CN117172751A - Construction method of intelligent operation and maintenance information analysis model - Google Patents
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Abstract
The invention discloses a construction method of an intelligent operation and maintenance information analysis model in the technical field of information analysis, which comprises the following steps: s100: collecting data and storing the collected data; s200: collecting data for preliminary treatment; s300: building and optimizing a model according to the acquired data; s400: optimizing and supporting decision-making on the information built by the model; s500: and displaying the result and monitoring the operation process. The beneficial effects of the invention are as follows: by adopting big data and artificial intelligence technology, the method can perform deep learning and pattern recognition on the operation and maintenance data and provide accurate analysis results; the method can predict and optimize the operation and data, provide decision support and improve the analysis efficiency of the operation and data.
Description
Technical Field
The invention relates to the technical field of information analysis, in particular to a construction method of an intelligent operation and maintenance information analysis model.
Background
Information, which refers to the objects of transmission and processing of audio, messaging, and communication systems, generally refers to everything that is spread by human society. People recognize and reform the world by obtaining and identifying different information of nature and society to distinguish different things. In all communication and control systems, information is a form of common association. The traditional operation and maintenance information analysis system is mainly based on rules and experience, and cannot fully utilize the advantages of big data and artificial intelligence technology. However, with the rapid development of cloud computing, big data, and artificial intelligence technology, it has become easier to collect and store massive amounts of operation and data. Therefore, an intelligent operation and maintenance information analysis system is needed, which can provide accurate operation and maintenance analysis and decision support by performing deep learning and pattern recognition on big data.
Disclosure of Invention
The invention aims to provide a construction method of an intelligent operation and maintenance information analysis model, which aims to solve the problems in the background technology.
In order to achieve the above purpose, the present invention provides the following technical solutions: a construction method of an intelligent operation and maintenance information analysis model comprises the following steps:
s100: collecting data and storing the collected data;
s200: collecting data for preliminary treatment;
s300: building and optimizing a model according to the acquired data;
s400: optimizing and supporting decision-making on the information built by the model;
s500: and displaying the result and monitoring the operation process.
In the step S100, the collected data is stored in a distributed database by collecting various data related to operation and maintenance, so as to facilitate subsequent analysis and inquiry, in the step S200, the raw data collected in the step S100 is preprocessed to improve the data quality, the step S300 uses a trained model to analyze and support decision on the operation and maintenance data, trend analysis and prediction can be performed on the basis of historical data, decision basis is provided for operation and maintenance personnel, and the step S400 displays the analysis result to the operation and maintenance personnel in a visual form.
As a further scheme of the invention: in the step S100, data collection and storage are performed by:
and (3) sensor data acquisition: the working state of the equipment is monitored in real time by installing various sensors;
and (3) collecting equipment interface data: the method comprises the steps of communicating with equipment through an equipment interface to obtain real-time state information and operation logs of the equipment;
collecting log files: periodically collecting log files generated by equipment and extracting key information in the log files;
and (3) collecting network packet capturing data: capturing and analyzing the data stream in real time by using a network packet capturing technology;
the collected data is formatted and cleaned to ensure accuracy and integrity of the data, the cleaning process includes removing duplicate data, repairing erroneous data, and filling missing data, and then the system stores the collected data in a distributed database for subsequent analysis and querying.
As still further aspects of the invention: in the step S100, the following method is adopted for data storage:
distributed database: the distributed database system is adopted, so that data can be stored on a plurality of nodes, and the distributed processing and high availability of the data are realized;
data compression and indexing: the collected data is reasonably compressed and indexed, so that the storage efficiency and the query speed of the data can be improved;
and (3) storage optimization: suitable storage formats and storage media may be selected for different types of data.
As still further aspects of the invention: in the step S200, the preliminary processing for data includes the steps of:
data cleaning: cleaning the acquired data, removing repeated data, repairing error data and filling missing data;
data normalization: carrying out normalization processing on the data, and converting the data with different dimensions into uniform dimensions;
feature extraction: converting the high-dimensional original data into more meaningful features by extracting the features of the original data;
smoothing data: smoothing the data to remove noise and abnormal points in the data, so that the data is more stable;
data dimension reduction: the dimension reduction processing is carried out on the high-dimension data, so that the dimension of the data can be reduced and the most important characteristics can be extracted;
data normalization: the processed data is normalized to fit a particular distribution or statistical characteristic.
As still further aspects of the invention: in the step S300, the following steps are adopted to build and optimize the model:
selecting a proper algorithm: according to the nature of the problem and the characteristics of the data, selecting a proper machine learning algorithm for training and optimizing;
feature selection and engineering: before training the model, feature selection and engineering are performed on the data to extract the most relevant and useful features;
model training and tuning: training a machine learning model by using a training data set, and optimizing according to the performance of the model on a verification data set;
model evaluation and selection: after training and tuning are completed, the model is evaluated by using a test data set, and the prediction performance and generalization capability of the model are evaluated;
model optimization and iteration: continuously monitoring and collecting operation and maintenance data, and iterating and optimizing the model;
system integration and deployment: integrating the trained and optimized model into an operation and maintenance system, and performing deployment and application.
As still further aspects of the invention: in the step S400, the information of model building is optimized by the following method:
data visualization: the processed data is presented in an intuitive mode through a visualization technology, so that operation and maintenance personnel can quickly understand and analyze the data;
fault diagnosis and prediction: performing fault diagnosis and prediction by using a machine learning and statistical algorithm based on the processed data;
early warning and intelligent recommendation: according to the result of data analysis, combining with preset rules and models, generating corresponding early warning information, and providing corresponding solutions or suggestions;
performance analysis and optimization: evaluating and optimizing the efficiency of the system through analysis of the operation and maintenance process and the performance data;
risk assessment and decision support: comprehensively considering system data, history records and external factors, and carrying out risk assessment and decision support;
automatic operation and maintenance: and combining the results of information analysis and decision support to realize the capability of automatic operation and decision.
As still further aspects of the invention: in the step S500, the model building result and the operation process are monitored through the following steps:
real-time data dashboard: the visual data instrument panel is designed, and real-time system performance indexes, early warning information and key data trends are displayed;
early warning and anomaly visualization: early warning and abnormality of the system are presented in a visual mode, so that operation and maintenance personnel can be helped to quickly locate and process problems;
historical data analysis: through analysis of historical data, the running trend and performance change of the system are displayed;
performance evaluation and reporting: and periodically generating a system efficiency evaluation report for summarizing and analyzing the running condition and performance index of the system.
Compared with the prior art, the invention has the beneficial effects that:
1. by adopting big data and artificial intelligence technology, the method can perform deep learning and pattern recognition on the operation and maintenance data and provide accurate analysis results;
2. the method can predict and optimize the operation and data, provide decision support and improve the analysis efficiency of the operation and data.
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FIG. 1 is a schematic diagram of steps of a method for constructing an intelligent operation and maintenance information analysis model according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In the description of the present invention, it should be understood that the terms "upper", "lower", "front", "rear", "left", "right", "top", "bottom", "inner", "outer", "one end", "one side", etc. indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, are merely for convenience in describing the present invention and simplifying the description, and do not indicate or imply that the devices or elements referred to must have a specific orientation, be configured and operated in a specific orientation, and thus should not be construed as limiting the present invention.
Referring to fig. 1, in an embodiment of the present invention, a method for constructing an intelligent operation and maintenance information analysis model includes the following steps:
s100: collecting data and storing the collected data;
s200: collecting data for preliminary treatment;
s300: building and optimizing a model according to the acquired data;
s400: optimizing and supporting decision-making on the information built by the model;
s500: and displaying the result and monitoring the operation process.
Wherein, step S100: data acquisition and storage
First, the system needs to collect various operation-related data including equipment status, fault information, maintenance records, etc. The data collection can be performed by means of sensors, device interfaces, log files or network packet capturing. The acquired data needs to be formatted and cleaned to ensure the accuracy and integrity of the data. The system then stores the collected data in a distributed database for subsequent analysis and querying.
Step S200: data preprocessing
The raw data collected often has noise and redundant information, and needs to be preprocessed to improve the data quality. In the preprocessing stage, operations such as denoising, normalization, feature extraction, dimension reduction and the like can be performed on the data so as to reduce the data volume and extract useful information.
Step S300: model training and optimization
In this step, the system trains and optimizes the preprocessed data using machine learning and deep learning algorithms. The operation and maintenance data are analyzed and predicted by constructing an appropriate model. Common models include support vector machines, random forests, neural networks, and the like. The system can improve the accuracy and generalization capability of the model through cross-validation and parameter optimization.
Step S400: information analysis and decision support
In this step, the system uses the trained model to analyze and support decision-making on the dimension of the fortune. The system can provide functions of fault early warning, abnormality detection, resource optimization and the like according to the characteristics of the operation and maintenance data. Meanwhile, the system can also perform trend analysis and prediction based on historical data, and provide decision basis for operation and maintenance personnel.
Step S500: results display and monitoring
Finally, the system presents the analysis results to the operation and maintenance personnel in a visual form. The trend and state of the operation and maintenance data can be displayed in a chart, a report, a dashboard and the like. In addition, the system can also provide a real-time monitoring function and timely feed back the change of the state and performance index of the equipment.
Preferably: first, the system needs to collect various operation-related data including equipment status, fault information, maintenance records, etc. The data acquisition is performed by the following modes:
and (3) sensor data acquisition: the system can be provided with various sensors, such as a temperature sensor, a humidity sensor, a vibration sensor and the like, for monitoring the working state of the equipment in real time. For example, temperature data of the device may be collected by a temperature sensor to determine if there is a risk of overheating.
And (3) collecting equipment interface data: the system can communicate with the equipment through the equipment interface to acquire the real-time state information and the running log of the equipment. For example, by communicating with the server interface, data such as CPU usage, memory usage, and disk space of the server may be obtained.
Collecting log files: the system may periodically collect device-generated log files and extract key information therefrom. For example, by analyzing the log file of the router, the abnormal condition of the network connection and the trend of the change in the network traffic can be obtained.
And (3) collecting network packet capturing data: the system may use network packet-grabbing techniques to capture and analyze the data streams in real time. By deeply analyzing the network traffic, the relevant information such as various protocols, the size of the data packet, the transmission time and the like in the network can be obtained.
The acquired data needs to be formatted and cleaned to ensure the accuracy and integrity of the data. The cleaning process comprises the operations of removing repeated data, repairing error data, filling missing data and the like. The system then stores the collected data in a distributed database for subsequent analysis and querying.
In order to achieve efficient data storage and management, the following techniques are employed to store data:
distributed database: by adopting the distributed database system, the data can be stored on a plurality of nodes, so that the distributed processing and high availability of the data are realized. Common distributed database systems include Apache Cassandra, hadoop HDFS, mongoDB, and the like.
Data compression and indexing: the collected data is reasonably compressed and indexed, so that the data storage efficiency and the query speed can be improved. For example, time-series data may employ time-interval sampling and aggregation techniques that reduce the amount of data without affecting the accuracy of the analysis.
And (3) storage optimization: suitable storage formats and storage media may be selected for different types of data. For example, for structured data, a columnar store or relational database may be used; for unstructured data, a document-type database or an object storage system may be used.
Through the acquisition and storage method, the system can efficiently acquire and store various operation and maintenance related data and provide sufficient data support for subsequent intelligent analysis and decision.
The raw data collected often has noise and redundant information, and needs to be preprocessed to improve the data quality. In the preprocessing stage, the following method is adopted to process data:
data cleaning: and cleaning the acquired data, removing repeated data, repairing error data and filling missing data. Data cleaning algorithms such as deduplication algorithms, anomaly detection algorithms, interpolation algorithms, and the like may be used.
Data normalization: and carrying out normalization processing on the data, and converting the data with different dimensions into uniform dimensions. Common normalization methods include max-min normalization, Z-Score normalization, decimal scale normalization, and the like.
Feature extraction: by feature extraction of the raw data, the high-dimensional raw data is converted into more meaningful features. The feature extraction may be performed using statistical features, frequency domain features, time domain features, etc., such as mean, standard deviation, power spectrum, etc.
Smoothing data: and smoothing the data to remove noise and abnormal points in the data, so that the data is more stable. Common data smoothing methods include moving average, exponential smoothing, kalman filtering, and the like.
Data dimension reduction: the dimensionality of the data can be reduced and the most important features can be extracted by performing dimensionality reduction processing on the high-dimensionality data. Common dimension reduction methods include Principal Component Analysis (PCA), linear Discriminant Analysis (LDA), t-SNE, and the like.
Data normalization: the processed data is normalized to fit a particular distribution or statistical characteristic. For example, the data is normalized or discretized for subsequent model training and analysis.
In the data preprocessing stage, a proper method can be selected according to different data characteristics and problem requirements. Meanwhile, the preprocessing process also considers the efficiency and real-time performance of data processing so as to ensure that the system can respond and process large-scale operation and maintenance data in real time.
Through the preprocessing step, the quality and accuracy of the data can be improved, and more meaningful data features are provided for subsequent model training and analysis.
After data acquisition, storage and preprocessing, the next step is to train and optimize the data using machine learning and optimization methods to achieve intelligent analysis and decision-making of the operation and maintenance problems. The following are some suggestions for enriching the training and optimization in the present invention:
selecting a proper algorithm: according to the nature of the problem and the characteristics of the data, a proper machine learning algorithm is selected for training and optimization. For example, for classification problems, algorithms such as decision trees, support vector machines, or neural networks may be tried; for the clustering problem, k-means clustering or hierarchical clustering algorithms or the like may be used.
Feature selection and engineering: prior to training the model, the data is feature selected and engineered to extract the most relevant and useful features. Feature selection algorithms, such as mutual information, L1 regularization, and a keni index, may be used to identify features that have an important impact on the target variable. Meanwhile, feature engineering can be performed by combining domain knowledge, and the performance of the model is improved through combining, converting or newly creating features.
Model training and tuning: the machine learning model is trained using the training dataset and tuning is performed according to the model's performance on the verification dataset. Different combinations of model parameters may be tried, using cross-validation techniques or the like to evaluate the accuracy and generalization ability of the model. Meanwhile, an integrated learning method such as random forest, boosting and Bagging can be used, so that the performance of the model is further improved.
Model evaluation and selection: after training and tuning is completed, the model is evaluated using the test dataset to evaluate the predictive performance and generalization ability of the model. And selecting an optimal model according to the evaluation result, and applying the optimal model to the actual operation and maintenance problem.
Model optimization and iteration: operation and maintenance data are continuously monitored and collected, and the model is iterated and optimized. The model can be adapted to the change of data and new operation and maintenance scene in time by means of the technologies such as incremental learning.
System integration and deployment: integrating the trained and optimized model into an operation and maintenance system, and performing deployment and application. The model is ensured to process and analyze the operation and maintenance data in real time, and instant intelligent support is provided for decision making.
Training and optimization are key steps for realizing intelligent operation and maintenance, and can continuously improve the efficiency and reliability of an operation and maintenance system through a data driving method. In practical application, more refined and customized training and optimizing strategies can be performed according to specific requirements and by combining domain knowledge and experience.
In the operation and maintenance system, through information analysis and decision support on the collected and processed data, operation and maintenance personnel can be helped to understand the system state and diagnose the problems more accurately, and make corresponding decisions. The following are sub-modules of the information analysis and decision support of the present invention:
data visualization: the processed data is presented in an intuitive mode through a visualization technology, so that operation and maintenance personnel can quickly understand and analyze the data. Suitable data visualization means may include line graphs, bar graphs, scatter graphs, thermodynamic diagrams, and the like. Meanwhile, the data can be displayed and analyzed in the geographic space by combining tools such as a Geographic Information System (GIS) and the like.
Fault diagnosis and prediction: based on the processed data, machine learning and statistical algorithms are used for fault diagnosis and prediction. By establishing a proper model, the operation and maintenance personnel can be helped to accurately identify abnormal behaviors and potential faults in the system, and corresponding preventive measures can be taken in advance.
Early warning and intelligent recommendation: and according to the result of the data analysis, combining with a preset rule and model, generating corresponding early warning information and providing corresponding solutions or suggestions. For example, when the system is abnormal, early warning is automatically generated, possible reasons and solutions are given, and the operation and maintenance personnel are helped to respond quickly and process faults.
Performance analysis and optimization: the performance of the system is assessed and optimized by analysis of the operation and maintenance process and performance data. The system can analyze indexes such as workload, response time, resource utilization rate and the like of the system, identify bottlenecks and optimization space, and propose corresponding improvement strategies so as to improve the efficiency and performance of the system.
Risk assessment and decision support: and comprehensively considering system data, history records and external factors, and carrying out risk assessment and decision support. Different decision schemes can be evaluated and compared by using methods such as decision trees, priority ordering, simulation and the like, and operation and maintenance personnel can be helped to make decisions based on data.
Automatic operation and maintenance: and combining the results of information analysis and decision support to realize the capability of automatic operation and decision. By establishing an automated decision engine and an execution strategy, common problems in the system can be automatically detected and processed, so that manual intervention is reduced, and the operation and maintenance efficiency is improved.
The information analysis and decision support are core functions of the invention, can provide accurate diagnosis and decision support by analyzing and processing a large number of operation and maintenance data, help operation and maintenance personnel to find and solve problems in time, and improve the usability and reliability of the system.
In the process of realizing intelligent operation and maintenance, result display and monitoring are a very critical ring. Through visual result display and real-time monitoring, operation and maintenance personnel can timely know the system state, problem trend and performance. For this step, the following modules are used for optimization:
real-time data dashboard: and (3) designing an intuitive data instrument panel, and displaying real-time system performance indexes, early warning information and key data trends. The change and state of the data are presented in the forms of charts, indicator lights, progress bars and the like, so that operation and maintenance personnel are helped to track and monitor the operation condition of the system in real time.
Early warning and anomaly visualization: early warning and abnormality of the system are presented in a visual mode, so that operation and maintenance personnel can be helped to quickly locate and process the problems. Colors, identifiers, pop-up windows and the like can be used for distinguishing early warning at different levels, and detailed abnormal information and suggestion processing methods are provided.
Historical data analysis: through analysis of historical data, the operation trend and the performance change of the system are displayed. The change rule of the system index can be visually displayed by using the forms of a line graph, a trend graph and the like, so that operation and maintenance personnel can be helped to identify potential problems and plan an optimization strategy.
Performance evaluation and reporting: and periodically generating a system efficiency evaluation report for summarizing and analyzing the running condition and performance index of the system. Reports may include trends in key metrics, suggestions for efficiency improvement, summaries of fault handling, etc., to help operators understand the overall status and direction of improvement of the system.
The method for intelligent operation and maintenance information analysis involves a plurality of steps and techniques, such as in the present embodiment, the intelligent operation and maintenance information analysis flow is as follows:
and (3) data collection: first, various data related to the operation and maintenance needs to be collected, which may include device sensor data, device operation logs, user feedback data, maintenance records, and the like. The collection of data may be performed by means of real-time monitoring, sensor devices, manual recording, etc.
And (3) data storage: the preprocessed data is stored in a suitable data storage system. Common options include relational databases, noSQL databases, data warehouses, and the like. Data storage should be high performance, scalable and fault tolerant to meet the needs of large-scale data storage and querying.
Data preprocessing: preprocessing the collected data is an important step. Preprocessing comprises data cleaning, data denoising, data format conversion and the like so as to ensure the accuracy and consistency of data. Feature selection and dimension reduction may also be performed for better modeling and analysis.
Data analysis: analysis of stored data is the core step of intelligent operation and maintenance information analysis. The data analysis may employ statistical analysis, machine learning, data mining, etc. Common analysis techniques include cluster analysis, anomaly detection, time series analysis, association rule mining, etc., to discover equipment failures, predict maintenance requirements, optimize operation and maintenance flows, etc.
Visualization of results: the analysis results are visually displayed in a form which is easy to understand and explain so as to help a user to quickly understand the data and the analysis results. The visualizations may take the form of charts, dashboards, reports, etc., which may help the user make decisions and take corresponding actions.
Model optimization and iteration: the model can be optimized and iterated according to feedback and requirements of analysis results. Model optimization may include steps of updating algorithms, adjusting parameters, adding features, etc. to improve accuracy and performance of the model.
In summary, the intelligent operation and maintenance information analysis method comprises links of data collection, data preprocessing, data storage, data analysis, result visualization, model optimization, iteration and the like. The method can help extract valuable operation and maintenance information, optimize equipment performance, improve maintenance efficiency and provide support for decisions.
The foregoing is only a preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art, who is within the scope of the present invention, should make equivalent substitutions or modifications according to the technical scheme of the present invention and the inventive concept thereof, and should be covered by the scope of the present invention.
Claims (7)
1. A construction method of an intelligent operation and maintenance information analysis model is characterized by comprising the following steps of: comprising the following steps:
s100: collecting data and storing the collected data;
s200: collecting data for preliminary treatment;
s300: building and optimizing a model according to the acquired data;
s400: optimizing and supporting decision-making on the information built by the model;
s500: and displaying the result and monitoring the operation process.
In the step S100, the collected data is stored in a distributed database by collecting various data related to operation and maintenance, so as to facilitate subsequent analysis and inquiry, in the step S200, the raw data collected in the step S100 is preprocessed to improve the data quality, the step S300 uses a trained model to analyze and support decision on the operation and maintenance data, trend analysis and prediction can be performed on the basis of historical data, decision basis is provided for operation and maintenance personnel, and the step S400 displays the analysis result to the operation and maintenance personnel in a visual form.
2. The method for constructing an intelligent operation and maintenance information analysis model according to claim 1, wherein the method comprises the following steps: in the step S100, data collection and storage are performed by:
and (3) sensor data acquisition: the working state of the equipment is monitored in real time by installing various sensors;
and (3) collecting equipment interface data: the method comprises the steps of communicating with equipment through an equipment interface to obtain real-time state information and operation logs of the equipment;
collecting log files: periodically collecting log files generated by equipment and extracting key information in the log files;
and (3) collecting network packet capturing data: capturing and analyzing the data stream in real time by using a network packet capturing technology;
the collected data is formatted and cleaned to ensure accuracy and integrity of the data, the cleaning process includes removing duplicate data, repairing erroneous data, and filling missing data, and then the system stores the collected data in a distributed database for subsequent analysis and querying.
3. The method for constructing an intelligent operation and maintenance information analysis model according to claim 1, wherein the method comprises the following steps: in the step S100, the following method is adopted for data storage:
distributed database: the distributed database system is adopted, so that data can be stored on a plurality of nodes, and the distributed processing and high availability of the data are realized;
data compression and indexing: the collected data is reasonably compressed and indexed, so that the storage efficiency and the query speed of the data can be improved;
and (3) storage optimization: suitable storage formats and storage media may be selected for different types of data.
4. The method for constructing an intelligent operation and maintenance information analysis model according to claim 1, wherein the method comprises the following steps: in the step S200, the preliminary processing for data includes the steps of:
data cleaning: cleaning the acquired data, removing repeated data, repairing error data and filling missing data;
data normalization: carrying out normalization processing on the data, and converting the data with different dimensions into uniform dimensions;
feature extraction: converting the high-dimensional original data into more meaningful features by extracting the features of the original data;
smoothing data: smoothing the data to remove noise and abnormal points in the data, so that the data is more stable;
data dimension reduction: the dimension reduction processing is carried out on the high-dimension data, so that the dimension of the data can be reduced and the most important characteristics can be extracted;
data normalization: the processed data is normalized to fit a particular distribution or statistical characteristic.
5. The method for constructing an intelligent operation and maintenance information analysis model according to claim 1, wherein the method comprises the following steps: in the step S300, the following steps are adopted to build and optimize the model:
selecting a proper algorithm: according to the nature of the problem and the characteristics of the data, selecting a proper machine learning algorithm for training and optimizing;
feature selection and engineering: before training the model, feature selection and engineering are performed on the data to extract the most relevant and useful features;
model training and tuning: training a machine learning model by using a training data set, and optimizing according to the performance of the model on a verification data set;
model evaluation and selection: after training and tuning are completed, the model is evaluated by using a test data set, and the prediction performance and generalization capability of the model are evaluated;
model optimization and iteration: continuously monitoring and collecting operation and maintenance data, and iterating and optimizing the model;
system integration and deployment: integrating the trained and optimized model into an operation and maintenance system, and performing deployment and application.
6. The method for constructing an intelligent operation and maintenance information analysis model according to claim 1, wherein the method comprises the following steps: in the step S400, the information of model building is optimized by the following method:
data visualization: the processed data is presented in an intuitive mode through a visualization technology, so that operation and maintenance personnel can quickly understand and analyze the data;
fault diagnosis and prediction: performing fault diagnosis and prediction by using a machine learning and statistical algorithm based on the processed data;
early warning and intelligent recommendation: according to the result of data analysis, combining with preset rules and models, generating corresponding early warning information, and providing corresponding solutions or suggestions;
performance analysis and optimization: evaluating and optimizing the efficiency of the system through analysis of the operation and maintenance process and the performance data;
risk assessment and decision support: comprehensively considering system data, history records and external factors, and carrying out risk assessment and decision support;
automatic operation and maintenance: and combining the results of information analysis and decision support to realize the capability of automatic operation and decision.
7. The method for constructing an intelligent operation and maintenance information analysis model according to claim 1, wherein the method comprises the following steps: in the step S500, the model building result and the operation process are monitored through the following steps:
real-time data dashboard: the visual data instrument panel is designed, and real-time system performance indexes, early warning information and key data trends are displayed;
early warning and anomaly visualization: early warning and abnormality of the system are presented in a visual mode, so that operation and maintenance personnel can be helped to quickly locate and process problems;
historical data analysis: through analysis of historical data, the running trend and performance change of the system are displayed;
performance evaluation and reporting: and periodically generating a system efficiency evaluation report for summarizing and analyzing the running condition and performance index of the system.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117376108A (en) * | 2023-12-07 | 2024-01-09 | 深圳市亲邻科技有限公司 | Intelligent operation and maintenance method and system for Internet of things equipment |
| CN117852719A (en) * | 2024-01-15 | 2024-04-09 | 重庆双江航运发展有限公司 | Full-flow management method and system for engineering data |
| CN117850784A (en) * | 2024-02-07 | 2024-04-09 | 北京燕华科技发展有限公司 | Visual equipment scene model building method |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117376108A (en) * | 2023-12-07 | 2024-01-09 | 深圳市亲邻科技有限公司 | Intelligent operation and maintenance method and system for Internet of things equipment |
| CN117376108B (en) * | 2023-12-07 | 2024-03-01 | 深圳市亲邻科技有限公司 | Intelligent operation and maintenance method and system for Internet of things equipment |
| CN117852719A (en) * | 2024-01-15 | 2024-04-09 | 重庆双江航运发展有限公司 | Full-flow management method and system for engineering data |
| CN117850784A (en) * | 2024-02-07 | 2024-04-09 | 北京燕华科技发展有限公司 | Visual equipment scene model building method |
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