CN116881083A - Information resource sharable distribution network equipment management system - Google Patents
Information resource sharable distribution network equipment management system Download PDFInfo
- Publication number
- CN116881083A CN116881083A CN202310900742.9A CN202310900742A CN116881083A CN 116881083 A CN116881083 A CN 116881083A CN 202310900742 A CN202310900742 A CN 202310900742A CN 116881083 A CN116881083 A CN 116881083A
- Authority
- CN
- China
- Prior art keywords
- data
- equipment
- module
- distribution network
- optimization
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Withdrawn
Links
- 238000009826 distribution Methods 0.000 title claims abstract description 61
- 238000005457 optimization Methods 0.000 claims abstract description 84
- 238000007726 management method Methods 0.000 claims abstract description 62
- 238000005265 energy consumption Methods 0.000 claims abstract description 57
- 238000007405 data analysis Methods 0.000 claims abstract description 38
- 230000000007 visual effect Effects 0.000 claims abstract description 22
- 238000013486 operation strategy Methods 0.000 claims abstract description 20
- 230000007613 environmental effect Effects 0.000 claims abstract description 18
- 238000012544 monitoring process Methods 0.000 claims abstract description 16
- 238000012545 processing Methods 0.000 claims abstract description 15
- 238000012806 monitoring device Methods 0.000 claims abstract description 8
- 238000000034 method Methods 0.000 claims description 20
- 238000004422 calculation algorithm Methods 0.000 claims description 17
- 230000003044 adaptive effect Effects 0.000 claims description 14
- 230000002159 abnormal effect Effects 0.000 claims description 13
- 238000010801 machine learning Methods 0.000 claims description 13
- 238000004458 analytical method Methods 0.000 claims description 12
- 238000007418 data mining Methods 0.000 claims description 12
- 238000005516 engineering process Methods 0.000 claims description 11
- 238000000605 extraction Methods 0.000 claims description 7
- 238000007619 statistical method Methods 0.000 claims description 7
- 230000005856 abnormality Effects 0.000 claims description 5
- 238000007781 pre-processing Methods 0.000 claims description 4
- 238000004140 cleaning Methods 0.000 claims description 3
- 230000006872 improvement Effects 0.000 abstract description 9
- 238000003745 diagnosis Methods 0.000 abstract description 4
- 238000012800 visualization Methods 0.000 abstract description 4
- 206010063385 Intellectualisation Diseases 0.000 abstract description 3
- 238000010586 diagram Methods 0.000 description 10
- 206010000117 Abnormal behaviour Diseases 0.000 description 7
- 238000013461 design Methods 0.000 description 6
- 230000000694 effects Effects 0.000 description 5
- 238000004891 communication Methods 0.000 description 4
- 230000008569 process Effects 0.000 description 4
- 238000007621 cluster analysis Methods 0.000 description 3
- 238000001514 detection method Methods 0.000 description 3
- 238000012423 maintenance Methods 0.000 description 3
- 230000009286 beneficial effect Effects 0.000 description 2
- 230000008859 change Effects 0.000 description 2
- 238000005065 mining Methods 0.000 description 2
- 230000003449 preventive effect Effects 0.000 description 2
- 238000000611 regression analysis Methods 0.000 description 2
- 230000004075 alteration Effects 0.000 description 1
- 238000012098 association analyses Methods 0.000 description 1
- 238000011263 association rule mining technique Methods 0.000 description 1
- 230000006399 behavior Effects 0.000 description 1
- 230000003542 behavioural effect Effects 0.000 description 1
- 230000008901 benefit Effects 0.000 description 1
- 238000007635 classification algorithm Methods 0.000 description 1
- 238000010224 classification analysis Methods 0.000 description 1
- 238000010219 correlation analysis Methods 0.000 description 1
- 238000013480 data collection Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 238000009499 grossing Methods 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000003909 pattern recognition Methods 0.000 description 1
- 238000005381 potential energy Methods 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
- 230000007704 transition Effects 0.000 description 1
- 230000001960 triggered effect Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/30—Monitoring
- G06F11/3065—Monitoring arrangements determined by the means or processing involved in reporting the monitored data
- G06F11/3072—Monitoring arrangements determined by the means or processing involved in reporting the monitored data where the reporting involves data filtering, e.g. pattern matching, time or event triggered, adaptive or policy-based reporting
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/30—Monitoring
- G06F11/3003—Monitoring arrangements specially adapted to the computing system or computing system component being monitored
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/30—Monitoring
- G06F11/3003—Monitoring arrangements specially adapted to the computing system or computing system component being monitored
- G06F11/3006—Monitoring arrangements specially adapted to the computing system or computing system component being monitored where the computing system is distributed, e.g. networked systems, clusters, multiprocessor systems
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Computing Systems (AREA)
- Quality & Reliability (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Data Mining & Analysis (AREA)
- Mathematical Physics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Bioinformatics & Computational Biology (AREA)
- Evolutionary Biology (AREA)
- Evolutionary Computation (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Artificial Intelligence (AREA)
- Testing And Monitoring For Control Systems (AREA)
Abstract
The application relates to the field of distribution network equipment, and discloses an information resource sharable distribution network equipment management system, which comprises distribution network equipment, a sensor and a monitoring device, wherein the distribution network equipment is provided with the sensor and the monitoring device; the data acquisition module is used for acquiring equipment state, energy consumption and environmental data; the data analysis module is used for comprehensively analyzing and processing the multidimensional data; the intelligent decision module is used for generating intelligent decision recommendation; the self-adaptive optimization module is used for automatically adjusting equipment configuration and operation strategies; and the visual interface module is used for displaying the equipment state and the optimization result. The intelligent and sustainable management system and the intelligent and sustainable management method of the distribution network equipment realize the intellectualization and sustainability of the distribution network equipment management through integrating the modules such as the sensor, the data acquisition, the data analysis, the intelligent decision, the self-adaptive optimization and the visualization, effectively solve the problem of information island, improve the management efficiency and the accuracy, provide the data analysis and the decision support, realize the fault diagnosis and the operation monitoring, and bring innovation and improvement to the distribution network equipment management.
Description
Technical Field
The application relates to the technical field of distribution network equipment, in particular to an information resource sharable distribution network equipment management system.
Background
In the traditional distribution network equipment management, a manual monitoring and manual adjustment mode is generally adopted to manage the state and operation of the equipment, however, with the increasing importance of energy consumption and sustainable development, the traditional management mode cannot meet the requirements on the performance and energy efficiency of the equipment. The information resource sharable distribution network equipment management system is generated; however, in conventional systems, different departments or systems may each maintain separate data sources, which are difficult to share and utilize. This results in isolated information, making comprehensive analysis and comprehensive decision making of the device difficult.
Disclosure of Invention
Aiming at the defects of the prior art, the application provides an information resource sharable distribution network equipment management system, which solves the problems that different departments or systems in the traditional system can maintain independent data sources respectively, the data are difficult to share and utilize, and the information is isolated.
In order to achieve the above purpose, the application is realized by the following technical scheme: an information resource shareable distribution network device management system, comprising:
a distribution network device on which a sensor and a monitoring device are mounted;
the data acquisition module is used for acquiring equipment state, energy consumption and environmental data;
the data analysis module is used for comprehensively analyzing and processing the multidimensional data;
the intelligent decision module is used for generating intelligent decision recommendation;
the self-adaptive optimization module is used for automatically adjusting equipment configuration and operation strategies;
and the visual interface module is used for displaying the equipment state and the optimization result.
Preferably, the data acquisition module includes:
the data receiving module is used for receiving and processing the data signals from the distribution network equipment;
the strategy acquisition module is used for acquiring equipment state, energy consumption and environmental data according to a predefined acquisition strategy;
and the data processing module is used for preprocessing and cleaning the acquired data.
Preferably, the data analysis module includes:
the modeling analysis module is used for analyzing and modeling the multidimensional data by using a data mining and machine learning technology;
the state extraction module is used for extracting equipment modes, association relations and abnormal conditions;
and the report generation module is used for generating equipment state reports, energy consumption trends and process optimization suggestions.
Preferably, the intelligent decision module includes:
the decision recommendation module is used for generating intelligent decision recommendation through machine learning and optimization algorithm based on the data analysis result;
and the policy making module is used for automatically adjusting equipment configuration and operation policies according to the optimization targets set by the user.
Preferably, the adaptive optimization module includes:
the model prediction module is used for predicting the running state of the equipment according to the real-time data and the prediction model;
and the operation optimization module is used for automatically adjusting equipment configuration and operation strategies according to the prediction data of the model prediction module.
Preferably, the visual interface includes:
the real-time state display module is used for displaying the equipment state, the energy consumption and the optimization result in real time;
and the visual display module is used for providing a chart and a graphical interface and displaying equipment management data and energy consumption trend.
The application provides a method for managing information resource sharable distribution network equipment, which is implemented based on the system and comprises the following steps:
collecting equipment state, energy consumption and environmental data from distribution network equipment;
carrying out multidimensional data analysis and modeling, and extracting equipment modes and association relations;
generating intelligent decision recommendation based on the data analysis result;
automatically adjusting equipment configuration and operation strategies by utilizing an adaptive optimization algorithm;
displaying equipment state, optimizing result and energy consumption trend.
Preferably, the multidimensional data analysis comprises:
analyzing and modeling the multidimensional data by using a data mining and statistical analysis technology;
detecting equipment abnormality and generating alarm information;
and extracting the equipment mode and the association relation, and predicting the equipment fault risk.
Preferably, the adaptive optimization includes:
monitoring equipment state, energy consumption and environmental conditions in real time;
comparing the real-time data with a prediction model, and detecting abnormal conditions and optimization opportunities;
based on the adaptive optimization algorithm, device configuration and operation strategies are automatically adjusted.
Preferably, the visual display includes:
real-time display of equipment state, energy consumption and optimization results;
and providing a chart and a graphical interface, and displaying equipment management data and energy consumption trend.
The application provides an information resource sharable distribution network equipment management system. The beneficial effects are as follows:
the intelligent and sustainable management system and the intelligent and sustainable management method of the distribution network equipment realize the intellectualization and sustainability of the distribution network equipment management through integrating the modules such as the sensor, the data acquisition, the data analysis, the intelligent decision, the self-adaptive optimization and the visualization, effectively solve the problem of information island, improve the management efficiency and the accuracy, provide the data analysis and the decision support, realize the fault diagnosis and the operation monitoring, and bring innovation and improvement to the distribution network equipment management.
Drawings
FIG. 1 is a system architecture diagram of the present application;
FIG. 2 is a schematic diagram of a data acquisition module according to the present application;
FIG. 3 is a schematic diagram of a data analysis module according to the present application;
FIG. 4 is a schematic diagram of an intelligent decision module according to the present application;
FIG. 5 is a schematic diagram of an adaptive optimization module according to the present application;
FIG. 6 is a schematic diagram of a visual interface module of the present application;
FIG. 7 is a schematic diagram of the method steps of the present application;
FIG. 8 is a schematic diagram of a multi-dimensional data analysis step according to the present application;
FIG. 9 is a schematic diagram of the adaptive optimization step of the present application.
Detailed Description
The following description of the embodiments of the present application 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 application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
Referring to fig. 1 to fig. 9, an embodiment of the present application provides an information resource sharable distribution network device management system, including:
a distribution network device on which a sensor and a monitoring device are mounted; the sensor monitors parameters such as current, voltage, temperature and the like, and the monitoring device records the running condition and fault information of equipment.
The data acquisition module is used for acquiring equipment state, energy consumption and environmental data; and acquiring and processing real-time data by connecting with distribution network equipment.
The data analysis module is used for comprehensively analyzing and processing the multidimensional data; the method identifies the running trend, the energy consumption mode and the abnormal behavior of the equipment through data mining, machine learning, statistical analysis and other methods.
The intelligent decision module is used for generating intelligent decision recommendation; the module provides suggestions for optimizing device configuration, power saving policies, and operational schedules based on device status and environmental conditions.
The self-adaptive optimization module is used for automatically adjusting equipment configuration and operation strategies; according to the real-time data and the optimization target, the equipment parameters are dynamically adjusted, and the energy consumption optimization and the equipment performance improvement are realized.
The visual interface module is used for displaying the equipment state and the optimization result; the user monitors the running condition of the equipment in real time through the interface, checks the optimizing effect, and performs manual intervention and decision.
In the embodiment, the sensor and the monitoring device are mounted so that the distribution network equipment can monitor and collect data in real time, the original information island is broken, and real-time and accurate equipment state and environment data are provided;
the system comprehensively analyzes and evaluates the running state of the distribution network equipment through a data analysis and intelligent decision module, provides optimization suggestions, helps management staff to make more reasonable decisions and scheduling strategies, and improves management efficiency and accuracy;
the data analysis module is used for excavating equipment operation trend, energy consumption mode and abnormal behavior, providing comprehensive data analysis and decision support for management personnel, helping the management personnel to better know equipment performance, making improvement measures and predicting potential faults;
through real-time monitoring and data analysis of distribution network equipment, the system timely discovers equipment faults and abnormal behaviors, provides fault diagnosis and operation monitoring functions, helps management personnel to take measures in time, and avoids accidents or shortens fault processing time.
In general, the information resource sharable distribution network equipment management system realizes the intellectualization and sustainability of distribution network equipment management by integrating modules such as sensors, data acquisition, data analysis, intelligent decision, self-adaptive optimization and visualization, effectively solves the problem of information island, improves the management efficiency and accuracy, provides data analysis and decision support, realizes fault diagnosis and operation monitoring, and brings innovation and improvement to distribution network equipment management.
In one embodiment, the data acquisition module comprises:
the data receiving module is used for receiving and processing the data signals from the distribution network equipment;
in this embodiment, it communicates with the network device, receives the data signal sent by the device, and parses and processes the data. The data receiving module supports different communication protocols and interfaces, such as ethernet, serial communication, etc., to adapt to different types of distribution network devices.
The strategy acquisition module is used for acquiring equipment state, energy consumption and environmental data according to a predefined acquisition strategy;
in this embodiment, the acquisition strategy is defined according to specific requirements and equipment characteristics, for example, data is acquired at time intervals, acquisition is triggered according to specific events, and the like. The policy acquisition module coordinates communication with the distribution network device and requests device data periodically or triggerably according to an acquisition policy.
The data processing module is used for preprocessing and cleaning the acquired data;
the data processing module performs a series of operations such as removing noise, filling in missing values, smoothing data, etc. In addition, the data is normalized, clustered or feature extracted so that the subsequent data analysis and decision module can better process the data.
In general, the system is capable of efficiently receiving, collecting and processing data from the distribution network devices through a combination of the above-described data collection modules. The data receiving module ensures reliable communication with the device, the policy acquisition module acquires the required data according to a predefined policy, and the data processing module pre-processes and cleans the acquired data to provide high quality data for use by subsequent modules. The design of the data acquisition module can ensure that the system obtains accurate, reliable and effective distribution network equipment data, and provides a good data basis for the subsequent modules such as data analysis, decision recommendation, optimization and the like.
In one embodiment, the data analysis module includes:
the modeling analysis module is used for analyzing and modeling the multidimensional data by using a data mining and machine learning technology;
in this embodiment, the modeling analysis module applies various data mining and machine learning algorithms, such as cluster analysis, classification algorithm, regression analysis, etc., to learn about the equipment operation trend, energy consumption pattern, and abnormal behavior. By modeling, it discovers potential laws and associations from the data and provides insight and predictive capability.
The state extraction module is used for extracting equipment modes, association relations and abnormal conditions;
in this embodiment, it identifies the operating state, mode switching, and abnormal event of the device according to the result of the modeling analysis module. For example, the state extraction module determines whether the device is in a normal operating state, discovers a mode transition of the device, and detects abnormal behavior of the device. Through state extraction, management personnel know the real-time state and problem of equipment and take corresponding measures in time.
The report generation module is used for generating equipment state reports, energy consumption trends and process optimization suggestions;
in this embodiment, the modeling analysis module and the state extraction module are integrated to generate a detailed report. The reports include status summaries of the devices, energy consumption trend charts, and targeted process optimization suggestions. In this way, the manager intuitively knows the performance of the device, the energy consumption, and makes decisions and improvements based on the advice in the report.
In general, the system is capable of comprehensive analysis and modeling of multidimensional data through a combination of the above-described data analysis modules. The modeling analysis module utilizes data mining and machine learning techniques to discover rules and associations from the data, providing insight and predictive capability. The state extraction module recognizes the equipment state, association relation and abnormal condition and provides real-time equipment state monitoring. The report generation module integrates the analysis results into an easy-to-read report and provides decision support and optimization suggestions for management staff. The data analysis module design can improve the intelligent level of distribution network equipment management, help management personnel to better know equipment states, optimize energy consumption and improve the operation efficiency and the sustainability of the distribution network.
In one embodiment, the intelligent decision module comprises:
the decision recommendation module is used for generating intelligent decision recommendation through machine learning and optimization algorithm based on the data analysis result;
in this embodiment, the module is able to identify performance bottlenecks, optimization potentials, and improvements of the device by analyzing device status, energy consumption, environmental data, and other relevant factors. By means of machine learning technology, the decision recommendation module learns and adapts to different scenes and requirements, and personalized decision recommendation is provided. Meanwhile, the optimization algorithm considers various constraint conditions such as cost, reliability and sustainability to realize comprehensive optimization.
The strategy making module is used for automatically adjusting equipment configuration and operation strategies according to the optimization targets set by the user;
in this embodiment, the user sets various constraint conditions and optimization parameters according to specific requirements and optimization objectives. The strategy making module combines the optimization target set by the user and the result of the decision recommending module to generate specific strategy and operation suggestion. The configuration parameters of the equipment, such as power setting, priority allocation and the like, are automatically adjusted to meet the set optimization target, and the operation strategy of the equipment is monitored and controlled to be adjusted in real time according to the needs.
In general, through the combination of the intelligent decision modules, the system can generate intelligent decision recommendations and automatically adjust device configuration and operation strategies according to optimization targets set by users. The decision recommendation module generates personalized decision recommendation by utilizing data analysis, machine learning and optimization algorithm, and helps management personnel to make more reasonable decisions and improve strategies. And the policy making module automatically adjusts equipment configuration and operation policy according to the optimization target of the user and the result of decision recommendation so as to realize the optimization effect. The intelligent decision module design can improve the intelligent level of distribution network equipment management, and enhance the automation and adaptability of the system so as to realize more efficient and sustainable operation strategies.
In one embodiment, the adaptive optimization module includes:
the model prediction module is used for predicting the running state of the equipment according to the real-time data and the prediction model;
in this embodiment, it uses historical data and machine learning techniques to construct a predictive model that predicts the device's operating state in the future by analyzing and comparing current data. The predictive model takes into account the state parameters of the device, environmental conditions, and other relevant factors, providing a possible future state situation of the device.
The operation optimization module is used for automatically adjusting equipment configuration and operation strategies according to the prediction data of the model prediction module;
in this embodiment, if the prediction model predicts that the device is about to enter a high-energy consumption state, the operation optimization module automatically adjusts the power setting or the operation policy of the device, so as to reduce energy consumption and improve efficiency. The method and the device are used for formulating an optimization scheme according to the prediction result, such as setting optimal operation parameters, adjusting equipment scheduling strategies, optimizing energy allocation and the like.
In general, by combining the adaptive optimization modules, the system can perform adaptive optimization adjustment according to real-time data and a prediction model. The model prediction module utilizes the real-time data and the historical data to construct a prediction model for predicting the future running state of the equipment. And the operation optimization module automatically adjusts equipment configuration and operation strategy according to the prediction result so as to optimize equipment performance to the greatest extent, reduce energy consumption and improve system efficiency. The self-adaptive optimization module design can improve the intelligent and automatic level of the system, so that the distribution network equipment can be optimally adjusted according to real-time conditions, and more sustainable and efficient operation is realized.
In one embodiment, the visual interface includes:
the real-time state display module is used for displaying the equipment state, the energy consumption and the optimization result in real time;
in this embodiment, the running state, the important parameters and the real-time index of the current device are displayed through a chart, an instrument panel or a real-time monitoring mode. The real-time state display module can provide real-time monitoring and alarming of the equipment, so that a manager can know the running condition of the equipment at any time and take measures in time. For example, key indicators of the switching status, power consumption, temperature, etc. of the device are displayed and an alarm or notification is raised to quickly respond to an abnormal situation or to allow for further optimization.
The visual display module is used for providing a chart and a graphical interface and displaying equipment management data and energy consumption trend;
in the embodiment, various charts, trend charts and visual reports are generated to intuitively display the performance, the energy consumption condition and the optimization result of the distribution network equipment. The visual display module displays historical data, real-time data and forecast data, and helps management personnel to comprehensively know the working condition and trend of the equipment. For example, the energy consumption trend graph is displayed, the equipment state and the energy consumption distribution are reported, and the user can more intuitively analyze and evaluate the effect of equipment management and make corresponding decisions.
In general, through the combination of the visual interfaces, the system can provide an intuitive and easily understood interface, so that a manager can monitor the running state and energy consumption of equipment in real time, and meanwhile, can display management data and energy consumption trend of distribution network equipment through a chart and a graphical interface. The real-time status display module provides real-time monitoring and alarming of the equipment and supports quick response to abnormal conditions. The visual display module provides charts, trend charts and reports, so that a user is helped to comprehensively know the performance and the optimization effect of the equipment, and better decision making and improvement are performed. The visual interface design can improve the visual level of the distribution network equipment management, increase the understanding of the equipment state and the energy consumption of the user, and help to realize more efficient and sustainable equipment management.
In one embodiment, the present application provides a method for managing information resource sharable distribution network equipment, including:
collecting equipment state, energy consumption and environmental data from distribution network equipment; such data is acquired by sensors, monitoring devices, or other means of data acquisition. The collected data includes the switching status of the device, power consumption, temperature, humidity, etc.
Carrying out multidimensional data analysis and modeling, and extracting equipment modes and association relations; this includes data preprocessing, feature selection, and application of data mining techniques. By analyzing and mining data, the performance mode, association relation and abnormal behavior of the equipment are found; and determining the working mode of the equipment, the mode switching and the association relation among the equipment through a mode identification and association analysis technology.
Generating intelligent decision recommendation based on the data analysis result; these decision recommendations relate to configuration parameters, operating policies and optimization methods of the device.
Automatically adjusting equipment configuration and operation strategies by utilizing an adaptive optimization algorithm; according to intelligent decision recommendation and real-time data, the optimization algorithm automatically adjusts parameters and strategies of the equipment so as to improve the performance and energy efficiency of the equipment.
Displaying equipment state, optimization result and energy consumption trend; through the diagrams, the graphical interfaces and the reports, the management personnel intuitively know the running condition, the optimizing effect and the energy consumption trend of the equipment.
Further, the multidimensional data analysis includes:
analyzing and modeling the multidimensional data by using a data mining and statistical analysis technology; and analyzing and modeling the multidimensional data by using data mining and statistical analysis technologies. Data mining techniques include cluster analysis, classification analysis, association rule mining, and the like. Statistical analysis techniques include descriptive statistics, hypothesis testing, regression analysis, and the like. These techniques help discover intrinsic laws, trends, and anomalies of the data
Detecting equipment abnormality and generating alarm information; the anomaly detection algorithm identifies an anomaly pattern or outlier in the device data, suggesting a possible problem with the device. Based on the abnormality detection result, the system generates alarm information notifying the manager to take corresponding measures such as maintenance equipment, replacement of parts, and the like.
Extracting equipment modes and association relations, and predicting equipment fault risks; in the multidimensional data analysis, patterns and association relations of devices are extracted. Through cluster analysis and pattern recognition technology, the working mode, typical operation mode and mode switching of the device are discovered. In addition, association relationships and interactions between devices are identified through association rule mining and correlation analysis. Such information helps to understand the behavioral characteristics and operating mechanisms of the device;
and establishing a predictive model of the equipment fault by utilizing statistical analysis and machine learning technology. The model predicts possible future fault conditions of the equipment according to the historical data and related characteristics of the equipment. This helps to take preventive maintenance measures to reduce the impact of equipment failure on the system.
In general, through these steps of multidimensional data analysis, information in the device data is mined deep, potential problems and opportunities are discovered, and more accurate and comprehensive support is provided for device management. And timely discovering the abnormal condition of the equipment through abnormality detection and alarm information generation. The behavior characteristics and the mutual influence of the equipment can be known by extracting the equipment modes and the association relations, and references are provided for decision making. The equipment failure risk prediction is beneficial to preventive maintenance, and the reliability and usability of the equipment are improved.
Further, the adaptive optimization includes:
monitoring equipment state, energy consumption and environmental conditions in real time; such data includes information on operating parameters of the device, energy consumption, temperature, humidity, etc. The real-time monitoring can provide the current state and the running condition of the equipment.
Comparing the real-time data with a prediction model, and detecting abnormal conditions and optimization opportunities; by comparison with the predictive model, differences between actual data and expected conditions are found, thereby identifying and reporting abnormal behavior of the device. In addition, comparing the real-time data to the predictive model also determines future possible optimization opportunities, such as identifying potential energy consumption reduction or efficiency improvement spaces.
Based on the self-adaptive optimization algorithm, automatically adjusting equipment configuration and operation strategies; according to the optimization targets and constraint conditions, the self-adaptive optimization algorithm searches and recommends optimal equipment configuration and operation strategies so as to optimize equipment performance, reduce energy consumption or improve environmental benefits. The self-adaptive optimization algorithm is dynamically adjusted according to the change of the real-time data so as to adapt to different working conditions and environmental requirements.
Through the steps, the self-adaptive optimization can realize real-time monitoring and analysis of the equipment, identify abnormal conditions and optimization opportunities, and automatically adjust the configuration and operation strategy of the equipment through a self-adaptive optimization algorithm. The automatic optimization process improves the performance and efficiency of the equipment, reduces the energy consumption, and can reduce the workload of manual intervention and management.
Further, the visual display includes:
real-time display of equipment state, energy consumption and optimization results; this is achieved by monitoring the data in real time and visualizing the results of the analysis. The interface displays key indexes such as the switching state, the power consumption, the temperature and the like of the equipment, and intuitively presents the information to a user. Real-time display helps management personnel to know current running condition and performance of equipment at any time
Providing a chart and a graphical interface, and displaying equipment management data and energy consumption trend; the device management data is visually presented in various chart forms, such as a line graph, a bar graph, a pie chart, and the like. Such data includes the operating time of the device, energy consumption, efficiency index, etc. Meanwhile, the interface also displays the trend of energy consumption, so that the user is helped to know the change trend of the energy use condition and make corresponding decisions.
Through these means of visual display, the manager intuitively observes the status of the device, the energy consumption and the optimization results. The real-time display can provide instant information of the equipment, and help to find problems or abnormal conditions in time. Meanwhile, the equipment management data and the energy consumption trend displayed by the chart and the graphical interface can help a user to more comprehensively know the working condition and efficiency of equipment, and provide a reference basis for decision making. Such a visual interface design improves the level of visualization of the device management data by the user, making it easier to understand and analyze, thereby supporting better device management and optimization decision making.
Although embodiments of the present application have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the application, the scope of which is defined in the appended claims and their equivalents.
Claims (10)
1. An information resource shareable distribution network device management system, comprising:
a distribution network device on which a sensor and a monitoring device are mounted;
the data acquisition module is used for acquiring equipment state, energy consumption and environmental data;
the data analysis module is used for comprehensively analyzing and processing the multidimensional data;
the intelligent decision module is used for generating intelligent decision recommendation;
the self-adaptive optimization module is used for automatically adjusting equipment configuration and operation strategies;
and the visual interface module is used for displaying the equipment state and the optimization result.
2. The information resource sharable distribution network device management system of claim 1 wherein said data acquisition module includes:
the data receiving module is used for receiving and processing the data signals from the distribution network equipment;
the strategy acquisition module is used for acquiring equipment state, energy consumption and environmental data according to a predefined acquisition strategy;
and the data processing module is used for preprocessing and cleaning the acquired data.
3. The information resource shareable distribution network device management system of claim 1 wherein said data analysis module includes:
the modeling analysis module is used for analyzing and modeling the multidimensional data by using a data mining and machine learning technology;
the state extraction module is used for extracting equipment modes, association relations and abnormal conditions;
and the report generation module is used for generating equipment state reports, energy consumption trends and process optimization suggestions.
4. The information resource shareable distribution network device management system of claim 1 wherein said intelligent decision module includes:
the decision recommendation module is used for generating intelligent decision recommendation through machine learning and optimization algorithm based on the data analysis result;
and the policy making module is used for automatically adjusting equipment configuration and operation policies according to the optimization targets set by the user.
5. The information resource shareable distribution network device management system of claim 1 wherein the adaptive optimization module includes:
the model prediction module is used for predicting the running state of the equipment according to the real-time data and the prediction model;
and the operation optimization module is used for automatically adjusting equipment configuration and operation strategies according to the prediction data of the model prediction module.
6. The information resource shareable distribution network device management system of claim 1 wherein said visual interface module comprises:
the real-time state display module is used for displaying the equipment state, the energy consumption and the optimization result in real time;
and the visual display module is used for providing a chart and a graphical interface and displaying equipment management data and energy consumption trend.
7. An information resource sharable distribution network device management method based on an information resource sharable distribution network device management system according to any one of claims 1 to 6, comprising:
collecting equipment state, energy consumption and environmental data from distribution network equipment;
carrying out multidimensional data analysis and modeling, and extracting equipment modes and association relations;
generating intelligent decision recommendation based on the data analysis result;
automatically adjusting equipment configuration and operation strategies by utilizing an adaptive optimization algorithm;
displaying equipment state, optimizing result and energy consumption trend.
8. The method for managing information resource sharable distribution network devices of claim 7 wherein said multidimensional data analysis comprises:
analyzing and modeling the multidimensional data by using a data mining and statistical analysis technology;
detecting equipment abnormality and generating alarm information;
and extracting the equipment mode and the association relation, and predicting the equipment fault risk.
9. The method for managing information resource shareable distribution network devices of claim 7, wherein the adaptively optimizing comprises:
monitoring equipment state, energy consumption and environmental conditions in real time;
comparing the real-time data with a prediction model, and detecting abnormal conditions and optimization opportunities;
based on the adaptive optimization algorithm, device configuration and operation strategies are automatically adjusted.
10. The method for managing information resource sharable distribution network equipment of claim 7 wherein the visual display includes:
real-time display of equipment state, energy consumption and optimization results;
and providing a chart and a graphical interface, and displaying equipment management data and energy consumption trend.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310900742.9A CN116881083A (en) | 2023-07-21 | 2023-07-21 | Information resource sharable distribution network equipment management system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310900742.9A CN116881083A (en) | 2023-07-21 | 2023-07-21 | Information resource sharable distribution network equipment management system |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116881083A true CN116881083A (en) | 2023-10-13 |
Family
ID=88256529
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310900742.9A Withdrawn CN116881083A (en) | 2023-07-21 | 2023-07-21 | Information resource sharable distribution network equipment management system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116881083A (en) |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117369603A (en) * | 2023-12-05 | 2024-01-09 | 广东迅扬科技股份有限公司 | Cabinet heat dissipation control system |
| CN117411184A (en) * | 2023-10-26 | 2024-01-16 | 唐山昌宏科技有限公司 | Intelligent command system for emergency treatment of medium-low voltage power supply |
| CN117540330A (en) * | 2024-01-09 | 2024-02-09 | 北京松岛菱电设备有限公司 | Power distribution cabinet system based on self-learning function |
| CN117555225A (en) * | 2024-01-10 | 2024-02-13 | 万桥信息技术有限公司 | Green building energy management control system |
| CN117829554A (en) * | 2024-03-05 | 2024-04-05 | 山东商业职业技术学院 | Intelligent perception finished product restoration decision support system |
| CN118195837A (en) * | 2024-05-15 | 2024-06-14 | 巨洋神州科技集团有限公司 | Energy monitoring method, device, equipment and medium based on micro-grid dual-carbon platform |
| CN118349600A (en) * | 2024-06-17 | 2024-07-16 | 成都中科合迅科技有限公司 | Multi-dimensional relation display control method and system based on materialized view |
-
2023
- 2023-07-21 CN CN202310900742.9A patent/CN116881083A/en not_active Withdrawn
Cited By (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117411184A (en) * | 2023-10-26 | 2024-01-16 | 唐山昌宏科技有限公司 | Intelligent command system for emergency treatment of medium-low voltage power supply |
| CN117411184B (en) * | 2023-10-26 | 2024-05-03 | 唐山昌宏科技有限公司 | Intelligent command system for emergency treatment of medium-low voltage power supply |
| CN117369603A (en) * | 2023-12-05 | 2024-01-09 | 广东迅扬科技股份有限公司 | Cabinet heat dissipation control system |
| CN117369603B (en) * | 2023-12-05 | 2024-03-22 | 广东迅扬科技股份有限公司 | Cabinet heat dissipation control system |
| CN117540330B (en) * | 2024-01-09 | 2024-04-09 | 北京松岛菱电设备有限公司 | Power distribution cabinet system based on self-learning function |
| CN117540330A (en) * | 2024-01-09 | 2024-02-09 | 北京松岛菱电设备有限公司 | Power distribution cabinet system based on self-learning function |
| CN117555225A (en) * | 2024-01-10 | 2024-02-13 | 万桥信息技术有限公司 | Green building energy management control system |
| CN117555225B (en) * | 2024-01-10 | 2024-04-26 | 万桥信息技术有限公司 | Green building energy management control system |
| CN117829554A (en) * | 2024-03-05 | 2024-04-05 | 山东商业职业技术学院 | Intelligent perception finished product restoration decision support system |
| CN117829554B (en) * | 2024-03-05 | 2024-05-14 | 山东商业职业技术学院 | Intelligent perception finished product restoration decision support system |
| CN118195837A (en) * | 2024-05-15 | 2024-06-14 | 巨洋神州科技集团有限公司 | Energy monitoring method, device, equipment and medium based on micro-grid dual-carbon platform |
| CN118349600A (en) * | 2024-06-17 | 2024-07-16 | 成都中科合迅科技有限公司 | Multi-dimensional relation display control method and system based on materialized view |
| CN118349600B (en) * | 2024-06-17 | 2024-08-23 | 成都中科合迅科技有限公司 | Multi-dimensional relation display control method and system based on materialized view |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN116881083A (en) | Information resource sharable distribution network equipment management system | |
| CN108009300A (en) | A kind of novel maintenance system based on big data technology | |
| CN116862442A (en) | Intelligent equipment management method and system based on big data analysis | |
| CN105325023A (en) | Method and network device for cell anomaly detection | |
| CN113063611B (en) | Equipment monitoring management method and system | |
| CN111290913A (en) | Fault location visualization system and method based on operation and maintenance data prediction | |
| CN103049365B (en) | Information and application resource running state monitoring and evaluation method | |
| CN116882426A (en) | Electromechanical equipment management platform based on identification analysis | |
| CN101989931A (en) | Operation alarm processing method and device | |
| CN116224137A (en) | UPS equipment fault early warning method and system | |
| CN105634796A (en) | Network device failure prediction and diagnosis method | |
| CN117607780A (en) | Abnormality analysis system and analysis method for electric energy metering device | |
| CN117761444B (en) | Method and system for monitoring service life of surge protector | |
| JP6176377B1 (en) | Equipment management system, equipment management method and program | |
| CN109840864A (en) | A kind of building energy consumption management system | |
| CN117235169A (en) | Wisdom fortune dimension data storage platform | |
| CN117453137A (en) | Cloud intelligent operation and maintenance system data management system | |
| CN117639251A (en) | Intelligent online monitoring system for high-voltage switch cabinet | |
| CN117010665A (en) | Intelligent operation and maintenance IDC machine room management system | |
| CN117489575A (en) | Fault monitoring system and method for digital energy air compression station | |
| CN117689373A (en) | Maintenance decision support method for energy router of flexible direct-current traction power supply system | |
| CN118011927A (en) | Rotational molding machine remote control device and method based on cloud platform | |
| CN117270937A (en) | Digital operation and maintenance management system | |
| CN118016268A (en) | Intelligent medical system based on artificial intelligence | |
| CN117575496A (en) | Real estate operation engineering construction progress intelligent supervision system based on digitization |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| WW01 | Invention patent application withdrawn after publication | ||
| WW01 | Invention patent application withdrawn after publication |
Application publication date: 20231013 |