CN112165161A - Intelligent power grid monitoring method and system based on Internet of things - Google Patents

Intelligent power grid monitoring method and system based on Internet of things Download PDF

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Publication number
CN112165161A
CN112165161A CN202010931782.6A CN202010931782A CN112165161A CN 112165161 A CN112165161 A CN 112165161A CN 202010931782 A CN202010931782 A CN 202010931782A CN 112165161 A CN112165161 A CN 112165161A
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target
thread
electric equipment
log file
working condition
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吴龙圣
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
    • H02J13/00002—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by monitoring
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/10—File systems; File servers
    • G06F16/18—File system types
    • G06F16/1805—Append-only file systems, e.g. using logs or journals to store data
    • G06F16/1815—Journaling file systems
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/23—Clustering techniques
    • G06F18/232—Non-hierarchical techniques
    • G06F18/2321—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Electricity, gas or water supply
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
    • H02J13/00006—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by information or instructions transport means between the monitoring, controlling or managing units and monitored, controlled or operated power network element or electrical equipment
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
    • Y02B70/00—Technologies for an efficient end-user side electric power management and consumption
    • Y02B70/30—Systems integrating technologies related to power network operation and communication or information technologies for improving the carbon footprint of the management of residential or tertiary loads, i.e. smart grids as climate change mitigation technology in the buildings sector, including also the last stages of power distribution and the control, monitoring or operating management systems at local level
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S10/00—Systems supporting electrical power generation, transmission or distribution
    • Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
    • Y04S10/52—Outage or fault management, e.g. fault detection or location
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S20/00—Management or operation of end-user stationary applications or the last stages of power distribution; Controlling, monitoring or operating thereof
    • Y04S20/20—End-user application control systems
    • Y04S20/242—Home appliances
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S40/00—Systems for electrical power generation, transmission, distribution or end-user application management characterised by the use of communication or information technologies, or communication or information technology specific aspects supporting them
    • Y04S40/12—Systems for electrical power generation, transmission, distribution or end-user application management characterised by the use of communication or information technologies, or communication or information technology specific aspects supporting them characterised by data transport means between the monitoring, controlling or managing units and monitored, controlled or operated electrical equipment
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S40/00—Systems for electrical power generation, transmission, distribution or end-user application management characterised by the use of communication or information technologies, or communication or information technology specific aspects supporting them
    • Y04S40/12—Systems for electrical power generation, transmission, distribution or end-user application management characterised by the use of communication or information technologies, or communication or information technology specific aspects supporting them characterised by data transport means between the monitoring, controlling or managing units and monitored, controlled or operated electrical equipment
    • Y04S40/128—Systems for electrical power generation, transmission, distribution or end-user application management characterised by the use of communication or information technologies, or communication or information technology specific aspects supporting them characterised by data transport means between the monitoring, controlling or managing units and monitored, controlled or operated electrical equipment involving the use of Internet protocol

Abstract

The invention provides an intelligent power grid monitoring method and system based on the Internet of things, which can acquire an operation state log file of each piece of electric equipment from data storage equipment, analyze and acquire a plurality of groups of operation parameters included in the operation state log file of each piece of electric equipment based on a log file analysis process, further determine a working condition track of each group of operation parameters of each group of electric equipment, and then determine the comprehensive fault occurrence rate of the target electric equipment according to the acquired safety factor and working condition track of each group of operation parameters of each group of electric equipment; and when the comprehensive fault occurrence rate reaches a set value, early warning information is pushed to the corresponding electric equipment. So, can carry out the analysis to consumer's operating mode orbit, through early warning information's propelling movement, effectively reduce consumer and break down the risk, ensure smart power grids's safe and reliable operation.

Description

Intelligent power grid monitoring method and system based on Internet of things
Technical Field
The invention relates to the technical field of smart power grids, in particular to a smart power grid monitoring method and system based on the Internet of things.
Background
With the development of the internet of things, the smart grid technology is mature. The smart grid technology can support remote payment of electric charge, remote maintenance and remote control of electric equipment, and unification and centralized management of equipment clusters. However, as the types and the number of the electric devices are gradually increased, the instability of the power grid is increased, and for a large power utilization cluster, if one of the electric devices in the power utilization cluster fails, a chain reaction of the whole power utilization cluster may be caused, so that the stability and the reliability of the power grid are affected, and in a severe case, a major production accident may be caused.
In view of the above problems, most of common solutions are to monitor the power consumption of each electrical device in real time, and to perform corresponding maintenance and processing measures when the electrical device fails. This solution only enables fault monitoring of the consumer, as described above, if the consumer has failed, it is difficult to ensure timely execution of maintenance and treatment measures due to time delays or other factors.
Disclosure of Invention
In order to solve the problems, the invention provides a smart grid monitoring method and system based on the Internet of things.
In a first aspect of the embodiments of the present invention, a smart grid monitoring method based on an internet of things is provided, which is applied to a smart grid controller that communicates with a data storage device and a plurality of power consumption devices, and the method includes:
monitoring the running state of each electric device in real time through a collector implanted into each electric device in advance, generating a running state log file corresponding to each electric device in the data storage device through the collector, and writing the collected running parameters of each electric device into the running state log file of each electric device in the data storage device through the collector;
determining a copy frequency for copying each operation state log file in the data storage device to the intelligent power grid controller according to the number of target log files, which have no operation parameter update in a set time period, in all operation state log files stored in the data storage device; copying each running state log file in the data storage equipment according to the copying frequency; the running state log file is used for recording running parameters of the electric equipment;
for each running state log file copied to the intelligent power grid controller, determining a device identifier of a target electric device corresponding to the running state log file, and searching a log file analysis packet corresponding to the device identifier in a preset database; starting a log file analysis process according to the log file analysis package, importing the running state log file into the log file analysis process, and analyzing to obtain multiple groups of running parameters of the target electric equipment in the running state log file based on the log file analysis process; the system comprises a database, a log file analysis package and a user terminal, wherein the operating parameters corresponding to electric equipment with different equipment identifiers are different, the preset database is used for storing the equipment identifiers and the log file analysis package, and the equipment identifiers and the log file analysis package in the database are in one-to-one correspondence;
determining the working condition track of each group of operation parameters of the target electric equipment;
acquiring the safety factor of each group of operation parameters of the target electric equipment, and determining the comprehensive fault occurrence rate of the target electric equipment according to the safety factor and the working condition track; and judging whether the comprehensive fault occurrence rate reaches a set value, if so, pushing early warning information to the target electric equipment.
Optionally, the determining the working condition trajectory of each set of operating parameters of the target electrical equipment includes:
calling a characteristic data list corresponding to each group of operation parameters from the data storage equipment aiming at each group of operation parameters of the target electric equipment;
extracting the characteristics of the group of operation parameters based on the characteristic data list to obtain target characteristic vectors corresponding to the group of operation parameters;
mapping the target characteristic vector to a preset coordinate plane, and determining a target clustering region where the target characteristic vector is located in the coordinate plane;
and determining the working condition track corresponding to the group of operation parameters according to the target clustering region.
Optionally, the determining a working condition track corresponding to the group of operating parameters according to the target clustering region includes:
acquiring clustering information of the target clustering area; classifying the clustering information of the target clustering area by using a classifier to obtain a classification result;
when the classification result shows that the target clustering information of the target clustering area is working condition information, acquiring a depreciation coefficient and a performance fluctuation coefficient of the target electric equipment, determining the number of track area nodes used for determining a track curve in the target clustering information according to the depreciation coefficient and the performance fluctuation coefficient of the target electric equipment, extracting the track curve from the target clustering information according to the determined track area nodes, and classifying the group of operation parameters based on the track curve to obtain a plurality of parameter groups;
extracting the median in each parameter group, and setting a fluctuation interval for each median according to the ratio of each median in each parameter group to obtain a first track interval corresponding to each median;
determining a first overlapping rate of two adjacent first track intervals; aiming at each determined first overlap rate, when the first overlap rate does not reach a set value, determining working condition tracks corresponding to the group of operation parameters according to all the determined first track intervals; when the first overlapping rate reaches the set value, at least one of two first track intervals corresponding to the first overlapping rate is reduced, and the two first track intervals corresponding to the first overlapping rate are determined to be two second track intervals; determining a second overlapping rate of the two second track intervals, and determining a working condition track corresponding to the group of operation parameters according to the two second track intervals and the determined first track interval when the second overlapping rate does not reach a set value; and when the second overlapping rate reaches a set value, executing a step of reducing at least one track section in two first track sections corresponding to the first overlapping rate.
Optionally, determining the comprehensive fault occurrence rate of the target electric equipment according to the safety factor and the working condition track includes:
determining the fault occurrence rate corresponding to each group of operation parameters according to the working condition track corresponding to each group of operation parameters;
sorting the fault occurrence rates corresponding to each group of operation parameters according to the sequence of the safety factors from large to small to obtain a sorting sequence;
determining a fault tolerance according to the equipment identifier of the target electric equipment, and dividing the fault occurrence rates in the sequencing sequence according to the fault tolerance to obtain a plurality of first fault occurrence rates and a plurality of second fault occurrence rates; the safety factor of the first fault occurrence rate is greater than the safety factor of the second fault occurrence rate;
setting the second fault occurrence rates to zero to obtain third fault occurrence rates;
and determining the average value of the plurality of first fault occurrence rates and the plurality of third fault occurrence rates as a comprehensive fault occurrence rate.
Optionally, the determining the fault occurrence rate corresponding to each group of operating parameters according to the working condition track corresponding to each group of operating parameters includes:
creating a working condition simulation thread, determining a parameter list of the working condition simulation thread, caching the parameter list of the working condition simulation thread according to a preset parameter storage mode, determining a parameter list of working condition tracks corresponding to each group of operation parameters, and filling the cached parameter list according to the parameter list to obtain a target list;
running the working condition simulation thread based on the target list, judging whether a thread record simulated by the working condition simulation thread is cached or not when a breakpoint occurs in the working condition simulation thread, and if so, acquiring the cached thread record simulated by the working condition simulation thread; if not, decoding the working condition simulation thread to obtain a thread record simulated by the working condition simulation thread; dynamically caching the thread records simulated by the working condition simulation thread according to a preset thread record storage mode;
obtaining a target thread record according to the thread record of the dynamic cache, obtaining at least part of thread data from the target thread record according to a preset thread record obtaining rule, judging whether mapping data of the thread data are cached or not, and obtaining the mapping data of the cached thread data if the mapping data of the thread data are cached; if not, performing feature extraction on the thread data based on a mapping relation of the mapping data relative to the thread data and/or a preset mapping data extraction mode to obtain mapping data corresponding to the thread data; according to the preset thread record storage mode, dynamically caching mapping data corresponding to the thread data;
calling thread records and mapping data of the cached working condition simulation threads, and dynamically dividing the working condition simulation threads into a first thread and at least one second thread according to the called thread records and mapping data, wherein the first thread is used for representing normal threads of working condition tracks corresponding to each group of operating parameters, and the second thread is used for representing abnormal threads of the working condition tracks corresponding to each group of operating parameters;
loading the thread record and the mapping data in the first thread to obtain first target data corresponding to the first thread; wherein the first target data is used to characterize the stability of the first thread;
loading the target data into the second thread, and operating the second thread to obtain second target data corresponding to the second thread; obtaining the fault occurrence rate corresponding to each group of operation parameters according to the second target data; wherein the second target data is used to characterize a probability of failure of the second thread.
Optionally, the determining the fault tolerance according to the device identifier of the target electrical device includes:
identifying the associated information of the equipment identifier from factory information of the target electric equipment;
outputting an operation numerical value distribution graph corresponding to the associated information according to the text content of the associated information, wherein a numerical value corresponding to each coordinate on the operation numerical value distribution graph is the maximum fault operation duration of the target electric equipment under the associated information corresponding to the numerical value;
determining the comprehensive fault operation time length of the target electric equipment based on the maximum fault operation time length corresponding to the value corresponding to each coordinate on the operation value distribution diagram;
determining the fault tolerance of the target electric equipment based on the comprehensive fault operation time length; wherein the fault tolerance is expressed in percentage.
Optionally, the method further comprises:
acquiring current data of the target electric equipment after the early warning information is received through the collector, wherein the current data is current data of the target electric equipment in normal operation;
determining the operation duration of the target electric equipment after the early warning information is received according to the current data;
and when the running time length exceeds a set time length, controlling the target electric equipment to be closed, wherein the set time length is determined by the comprehensive fault rate of the target electric equipment and the accumulated use time length of the target electric equipment.
In a second aspect of the embodiments of the present invention, an intelligent power grid monitoring system based on the internet of things is provided, which includes an intelligent power grid controller, a data storage device, a plurality of electric devices, and a collector implanted in each electric device, where the intelligent power grid controller and the data storage device are in communication with each other;
the collector is used for monitoring the running state of each piece of electric equipment in real time, generating a running state log file corresponding to each piece of electric equipment in the data storage equipment, and writing the collected running parameters of each piece of electric equipment into the running state log file of each piece of electric equipment in the data storage equipment;
the intelligent power grid controller is configured to determine, according to the number of target log files, which are not updated by the operating parameters, in all the operating state log files stored in the data storage device within a set time period, a copy frequency at which each operating state log file in the data storage device is copied to the intelligent power grid controller; copying each running state log file in the data storage equipment according to the copying frequency; for each running state log file copied to the intelligent power grid controller, determining a device identifier of a target electric device corresponding to the running state log file, and searching a log file analysis packet corresponding to the device identifier in a preset database; starting a log file analysis process according to the log file analysis package, importing the running state log file into the log file analysis process, and analyzing to obtain multiple groups of running parameters of the target electric equipment in the running state log file based on the log file analysis process; determining the working condition track of each group of operation parameters of the target electric equipment; acquiring the safety factor of each group of operation parameters of the target electric equipment, and determining the comprehensive fault occurrence rate of the target electric equipment according to the safety factor and the working condition track; judging whether the comprehensive fault occurrence rate reaches a set value or not, if so, pushing early warning information to the target electric equipment; the running state log file is used for recording running parameters of the electric equipment, the running parameters corresponding to the electric equipment with different equipment identifications are different, the preset database is used for storing the equipment identifications and the log file analysis packets, and the equipment identifications and the log file analysis packets in the database are in one-to-one correspondence.
According to the intelligent power grid monitoring method and system based on the Internet of things, the running state log file of each piece of electric equipment can be obtained from the data storage device, multiple groups of running parameters included in the running state log file of each piece of electric equipment are obtained through analysis based on the log file analysis process, the working condition track of each group of running parameters of each group of electric equipment is further determined, and then the comprehensive fault occurrence rate of the target electric equipment is determined according to the safety coefficient and the working condition track of each group of running parameters of each group of electric equipment; and when the comprehensive fault occurrence rate reaches a set value, early warning information is pushed to the corresponding electric equipment. So, can carry out the analysis to consumer's operating mode orbit, through early warning information's propelling movement, effectively reduce consumer and break down the risk, ensure smart power grids's safe and reliable operation.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.
Fig. 1 is a schematic diagram of an architecture of an intelligent power grid monitoring system based on the internet of things according to an embodiment of the present invention.
Fig. 2 is a flowchart of a smart grid monitoring method based on the internet of things according to an embodiment of the present invention.
Fig. 3 is a functional block diagram of an intelligent power grid controller according to an embodiment of the present invention.
Icon:
100-an intelligent power grid monitoring system based on the internet of things;
1-a smart grid controller; 11-a monitoring module; 12-a copy module; 13-an analysis module; 14-a determination module; 15-a push module;
2-a data storage device;
3-electric equipment.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
In order to better understand the technical solutions of the present invention, the following detailed descriptions of the technical solutions of the present invention are provided with the accompanying drawings and the specific embodiments, and it should be understood that the specific features in the embodiments and the examples of the present invention are the detailed descriptions of the technical solutions of the present invention, and are not limitations of the technical solutions of the present invention, and the technical features in the embodiments and the examples of the present invention may be combined with each other without conflict.
The inventor finds through investigation and research that most of common solutions for smart grid faults are based on real-time monitoring results to maintain, process and investigate the faulty electric equipment, and the fault early warning of the electric equipment is not realized by analyzing the performance, state and other data of the electric equipment based on the historical working conditions of the electric equipment.
Therefore, the embodiment of the invention provides an intelligent power grid monitoring method and system based on the internet of things, which can be used for carrying out big data analysis on historical working conditions of electric equipment, so that data such as performance data and state data of the electric equipment, which are used for describing working condition tracks of the electric equipment, are determined, the working condition tracks of the electric equipment are determined according to the data, and then fault early warning is carried out on the electric equipment based on the working condition tracks, so that the risk of faults of the electric equipment can be effectively reduced, and the safe and reliable operation of an intelligent power grid is ensured.
Referring to fig. 1, an architecture schematic diagram of an internet-of-things-based smart grid monitoring system 100 according to an embodiment of the present invention is provided, where the internet-of-things-based smart grid monitoring system 100 includes a smart grid controller 1, a data storage device 2, and a plurality of power consumers 3, the smart grid controller 1 is respectively in communication with the data storage device 2 and the plurality of power consumers 3, and the data storage device 2 is in communication with the plurality of power consumers 3.
In this embodiment, the electric device 3 may be an intelligent household appliance, or may be an automatic production device, which is not limited herein. Further, the data storage device 2 is used for storing operation data uploaded by the electric equipment 3, such as instantaneous current/voltage data of the electric equipment 3 during start-up and shut-down, power quality data of the electric equipment 3 during stable operation, and the like.
Referring to fig. 1, the intelligent grid controller 1 may obtain the operation data of each electric device 3 in a set time period from the data storage device 2, process and analyze the operation data, determine a working condition trajectory of each electric device 3, and perform fault early warning on the electric devices according to the working condition trajectory.
In this embodiment, intelligent electric network controller 1 is through obtaining the operating data from data storage device 2, can avoid operating data direct storage to influence intelligent electric network controller 1's operating efficiency in intelligent electric network controller 1, so, can ensure that intelligent electric network controller 1 can be fast, accurately based on the operating data that obtains from data storage device 2 confirms the operating mode orbit, and then realize fast, accurately the early warning of trouble, effectively reduce the risk that the consumer broke down, ensure the safety and the reliable operation of intelligent electric network.
On the basis of the above, please refer to fig. 2, which is a flowchart of a smart grid monitoring method based on the internet of things according to an embodiment of the present invention, the method is applied to the smart grid controller 1 in fig. 1, and further, the method may include the following steps.
Step S21, monitoring the running state of each electric device in real time through a collector implanted into each electric device in advance, generating a running state log file corresponding to each electric device in the data storage device through the collector, and enabling the collector to write the collected running parameters of each electric device into the running state log file of each electric device in the data storage device.
Step S22, determining, according to the number of target log files for which there is no operation parameter update in a set time period among all the operation state log files stored in the data storage device, a copy frequency for copying each operation state log file in the data storage device to the smart grid controller; and copying each running state log file in the data storage equipment according to the copying frequency.
In this embodiment, the operation state log file is used to record the operation parameters of the electric device, and it can be understood that, if the electric device M is not started within a period of time, the collector does not need to collect the operation parameters of the electric device M, and further, the operation state log file of the electric device M stored in the data storage device may not have an update of the operation parameters.
Therefore, the copying frequency can be flexibly determined according to the number of the target log files, the efficiency of copying the running state log files by the intelligent power grid controller is further ensured, the copying time is saved, and the timeliness of determining the working condition track of the electric equipment is improved.
Step S23, determining an equipment identifier of the target electric equipment corresponding to each running state log file copied to the intelligent power grid controller, and searching a log file analysis packet corresponding to the equipment identifier in a preset database; and starting a log file analysis process according to the log file analysis package, importing the running state log file into the log file analysis process, and analyzing to obtain multiple groups of running parameters of the target electric equipment in the running state log file based on the log file analysis process.
In this embodiment, the device identifier is used to characterize the type of the target powered device. For example, the target electric equipment may be a water heater or a boiler motor of a steel mill. It will be appreciated that the types of water heater and boiler motor are different and therefore the appliance identification of the water heater and boiler motor is different.
In this embodiment, the operating parameters corresponding to the electric devices with different device identifiers are different, so the device identifiers of the electric devices also need to be considered when analyzing the operating status log file. The preset database can be used for storing the device identifier and the log file analysis packet, and the device identifier and the log file analysis packet in the database are in one-to-one correspondence.
And step S24, determining working condition tracks corresponding to each group of operating parameters of the target electric equipment.
Step S25, obtaining the safety factor of each group of operation parameters of the target electric equipment, and determining the comprehensive fault occurrence rate of the target electric equipment according to the safety factor and the working condition track; and judging whether the comprehensive fault occurrence rate reaches a set value, if so, pushing early warning information to the target electric equipment.
It can be understood that through steps S21-S25, the intelligent power grid controller can obtain an operation state log file of each electrical device from the data storage device, and obtain a plurality of sets of operation parameters included in the operation state log file of each electrical device based on log file analysis process, so as to determine a working condition track of each set of operation parameters of each set of electrical devices, and then determine a comprehensive failure occurrence rate of the target electrical device according to the obtained safety factor and working condition track of each set of operation parameters of each set of electrical devices; and when the comprehensive fault occurrence rate reaches a set value, early warning information is pushed to the corresponding electric equipment. So, can carry out the analysis to consumer's operating mode orbit, through early warning information's propelling movement, effectively reduce consumer and break down the risk, ensure smart power grids's safe and reliable operation.
In an alternative embodiment, in step S23, the parsing to obtain multiple sets of operating parameters of the target electrical device included in the operating status log file based on the log file parsing process may specifically include the following.
Step S231, monitoring a dynamic process parameter of the log file analysis process, and if it is determined that the dynamic process parameter is located in a preset parameter interval, acquiring a script file of the log file analysis process at the current time.
In this embodiment, the dynamic process parameter is used to describe a state of the log file parsing process, where the state of the log file parsing process may include a ready state, a running state, a blocking state, and the like. The preset parameter interval is used for representing the interval of the corresponding dynamic process parameter when the log file analysis process is in the ready state.
Step S232, determining the analysis logic list of the script file according to the process percentage of the log file analysis process.
Step S233, querying a node identifier preset by the data storage device for the running state log file, and acquiring a target node identifier marked with parsing information corresponding to the parameter interval, the script file, and the parsing logic list; the data storage device identifies the text information of a file list in the running state log file in advance, determines the data characteristics corresponding to the text information in the file list and the incidence relation with the parameter interval, compares the list characteristic information of the file list in the running state log file with the preset list information in advance, sets a first label for the list characteristic information of the file list, identifies the file list in the running state log file in advance by list categories, sets a second label for the list categories in the file list, identifies the file list in the running state log file in advance by data dimensions, sets a third label for the number of the data dimensions in the file list, and sets the first label according to the data characteristics, the incidence relation and the preset data dimensions, And the second label and the third label generate a node identifier of the running state log file.
In this embodiment, the data storage device sets the node identifier for the running state log file in advance, so that the workload of the intelligent power grid controller can be effectively reduced.
Step S234, blocking the running state log file according to the target node identifier to obtain a plurality of file blocks, and pushing each file block to the log file parsing process, so that the log file parsing process parses each file block to obtain a running parameter corresponding to each file block.
In this embodiment, through steps S231 to S234, the operation status log file can be comprehensively analyzed, so as to determine multiple sets of operation parameters of the target electrical equipment, and improve reliability and accuracy of subsequently determining the comprehensive risk rate.
In a specific implementation, in step S24, the determining a working condition trajectory of each set of operating parameters of the target electrical equipment may specifically include the following.
Step S241, for each group of operating parameters of the target electrical device, calling a feature data list corresponding to the group of operating parameters from the data storage device.
Step S242, performing feature extraction on the set of operating parameters based on the feature data list to obtain target feature vectors corresponding to the set of operating parameters.
Step S243, mapping the target feature vector to a preset coordinate plane, and determining a target clustering region where the target feature vector is located in the coordinate plane.
And step S244, determining the working condition track corresponding to the group of operation parameters according to the target clustering region.
In this embodiment, the preset coordinate plane is established according to the historical feature vector corresponding to the historical operating parameter of the target electrical equipment, the coordinate plane is a two-dimensional coordinate plane, the two-dimensional coordinate plane includes a plurality of clusters, each cluster corresponds to a cluster region, and each cluster region corresponds to a working condition track of the operating parameter. It is understood that the clusters in the two-dimensional coordinate plane may be obtained by a K-means clustering method.
In a specific implementation, in order to ensure the accuracy of the working condition trajectory, in step S244, the determining the working condition trajectory corresponding to the group of operation parameters according to the target clustering region may specifically include the following.
Step S2441, acquiring clustering information of the target clustering area; and classifying the clustering information of the target clustering area by using a classifier to obtain a classification result.
Step S2442, when the classification result indicates that the target clustering information of the target clustering area is the working condition information, obtaining a depreciation coefficient and a performance fluctuation coefficient of the target electric equipment, determining the number of track area nodes used for determining a track curve in the target clustering information according to the depreciation coefficient and the performance fluctuation coefficient of the target electric equipment, extracting the track curve from the target clustering information according to the determined track area nodes, and classifying the group of operation parameters to obtain a plurality of parameter groups based on the track curve.
And step S2443, extracting the median in each parameter group, and setting a fluctuation interval for each median according to the ratio of each median in each parameter group to obtain a first track interval corresponding to each median.
In the present embodiment, the fluctuation section may be a two-dimensional section.
Step S2444, determining a first overlapping rate of two adjacent first track intervals; aiming at each determined first overlap rate, when the first overlap rate does not reach a set value, determining working condition tracks corresponding to the group of operation parameters according to all the determined first track intervals; when the first overlapping rate reaches the set value, at least one of two first track intervals corresponding to the first overlapping rate is reduced, and the two first track intervals corresponding to the first overlapping rate are determined to be two second track intervals; determining a second overlapping rate of the two second track intervals, and determining a working condition track corresponding to the group of operation parameters according to the two second track intervals and the determined first track interval when the second overlapping rate does not reach a set value; and when the second overlapping rate reaches a set value, executing a step of reducing at least one track section in two first track sections corresponding to the first overlapping rate.
In this embodiment, the operating condition trajectory may be a two-dimensional curve, and is used to represent a historical operating condition of the target electrical device, and may also be used to predict a subsequent operating condition of the target electrical device. In this embodiment, the failure occurrence rate of the target electrical equipment under each set of operating parameters can be determined according to the working condition track, and then the comprehensive failure occurrence rate of the target electrical equipment is determined.
It can be understood that through steps S2441-S2444, the operating condition trajectory corresponding to each set of operating parameters can be accurately determined.
In a specific implementation, in step S25, the determining a comprehensive failure occurrence rate of the target electrical equipment according to the safety factor and the working condition trajectory may specifically include the following.
And step S251, determining the fault occurrence rate corresponding to each group of operation parameters according to the working condition track corresponding to each group of operation parameters.
And step S252, sorting the fault occurrence rates corresponding to each group of operation parameters according to the sequence of the safety factors from large to small to obtain a sorting sequence.
Step S253, determining fault tolerance according to the equipment identification of the target electric equipment, and dividing the fault occurrence rates in the sequencing sequence according to the fault tolerance to obtain a plurality of first fault occurrence rates and a plurality of second fault occurrence rates; the safety factor of the first failure occurrence rate is greater than the safety factor of the second failure occurrence rate.
And step S254, setting the plurality of second failure occurrence rates to zero to obtain a plurality of third failure occurrence rates.
Step S255, determining an average value of the plurality of first failure occurrence rates and the plurality of third failure occurrence rates as a comprehensive failure occurrence rate.
It can be understood that, through steps S251 to S255, the failure occurrence rates can be sorted, divided, and zeroed based on the magnitude of the safety factor corresponding to each set of operating parameters, so as to provide a certain margin for determining the comprehensive failure occurrence rate, and thus, the comprehensive failure occurrence rate can be accurately determined under the condition of ensuring the normal use of the target electrical equipment.
In practical application, the comprehensive fault occurrence rate may be used to represent a probability that the electric device may have a fault, and therefore, in order to ensure accuracy of the comprehensive fault occurrence rate, the fault occurrence rate corresponding to each group of operating parameters needs to be accurately determined. Therefore, in step S251, the determining the fault occurrence rate corresponding to each set of operating parameters according to the working condition trajectory corresponding to each set of operating parameters may specifically include the following.
Step S2511, a working condition simulation thread is created, a parameter list of the working condition simulation thread is determined, the parameter list of the working condition simulation thread is cached according to a preset parameter storage mode, a parameter list of working condition tracks corresponding to each group of operation parameters is determined, and the cached parameter list is filled according to the parameter list to obtain a target list.
Step S2512, running the working condition simulation thread based on the target list, judging whether the thread record simulated by the working condition simulation thread is cached or not when the working condition simulation thread has a breakpoint, and if so, acquiring the cached thread record simulated by the working condition simulation thread; if not, decoding the working condition simulation thread to obtain a thread record simulated by the working condition simulation thread; and dynamically caching the thread records simulated by the working condition simulation thread according to a preset thread record storage mode.
In this embodiment, the dynamic cache may be understood as storing the thread record in a section that is available at any time, and supporting direct modification of the thread record in the section.
Step S2513, obtaining a target thread record according to the dynamically cached thread record, obtaining at least part of thread data from the target thread record according to a preset thread record obtaining rule, judging whether the mapping data of the thread data is cached, and obtaining the cached mapping data of the thread data if the mapping data of the thread data is cached; if not, performing feature extraction on the thread data based on a mapping relation of the mapping data relative to the thread data and/or a preset mapping data extraction mode to obtain mapping data corresponding to the thread data; and according to the preset thread record storage mode, dynamically caching the mapping data corresponding to the thread data.
In this embodiment, the mapping data of the thread data is used to represent the weight value of the failed target device.
Step S2514, calling the thread record and mapping data of the buffered working condition simulation thread, and dynamically dividing the working condition simulation thread into a first thread and at least one second thread according to the called thread record and mapping data, wherein the first thread is used for representing a normal thread of the working condition track corresponding to each group of operation parameters, and the second thread is used for representing an abnormal thread of the working condition track corresponding to each group of operation parameters.
Step S2515, loading the thread record and the mapping data in the first thread to obtain first target data corresponding to the first thread; wherein the first target data is used to characterize stability of the first thread.
Step S2516, loading the target data into the second thread, and running the second thread to obtain second target data corresponding to the second thread; obtaining the fault occurrence rate corresponding to each group of operation parameters according to the second target data; wherein the second target data is used to characterize a probability of failure of the second thread.
It can be understood that through steps S2511 to S2516, the fault occurrence rate corresponding to each set of operating parameters can be accurately determined based on the created working condition simulation thread, so as to ensure the accuracy of the comprehensive fault occurrence rate.
In a specific implementation, in order to ensure an accurate margin reserved when determining the comprehensive failure occurrence rate, in step S253, the determining a failure tolerance according to the device identifier of the target electrical device may specifically include the following.
And step S2531, identifying the associated information of the device identifier from the factory information of the target electrical device.
In this embodiment, the related information may include safe operation parameters of the target electrical device, and the like.
Step S2532, outputting an operation numerical value distribution graph corresponding to the associated information according to the text content of the associated information, wherein a numerical value corresponding to each coordinate on the operation numerical value distribution graph is the maximum fault operation duration of the target electric equipment under the associated information corresponding to the numerical value.
In this embodiment, the operation numerical value distribution map may be a two-dimensional coordinate map.
Step S2533, determining the comprehensive fault operation time length of the target electric equipment based on the maximum fault operation time length corresponding to the value corresponding to each coordinate on the operation value distribution diagram.
Step S2534, determining the fault tolerance of the target electric equipment based on the comprehensive fault operation time length; wherein the fault tolerance is expressed in percentage.
It can be understood that through steps S2531 to S2534, the associated information of the device identifier can be identified from the factory information of the target electrical device, and the operation value distribution map corresponding to the associated information is determined based on the text content of the associated information, so as to determine the comprehensive fault operation duration of the target electrical device, so as to determine the fault tolerance. Therefore, the fault tolerance can be accurately determined, and the reserved accurate allowance in the process of determining the comprehensive fault occurrence rate is further ensured.
On the basis, in order to further ensure safe and reliable operation of the target electric equipment, after the step of pushing the warning information to the target electric equipment, the method may further include the following steps.
Step S31, collecting, by the collector, current data of the target electrical equipment after receiving the warning information, where the current data is current data of the target electrical equipment during normal operation.
Step S32, determining the operation duration of the target electric equipment after receiving the early warning information according to the current data.
And step S33, controlling the target electric equipment to be closed when the running time length exceeds a set time length, wherein the set time length is determined by the comprehensive fault rate of the target electric equipment and the accumulated use time length of the target electric equipment.
It can be understood that through steps S31-S33, the operation duration of the target electrical equipment can be monitored after the warning information is pushed to the target electrical equipment, and the target electrical equipment is controlled to be turned off when the operation duration exceeds a set duration. Therefore, safe and reliable operation of the target electric equipment can be further ensured, and the probability of failure occurrence of the target electric equipment is reduced.
On the basis, the embodiment of the present invention further provides an internet of things-based smart grid monitoring method, which is applied to the internet of things-based smart grid monitoring system 100 in fig. 1, and the method may include the following steps.
Step S41, a collector implanted into each electric device in advance monitors the running state of each electric device in real time, an running state log file corresponding to each electric device is generated in the data storage device, and the collector writes the collected running parameters of each electric device into the running state log file of each electric device in the data storage device.
Step S42, the smart grid controller determines, according to the number of target log files in which no operation parameter update exists in all the operation state log files stored in the data storage device within a set time period, a copy frequency at which each operation state log file in the data storage device is copied to the smart grid controller; and copying each running state log file in the data storage equipment according to the copying frequency.
Step S43, the intelligent electric network controller determines the device identifier of the target electric equipment corresponding to each running state log file copied to the intelligent electric network controller, and searches a log file analysis packet corresponding to the device identifier in a preset database; and starting a log file analysis process according to the log file analysis package, importing the running state log file into the log file analysis process, and analyzing to obtain multiple groups of running parameters of the target electric equipment in the running state log file based on the log file analysis process.
And step S44, the intelligent electric network controller determines working condition tracks corresponding to each group of operation parameters of the target electric equipment.
Step S45, the intelligent electric network controller obtains the safety factor of each group of operation parameters of the target electric equipment, and determines the comprehensive failure occurrence rate of the target electric equipment according to the safety factor and the working condition track; and judging whether the comprehensive fault occurrence rate reaches a set value, if so, pushing early warning information to the target electric equipment.
In the present embodiment, since the implementation principle of step S41-step S45 is similar to that of step S21-step S25, no further description is made here.
On the basis of the above, please refer to fig. 3, which is a block diagram of an intelligent power grid controller 1 according to an embodiment of the present invention, where the intelligent power grid controller 1 may include the following modules.
The monitoring module 11 is configured to perform real-time monitoring on the operation state of each electrical device through a collector implanted in each electrical device in advance, and generate an operation state log file corresponding to each electrical device in the data storage device through the collector, so that the collector writes the acquired operation parameters of each electrical device into the operation state log file of each electrical device in the data storage device.
A copy module 12, configured to determine, according to the number of target log files for which an operation parameter update does not exist in all operation state log files stored in the data storage device within a set time period, a copy frequency for copying each operation state log file in the data storage device to the smart grid controller; and copying each running state log file in the data storage equipment according to the copying frequency.
The analysis module 13 is configured to determine, for each operation status log file copied to the intelligent power grid controller, an equipment identifier of the target electrical equipment corresponding to the operation status log file, and search a log file analysis packet corresponding to the equipment identifier in a preset database; and starting a log file analysis process according to the log file analysis package, importing the running state log file into the log file analysis process, and analyzing to obtain multiple groups of running parameters of the target electric equipment in the running state log file based on the log file analysis process.
And the determining module 14 is configured to determine a working condition trajectory of each set of operating parameters of the target electrical equipment.
The pushing module 15 is configured to obtain a safety factor of each set of operating parameters of the target electrical equipment, and determine a comprehensive fault occurrence rate of the target electrical equipment according to the safety factor and the working condition track; and judging whether the comprehensive fault occurrence rate reaches a set value, if so, pushing early warning information to the target electric equipment.
The embodiment of the invention also provides a readable storage medium, wherein a program is stored on the readable storage medium, and when the program is executed by a processor, the method for monitoring the smart grid based on the internet of things is realized.
The embodiment of the invention provides a processor, which is used for running a program, wherein the intelligent power grid monitoring method based on the Internet of things is executed when the program runs.
In this embodiment, the smart grid controller 1 includes at least one processor, and at least one memory and a bus connected to the processor. The processor and the memory complete mutual communication through the bus. The processor is used for calling the program instructions in the memory so as to execute the intelligent power grid monitoring method based on the Internet of things.
To sum up, the method and the system for monitoring the smart grid based on the internet of things provided by the embodiment of the invention can acquire the running state log file of each piece of electric equipment from the data storage device, and analyze the running state log file to obtain multiple groups of running parameters included in the running state log file of each piece of electric equipment based on the log file analysis process, so as to determine the working condition track of each group of running parameters of each group of electric equipment, and then determine the comprehensive fault occurrence rate of the target electric equipment according to the safety coefficient and the working condition track of each group of running parameters of each group of electric equipment; and when the comprehensive fault occurrence rate reaches a set value, early warning information is pushed to the corresponding electric equipment. So, can carry out the analysis to consumer's operating mode orbit, through early warning information's propelling movement, effectively reduce consumer and break down the risk, ensure smart power grids's safe and reliable operation.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, cloud intelligent grid controllers (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing cloud smart grid controller to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing cloud smart grid controller, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In a typical configuration, a cloud smart grid controller includes one or more processors (CPUs), memory, and buses. The cloud smart grid controller may also include input/output interfaces, network interfaces, and the like.
The memory may include volatile memory in a computer readable medium, Random Access Memory (RAM) and/or nonvolatile memory such as Read Only Memory (ROM) or flash memory (flash RAM), and the memory includes at least one memory chip. The memory is an example of a computer-readable medium.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technologies, compact disc read only memory (CD-ROM), Digital Versatile Disc (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage cloud smart grid controllers, or any other non-transmission medium that can be used to store information that can be matched by a computing cloud smart grid controller. As defined herein, computer readable media does not include transitory computer readable media (transmyedia) such as modulated data signals and carrier waves.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or cloud smart grid controller comprising a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or cloud smart grid controller. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of additional like elements in a process, method, article, or cloud smart grid controller comprising the element.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The above are merely examples of the present application and are not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims (8)

1. An Internet of things-based smart grid monitoring method applied to a smart grid controller in communication with a data storage device and a plurality of electric devices, the method comprising:
monitoring the running state of each electric device in real time through a collector implanted into each electric device in advance, generating a running state log file corresponding to each electric device in the data storage device through the collector, and writing the collected running parameters of each electric device into the running state log file of each electric device in the data storage device through the collector;
determining a copy frequency for copying each operation state log file in the data storage device to the intelligent power grid controller according to the number of target log files, which have no operation parameter update in a set time period, in all operation state log files stored in the data storage device; copying each running state log file in the data storage equipment according to the copying frequency; the running state log file is used for recording running parameters of the electric equipment;
for each running state log file copied to the intelligent power grid controller, determining a device identifier of a target electric device corresponding to the running state log file, and searching a log file analysis packet corresponding to the device identifier in a preset database; starting a log file analysis process according to the log file analysis package, importing the running state log file into the log file analysis process, and analyzing to obtain multiple groups of running parameters of the target electric equipment in the running state log file based on the log file analysis process; the system comprises a database, a log file analysis package and a user terminal, wherein the operating parameters corresponding to electric equipment with different equipment identifiers are different, the preset database is used for storing the equipment identifiers and the log file analysis package, and the equipment identifiers and the log file analysis package in the database are in one-to-one correspondence;
determining the working condition track of each group of operation parameters of the target electric equipment;
acquiring the safety factor of each group of operation parameters of the target electric equipment, and determining the comprehensive fault occurrence rate of the target electric equipment according to the safety factor and the working condition track; and judging whether the comprehensive fault occurrence rate reaches a set value, if so, pushing early warning information to the target electric equipment.
2. The Internet of things-based smart grid monitoring method according to claim 1, wherein the determining of the working condition trajectory of each set of operating parameters of the target electric equipment comprises:
calling a characteristic data list corresponding to each group of operation parameters from the data storage equipment aiming at each group of operation parameters of the target electric equipment;
extracting the characteristics of the group of operation parameters based on the characteristic data list to obtain target characteristic vectors corresponding to the group of operation parameters;
mapping the target characteristic vector to a preset coordinate plane, and determining a target clustering region where the target characteristic vector is located in the coordinate plane;
and determining the working condition track corresponding to the group of operation parameters according to the target clustering region.
3. The Internet of things-based smart grid monitoring method according to claim 2, wherein the determining of the working condition track corresponding to the group of operating parameters according to the target clustering region comprises:
acquiring clustering information of the target clustering area; classifying the clustering information of the target clustering area by using a classifier to obtain a classification result;
when the classification result shows that the target clustering information of the target clustering area is working condition information, acquiring a depreciation coefficient and a performance fluctuation coefficient of the target electric equipment, determining the number of track area nodes used for determining a track curve in the target clustering information according to the depreciation coefficient and the performance fluctuation coefficient of the target electric equipment, extracting the track curve from the target clustering information according to the determined track area nodes, and classifying the group of operation parameters based on the track curve to obtain a plurality of parameter groups;
extracting the median in each parameter group, and setting a fluctuation interval for each median according to the ratio of each median in each parameter group to obtain a first track interval corresponding to each median;
determining a first overlapping rate of two adjacent first track intervals; aiming at each determined first overlap rate, when the first overlap rate does not reach a set value, determining working condition tracks corresponding to the group of operation parameters according to all the determined first track intervals; when the first overlapping rate reaches the set value, at least one of two first track intervals corresponding to the first overlapping rate is reduced, and the two first track intervals corresponding to the first overlapping rate are determined to be two second track intervals; determining a second overlapping rate of the two second track intervals, and determining a working condition track corresponding to the group of operation parameters according to the two second track intervals and the determined first track interval when the second overlapping rate does not reach a set value; and when the second overlapping rate reaches a set value, executing a step of reducing at least one track section in two first track sections corresponding to the first overlapping rate.
4. The Internet of things-based smart grid monitoring method according to any one of claims 1 to 3, wherein the determining of the comprehensive fault occurrence rate of the target electric equipment according to the safety factor and the working condition track comprises:
determining the fault occurrence rate corresponding to each group of operation parameters according to the working condition track corresponding to each group of operation parameters;
sorting the fault occurrence rates corresponding to each group of operation parameters according to the sequence of the safety factors from large to small to obtain a sorting sequence;
determining a fault tolerance according to the equipment identifier of the target electric equipment, and dividing the fault occurrence rates in the sequencing sequence according to the fault tolerance to obtain a plurality of first fault occurrence rates and a plurality of second fault occurrence rates; the safety factor of the first fault occurrence rate is greater than the safety factor of the second fault occurrence rate;
setting the second fault occurrence rates to zero to obtain third fault occurrence rates;
and determining the average value of the plurality of first fault occurrence rates and the plurality of third fault occurrence rates as a comprehensive fault occurrence rate.
5. The Internet of things-based smart grid monitoring method according to claim 4, wherein the determining the fault occurrence rate corresponding to each group of operation parameters according to the working condition track corresponding to each group of operation parameters comprises:
creating a working condition simulation thread, determining a parameter list of the working condition simulation thread, caching the parameter list of the working condition simulation thread according to a preset parameter storage mode, determining a parameter list of working condition tracks corresponding to each group of operation parameters, and filling the cached parameter list according to the parameter list to obtain a target list;
running the working condition simulation thread based on the target list, judging whether a thread record simulated by the working condition simulation thread is cached or not when a breakpoint occurs in the working condition simulation thread, and if so, acquiring the cached thread record simulated by the working condition simulation thread; if not, decoding the working condition simulation thread to obtain a thread record simulated by the working condition simulation thread; dynamically caching the thread records simulated by the working condition simulation thread according to a preset thread record storage mode;
obtaining a target thread record according to the thread record of the dynamic cache, obtaining at least part of thread data from the target thread record according to a preset thread record obtaining rule, judging whether mapping data of the thread data are cached or not, and obtaining the mapping data of the cached thread data if the mapping data of the thread data are cached; if not, performing feature extraction on the thread data based on a mapping relation of the mapping data relative to the thread data and/or a preset mapping data extraction mode to obtain mapping data corresponding to the thread data; according to the preset thread record storage mode, dynamically caching mapping data corresponding to the thread data;
calling thread records and mapping data of the cached working condition simulation threads, and dynamically dividing the working condition simulation threads into a first thread and at least one second thread according to the called thread records and mapping data, wherein the first thread is used for representing normal threads of working condition tracks corresponding to each group of operating parameters, and the second thread is used for representing abnormal threads of the working condition tracks corresponding to each group of operating parameters;
loading the thread record and the mapping data in the first thread to obtain first target data corresponding to the first thread; wherein the first target data is used to characterize the stability of the first thread;
loading the target data into the second thread, and operating the second thread to obtain second target data corresponding to the second thread; obtaining the fault occurrence rate corresponding to each group of operation parameters according to the second target data; wherein the second target data is used to characterize a probability of failure of the second thread.
6. The Internet of things-based smart grid monitoring method according to claim 5, wherein the determining of the fault tolerance according to the device identifier of the target electric device comprises:
identifying the associated information of the equipment identifier from factory information of the target electric equipment;
outputting an operation numerical value distribution graph corresponding to the associated information according to the text content of the associated information, wherein a numerical value corresponding to each coordinate on the operation numerical value distribution graph is the maximum fault operation duration of the target electric equipment under the associated information corresponding to the numerical value;
determining the comprehensive fault operation time length of the target electric equipment based on the maximum fault operation time length corresponding to the value corresponding to each coordinate on the operation value distribution diagram;
determining the fault tolerance of the target electric equipment based on the comprehensive fault operation time length; wherein the fault tolerance is expressed in percentage.
7. The Internet of things-based smart grid monitoring method according to claim 1, further comprising:
acquiring current data of the target electric equipment after the early warning information is received through the collector, wherein the current data is current data of the target electric equipment in normal operation;
determining the operation duration of the target electric equipment after the early warning information is received according to the current data;
and when the running time length exceeds a set time length, controlling the target electric equipment to be closed, wherein the set time length is determined by the comprehensive fault rate of the target electric equipment and the accumulated use time length of the target electric equipment.
8. An intelligent power grid monitoring system based on the Internet of things is characterized by comprising an intelligent power grid controller, a data storage device, a plurality of electric equipment and a collector implanted into each electric equipment, wherein the intelligent power grid controller, the data storage device, the plurality of electric equipment and the collector are communicated with each other;
the collector is used for monitoring the running state of each piece of electric equipment in real time, generating a running state log file corresponding to each piece of electric equipment in the data storage equipment, and writing the collected running parameters of each piece of electric equipment into the running state log file of each piece of electric equipment in the data storage equipment;
the intelligent power grid controller is configured to determine, according to the number of target log files, which are not updated by the operating parameters, in all the operating state log files stored in the data storage device within a set time period, a copy frequency at which each operating state log file in the data storage device is copied to the intelligent power grid controller; copying each running state log file in the data storage equipment according to the copying frequency; for each running state log file copied to the intelligent power grid controller, determining a device identifier of a target electric device corresponding to the running state log file, and searching a log file analysis packet corresponding to the device identifier in a preset database; starting a log file analysis process according to the log file analysis package, importing the running state log file into the log file analysis process, and analyzing to obtain multiple groups of running parameters of the target electric equipment in the running state log file based on the log file analysis process; determining the working condition track of each group of operation parameters of the target electric equipment; acquiring the safety factor of each group of operation parameters of the target electric equipment, and determining the comprehensive fault occurrence rate of the target electric equipment according to the safety factor and the working condition track; judging whether the comprehensive fault occurrence rate reaches a set value or not, if so, pushing early warning information to the target electric equipment; the running state log file is used for recording running parameters of the electric equipment, the running parameters corresponding to the electric equipment with different equipment identifications are different, the preset database is used for storing the equipment identifications and the log file analysis packets, and the equipment identifications and the log file analysis packets in the database are in one-to-one correspondence.
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