CN117422434A - Wisdom fortune dimension dispatch platform - Google Patents
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Abstract
The invention discloses an intelligent operation and maintenance scheduling platform in the technical field of operation and maintenance, which comprises a main control center, a data acquisition module, a data analysis module, a task scheduling module, a fault diagnosis module, an operation log module and a control module, wherein the data acquisition module is used for automatically collecting and storing key data related to operation and maintenance, the data analysis module is used for analyzing the acquired data based on a machine learning algorithm and generating early warning and diagnosis results, the task scheduling module is used for automatically distributing tasks to operation and maintenance personnel according to real-time data analysis results and operation and maintenance personnel priority settings, the fault diagnosis module is used for identifying system faults and providing a repairing scheme, and the operation log module is used for recording execution details and results of each task. The beneficial effects of the invention are as follows: work efficiency is improved: by means of automatic task scheduling and fault diagnosis, manual intervention and errors are reduced, the working efficiency and the operation and maintenance response speed are improved, and the system stability is improved: through intelligent data analysis and early warning functions, timely discovery and preventive questions are provided.
Description
Technical Field
The invention relates to the technical field of operation and maintenance, in particular to an intelligent operation and maintenance scheduling platform.
Background
Currently, production operations for various industries are increasingly dependent on complex information technology systems, including hardware devices, network infrastructure, and software applications. In order to ensure that these systems are always in an efficient, stable state, the operation and maintenance team needs to perform close monitoring, maintenance and scheduling work. However, due to the complexity and diversity of operation and maintenance tasks, the conventional manual scheduling method often faces many challenges, for example, in conventional airport operation and maintenance scheduling often depends on manual experience and complex manual coordination, and is easily affected by various factors, so that the operation effect is poor. Therefore, an intelligent operation and maintenance scheduling platform is needed to assist the management unit in scheduling people and objects.
Disclosure of Invention
The invention aims to provide an intelligent operation and maintenance scheduling platform, which aims to improve the working efficiency and the system stability of an operation and maintenance team by combining artificial intelligence and an automation technology. The platform can automatically collect, analyze and respond to key operation and maintenance data, and provide comprehensive decision support and automatic task scheduling for operation and maintenance personnel so as to solve the problems in the background technology.
In order to achieve the above purpose, the present invention provides the following technical solutions: an intelligent operation and maintenance scheduling platform comprises a main control center and the following modules:
the data acquisition module is used for automatically collecting and storing key data related to operation and maintenance;
the data analysis module is used for analyzing the acquired data based on a machine learning algorithm and generating a pre-set
Police and diagnostic results;
the task scheduling module is used for automatically distributing tasks to operation and maintenance personnel according to the real-time data analysis result and the priority setting of the operation and maintenance personnel;
the fault diagnosis module is used for identifying system faults and providing a repairing scheme;
the operation log module is used for recording the execution details and the results of each task;
an analysis report module for generating a detailed analysis report based on the operation log and the data analysis;
the user interface module is used for displaying data, early warning, task allocation and operation logs to operation and maintenance personnel;
the system comprises a data acquisition module, a data analysis module, a task scheduling module, a fault diagnosis module, an operation log module and a user interface module, wherein the data acquisition module can automatically collect key data related to operation and maintenance and store the key data to a data storage device, the data analysis module can analyze the acquired data based on a machine learning algorithm and generate early warning and diagnosis results, the task scheduling module can automatically distribute tasks to the operation and maintenance personnel according to the real-time data analysis results and operation and maintenance personnel priority settings, the fault diagnosis module can identify system faults and provide a repairing scheme, the operation log module can record execution details and results of each task, the analysis report module can generate a detailed analysis report based on operation logs and data analysis, and the user interface module can display data, early warning, task distribution and operation logs to the operation and maintenance personnel.
As a further scheme of the invention: the data acquisition module comprises a multi-metadata source acquisition: the module is capable of collecting data from a plurality of data sources, real-time data acquisition: the module can collect data in a real-time or near real-time mode, so that the system can acquire latest operation and maintenance related information in time, and the data is cleaned and preprocessed: the collected raw data generally needs to be cleaned and preprocessed to improve the quality and usability of the data, and abnormal data detection and processing: during the data collection process, abnormal data or data missing may be encountered, and the data storage and management: the module can store and manage the collected data efficiently, and control the security and authority of the data: the data acquisition module is very important to the safety and privacy protection of data, and expansibility and flexibility: the module has good expansibility and flexibility, and can be customized and expanded according to actual requirements.
As still further aspects of the invention: the data analysis module is an important component of the intelligent operation and maintenance scheduling platform, and has the main functions of processing, analyzing and mining the collected data to extract valuable information and generate early warning and diagnosis results, and cleaning and preprocessing the data: the data analysis module firstly cleans and preprocesses the collected original data, and extracts the characteristics: after cleaning and preprocessing, the data analysis module extracts key features from the original data according to specific operation and maintenance requirements, and data modeling: based on the extracted features, the data analysis module applies various machine learning algorithms and statistical methods to construct an appropriate mathematical model to describe the relationships and rules of the data, analyze and mine: after the model is built, the data analysis module further analyzes and mines the data, and results are generated and displayed: the data analysis module converts the analysis and mining results into a form which is easy to understand and use, and generates early warning and diagnosis results.
As still further aspects of the invention: the task scheduling module is a component for managing and scheduling task execution. The system can automatically and optimally execute tasks, improve the efficiency and reliability of the system, and manage the tasks: the task scheduling module is responsible for managing the creation, configuration and monitoring of tasks, and task scheduling: the task scheduling module determines the execution sequence and time of the tasks through an intelligent scheduling algorithm, and performs task execution: the task scheduling module can start and manage the execution process of the task and time the task: the task scheduling module supports the execution of timed tasks, task dependencies and flows: the task scheduling module supports the dependency relationship and flow control between tasks, monitoring and reporting: the task scheduling module has monitoring and reporting functions and can record and report logs, indexes and events of task execution.
As still further aspects of the invention: the fault diagnosis module is a key component for detecting, analyzing and solving system faults, and fault detection: the fault diagnosis module can actively or passively detect faults in the system, and perform fault analysis: the fault diagnosis module has the capability of analyzing the cause and influence of the fault, and fault prevention and optimization: the fault diagnosis module can help prevent system faults and optimize system performance, fault management and collaboration: the fault diagnosis module may assist in fault management and team collaboration, visualization and reporting: the fault diagnosis module has the functions of visualization and report, automation and integration: the fault diagnosis module reduces manual operation and improves efficiency through automation and integration.
As still further aspects of the invention: the operation log module is a very important part in the system, records various operation behaviors and events in the system, and has important significance for operation tracking, fault detection, security audit and business analysis, log level and classification: defining a plurality of levels for the log messages, classifying the log messages according to the log levels to filter and analyze the log as needed, key operations log: for key operations in the system, special log entries are added to facilitate security audit and track key operations, and sensitive data is desensitized: for operations involving sensitive data, the data should be desensitized at the time of logging to protect user privacy and information security, exception log: recording anomalies, errors and warnings in the system, facilitating troubleshooting and problem localization, operating metadata records: in addition to log messages, metadata for some operations may be recorded for subsequent auditing and tracking, data change records: for the change operation of important data in the system, the data state before and after the change is recorded, so that the change history of the data and the recovery data can be traced, and various log output modes are realized: support to export logs to different targets to meet different needs and scenarios, log rotation and archiving: the log files are periodically rotated and archived, so that the single log file is prevented from being too large, and meanwhile, the history log is convenient to manage and consult, and the log is searched and filtered: the function of searching and filtering the logs is provided, the related logs can be conveniently and rapidly positioned according to the keyword, the time range and the log level, and the centralized log management is realized: the logs of a plurality of systems are managed in a centralized way, the logs can be centralized to a central server through a log collector or a log monitoring system, comprehensive analysis and management are convenient, and visual analysis of the logs is convenient: and (3) carrying out statistics and analysis on the log data through a data visualization tool to generate a visualization chart and a report form, so as to help know the running state of the system and find out the abnormality and the trend.
Compared with the prior art, the invention has the beneficial effects that: the platform has strong data acquisition and analysis capability, and can automatically collect and process key operation and maintenance data. By adopting an advanced machine learning algorithm, the platform can accurately analyze a large amount of data, find potential problems and abnormal conditions, timely send out early warning to operation and maintenance personnel, and intelligently schedule tasks and resources according to the real-time operation and maintenance data and priority setting of the operation and maintenance personnel. By comprehensively considering various factors such as equipment state, task emergency degree and personnel capability, the platform can automatically distribute the most suitable tasks to suitable operation and maintenance personnel, avoids errors and delays caused by traditional manual scheduling, and is also provided with automatic fault diagnosis and repair functions. The platform can automatically identify and find the best solution when the system fails or is abnormal. By combining an advanced reasoning engine and a professional knowledge base, the platform can efficiently locate fault points and provide a detailed repairing scheme, so that the fault recovery time is greatly shortened, and the platform can also automatically generate detailed operation logs and analysis reports. By recording the execution details and results of each task, as well as the overall operation and maintenance situation, the platform can provide valuable references and improvement suggestions to the operation and maintenance personnel, and embodiments of the present invention can be implemented by customized software applications based on existing information technology equipment and network infrastructure. The software application can be deployed on an internal server or cloud platform, interfaces with various operation and maintenance devices and systems, and performs data analysis and task scheduling using advanced algorithms and models.
Work efficiency is improved: by automating task scheduling and fault diagnosis, manual intervention and errors are reduced, and the working efficiency and the operation and maintenance response speed are improved.
And the stability of the system is improved: through intelligent data analysis and early warning functions, timely discovery and preventive questions are provided.
Drawings
Fig. 1 is a schematic diagram of a system structure 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, the data acquisition module is one of the core components of the intelligent operation and maintenance scheduling platform. It has the following functions and characteristics:
and (3) collecting a multiple data source: the module is capable of collecting data from a plurality of data sources including, but not limited to, servers, network devices, databases, application logs, and the like. It may communicate with and extract key data from various data sources via different protocols (e.g., SNMP, SSH, API, etc.).
And (3) real-time data acquisition: the module can collect data in a real-time or near real-time mode, and the system can be ensured to acquire the latest operation and maintenance related information in time. For example, it may stay connected to the data source through a polling or subscription mechanism to obtain updates of data changes and status in real time.
Data cleaning and pretreatment: the raw data collected typically requires cleaning and preprocessing to improve the quality and usability of the data. The module can perform operations such as filtering, denoising, deduplication, format conversion and the like on the acquired data, and ensures that the data is accurate, consistent and normalized.
Abnormal data detection and processing: during data acquisition, abnormal data or data loss conditions may be encountered. The module has the capability of detecting and processing abnormal data, and can automatically identify and process the abnormal data, such as interpolation, smoothing or filling missing data by using default values.
Data storage and management: the module can store and manage the collected data efficiently. It may use databases, data warehouses, or distributed storage systems to store data, supporting fast retrieval of data and high throughput data access.
Data security and authority control: the data acquisition module is very important to the security and privacy protection of the data. It can take various measures such as encrypted transmission, authentication, rights control, etc., ensuring that only authorized users can access and manipulate sensitive data.
Extensibility and flexibility: the module has good expansibility and flexibility, and can be customized and expanded according to actual requirements. It supports the addition of new data sources, custom acquisition rules, and data processing logic to accommodate changes in different scenarios and data sources.
In summary, the data acquisition module plays a key role in the intelligent operation and maintenance scheduling platform, and can efficiently and accurately collect various operation and maintenance related data, thereby providing a reliable basis for subsequent data analysis and decision.
The data analysis module is an important component of the intelligent operation and maintenance scheduling platform, and has the main functions of processing, analyzing and mining the acquired data to extract valuable information and generate early warning and diagnosis results. The module has the following working principle and operation program:
data cleaning and pretreatment: the data analysis module firstly cleans and preprocesses the collected original data, and comprises operations of removing noise, supplementing missing values, converting data formats and the like. This step helps to improve the quality and usability of the data.
In combination, the data acquisition module may acquire data through a variety of means, with the particular choice depending on the type, source, and acquisition requirements of the data. The following are common data acquisition devices:
a sensor: the sensor is a common data acquisition device used for monitoring and measuring various physical quantities in real time. For example, temperature sensors, pressure sensors, humidity sensors, acceleration sensors, etc. may be used to collect physical parameters in an environment, device, or structure. The sensor may be coupled to the data acquisition module via an analog or digital interface and provide a real-time value or signal.
Instrument apparatus: some acquisition requirements may require the use of specialized instrumentation to acquire data. For example, spectrometers, vibrometers, electronic test equipment, etc. may be used to collect data in specific fields, such as optical signals, vibration modes, electrical parameters, etc. Such instrumentation typically requires physical connection to a data acquisition module or communication using a standard interface.
Database and log system: the data collection module may also collect data by accessing a database and log system. Various structured data are stored in the database, and the log system records the operating state and events of the system and the application program. The data acquisition module can extract the required data through database inquiry, log subscription or log file analysis and the like.
Network device and protocol: for the collection of network data, the data collection module may obtain data packets or traffic information by interacting with the network device. For example, network traffic is captured and analyzed by a network monitoring device or sniffer, or communicated with a network device via a network protocol (e.g., SNMP, modbus, OPC, etc.) to obtain device status and metric data.
API interface: many cloud services, web applications, and third party data services provide open API interfaces for access and retrieval of data. The data acquisition module may pull data by interacting with these API interfaces. For example, the required data is acquired by communicating with a data source through HTTP requests, restful APIs, websockets, or the like.
In general, the data acquisition module may select different devices according to specific data types and acquisition requirements. Whether sensors, instrumentation, databases, network devices, or API interfaces, they provide a diverse solution to data collection to meet the requirements of various application scenarios and data sources.
Feature extraction: after cleaning and preprocessing, the data analysis module extracts key features from the raw data according to specific operation and maintenance requirements. The features can be numerical, classified or text data, and through extracting and selecting the features, the data dimension can be effectively reduced, and the processing complexity can be reduced.
Modeling data: based on the extracted features, the data analysis module applies various machine learning algorithms and statistical methods to construct an appropriate mathematical model to describe the relationships and rules of the data. Common models include regression models, classification models, cluster models, and the like. And selecting a proper model to model the data, and facilitating tasks such as prediction, classification, anomaly detection and the like.
Analysis and excavation: after the model is built, the data analysis module further analyzes and mines the data. By applying statistical, data mining and machine learning techniques, it can discover patterns, trends and anomalies in the data, extracting useful information hidden behind the data.
Results generation and display: the data analysis module converts the analysis and mining results into a form which is easy to understand and use, and generates early warning and diagnosis results. The results can be presented in a report form, a visual graph, a text prompt and the like, so that an operation and maintenance person can know the state and the problem of the system in time.
Running a program:
loading data: the data analysis module first loads the preprocessed data into memory or connects to a data storage system, ensuring that the data is available and ready for analysis.
Characteristic engineering: feature sets meeting modeling requirements are generated through feature extraction, conversion and selection of data.
And (3) establishing a model: and inputting the data and the characteristics into the model for training and optimizing according to the selected machine learning algorithm and the statistical method.
Data analysis and mining: and analyzing and mining the new data by using the trained model, and finding rules, trends and anomalies in the data.
Results generation and display: and generating a visual chart, a report or a text prompt from the analysis and mining results, and displaying the visual chart, the report or the text prompt to operation and maintenance personnel through a user interface module.
Iterative optimization: and according to feedback and requirements of operation and maintenance personnel, iterative optimization is carried out on the model and the analysis process, and the accuracy and the effect of analysis are improved.
In summary, the data analysis module extracts valuable information from a large amount of data through the steps of data cleaning, feature extraction, model building, analysis mining and the like, and displays the valuable information to operation and maintenance personnel in an easy-to-understand manner to support operation and maintenance decision-making and fault diagnosis.
The task scheduling module is a component for managing and scheduling task execution. The system can automatically and optimally execute tasks, and improves the efficiency and reliability of the system. The following are some of the functions and features of the task scheduling module:
and (3) task management: the task scheduling module is responsible for managing the creation, configuration and monitoring of tasks. It may provide a user interface or API that enables an administrator to create and configure different types of tasks, and set parameters, scheduling rules, priorities, etc. for the tasks. The task scheduling module can also monitor the state, progress and execution result of the task and provide real-time task state information and statistical data.
Task scheduling: the task scheduling module determines the execution sequence and time of the tasks through an intelligent scheduling algorithm. The scheduling decision can be made according to factors such as the priority of tasks, the dependency relationship, the availability of resources and the like. The task scheduling module can consider the load condition of the system and reasonably allocate resources so as to avoid resource conflict and overload and ensure that the tasks can be completed on time.
Task execution: the task scheduling module is capable of initiating and managing execution of tasks. It may be integrated with a task execution environment, job scheduler, or distributed computing platform, assigning tasks to appropriate executors for processing. The task scheduling module may monitor the execution status and progress of the task and perform error handling, retry mechanisms, or failover as necessary to ensure reliable completion of the task.
Timing tasks: the task scheduling module supports the execution of timed tasks. It may periodically trigger the execution of tasks according to a specified schedule or scheduling rule. For example, daily, weekly, or monthly timed tasks may be set to perform specific data processing, backup, or cleaning operations. The task scheduling module can also consider the factors such as summer time, time zone difference and the like, and ensure the accuracy and reliability of timing tasks.
Task dependency and flow: the task scheduling module supports dependency relationships and flow control between tasks. It can configure the pre-conditions and post-actions of the tasks to ensure that the tasks are executed in the correct order and conditions. The task scheduling module can also handle data transfer and sharing among tasks so as to realize complex task flows and collaboration.
Monitoring and reporting: the task scheduling module has monitoring and reporting functions and can record and report logs, indexes and events of task execution. The system can generate reports, operation statistics and error logs of task execution, and simultaneously provide alarm and notification mechanisms so that an administrator can know the operation condition and abnormal condition of the task in time.
In summary, the task scheduling module may help an organization to achieve efficient, automated task management and execution. The task scheduling module can improve the efficiency and stability of the system and reduce the manual intervention and errors by reasonably scheduling the execution sequence, time and resources of the tasks. The system also provides support for monitoring, reporting and notifying of the task, and helps an administrator to timely grasp the state and problems of the task so as to make corresponding response and adjustment.
The fault diagnosis module is a key component for detecting, analyzing and solving system faults. It has the following functions and characteristics:
and (3) fault detection: the fault diagnosis module is capable of actively or passively detecting faults in the system. It can identify potential fault conditions by monitoring various indicators and log information of the system. The fault diagnosis module can detect various types of faults such as hardware faults, software errors, network problems and the like, and can timely send out an alarm when the faults occur.
And (3) fault analysis: the fault diagnosis module has the capability of analyzing the cause and influence of the fault. It may collect and analyze fault-related data, logs, and events to determine the root cause that caused the fault. The fault diagnosis module can also use various fault analysis techniques, models and algorithms to locate, remove and predict faults, helping to quickly resolve faults and avoid the re-occurrence of similar faults.
Fault prevention and optimization: the fault diagnosis module may help prevent system faults and optimize system performance. It can periodically perform system inspection and health assessment to find potential failure risks and performance bottlenecks. The fault diagnosis module may also provide advice and guidance to improve reliability, fault tolerance, and availability of the system, reducing the likelihood of a fault occurring.
Fault management and collaboration: the fault diagnosis module may assist in fault management and team collaboration. The fault work order and tracking system can be provided for recording and tracking the fault processing process, and timely response and solution of faults are ensured. The fault diagnosis module can also support fault knowledge base and experience sharing, help team to quickly acquire solutions and cases, and improve the efficiency of fault removal and recovery.
Visualization and reporting: the fault diagnosis module has the functions of visualization and reporting. The health state and fault information of the system can be displayed through the visual charts, the instrument panels and the report forms. The fault diagnosis module may also generate fault analysis reports and performance reports that provide analysis results and advice of system faults and performance issues to management layers and related personnel.
Automation and integration: the fault diagnosis module reduces manual operation and improves efficiency through automation and integration. The method can automate fault detection and analysis processes and reduce manual intervention and response time. The fault diagnosis module can be integrated with other systems and tools, such as a monitoring system, an event management system and a service management system, so as to realize sharing and collaborative processing of fault data.
In summary, the fault diagnosis module plays an important role in ensuring system reliability and business continuity. By timely detecting, analyzing and solving the system faults, the fault diagnosis module can reduce the influence of the faults on the service and improve the stability and reliability of the system. It also prevents the occurrence of faults and optimizes system performance.
Task queue management: the basis of task scheduling is the management of task queues. Tasks in the system are submitted to a task queue, which is ordered according to a certain priority or other attribute. The task queue may employ first-in-first-out (F IFO), first-in-last-out (LIFO), or other ordering strategies to manage tasks. Task queue management typically includes tasks addition, deletion, querying, and priority adjustment.
Scheduling algorithm: the scheduling algorithm is the core of task scheduling. According to different application scenes and requirements, different scheduling algorithms can be adopted to determine the order of task execution. Common scheduling algorithms include:
first Come First Served (FCFS): scheduling is carried out according to the task submitting sequence, and the method is suitable for a scene without considering task priority.
Shortest Job First (SJF): and selecting the task with the shortest execution time as the next task to be executed.
Priority scheduling: the tasks are scheduled according to the priorities, and the tasks with high priorities are executed first.
Time slice rotation (RR): each task is assigned a time slice, and after the time slice is finished, if the task is not executed, the task is put into the tail of the queue to wait for the next round of scheduling.
And (3) resource management: task scheduling also requires consideration of management of available resources. Resources in the system include CPU, memory, disk, network, etc. The task scheduling needs to reasonably allocate and utilize resources according to the demands of the tasks and the availability of the resources, so that resource conflict and overload are avoided. Resource management involves allocation, release, reclamation, monitoring, etc. of resources.
Exception handling and fault tolerance: during the task scheduling process, various abnormal conditions may occur, such as task execution failure, insufficient resources, and the like. The task scheduling needs to have an exception handling and fault tolerant mechanism, which can adapt and cope with different fault conditions. Common fault tolerance mechanisms include task retry, failover, task dependency detection, error handling, and the like.
In summary, the task scheduling principle includes aspects of task queue management, scheduling algorithm, resource management, exception handling, and the like. By reasonably managing task queues, selecting proper scheduling algorithms, optimizing resource allocation and implementing fault tolerance mechanisms, task scheduling can realize efficient execution of tasks, and the responsiveness, stability and reliability of the system are improved.
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 (6)
1. An wisdom fortune dimension dispatch platform, its characterized in that: the system comprises a main control center and the following modules:
the data acquisition module is used for automatically collecting and storing key data related to operation and maintenance;
the data analysis module is used for analyzing the collected data based on a machine learning algorithm and generating early warning and diagnosis results;
the task scheduling module is used for automatically distributing tasks to operation and maintenance personnel according to the real-time data analysis result and the priority setting of the operation and maintenance personnel;
the fault diagnosis module is used for identifying system faults and providing a repairing scheme;
the operation log module is used for recording the execution details and the results of each task;
an analysis report module for generating a detailed analysis report based on the operation log and the data analysis;
the user interface module is used for displaying data, early warning, task allocation and operation logs to operation and maintenance personnel;
the system comprises a data acquisition module, a data analysis module, a task scheduling module, a fault diagnosis module, an operation log module and a user interface module, wherein the data acquisition module can automatically collect key data related to operation and maintenance and store the key data to a data storage device, the data analysis module can analyze the acquired data based on a machine learning algorithm and generate early warning and diagnosis results, the task scheduling module can automatically distribute tasks to the operation and maintenance personnel according to the real-time data analysis results and operation and maintenance personnel priority settings, the fault diagnosis module can identify system faults and provide a repairing scheme, the operation log module can record execution details and results of each task, the analysis report module can generate a detailed analysis report based on operation logs and data analysis, and the user interface module can display data, early warning, task distribution and operation logs to the operation and maintenance personnel.
2. The intelligent operation and maintenance scheduling platform according to claim 1, wherein: the data acquisition module comprises a multi-metadata source acquisition: the module is capable of collecting data from a plurality of data sources, real-time data acquisition: the module can collect data in a real-time or near real-time mode, so that the system can acquire latest operation and maintenance related information in time, and the data is cleaned and preprocessed: the collected raw data generally needs to be cleaned and preprocessed to improve the quality and usability of the data, and abnormal data detection and processing: during the data collection process, abnormal data or data missing may be encountered, and the data storage and management: the module can store and manage the collected data efficiently, and control the security and authority of the data: the data acquisition module is very important to the safety and privacy protection of data, and expansibility and flexibility: the module has good expansibility and flexibility, and can be customized and expanded according to actual requirements.
3. The intelligent operation and maintenance scheduling platform according to claim 1, wherein: the data analysis module is an important component of the intelligent operation and maintenance scheduling platform, and has the main functions of processing, analyzing and mining the collected data to extract valuable information and generate early warning and diagnosis results, and cleaning and preprocessing the data: the data analysis module firstly cleans and preprocesses the collected original data, and extracts the characteristics: after cleaning and preprocessing, the data analysis module extracts key features from the original data according to specific operation and maintenance requirements, and data modeling: based on the extracted features, the data analysis module applies various machine learning algorithms and statistical methods to construct an appropriate mathematical model to describe the relationships and rules of the data, analyze and mine: after the model is built, the data analysis module further analyzes and mines the data, and results are generated and displayed: the data analysis module converts the analysis and mining results into a form which is easy to understand and use, and generates early warning and diagnosis results.
4. The intelligent operation and maintenance scheduling platform according to claim 1, wherein: the task scheduling module is a component for managing and scheduling task execution. The system can automatically and optimally execute tasks, improve the efficiency and reliability of the system, and manage the tasks: the task scheduling module is responsible for managing the creation, configuration and monitoring of tasks, and task scheduling: the task scheduling module determines the execution sequence and time of the tasks through an intelligent scheduling algorithm, and performs task execution: the task scheduling module can start and manage the execution process of the task and time the task: the task scheduling module supports the execution of timed tasks, task dependencies and flows: the task scheduling module supports the dependency relationship and flow control between tasks, monitoring and reporting: the task scheduling module has monitoring and reporting functions and can record and report logs, indexes and events of task execution.
5. The intelligent operation and maintenance scheduling platform according to claim 1, wherein: the fault diagnosis module is a key component for detecting, analyzing and solving system faults, and fault detection: the fault diagnosis module can actively or passively detect faults in the system, and perform fault analysis: the fault diagnosis module has the capability of analyzing the cause and influence of the fault, and fault prevention and optimization: the fault diagnosis module can help prevent system faults and optimize system performance, fault management and collaboration: the fault diagnosis module may assist in fault management and team collaboration, visualization and reporting: the fault diagnosis module has the functions of visualization and report, automation and integration: the fault diagnosis module reduces manual operation and improves efficiency through automation and integration.
6. The intelligent operation and maintenance scheduling platform according to claim 1, wherein: the operation log module is a very important part in the system, records various operation behaviors and events in the system, and has important significance for operation tracking, fault detection, security audit and business analysis, log level and classification: defining a plurality of levels for the log messages, classifying the log messages according to the log levels to filter and analyze the log as needed, key operations log: for key operations in the system, special log entries are added to facilitate security audit and track key operations, and sensitive data is desensitized: for operations involving sensitive data, the data should be desensitized at the time of logging to protect user privacy and information security, exception log: recording anomalies, errors and warnings in the system, facilitating troubleshooting and problem localization, operating metadata records: in addition to log messages, metadata for some operations may be recorded for subsequent auditing and tracking, data change records: for the change operation of important data in the system, the data state before and after the change is recorded, so that the change history of the data and the recovery data can be traced, and various log output modes are realized: support to export logs to different targets to meet different needs and scenarios, log rotation and archiving: the log files are periodically rotated and archived, so that the single log file is prevented from being too large, and meanwhile, the history log is convenient to manage and consult, and the log is searched and filtered: the function of searching and filtering the logs is provided, the related logs can be conveniently and rapidly positioned according to the keyword, the time range and the log level, and the centralized log management is realized: the logs of a plurality of systems are managed in a centralized way, the logs can be centralized to a central server through a log collector or a log monitoring system, comprehensive analysis and management are convenient, and visual analysis of the logs is convenient: and (3) carrying out statistics and analysis on the log data through a data visualization tool to generate a visualization chart and a report form, so as to help know the running state of the system and find out the abnormality and the trend.
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| CN117764422A (en) * | 2024-02-22 | 2024-03-26 | 北京洁禹通环保科技有限公司 | Intelligent energy-saving operation and maintenance management cloud platform |
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| CN117764422A (en) * | 2024-02-22 | 2024-03-26 | 北京洁禹通环保科技有限公司 | Intelligent energy-saving operation and maintenance management cloud platform |
| CN117764422B (en) * | 2024-02-22 | 2024-04-26 | 北京洁禹通环保科技有限公司 | Intelligent energy-saving operation and maintenance management cloud platform |
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