Project operation and maintenance monitoring method and system, readable storage medium and computer

Abstract

The invention provides a project operation and maintenance monitoring method and system, a readable storage medium and a computer, and the method comprises the steps: deploying a corresponding monitoring tool in a service terminal, and configuring a corresponding probe interface in the service terminal; performing data acquisition by using the probe interface to obtain index data, and selecting a corresponding machine learning model for training according to the operation and maintenance target and the data type of the index data to obtain a data processing model; performing data prediction on the to-be-processed data by using the data processing model to output a corresponding prediction result, and defining an automation strategy and a corresponding execution rule according to the prediction result and the operation and maintenance target; and performing automatic execution based on the automatic strategy and the execution rule, and performing real-time monitoring according to an automatic execution result so as to realize real-time monitoring of the operation state and the operation and maintenance effect. According to the invention, comprehensive monitoring is carried out by using a probe technology, and automatic operation and maintenance of the IT project are realized by combining a human function and a machine learning technology, so that the operation and maintenance efficiency can be improved.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a project operation and maintenance monitoring method, system, readable storage medium and computer. Background Art

[0002] As IT systems become more complex, some large projects have hundreds or thousands of servers, middleware, and databases online, requiring real-time monitoring of system operating status to promptly detect potential problems and locate the source of failures.

[0003] Traditional operation and maintenance methods often use manual monitoring and manual processing, which leads to the following problems: 1. Dependence on manual inspections: Traditional operation and maintenance is highly dependent on manual inspections, which makes the operation and maintenance work passive and inefficient. Operation and maintenance personnel need to spend a lot of time running between various business departments to solve and handle problems.

[0004] 2. Long problem-solving cycle: When a business system fails, the traditional operation and maintenance department usually needs to convene various operation and maintenance positions to conduct self-inspection, and may require equipment providers, system developers and other parties to participate in the consultation. The entire process takes a long time, often more than a week.

[0005] 3. Human cost: As IT systems become more complex, human input is required to continuously increase, resulting in high operation and maintenance costs.

[0006] 4. Hardware cost: Under the traditional operation and maintenance method, the purchase and maintenance costs of hardware equipment are also relatively high. Summary of the invention Based on this, the purpose of the present invention is to provide a project operation and maintenance monitoring method, system, readable storage medium and computer to solve the deficiencies in the above-mentioned technology.

[0007] The present invention proposes a project operation and maintenance monitoring method, comprising: Deploy corresponding monitoring tools in a preset service terminal, and configure corresponding probe interfaces in the service terminal; The probe interface is used to collect data to obtain corresponding indicator data, and a corresponding machine learning model is selected for training according to preset operation and maintenance goals and the data type of the indicator data to obtain a data processing model; Using the data processing model to perform data prediction on the data to be processed to output corresponding prediction results, and defining automation strategies and corresponding execution rules according to the prediction results and the operation and maintenance objectives; Automated execution is performed based on the automation strategy and the execution rules, and real-time monitoring is performed based on the automated execution results to achieve real-time monitoring of the operating status and operation and maintenance effects.

[0008] Furthermore, the steps of deploying a corresponding monitoring tool in a preset service terminal and configuring a corresponding probe interface in the service terminal include: Deploy the corresponding monitoring tools in the preset service terminal to configure the warning rules and define the query statements for the collection database; A data acquisition interface is obtained from a preset monitored node, wherein a return value of the data acquisition interface is in the same format as a return value of the monitoring tool.

[0009] Furthermore, the step of selecting a corresponding machine learning model for training according to the preset operation and maintenance objectives and the data type of the indicator data to obtain a data processing model includes: Selecting a corresponding machine learning model according to the preset operation and maintenance goal and the data type of the indicator data, and obtaining historical data of the operation and maintenance goal; The machine learning model is trained according to the historical data so that the machine learning model learns the characteristics and rules of the historical data to obtain a data processing model.

[0010] Further, the steps of performing automatic execution based on the automatic strategy and the execution rules, and performing real-time monitoring according to the automatic execution results to achieve real-time monitoring of the operation status and operation and maintenance effect include: Performing automated execution according to the automated strategy and the execution rules to obtain corresponding automated execution results; Real-time monitoring and evaluation are performed based on the automation execution results, and the automation execution results are analyzed, and the data processing model, the automation strategy and the execution rules are continuously improved and optimized based on the obtained analysis results.

[0011] The present invention also proposes a project operation and maintenance monitoring system, comprising: A data configuration module, used to deploy corresponding monitoring tools in a preset service terminal and configure corresponding probe interfaces in the service terminal; A model training module is used to collect data using the probe interface to obtain corresponding indicator data, and select a corresponding machine learning model for training according to preset operation and maintenance goals and the data type of the indicator data to obtain a data processing model; A data prediction module, used to perform data prediction on the data to be processed using the data processing model to output corresponding prediction results, and define an automation strategy and corresponding execution rules according to the prediction results and the operation and maintenance objectives; The real-time monitoring module is used to perform automated execution based on the automation strategy and the execution rules, and to perform real-time monitoring according to the automated execution results, so as to achieve real-time monitoring of the operating status and operation and maintenance effects.

[0012] Furthermore, the data configuration module includes: A data configuration unit is used to deploy corresponding monitoring tools in a preset service terminal to configure early warning rules and define query statements for the collection database; The interface acquisition unit is used to obtain a data acquisition interface from a preset monitored node, wherein the return value of the data acquisition interface is the same as the return format of the monitoring tool.

[0013] Furthermore, the model training module includes: A model selection unit, configured to select a corresponding machine learning model according to the preset operation and maintenance target and the data type of the indicator data, and obtain historical data of the operation and maintenance target; A model training unit is used to train the machine learning model according to the historical data so that the machine learning model learns the characteristics and rules of the historical data to obtain a data processing model.

[0014] Furthermore, the real-time monitoring module includes: An automation execution unit, used to perform automation execution according to the automation strategy and the execution rules to obtain a corresponding automation execution result; A result analysis unit is used to perform real-time monitoring and evaluation based on the automation execution results, analyze the automation execution results, and continuously improve and optimize the data processing model, the automation strategy and the execution rules based on the obtained analysis results.

[0015] The present invention also provides a readable storage medium on which a computer program is stored, and when the program is executed by a processor, the above-mentioned project operation and maintenance monitoring method is implemented.

[0016] The present invention also proposes a computer, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned project operation and maintenance monitoring method when executing the computer program.

[0017] The project operation and maintenance monitoring method, system, readable storage medium and computer in the present invention deploy monitoring tools in the service terminal, configure the corresponding probe interface in the service terminal, use the probe interface to collect indicator data, train the machine learning model according to the data type and operation and maintenance goals of the indicator data, use probe technology to comprehensively monitor the server, database, middleware and application system, and combine human functions and machine learning technology to realize automated operation and maintenance of IT projects, which can improve operation and maintenance efficiency, predict, analyze operation and maintenance direction, reduce human errors, monitor in real time and respond quickly, reduce operation and maintenance costs and improve the level of operation and maintenance intelligence, and produce significant results. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 Flow chart of the project operation and maintenance monitoring method in the first embodiment of the present invention; Figure 2 for Figure 1 Detailed flow chart of step S101; Figure 3 for Figure 1 Detailed flow chart of step S102; Figure 4 for Figure 1 Detailed flow chart of step S104; Figure 5 It is a structural block diagram of a project operation and maintenance monitoring system in a second embodiment of the present invention; Figure 6 FIG. 4 is a structural block diagram of a computer in a third embodiment of the present invention.

[0019] The following specific implementation manner will further illustrate the present invention in conjunction with the above-mentioned drawings. DETAILED DESCRIPTION

[0020] In order to facilitate the understanding of the present invention, the present invention will be described more fully below with reference to the relevant drawings. Several embodiments of the present invention are given in the drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. On the contrary, the purpose of providing these embodiments is to make the disclosure of the present invention more thorough and comprehensive.

[0021] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which the present invention belongs. The terms used herein in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more of the related listed items.

[0022] Embodiment 1 See also Figure 1, which shows a project operation and maintenance monitoring method in a first embodiment of the present invention, and the method specifically includes steps S101 to S104: S101, deploying a corresponding monitoring tool in a preset service terminal, and configuring a corresponding probe interface in the service terminal; For further information, see Figure 2 , the step S101 specifically includes steps S1011~S1012: S1011, deploying corresponding monitoring tools in a preset service terminal to configure warning rules and define query statements for the collection database; S1012, obtaining a data acquisition interface from a preset monitored node, wherein a return value of the data acquisition interface is in the same format as a return value of the monitoring tool.

[0023] In the specific implementation, probe technology is used to monitor the server, database, middleware, and application system in all aspects. Prometheus technology is used to deploy the Prometheus server. The corresponding monitoring tool Node_Exporter is deployed in the servers, middleware, and databases that need to be monitored, and the warning rules are configured.

[0024] Specifically, the collection of indicators that Prometheus cannot collect requires the use of SQL statements to collect customized slow SQL statements of the database, visitor IP, execution time, etc.

[0025] For indicators that cannot be collected using the above methods, use the interface method to collect them. Specifically, provide an output interface on the monitored node, and the return value of the interface should be set to the same format as the return value of Prometheus's Node_Exporter.

[0026] S102, using the probe interface to collect data to obtain corresponding indicator data, and selecting a corresponding machine learning model for training according to a preset operation and maintenance goal and a data type of the indicator data to obtain a data processing model; For further information, see Figure 3 , the step S102 specifically includes steps S1021~S1022: S1021, selecting a corresponding machine learning model according to the preset operation and maintenance target and the data type of the indicator data, and obtaining historical data of the operation and maintenance target; S1022: Train the machine learning model according to the historical data so that the machine learning model learns the characteristics and rules of the historical data to obtain a data processing model.

[0027] In the specific implementation, the data collected through the Prometheus, SQL, and interface constructed above are queried and aggregated, and the collected data are marked as indicator data. According to the specific operation and maintenance goals and the data type of the above indicator data, a suitable machine learning model is selected for training. For example, for fault prediction, a time series analysis model or a classification model can be selected.

[0028] Specifically, historical data is used to train the model to learn the characteristics and patterns of the data. During the training process, the parameters and structure of the model need to be continuously adjusted to improve the accuracy and generalization ability of the model.

[0029] The following is the core code for model training and prediction: # Load data data = pd.read_csv('it_monitoring_data.csv') # Assume that the CSV file contains indicators such as timestamp, CPU usage, memory usage, etc. # Parse the timestamp data['timestamp'] = pd.to_datetime(data['timestamp']) # Select features and labels (here we assume that we want to predict whether the CPU usage exceeds a certain threshold) features = data[['memory_usage', 'cpu_usage', 'network_io']] # Example features labels = (data['cpu_usage']>80).astype(int) # Binary classification label, whether CPU usage exceeds 80% # Divide into training set and test set X_train, X_test, y_train, y_test = train_test_split(features, labels,test_size=0.2, random_state=42) # Feature Scaling scaler = StandardScaler() X_train_scaled = scaler.fit_transform(X_train) X_test_scaled = scaler.transform(X_test) # Model training from sklearn.ensemble import RandomForestClassifier from sklearn.metrics import accuracy_score, classification_report # Select model (Random Forest Classifier) model=RandomForestClassifier(n_estimators=100,random_state=42) # Train the model model.fit(X_train_scaled, y_train); S103, using the data processing model to perform data prediction on the data to be processed to output a corresponding prediction result, and defining an automation strategy and corresponding execution rules according to the prediction result and the operation and maintenance goal; In the specific implementation, the trained model is used to predict new data to find potential faults or problems. The prediction results can be used to guide operation and maintenance decisions and actions. The specific code is as follows: # New data for the example new_data = pd.DataFrame({ 'memory_usage': [50, 60,50, 35, 43], / / Memory usage data (a new data is generated every 5 minutes) 'cpu_usage': [30, 40, 50, 60,70], / / CPU usage data (a new data is generated every 5 minutes) 'network_io': [100, 150, 160, 200, 300] / / Network IO times per minute (a new data is generated every 5 minutes) }) # Scale the new data new_data_scaled = scaler.transform(new_data) # predict predictions = model.predict(new_data_scaled) print(f'Predictions: {predictions}') / / Print predicted data# Prediction predictions = model.predict(new_data_scaled); Specifically, the prediction algorithm formula is: y^= β0 + β1 x 1 + β2 x 2 +⋯+ β n x n In the formula, y^ is the predicted value, β0 is the intercept term, β1, β2, …, β n are the weights (coefficients) learned by the model, and x1, x2, …, xn are the values ​​of the preprocessed input data.

[0030] After project iterations, the above formula was improved by combining adaptive weight adjustment and dynamic evaluation of feature importance. The improved formula is: ; In the formula, Indicates that over time A changing intercept term allows the model to adapt to trend changes in the data when dealing with time series data; Indicates that over time , eigenvalue and variance The weight of the change reflects the change in the importance of the feature value at different time points and different values; is a nonlinear transformation function used to capture eigenvalues and its parameters Nonlinear relationships between them, such as polynomials, exponential functions, logarithmic functions, or neural network layers; Represents the parameters of the nonlinear transformation function, which is learned by the model to best fit the data.

[0031] It can be understood that by introducing an intercept term that changes over time, the model can capture the global trend in the data and The adaptive adjustment allows the model to consider the changes in importance over time, feature values, and feature uncertainty when processing different features. This is achieved through learning algorithms such as gradient descent, in which the weights are updated according to the gradient of the loss function. Furthermore, by introducing nonlinear transformation functions, the model can capture the nonlinear relationship between features and target variables, significantly improving the expressiveness of the model and enabling it to fit more complex data sets. Combined with adaptive weights and nonlinear transformations, the model can dynamically evaluate the importance of each feature and adjust its contribution to the predicted value based on its importance.

[0032] Furthermore, according to the prediction results and operation and maintenance goals, automatic decision-making and execution rules can be set. For example, when it is predicted that the CPU usage of a server will exceed the threshold, resource scheduling or capacity expansion operations can be automatically triggered.

[0033] Specifically, the above prediction value predictions is a continuous value, which indicates the model's prediction of the future of the monitored object. For example, the prediction value of the memory usage data is: [50, 43, 50, 35, 53], ps: the unit is %, which means that the memory utilization rate will remain stable in the next hour, so there is no need to trigger an alarm or automatic operation and maintenance. However, the prediction value of the CPU usage data is: [80, 90, 100, 100, 100], which means that the CPU usage rate will reach 100% in the next ten minutes. Then the operation and maintenance system needs to write code to warn the system manager based on these prediction values ​​and take immediate measures for automatic operation and maintenance.

[0034] S104, performing automated execution based on the automated strategy and the execution rules, and performing real-time monitoring according to the automated execution result, so as to achieve real-time monitoring of the operating status and operation and maintenance effect.

[0035] For further information, see Figure 4 , the step S104 specifically includes steps S1041-S1042: S1041, performing automated execution according to the automated strategy and the execution rule to obtain a corresponding automated execution result; S1042, real-time monitoring and evaluation are performed according to the automation execution results, and the automation execution results are analyzed, and the obtained analysis results are used to continuously improve and optimize the data processing model, the automation strategy and the execution rules.

[0036] In the specific implementation, the automated operation and maintenance operations are realized by using scripts, API and other technologies. When the set rules are met, the system will automatically perform the corresponding operation and maintenance tasks, such as fault repair, resource scheduling, etc. For example, the script code for elegantly solving the problem of high CPU usage of the server in this embodiment is as follows: # Function to terminate high CPU usage process kill_high_cpu_processes() { # Get the process ID and usage rate that occupies the highest CPU (may require root privileges); # Note: The ps and awk commands here may need to be adjusted according to your system; ps -eo pid,%cpu --sort=-%cpu | awk 'NR>1 {if ($2>'$CPU_THRESHOLD')print $1}' | while read -r pid; do echo "Terminate process $pid, CPU usage is too high." | tee -a "$LOG_FILE" # Try to terminate the process gracefully (may need to be adjusted depending on the process type) kill -15 "$pid" || echo "Unable to kill process $pid" | tee -a "$LOG_FILE" # If graceful termination fails, consider using forced termination (please use with caution) # sleep 5 # kill -9 "$pid" done }; Furthermore, the effects of automated operation and maintenance are monitored and evaluated in real time, and by collecting and analyzing operation and maintenance data, the operating status and operation and maintenance effects of the system are understood, so as to timely adjust and optimize the operation and maintenance strategy.

[0037] Specifically, based on the monitoring and evaluation results, the models, rules and execution processes are continuously improved and optimized. For example, the model parameters can be adjusted, the feature extraction method can be optimized, the automated execution process can be improved, etc., to improve the efficiency and accuracy of operation and maintenance.

[0038] In summary, the project operation and maintenance monitoring method in the above-mentioned embodiment of the present invention, by deploying a monitoring tool in the service terminal, and configuring a corresponding probe interface in the service terminal, uses the probe interface to collect indicator data, trains the machine learning model according to the data type and operation and maintenance goals of the indicator data, and uses probe technology to perform comprehensive monitoring of servers, databases, middleware, and application systems. It also combines human functions and machine learning technology to achieve automated operation and maintenance of IT projects, which can improve operation and maintenance efficiency, predict, analyze operation and maintenance directions, reduce human errors, monitor in real time and respond quickly, reduce operation and maintenance costs, and improve the level of operation and maintenance intelligence, and has produced significant results.

[0039] Embodiment 2 Another aspect of the present invention is to provide a project operation and maintenance monitoring system. Figure 5 , shown is a project operation and maintenance monitoring system in a second embodiment of the present invention, the system comprising: The data configuration module 11 is used to deploy the corresponding monitoring tool in the preset service terminal and configure the corresponding probe interface in the service terminal; Furthermore, the data configuration module 11 includes: A data configuration unit is used to deploy corresponding monitoring tools in a preset service terminal to configure early warning rules and define query statements for the collection database; The interface acquisition unit is used to obtain a data acquisition interface from a preset monitored node, wherein the return value of the data acquisition interface is the same as the return format of the monitoring tool.

[0040] The model training module 12 is used to collect data using the probe interface to obtain corresponding indicator data, and select a corresponding machine learning model for training according to the preset operation and maintenance objectives and the data type of the indicator data to obtain a data processing model; Furthermore, the model training module 12 includes: A model selection unit, configured to select a corresponding machine learning model according to the preset operation and maintenance target and the data type of the indicator data, and obtain historical data of the operation and maintenance target; A model training unit is used to train the machine learning model according to the historical data so that the machine learning model learns the characteristics and rules of the historical data to obtain a data processing model.

[0041] A data prediction module 13 is used to perform data prediction on the data to be processed using the data processing model to output corresponding prediction results, and define an automation strategy and corresponding execution rules according to the prediction results and the operation and maintenance objectives; The real-time monitoring module 14 is used to perform automated execution based on the automated strategy and the execution rules, and to perform real-time monitoring according to the automated execution results, so as to achieve real-time monitoring of the operating status and operation and maintenance effect.

[0042] Furthermore, the real-time monitoring module 14 includes: An automation execution unit, used to perform automation execution according to the automation strategy and the execution rules to obtain a corresponding automation execution result; A result analysis unit is used to perform real-time monitoring and evaluation based on the automation execution results, analyze the automation execution results, and use the obtained analysis results to continuously improve and optimize the data processing model, the automation strategy and the execution rules.

[0043] The functions or operation steps implemented when the above modules and units are executed are generally the same as those in the above method embodiments, and will not be repeated here.

[0044] The project operation and maintenance monitoring system provided in the embodiment of the present invention has the same implementation principle and technical effects as those of the aforementioned method embodiment. For the sake of brief description, for matters not mentioned in the system embodiment, reference may be made to the corresponding contents in the aforementioned method embodiment.

[0045] Embodiment 3 The present invention also provides a computer, see Figure 6 , shown is a computer in the third embodiment of the present invention, including a memory 10, a processor 20, and a computer program 30 stored in the memory 10 and executable on the processor 20, and the processor 20 implements the above-mentioned project operation and maintenance monitoring method when executing the computer program 30.

[0046] The memory 10 includes at least one type of readable storage medium, which includes a flash memory, a hard disk, a multimedia card, a card-type memory (e.g., an SD or DX memory, etc.), a magnetic memory, a magnetic disk, an optical disk, etc. In some embodiments, the memory 10 may be an internal storage unit of a computer, such as a hard disk of the computer. In other embodiments, the memory 10 may also be an external storage device, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card, etc. Further, the memory 10 may also include both an internal storage unit of the computer and an external storage device. The memory 10 may be used not only to store application software and various types of data installed in the computer, but also to temporarily store data that has been output or is to be output.

[0047] Among them, in some embodiments, the processor 20 can be an electronic control unit (Electronic Control Unit, abbreviated as ECU, also known as a vehicle computer), a central processing unit (Central Processing Unit, CPU), a controller, a microcontroller, a microprocessor or other data processing chip, used to run the program code stored in the memory 10 or process data, such as executing access restriction programs, etc.

[0048] It should be pointed out that Figure 6 The structure shown does not constitute a limitation on the computer. In other embodiments, the computer may include fewer or more components than shown in the figure, or combine certain components, or arrange the components differently.

[0049] The embodiment of the present invention further provides a readable storage medium having a computer program stored thereon, which implements the project operation and maintenance monitoring method as described above when the program is executed by a processor.

[0050] Those skilled in the art will appreciate that the logic and / or steps represented in the flowchart or otherwise described herein, for example, may be considered as an ordered list of executable instructions for implementing logical functions, and may be specifically implemented in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For purposes of this specification, "computer-readable medium" may be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0051] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0052] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or a combination thereof: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0053] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0054] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached claims.

Claims

1. A project operation and maintenance monitoring method, characterized in that: include: Deploy corresponding monitoring tools in a preset service terminal, and configure corresponding probe interfaces in the service terminal; The probe interface is used to collect data to obtain corresponding indicator data, and a corresponding machine learning model is selected for training according to preset operation and maintenance goals and the data type of the indicator data to obtain a data processing model; Using the data processing model to perform data prediction on the data to be processed to output corresponding prediction results, and defining automation strategies and corresponding execution rules according to the prediction results and the operation and maintenance objectives; Automated execution is performed based on the automation strategy and the execution rules, and real-time monitoring is performed based on the automated execution results to achieve real-time monitoring of the operating status and operation and maintenance effects.

2. The project operation and maintenance monitoring method according to claim 1, characterized in that: The steps of deploying a corresponding monitoring tool in a preset service terminal and configuring a corresponding probe interface in the service terminal include: Deploy the corresponding monitoring tools in the preset service terminal to configure the warning rules and define the query statements for the collection database; A data acquisition interface is obtained from a preset monitored node, wherein a return value of the data acquisition interface is in the same format as a return value of the monitoring tool.

3. The project operation and maintenance monitoring method according to claim 1, characterized in that: The steps of selecting a corresponding machine learning model for training according to the preset operation and maintenance objectives and the data type of the indicator data to obtain a data processing model include: Selecting a corresponding machine learning model according to the preset operation and maintenance goal and the data type of the indicator data, and obtaining historical data of the operation and maintenance goal; The machine learning model is trained according to the historical data so that the machine learning model learns the characteristics and rules of the historical data to obtain a data processing model.

4. The project operation and maintenance monitoring method according to claim 1, characterized in that: The steps of performing automatic execution based on the automatic strategy and the execution rules, and performing real-time monitoring according to the automatic execution results to achieve real-time monitoring of the operation status and operation and maintenance effect include: Performing automated execution according to the automated strategy and the execution rules to obtain corresponding automated execution results; Real-time monitoring and evaluation are performed based on the automation execution results, and the automation execution results are analyzed, and the data processing model, the automation strategy and the execution rules are continuously improved and optimized based on the obtained analysis results.

5. A project operation and maintenance monitoring system, characterized in that: include: A data configuration module, used to deploy corresponding monitoring tools in a preset service terminal and configure corresponding probe interfaces in the service terminal; A model training module is used to collect data using the probe interface to obtain corresponding indicator data, and select a corresponding machine learning model for training according to preset operation and maintenance goals and the data type of the indicator data to obtain a data processing model; A data prediction module, used to perform data prediction on the data to be processed using the data processing model to output corresponding prediction results, and define an automation strategy and corresponding execution rules according to the prediction results and the operation and maintenance objectives; The real-time monitoring module is used to perform automated execution based on the automation strategy and the execution rules, and to perform real-time monitoring according to the automated execution results, so as to achieve real-time monitoring of the operating status and operation and maintenance effects.

6. The project operation and maintenance monitoring system according to claim 5, characterized in that: The data configuration module includes: A data configuration unit is used to deploy corresponding monitoring tools in a preset service terminal to configure early warning rules and define query statements for the collection database; The interface acquisition unit is used to obtain a data acquisition interface from a preset monitored node, wherein the return value of the data acquisition interface is the same as the return format of the monitoring tool.

7. The project operation and maintenance monitoring system according to claim 5, characterized in that: The model training module includes: A model selection unit, configured to select a corresponding machine learning model according to the preset operation and maintenance target and the data type of the indicator data, and obtain historical data of the operation and maintenance target; A model training unit is used to train the machine learning model according to the historical data so that the machine learning model learns the characteristics and rules of the historical data to obtain a data processing model.

8. The project operation and maintenance monitoring system according to claim 5, characterized in that: The real-time monitoring module includes: An automation execution unit, used to perform automation execution according to the automation strategy and the execution rules to obtain a corresponding automation execution result; A result analysis unit is used to perform real-time monitoring and evaluation based on the automation execution results, analyze the automation execution results, and use the obtained analysis results to continuously improve and optimize the data processing model, the automation strategy and the execution rules.

9. A readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the project operation and maintenance monitoring method as described in any one of claims 1 to 4 is implemented.

10. A computer comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the project operation and maintenance monitoring method as described in any one of claims 1 to 4 is implemented.

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