and the model input layer, multiple convolutional layers, pooling layers, fully connected layers and output layer architecture are designed and initialized. The training set is input into the CNN model. The model forward propagates the training set data from the input layer, and then the output layer outputs the predicted value, and the loss value between the predicted value and the actual value is calculated through the cross entropy loss function. The loss value is reversed from the output layer. The gradient descent method is used to optimize the parameters of each network layer. After each round of training, the performance indicators of the CNN model are evaluated on the validation set, and the hyperparameters of the model are optimized based on the results of the validation set. The CNN model is trained and verified repeatedly until the model performance indicators reach the preset threshold. The training is stopped and the final optimized CNN model is evaluated on the test set. The performance of the model on unseen data is calculated. After that, during the operation of the fiber optic switch, various data are collected and preprocessed in real time. The preprocessed data is inferred through the trained CNN model, and the fault prediction result is output. If the prediction result exceeds the preset threshold, it is determined that there is a potential fault.

[0052] Specifically, based on the real-time collected optical fiber switches and prediction results, the state space of the self-check of the optical fiber switches is constructed, and the actions that can be executed in each state are used to form an action space. Then, the starting state of the self-check process is used as the root node of the search tree to initialize the search tree. Starting from the root node, child nodes are selected according to the maximum upper confidence strategy until a node that has not been fully expanded or visited is reached. Then a set of unexplored actions are selected from the action space to expand the node. Starting from the newly expanded node, a random strategy or a heuristic strategy is used to simulate the execution of the self-check process until all detection steps are completed and the simulation is stopped. The cumulative reward obtained during the simulation is recorded, and then the cumulative reward obtained by the simulation is transmitted back from the newly expanded node to the root node, and the number of visits and cumulative rewards of each node on the path are updated. Repeat multiple selections, expansions, simulations and back propagation until the preset iteration time is reached and stop. The node path with the highest reward value under the root node is selected as the optimal decision path in the current state, and according to the selected optimal decision path, the various steps of the self-check process are executed, and the actual self-check process results are monitored and recorded.

[0053] Collect external device information and optimize workload distribution among devices.

[0054] Analyze the self-test information of each switch in real time and optimize the self-test parameters of the fiber optic switch.

[0055] Specifically, the range and value space of the self-test parameters are set according to the parameter information of the optical fiber switch, multiple groups of initial sample points are selected in the parameter space by Latin hypercube sampling, and the objective function value of each sample point is evaluated, and a Gaussian process model is constructed based on the selected groups of initial sample points, the objective function is modeled by the Gaussian process model, and a prediction distribution is provided for each unobserved point, the expected improvement function is maximized in the parameter space to determine the parameter point for the next objective function evaluation, and the objective function is evaluated at the selected parameter point, the new data point is added to the existing data set, the Gaussian process model is retrained with the updated data set, the predicted distribution of the objective function is corrected, and the iterative update is repeated until the change value of the objective function value is less than the preset threshold value, and after the stopping condition is met, the parameter point with the best objective function value among the currently evaluated parameter points is selected as the final optimization result, and then the self-test parameter corresponding to the parameter point replaces the original self-test parameter.

[0056] Various data and model parameters in the self-inspection process are stored through blockchain.

[0057] Comprehensively analyze all test results and generate a self-test report.

[0058] Example 2

[0059] This embodiment discloses a fast self-check device for an optical fiber switch, including a sensor module, a central processing unit, a graphics processing unit, an analog-to-digital converter, a communication module, a storage unit, a power management module, and a display alarm module;

[0060] The sensor module specifically includes an optical sensor, a temperature sensor, and a current sensor, which are used to detect the optical signal strength and transmission quality in the fiber optic switch, the temperature inside the fiber optic switch and the surrounding environment, and the current consumption of the device;

[0061] The central processing unit is used to process data from various sensors; the graphics processing unit is used to provide accelerated computing capabilities when performing complex data analysis; the analog-to-digital converter is used to convert the analog signals collected by the sensor into digital signals;

[0062] The communication module is used to communicate with the remote monitoring system and network equipment through wireless and wired network connections; the storage unit is used to store the firmware, detection algorithm and historical self-test data of the self-test device;

[0063] The power management module is used to manage the power distribution of the entire device; the display alarm module is used to display the working status of the device, and when a serious fault or abnormality is detected, an alarm will be sounded to prompt the operation and maintenance personnel to deal with it in time.

Claims

1. A fast self-test method for an optical fiber switch, characterized in that: The specific steps of the self-test method are as follows: Ⅰ. Collect the operation data of the fiber optic switch and build a virtual model of the switch; II. Identify and predict potential failures of fiber optic switches and optimize the self-check process; III. Collect external device information and optimize workload distribution among devices; IV. Real-time analysis of the self-test information of each switch and optimization of the self-test parameters of the optical fiber switch; V. Store various data and model parameters during the self-checking process through blockchain; VI. Comprehensively analyze all test results and generate a self-test report.

2. A fast self-check method for an optical fiber switch according to claim 1, characterized in that: The specific steps of building a switch virtual model in step I are as follows: S1.1: The sensors installed in the fiber optic switch collect the operation data of the equipment in real time and the historical data accumulated from the long-term operation of the fiber optic switch. Then, each group of data is processed by removing noise, correcting erroneous data and filling missing values. The outliers in the data are identified and eliminated by the boxplot method, and the data of different dimensions are converted into dimensionless form by normalization or standardization methods. S1.2: According to the historical data and the equipment specifications of the optical fiber switch, various parameter information of the optical fiber switch is extracted, and a three-dimensional geometric model of the optical fiber switch is constructed. The three-dimensional geometric model is divided into multiple groups of finite element units through the adaptive meshing technology, and each unit is connected through nodes; S1.3: Model the structure, materials, and external forces of the equipment, and simulate the stress and temperature distribution of the equipment under different working conditions. Compare the output of the virtual model with historical data, verify the accuracy of the model, and make real-time adjustments to the model.

3. A fast self-check method for an optical fiber switch according to claim 2, characterized in that: The operating data of the device described in S1.1 specifically includes optical signal strength, device temperature, voltage, current and error log information; S1.1 The historical data specifically includes the operation status information and fault records of the fiber optic switch at different time points; S1.2 The various parameter information of the optical fiber switch specifically includes the optical characteristics, physical dimensions, electrical characteristics, thermal characteristics, mechanical characteristics, information transmission rate and transmission delay of the device.

4. A fast self-check method for an optical fiber switch according to claim 2, characterized in that: The specific steps for identifying and predicting potential failures of fiber switches described in step II are as follows: S2.1: collect the pre-processed operation data and historical fault records of the optical fiber switch, annotate the collected data manually or automatically using an existing fault diagnosis system according to the historical fault records, and divide each group of annotated data into a training set, a validation set, and a test set; S2.2: Build a CNN model, design and initialize the model input layer, multiple convolutional layers, pooling layers, fully connected layers, and output layer architecture, input the training set into the CNN model, and the model forward propagates the training set data from the input layer. After that, the output layer outputs the predicted value, and calculates the loss value between the predicted value and the actual value through the cross entropy loss function; S2.3: The loss value is back-propagated from the output layer, and the gradient descent method is used to optimize the parameters of each network layer. After each round of training, the performance indicators of the CNN model are evaluated on the validation set, and the hyperparameters of the model are optimized based on the results of the validation set. The CNN model is repeatedly trained and validated until the model performance indicators reach the preset threshold, and then the training is stopped; S2.4: Evaluate the final optimized CNN model on the test set, calculate the performance of the model on unseen data, and then collect and preprocess various data in real time during the operation of the fiber optic switch. Use the trained CNN model to infer the preprocessed data and output the fault prediction result. If the prediction result exceeds the preset threshold, it is determined that there is a potential fault.

5. A fast self-check method for an optical fiber switch according to claim 4, characterized in that: The specific steps of the optimized self-check process described in step II are as follows: S3.1: Based on the real-time collected fiber optic switch and the prediction results, the state space of the fiber optic switch self-check is constructed, and the actions that can be executed in each state are combined into an action space. Then, the starting state of the self-check process is used as the root node of the search tree to initialize the search tree; S3.2: Starting from the root node, select child nodes according to the maximum upper confidence strategy until reaching a node that has not been fully expanded or visited, and then select a set of unexplored actions from the action space to expand the node; S3.3: Starting from the newly expanded node, use a random strategy or a heuristic strategy to simulate the self-check process until all the detection steps are completed and then stop the simulation. Record the cumulative reward obtained during the simulation, and then transmit the cumulative reward obtained from the simulation from the newly expanded node to the root node, and update the number of visits and cumulative rewards of each node on the path. S3.4: Repeat multiple selections, expansions, simulations, and backpropagation until the preset iteration time is reached, and then stop. Select the node path with the highest reward value under the root node as the optimal decision path in the current state. According to the selected optimal decision path, execute each step of the self-check process, monitor and record the actual self-check process results.

6. A fast self-check method for an optical fiber switch according to claim 4, characterized in that: The specific steps for optimizing the self-test parameters of the fiber optic switch described in step IV are as follows S4.1: according to the parameter information of the optical fiber switch, the range and value space of the self-test parameters are set, multiple groups of initial sample points are selected in the parameter space by Latin hypercube sampling, and the objective function value of each sample point is evaluated, and a Gaussian process model is constructed based on the selected groups of initial sample points; S4.2: Model the objective function through the Gaussian process model and provide a prediction distribution for each unobserved point, maximize the expected improvement function in the parameter space to determine the parameter point for the next objective function evaluation, and evaluate the objective function at the selected parameter point, adding the new data point to the existing data set; S4.3: Use the updated data set to retrain the Gaussian process model, correct the predicted distribution of the objective function, and repeat the iterative update until the change in the objective function value is less than the preset threshold. After the stopping condition is met, select the parameter point with the best objective function value among the currently evaluated parameter points as the final optimization result, and then replace the original self-test parameters with the self-test parameters corresponding to the parameter point.

7. A fast self-check device for an optical fiber switch, used to implement the fast self-check method for an optical fiber switch according to any one of claims 1 to 6, characterized in that: It includes a sensor module, a central processing unit, a graphics processing unit, an analog-to-digital converter, a communication module, a storage unit, a power management module, and a display alarm module; The sensor module specifically includes an optical sensor, a temperature sensor, and a current sensor, which are used to detect the optical signal strength and transmission quality in the optical fiber switch, the temperature inside the optical fiber switch and the surrounding environment, and the current consumption of the device; The central processing unit is used to process data from various sensors; the graphics processor is used to provide accelerated computing capabilities when performing complex data analysis; the analog-to-digital converter is used to convert analog signals collected by the sensor into digital signals; The communication module is used to communicate with the remote monitoring system and network equipment through wireless and wired network connections; the storage unit is used to store the firmware, detection algorithm and historical self-test data of the self-test device; The power management module is used to manage the power distribution of the entire device; the display alarm module is used to display the working status of the device and, when a serious fault or abnormality is detected, issue an alarm to prompt the operation and maintenance personnel to handle it in time.

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