A solution method for edge computing operation and maintenance alarm

Abstract

The application discloses a kind of edge computing operation and maintenance alarm solutions, it is related to edge computing technical field;Including: step 1: collect all equipment information in edge computing environment, including the running state of equipment, performance index, fault record;Step 2: according to the running state of equipment and performance index, the state model of equipment is constructed;Step 3: according to the state model of equipment, the alarm model of equipment is constructed;Step 4: when equipment occurs alarm, according to the alarm model of equipment, the reason of alarm is determined;Step 5: according to the reason of alarm, corresponding measures are taken to handle;Step 6: the running data of equipment is carried out deep learning and pattern recognition by applying artificial neural network algorithm.The application can automatically identify the complex relationship between equipment state and alarm by applying artificial neural network and reinforcement learning technology, realize the automatic detection and classification of alarm;Greatly reduce the need of manual intervention, improve operation and maintenance efficiency.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of edge computing, and in particular to a solution method for edge computing operation and maintenance alarm. BACKGROUND

[0002] Edge computing is one of the key solutions for processing large amounts of data, especially in application scenarios that require fast response and local data processing. Edge computing reduces the dependence on central cloud servers by processing and analyzing data near the data source, reducing data transmission delay, and improving processing speed and system reliability. However, the dispersion and heterogeneity of edge computing environments increase the complexity of system operation and maintenance, especially in terms of device monitoring and fault alarm processing. Since edge devices are usually deployed in resource-constrained environments, and the number of devices is large and the types are diverse, how to effectively monitor device status and timely and accurately process alarms is one of the problems to be considered.

[0003] Traditional alarm processing methods often rely on predefined thresholds or simple rules, which are difficult to adapt to dynamic operating conditions and complex environmental factors. In addition, manually configuring and managing alarm rules for a large number of edge devices is not only inefficient, but also prone to errors.

[0004] After searching, the application scheme of Chinese patent application No. 200810118702.4 discloses an operation and maintenance device, a network element device, and a reporting and processing method for alarms of the network element device. The operation and maintenance device is used for alarm reporting and processing, and includes a saving module that saves the linkage relationship between all possible alarms of the network element device; an acquisition module that acquires original alarms generated during the operation of the network element device; an analysis module that selects a first alarm and a second alarm from the original alarms according to the linkage relationship, wherein any one alarm in the first alarm is a master alarm of at least one alarm in the original alarms, and is not a slave alarm of any alarm in the original alarms, and any one alarm in the second alarm is neither a master alarm nor a slave alarm of any alarm in the original alarms; and a sending module that sends the first alarm and the second alarm to a management station. The above patent has the following deficiencies: lack of intelligent self-adaptation and learning ability, and the analysis module is mainly based on fixed linkage relationship, which may not accurately capture complex alarm patterns and abnormal behaviors. SUMMARY

[0005] The purpose of the present application is to solve the problems existing in the prior art and provide a solution method for edge computing operation and maintenance alarm.

[0006] To achieve the above purpose, the present application adopts the following technical scheme:

[0007] A solution method for edge computing operation and maintenance alarm, comprising:

[0008] Step 1: Collect all device information in the edge computing environment, including the running status, performance indicators, and fault records of the device;

[0009] Step 2: Construct a state model of the device based on the running status and performance indicators of the device;

[0010] Step 3: Construct an alarm model of the device based on the state model of the device;

[0011] Step 4: When an alarm occurs, determine the cause of the alarm based on the alarm model of the device;

[0012] Step 5: Take appropriate measures to handle the alarm based on the cause of the alarm;

[0013] Step 6: Apply artificial neural network algorithms to deep learning and pattern recognition of device running data; automatically identify the complex relationship between device status and alarms by constructing a multi-layer perception network;

[0014] Step 7: Use logistic regression algorithms to classify and process alarms; determine whether a certain state of the device belongs to an abnormal state;

[0015] Step 8: Based on reinforcement learning algorithms, optimize alarm handling strategies; through interaction with the edge computing environment, reinforcement learning algorithms gradually learn and optimize behavior strategies for handling alarms to minimize alarm handling time and maximize system stability;

[0016] Step 9: Use swarm intelligence algorithms to achieve self-organization and collaborative processing of alarm information; solve the problem of distributed alarm processing in edge computing;

[0017] Step 10: Repeat the above steps until all alarms are handled.

[0018] Preferably, the step 1 specifically includes:

[0019] Step 1.1: Real-time monitoring of device information to ensure timeliness and accuracy of data collection;

[0020] Step 1.2: Use automated tools to extract key performance indicators from devices and applications;

[0021] Step 1.3: Store collected data in a unified data management platform for subsequent analysis and processing;

[0022] The step 2 specifically includes:

[0023] Step 2.1: Preprocess the collected data, including cleaning, normalization, and standardization;

[0024] Step 2.2: Use statistical analysis methods to reduce dimensions and identify key influencing factors;

[0025] Step 2.3: Establish a state model using mathematical modeling techniques to express device behavior.

[0026] Preferably, the step 3 specifically includes:

[0027] Step 3.1: Define various alarm types and their corresponding relationship with device status;

[0028] Step 3.2: Use threshold setting or pattern recognition techniques to identify state changes that require attention;

[0029] Step 3.3: Build an alarm model to map operating status to alarm levels for triggering corresponding alarm notifications;

[0030] The step 4 specifically includes:

[0031] Step 4.1: Real-time monitoring of alarm notifications, and starting the alarm cause analysis process as soon as an alarm signal is received;

[0032] Step 4.2: Query current device status and historical data, and use the alarm model to deduce possible alarm causes;

[0033] Step 4.3: Combine device characteristics and usage scenarios to further refine alarm causes to assist in developing targeted handling measures.

[0034] Preferably, the step 5 specifically includes:

[0035] Step 5.1: Design a knowledge base for alarm handling, containing preset handling strategies and experience summaries;

[0036] Step 5.2: Match the knowledge base's handling solutions or suggestions based on alarm causes;

[0037] Step 5.3: Automatically or manually execute selected handling measures and monitor execution and effects.

[0038] Preferably, the step 6 specifically includes:

[0039] Step 6.1: Build a multi-layer perceptron network structure, with the input layer accepting multi-dimensional performance indicators;

[0040] Step 6.2: Train network weights through training set data for feature learning and pattern recognition;

[0041] Step 6.3: Use validation and test sets to evaluate model accuracy and adjust network parameters to improve prediction accuracy;

[0042] Wherein, the input layer of the neural network receives multiple performance indicators of the device, the hidden layer processes the inputs through weighting and activation function, and the output layer gives the current state of the device or predicts the type of alarm that may occur in the future, and the specific formula is:

[0043]

[0044] Wherein:

[0045] Q j is the output of neuron j;

[0046] w ij is the weight of input i to neuron j;

[0047] x i is the input signal;

[0048] θ j is the threshold of neuron j;

[0049] f is the activation function.

[0050] Preferably, the step 7 specifically comprises:

[0051] Step 7.1: Prepare label data, mark normal operation and abnormal state;

[0052] Step 7.2: Learn the mapping relationship from features to alarm categories based on a logistic regression model;

[0053] Step 7.3: Apply the model to make classification prediction of new data, and take different response measures corresponding to different alarm levels according to the prediction results;

[0054] When judging whether a certain state of the device belongs to an abnormal state, the result of linear combination is mapped to the interval (0, 1) by the sigmoid function, which is expressed as a probability value, so as to predict the probability of the device appearing alarm, which is specifically:

[0055]

[0056] Wherein:

[0057] P(y=1│x) is the probability of output being 1 under given input x;

[0058] σ is the sigmoid function;

[0059] w i is the weight;

[0060] c is the bias term.

[0061] Preferably, the step 8 specifically comprises:

[0062] Step 8.1: Design a reinforcement learning problem framework, including state space, action space, reward function, and discount factor;

[0063] Step 8.2: Adjust the action selection policy through continuous interaction learning to maximize cumulative rewards;

[0064] Step 8.3: Implement the policy and track the effect of the policy, and constantly adjust until the optimal solution is found;

[0065] Its state value function and action value function are represented as follows:

[0066] Q(s,a) = E[R t+1 + r·V(s t+1 )|S t = s,A t = a]

[0067]

[0068] Where:

[0069] Q(s,a) is the expected return of taking action a from state s;

[0070] V(s) is the value of state s, the expected return of acting according to the optimal policy in state s;

[0071] R t+1 is the reward at the next time;

[0072] r is the discount factor.

[0073] Preferably, the step 9 specifically includes:

[0074] Step 9.1: Initialize ant colony algorithm parameters, including pheromone concentration, heuristic information, and evaporation rate;

[0075] Step 9.2: Distribute alarm pheromone in the network through ant colony algorithm and guide the allocation of alarm processing tasks;

[0076] Step 9.3: Dynamically update pheromone trails, optimize alarm processing procedures, and enhance the overall processing capacity and robustness of the system.

[0077] Preferably, in the step 9, based on swarm intelligence algorithm, multiple simple individuals produce complex collective behavior through local interaction, thereby solving the problem of distributed alarm processing in edge computing; specifically:

[0078]

[0079] Where:

[0080] r ij(t) denotes the pheromone concentration on the path (i,j) at time t;

[0081] n ij is the heuristic information, inversely proportional to the path length;

[0082] N i is the neighbor set of position i;

[0083] a and β control the relative importance of pheromone and heuristic information, respectively.

[0084] Preferably, the step 10 specifically includes:

[0085] Step 10.1: Establish a feedback loop to regularly check system status and alarm handling results;

[0086] Step 10.2: Adjust the alarm model and response strategy according to the feedback to adapt to changes in the environment;

[0087] Step 10.3: Keep the system's ability to continuously learn and improve alarm detection and handling mechanisms.

[0088] The beneficial effects of the present application are:

[0089] 1. The present application can automatically identify the complex relationship between device state and alarm by applying artificial neural network and reinforcement learning technology, realize automatic detection and classification of alarm; greatly reduce the need for manual intervention, improve the operation and maintenance efficiency.

[0090] 2. The present application uses logistic regression and swarm intelligence algorithm to accurately classify and process alarms in real time, and realizes self-organization and collaborative processing of alarm information; the system can quickly respond to various alarms and reduce fault recovery time.

[0091] 3. The application of reinforcement learning algorithm in the present application enables the system to adjust and optimize the alarm handling strategy according to the changing environmental conditions, improving the overall stability and adaptability of the system. BRIEF DESCRIPTION OF DRAWINGS

[0092] Figure 1 A flowchart of a solution method for edge computing operation and maintenance alarm is proposed in the present application. DETAILED DESCRIPTION

[0093] The technical solutions of the present application will be further described in detail below in combination with specific embodiments.

[0094] Example 1:

[0095] A solution method for edge computing operation and maintenance alarm, comprising:

[0096] Step 1: Collect all device information in the edge computing environment, including the running status, performance indicators, fault records, etc.

[0097] Step 2: Construct a state model of the device based on its running status and performance indicators;

[0098] Step 3: Construct an alarm model of the device based on its state model;

[0099] Step 4: When an alarm occurs, determine the cause of the alarm based on the device's alarm model;

[0100] Step 5: Take appropriate measures to handle the alarm based on its cause; for example, if the alarm is due to the device's performance indicators exceeding the threshold, you can solve the problem by adjusting the device's configuration parameters;

[0101] Step 6: Apply artificial neural network algorithms to deep learning and pattern recognition of device operation data; through the construction of multi-layer perception network, automatically identify the complex relationship between device status and alarms;

[0102] Step 7: Use logistic regression algorithms to classify and process alarms; determine whether a certain state of the device belongs to an abnormal state;

[0103] Step 8: Use swarm intelligence algorithms to achieve self-organization and collaborative processing of alarm information; solve the problem of distributed alarm processing in edge computing;

[0104] Step 9: Repeat the above steps until all alarms are handled.

[0105] The step 1 specifically includes:

[0106] Step 1.1: Real-time monitoring of device information to ensure the timeliness and accuracy of data collection;

[0107] Step 1.2: Use automated tools to extract key performance indicators from devices and applications, such as CPU usage, memory usage, network traffic, and response time, etc.

[0108] Step 1.3: Store the collected data in a unified data management platform for subsequent analysis and processing.

[0109] The step 2 specifically includes:

[0110] Step 2.1: Preprocess the collected data, including cleaning, normalization, and standardization, etc.

[0111] Step 2.2: Use statistical analysis methods such as principal component analysis (PCA) or factor analysis to reduce dimensions and identify key influencing factors.

[0112] Step 2.3: Establish a state model using mathematical modeling techniques such as regression analysis or state estimation algorithms to express device behavior.

[0113] wherein the step 3 specifically includes:

[0114] Step 3.1: Define various alarm types and their corresponding relationships with device states;

[0115] Step 3.2: Identify state changes that require attention using threshold setting or pattern recognition techniques;

[0116] Step 3.3: Build an alarm model that maps operating states to alarm levels for triggering corresponding alarm notifications.

[0117] wherein the step 4 specifically includes:

[0118] Step 4.1: Real-time monitoring of alarm notifications, and starting the alarm cause analysis process as soon as an alarm signal is received;

[0119] Step 4.2: Querying current device states and historical data, and using the alarm model to deduce possible alarm causes;

[0120] Step 4.3: Combining device characteristics and usage scenarios to further refine alarm causes to assist in developing targeted handling measures.

[0121] wherein the step 5 specifically includes:

[0122] Step 5.1: Designing a knowledge base for alarm handling, containing preset handling strategies and experience summaries;

[0123] Step 5.2: Matching handling solutions or suggestions in the knowledge base according to alarm causes;

[0124] Step 5.3: Automatically or manually executing selected handling measures, and monitoring execution and effects.

[0125] wherein the step 6 specifically includes:

[0126] Step 6.1: Building a multi-layer perceptron (MLP) network structure, with the input layer accepting multi-dimensional performance indicators;

[0127] Step 6.2: Training network weights through training set data for feature learning and pattern recognition;

[0128] Step 6.3: Evaluating model accuracy using validation and test sets, and adjusting network parameters to improve prediction accuracy;

[0129] Wherein, the input layer of the neural network receives multiple performance indicators of the device, such as CPU usage, memory occupation and network traffic, the hidden layer processes the inputs through weighting and activation function, and the output layer gives the current state of the device or predicts the alarm type that may occur in the future, and the specific formula is:

[0130]

[0131] Wherein:

[0132] Q j is the output of neuron j;

[0133] w ij is the weight of input i to neuron j;

[0134] x i is the input signal;

[0135] θ j is the threshold value of neuron j;

[0136] f is the activation function.

[0137] Wherein, the step 7 specifically comprises:

[0138] Step 7.1: Prepare label data, mark normal operation and abnormal state;

[0139] Step 7.2: Based on the logistic regression model, learn the mapping relationship from features to alarm categories;

[0140] Step 7.3: Apply the model to classify and predict new data, and take different response measures corresponding to different alarm levels according to the prediction results;

[0141] When judging whether a certain state of the device belongs to an abnormal state, the result of linear combination is mapped to the interval (0, 1) by the sigmoid function, which is expressed as a probability value, so as to predict the probability of the device appearing alarm, which is specifically:

[0142]

[0143] Wherein:

[0144] P(y=1│x) is the probability of output being 1 under the given input x;

[0145] σ is the sigmoid function;

[0146] w i is the weight;

[0147] c is the bias term.

[0148] Wherein, the step 8 specifically comprises:

[0149] Step 8.1: Initialize parameters of ant colony algorithm, such as pheromone concentration, heuristic information and evaporation rate, etc.

[0150] Step 8.2: Distribute alarm pheromone in the network and guide the allocation of alarm processing tasks through ant colony algorithm;

[0151] Step 8.3: Dynamically update pheromone trail, optimize alarm processing flow, and enhance the overall processing capacity and robustness of the system;

[0152] Based on swarm intelligence algorithm, multiple simple individuals interact locally to produce complex collective behavior, thus solving the problem of distributed alarm processing in edge computing; Specifically:

[0153]

[0154] Where:

[0155] r ij (t) represents the pheromone concentration on path (i,j) at time t;

[0156] n ij is the heuristic information, which is inversely proportional to the path length;

[0157] N i is the neighbor set of position i;

[0158] a and β control the relative importance of pheromone and heuristic information, respectively.

[0159] Wherein, the step 9 specifically includes:

[0160] Step 9.1: Establish a feedback loop to regularly check the system state and alarm processing results;

[0161] Step 9.2: Adjust the alarm model and response strategy according to the feedback to adapt to changes in the environment;

[0162] Step 9.3: Maintain the ability of the system to continuously learn and constantly improve the alarm detection and processing mechanism.

[0163] Embodiment 2:

[0164] A solution to edge computing operation and alarm, comprising:

[0165] Step 1: Collect all device information in the edge computing environment, including the running state, performance indicators, fault records, etc.

[0166] Step 2: Construct the state model of the device according to the running state and performance indicators of the device;

[0167] Step 3: Construct an alarm model for the device based on its state model;

[0168] Step 4: When an alarm occurs in the device, determine the cause of the alarm based on the device's alarm model;

[0169] Step 5: Take appropriate measures to handle the alarm based on its cause; for example, if the alarm is due to the device's performance indicators exceeding thresholds, you can solve the problem by adjusting the device's configuration parameters;

[0170] Step 6: Apply artificial neural network algorithms to deep learning and pattern recognition of device operation data; through the construction of multi-layer perception network, automatically identify the complex relationship between device state and alarm;

[0171] Step 7: Use logistic regression algorithm to classify and process alarms; determine whether a certain state of the device belongs to an abnormal state;

[0172] Step 8: Based on reinforcement learning algorithm, optimize the alarm handling strategy; through interaction with edge computing environment, reinforcement learning algorithm gradually learns and optimizes the behavior strategy for handling alarms to minimize alarm handling time and maximize system stability;

[0173] Step 9: Use swarm intelligence algorithm to realize self-organization and collaborative processing of alarm information; solve the problem of distributed alarm processing in edge computing;

[0174] Step 10: Repeat the above steps until all alarms are handled.

[0175] Among them, the step 8 specifically includes:

[0176] Step 8.1: Design the problem framework of reinforcement learning, including state space, action space, reward function and discount factor;

[0177] Step 8.2: Through continuous interaction learning, adjust the action selection strategy to maximize cumulative rewards;

[0178] Step 8.3: Implement the strategy and track its effectiveness, and constantly adjust until the optimal solution is found;

[0179] Its state value function and action value function are represented as follows:

[0180] Q(s,a) = E[R t+1 + r·V(s t+1 )|S t = s,A t = a]

[0181]

[0182] Where:

[0183] Q(s, a) is the expected return of taking action a from state s;

[0184] V(s) is the value of state s, the expected return of acting according to the optimal policy in state s;

[0185] R t+1 is the reward at the next time step;

[0186] r is the discount factor.

[0187] The above merely describes the preferred embodiments of the present application, but the protection scope of the present application is not limited thereto, and any person skilled in the art, according to the technical solution and the inventive concept of the present application, makes equivalent replacement or change within the technical range disclosed by the present application, should be covered within the protection scope of the present application.

Claims

1. A method for edge computing operation and maintenance alarm, characterized in that, Comprise: Step 1: Collect all device information in the edge computing environment, including the running status, performance indicators, and fault records of the device; Step 2: According to the running status and performance indicators of the device, construct the state model of the device; Step 3: According to the state model of the device, construct the alarm model of the device; Step 4: When the device occurs alarm, according to the alarm model of the device, determine the cause of the alarm; Step 5: According to the cause of the alarm, take corresponding measures for processing; Step 6: Apply artificial neural network algorithm to deep learning and pattern recognition of device running data; Through the construction of multi-layer perception network, the complex relationship between device state and alarm is automatically identified; Step 7: Use logistic regression algorithm to classify and process alarms; Determine whether a certain state of the device belongs to an abnormal state; Step 8: Based on reinforcement learning algorithm, optimize the alarm handling strategy; Through interaction with the edge computing environment, the reinforcement learning algorithm gradually learns and optimizes the behavior strategy for handling alarms to minimize alarm handling time and maximize system stability; Step 9: Use swarm intelligence algorithm to realize self-organization and collaborative processing of alarm information; Solve the problem of distributed alarm processing in edge computing; Step 10: Repeat the above steps until all alarms are processed. 2.The method of claim 1, wherein, The step 1 specifically comprises: Step 1.1: Real-time monitoring of device information to ensure the timeliness and accuracy of data collection; Step 1.2: Use automated tools to extract key performance indicators from devices and applications; Step 1.3: Store the collected data in a unified data management platform for subsequent analysis and processing; The step 2 specifically comprises: Step 2.1: Preprocess the collected data, including cleaning, normalization and standardization; Step 2.2: Use statistical analysis methods to reduce dimensions and identify key influencing factors; Step 2.3: Establish a state model using mathematical modeling techniques to express device behavior. 3.The method of claim 1, wherein, The step 3 specifically comprises: Step 3.1: Define various alarm types and their corresponding relationship with device status; Step 3.2: Use threshold setting or pattern recognition techniques to identify state changes that need attention; Step 3.3: Build an alarm model to map running status to alarm levels for triggering appropriate alarm notifications; The step 4 specifically comprises: Step 4.1: Real-time monitoring of alarm notifications, start alarm cause analysis process as soon as alarm signal is received; Step 4.2: Query current device status and historical data, use alarm model to deduce possible alarm causes; Step 4.3: Combine device characteristics and usage scenarios to further refine alarm causes to assist in developing targeted handling measures. 4.The method of claim 3, wherein, The step 5 specifically comprises: Step 5.1: Design a knowledge base for alarm handling, containing preset handling strategies and experience summary; Step 5.2: According to the alarm cause, match the handling scheme or suggestion in the knowledge base; Step 5.3: Automatically or manually execute the selected handling measures and monitor the execution and effect.

5. The method of claim 1, wherein the edge computing operation and maintenance alarm is solved by, The step 6 specifically comprises: Step 6.1: Build a multi-layer perceptron network structure, the input layer accepts multi-dimensional performance indicators; Step 6.2: Train the network weights with the training set data for feature learning and pattern recognition; Step 6.3: Evaluate the model accuracy using the validation and test sets, and adjust the network parameters to improve prediction accuracy; Where the input layer of the neural network receives multiple performance indicators of the device, the hidden layer processes these inputs through weighting and activation functions, and the output layer gives the current state of the device or predicts the type of alarm that may occur in the future, with the specific formula being: Where: Q j is the output of neuron j; w ij is the weight of input i to neuron j; x i is an input signal; θ j is a threshold value of the neuron j; f is the activation function.

6. The method of claim 1, wherein the edge computing operation and maintenance alarm is solved. The step 7 specifically includes: Step 7.1: Prepare label data, mark normal operation and abnormal state; Step 7.2: Learn the mapping relationship from features to alarm categories based on the logistic regression model; Step 7.3: Apply the model for classification prediction of new data, and take different response measures corresponding to different alarm levels according to the prediction results; When judging whether a certain state of the device belongs to an abnormal state, the result of linear combination is mapped to the (0, 1) interval through the sigmoid function, represented as a probability value, to predict the probability of the device appearing an alarm, specifically: Where: P(y=1│x) is the probability of output being 1 under given input x; σ is the sigmoid function; w i are weights; c is the bias term.

7. The method of claim 1, wherein the edge computing operation and maintenance alarm is solved. The step 8 specifically includes: Step 8.1: Design the problem framework of reinforcement learning, including state space, action space, reward function and discount factor; Step 8.2: Through continuous interaction learning, adjust the action selection strategy to maximize the cumulative reward; Step 8.3: Implement the strategy and track the effect of the strategy, and continuously adjust until the optimal solution is found; Its state value function and action value function are represented as follows: Q(s, a) = E[R t+1 + γ · V(s t+1 )| S t = s, A t = a] Where: Q(s,a) is the expected return of taking action a from state s; V(s) is the value of state s, which is the expected return of acting optimally in state s; R t+1 is the reward at the next time; r is the discount factor. 8.The method of claim 1, wherein, The step 9 specifically includes: Step 9.1: Initialize ant colony algorithm parameters, including pheromone concentration, heuristic information and evaporation rate; Step 9.2: Distribute alarm pheromones in the network through ant colony algorithm and guide the allocation of alarm handling tasks; Step 9.3: Dynamically update pheromone trails, optimize alarm handling processes, and enhance the overall processing capacity and robustness of the system. 9.The method of claim 8, wherein, In the step 9, based on swarm intelligence algorithm, multiple simple individuals produce complex collective behavior through local interaction, thereby solving the distributed alarm handling problem in edge computing; specifically: Where: r ij (t) denotes the pheromone concentration on the path (i,j) at time t; n ij is heuristic information, inversely proportional to path length; N i is the set of neighbors of position i; a and β control the relative importance of pheromone and heuristic information, respectively. 10.The method of claim 1, wherein, The step 10 specifically includes: Step 10.1: Establish a feedback loop to regularly check the system status and alarm handling results; Step 10.2: Adjust the alarm model and response strategy according to the feedback to adapt to changes in the environment; Step 10.3: Maintain the ability of the system to continuously learn and improve the alarm detection and handling mechanism.

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