CN110867964B - Intelligent electricity utilization safety monitoring method based on Internet of things and main control computer - Google Patents
Intelligent electricity utilization safety monitoring method based on Internet of things and main control computer Download PDFInfo
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- CN110867964B CN110867964B CN201911172146.3A CN201911172146A CN110867964B CN 110867964 B CN110867964 B CN 110867964B CN 201911172146 A CN201911172146 A CN 201911172146A CN 110867964 B CN110867964 B CN 110867964B
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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
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/04—Circuit arrangements for ac mains or ac distribution networks for connecting networks of the same frequency but supplied from different sources
- H02J3/06—Controlling transfer of power between connected networks; Controlling sharing of load between connected networks
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- 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
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- 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
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- 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
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- 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 embodiment of the invention belongs to the technical field of smart power grids, and particularly relates to an intelligent power utilization safety monitoring method and a main control computer based on the Internet of things. In the embodiment of the invention, on the premise of determining the power consumption state recording table, the mark information can be added to the characteristic value in the power consumption state recording table for multiple times, so that the state of the first electric equipment in other information dimensions can be determined based on the limited power consumption information of the first electric equipment, and further the main control computer can determine the predicted value of the power consumption load according to the risk weight grades of different mark information, so that the main control computer can generate a control strategy for adjusting the power consumption state of the first electric equipment based on the predicted value of the power consumption load, the power consumption safety of the first electric equipment is ensured, and the power consumption risk of the first electric equipment is prevented.
Description
Technical Field
The invention relates to the technical field of smart power grids, in particular to an intelligent power consumption safety monitoring method and a main control computer based on the Internet of things.
Background
Along with the development of the internet of things technology, the development of the smart power grid is more and more mature, and nowadays, the smart power utilization becomes an indispensable link for production and life of residents.
However, with the increase of electric equipment, production and life accidents caused by electric safety are more and more, so the electric safety becomes an urgent problem at the present stage.
The inventor finds that, based on the consideration of data security and privacy, the information sealing performance of common electric equipment in the environment of the internet of things is high, so that it is difficult to predict the electricity utilization risk directly according to the related electricity utilization information of the electric equipment, and it is difficult to ensure safe and effective adjustment of the electricity utilization condition of the electric equipment.
Disclosure of Invention
In order to overcome at least the above disadvantages in the prior art, an object of the present invention is to provide an intelligent power consumption safety monitoring method and a main control computer based on the internet of things.
The embodiment of the invention provides an intelligent electricity safety monitoring method based on the Internet of things, which is applied to a main control computer, and at least comprises the following steps:
the method comprises the steps of obtaining real-time current information and real-time voltage information of first electric equipment in a set time period, obtaining power utilization state information of the first electric equipment in the set time period according to the real-time current information and the real-time voltage information, and determining a power utilization state record table of the first electric equipment according to the power utilization state information, wherein the power utilization state record table comprises a characteristic value of the power utilization state of the first electric equipment in the set time period;
acquiring influence factors among the characteristic values without adding the first mark information in the electricity consumption state recording table, weighting the characteristic values without being marked according to the influence factors to obtain weighted characteristic values, and adding second mark information to the weighted characteristic values;
when the characteristic value without the first mark information or the second mark information is still present in the electricity consumption state recording table, clustering the characteristic value without the first mark information or the second mark information to obtain a clustering characteristic value, and adding third mark information to the clustering characteristic value;
when a target characteristic value which is not added with the first mark information, the second mark information or the third mark information still exists in the electricity utilization state recording table, determining fourth mark information of the target characteristic value according to a power flow distribution coefficient between the target characteristic value and the clustering characteristic value;
respectively determining the risk weight levels of the first mark information, the second mark information, the third mark information and the fourth mark information;
and determining a power consumption load predicted value on the basis of the power consumption state recording table according to the risk weight grade, and generating a control strategy for adjusting the power consumption state of the first power consumption equipment according to the power consumption load predicted value.
In an optional manner, the generating a control strategy for adjusting the power consumption state of the first electrical device according to the power consumption load predicted value includes:
acquiring a historical control strategy execution success rate of second electrical equipment in an electrical utilization area of the first electrical equipment, wherein the historical control strategy execution success rate is a success rate of adjusting electrical loads according to a plurality of historical control strategies when the second electrical equipment executes the plurality of historical control strategies issued by the main control computer;
determining a target historical control strategy meeting preset conditions in the set time period according to the adjustment coefficient of each historical control strategy; the preset condition is that the harmonic occupancy of the second electrical equipment is lower than a preset ratio when a historical control strategy is executed;
determining the execution success rate of the target historical control strategy on the basis of the execution success rate of the historical control strategy;
when the execution success rate reaches a preset value, correcting the power load predicted value according to a target historical control strategy, and predicting a signal disturbance level when the first electric equipment communicates with the main control computer according to the corrected power load predicted value;
and generating a control strategy for adjusting the power utilization state of the first electric equipment according to the signal disturbance grade and the corrected power utilization load predicted value.
In an optional manner, the determining fourth label information of the target feature value according to the power flow distribution coefficient between the target feature value and the cluster feature value includes:
carrying out binary processing on the characteristic vector of the target characteristic value to obtain a first characteristic vector;
denoising the first eigenvector to obtain a noise eigenvalue in the first eigenvector, and partially deleting the noise eigenvalue according to the character string length of the target eigenvalue to realize the dimension reduction processing of the first eigenvector to obtain a second eigenvector;
determining a stability coefficient of the target characteristic value according to the Hamming distance between the characteristic values in the second characteristic vector;
determining a power flow distribution coefficient between the power flow distribution coefficient and the clustering characteristic value based on the stability coefficient;
determining a target node of the power flow distribution coefficient in the power grid where the first electric equipment is located;
detecting an important coefficient of the target node in the power flow distribution coefficient;
selecting a marking mode for carrying out information marking on the target characteristic value according to an important coefficient of the target node in the power flow distribution coefficient;
and adding the fourth marking information to the target characteristic value according to the marking mode.
In an optional manner, the obtaining, according to the real-time current information and the real-time voltage information, power consumption state information of the first electrical device in the set time period includes:
obtaining power information of the first electric equipment in the set time period according to the real-time current information and the real-time voltage information;
obtaining a power flow distribution record table of the power information and each power flow node;
when the power information is determined to contain the phase distribution sequence according to the power flow distribution record table, determining node power values between the power flow nodes under the non-phase distribution sequence of the power information and the power flow nodes under the phase distribution sequence of the power information according to the power flow nodes and phase angles in the pre-stored phase distribution sequences of a plurality of historical powers, and modifying the power flow nodes under the non-phase distribution sequence of the power information, which are similar to the power flow nodes under the phase distribution sequence, into the power flow nodes under the corresponding phase distribution sequence according to the node power values;
when the current non-phase distribution sequence of the power information contains a plurality of power flow nodes, determining node power values among the power flow nodes in the current non-phase distribution sequence of the power information according to power flow points and phase angles of the power flow points in the phase distribution sequences of the historical powers, and injecting node power into the power flow nodes in the current non-phase distribution sequence according to the node power values among the power flow nodes;
allocating a migration interface for each power flow node obtained by injecting the node power according to the power flow nodes in the plurality of historical power phase distribution sequences and phase angles thereof, and migrating each power flow node to a phase distribution sequence corresponding to the migration interface to obtain a target phase distribution sequence;
and determining the power utilization state information of the first electric equipment in the set time period according to the target phase distribution sequence.
An embodiment of the present invention provides a master control computer, where the master control computer at least includes:
the acquisition module is used for acquiring real-time current information and real-time voltage information of first electric equipment in a set time period, acquiring power utilization state information of the first electric equipment in the set time period according to the real-time current information and the real-time voltage information, and determining a power utilization state record table of the first electric equipment according to the power utilization state information, wherein the power utilization state record table comprises a characteristic value of the power utilization state of the first electric equipment in the set time period;
the adding module is used for acquiring influence factors among the characteristic values which are not added with the first mark information in the power consumption state recording table, weighting the characteristic values which are not marked according to the influence factors to obtain weighted characteristic values, and adding second mark information to the weighted characteristic values;
the clustering module is used for clustering the characteristic values without the first mark information or the second mark information to obtain a clustering characteristic value and adding third mark information for the clustering characteristic value when the characteristic values without the first mark information or the second mark information are still existed in the electricity utilization state record table and the short circuit risk coefficient between the characteristic values without the first mark information or the second mark information is not added;
a first determining module, configured to determine, when a target characteristic value to which the first flag information, the second flag information, or the third flag information is not added still exists in the power consumption state record table, fourth flag information of the target characteristic value according to a power flow distribution coefficient between the target characteristic value and the cluster characteristic value;
a second determining module, configured to determine risk weight levels of the first marker information, the second marker information, the third marker information, and the fourth marker information, respectively;
and the generating module is used for determining an electricity load predicted value on the basis of the electricity utilization state recording table according to the risk weight grade and generating a control strategy for adjusting the electricity utilization state of the first electric equipment according to the electricity load predicted value.
In an optional manner, the generating module is specifically configured to:
acquiring a historical control strategy execution success rate of second electrical equipment in an electrical utilization area of the first electrical equipment, wherein the historical control strategy execution success rate is a success rate of adjusting electrical loads according to a plurality of historical control strategies when the second electrical equipment executes the plurality of historical control strategies issued by the main control computer;
determining a target historical control strategy meeting preset conditions in the set time period according to the adjustment coefficient of each historical control strategy; the preset condition is that the harmonic occupancy of the second electrical equipment is lower than a preset ratio when a historical control strategy is executed;
determining the execution success rate of the target historical control strategy on the basis of the execution success rate of the historical control strategy;
when the execution success rate reaches a preset value, correcting the power load predicted value according to a target historical control strategy, and predicting a signal disturbance level when the first electric equipment communicates with the main control computer according to the corrected power load predicted value;
and generating a control strategy for adjusting the power utilization state of the first electric equipment according to the signal disturbance grade and the corrected power utilization load predicted value.
In an optional manner, the first determining module is specifically configured to:
carrying out binary processing on the characteristic vector of the target characteristic value to obtain a first characteristic vector;
denoising the first eigenvector to obtain a noise eigenvalue in the first eigenvector, and partially deleting the noise eigenvalue according to the character string length of the target eigenvalue to realize the dimension reduction processing of the first eigenvector to obtain a second eigenvector;
determining a stability coefficient of the target characteristic value according to the Hamming distance between the characteristic values in the second characteristic vector;
determining a power flow distribution coefficient between the power flow distribution coefficient and the clustering characteristic value based on the stability coefficient;
determining a target node of the power flow distribution coefficient in the power grid where the first electric equipment is located;
detecting an important coefficient of the target node in the power flow distribution coefficient;
selecting a marking mode for carrying out information marking on the target characteristic value according to an important coefficient of the target node in the power flow distribution coefficient;
and adding the fourth marking information to the target characteristic value according to the marking mode.
In an optional manner, the obtaining module is specifically configured to:
obtaining power information of the first electric equipment in the set time period according to the real-time current information and the real-time voltage information;
obtaining a power flow distribution record table of the power information and each power flow node;
when the power information is determined to contain the phase distribution sequence according to the power flow distribution record table, determining node power values between the power flow nodes under the non-phase distribution sequence of the power information and the power flow nodes under the phase distribution sequence of the power information according to the power flow nodes and phase angles in the pre-stored phase distribution sequences of a plurality of historical powers, and modifying the power flow nodes under the non-phase distribution sequence of the power information, which are similar to the power flow nodes under the phase distribution sequence, into the power flow nodes under the corresponding phase distribution sequence according to the node power values;
when the current non-phase distribution sequence of the power information contains a plurality of power flow nodes, determining node power values among the power flow nodes in the current non-phase distribution sequence of the power information according to power flow points and phase angles of the power flow points in the phase distribution sequences of the historical powers, and injecting node power into the power flow nodes in the current non-phase distribution sequence according to the node power values among the power flow nodes;
allocating a migration interface for each power flow node obtained by injecting the node power according to the power flow nodes in the plurality of historical power phase distribution sequences and phase angles thereof, and migrating each power flow node to a phase distribution sequence corresponding to the migration interface to obtain a target phase distribution sequence;
and determining the power utilization state information of the first electric equipment in the set time period according to the target phase distribution sequence.
The embodiment of the invention provides a main control computer, which comprises a processor, a memory and a bus, wherein the memory and the bus are connected with the processor; wherein, 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 electricity utilization safety monitoring method based on the Internet of things.
The embodiment of the invention provides a storage medium, wherein a program is stored on the storage medium, and when the program is executed by a processor, the intelligent electricity safety monitoring method based on the internet of things is realized.
According to the intelligent electricity utilization safety monitoring method and the main control computer based on the Internet of things, provided by the embodiment of the invention, under the premise of determining the electricity utilization state recording table, the mark information can be added to the characteristic value in the electricity utilization state recording table for multiple times, so that the state of the first electric equipment in other information dimensions can be determined based on the limited electricity utilization information of the first electric equipment, and the main control computer can determine the electricity utilization load predicted value according to the risk weight grades of different mark information, so that the main control computer can generate a control strategy for adjusting the electricity utilization state of the first electric equipment based on the electricity utilization load predicted value, the electricity utilization safety of the first electric equipment is ensured, and the electricity utilization risk of the first electric equipment is prevented.
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 flowchart of an intelligent electricity consumption safety monitoring method based on the internet of things according to an embodiment of the present invention.
Fig. 2 is a functional block diagram of a host computer according to an embodiment of the present invention.
Fig. 3 is a block diagram of a host computer according to an embodiment of the present invention.
Icon:
200-a master control computer; 201-an acquisition module; 202-add module; 203-clustering module; 204-a first determination module; 205-a second determination module; 206-a generation module;
301-a processor; 302-a memory; 303-bus.
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.
The embodiment of the invention provides an intelligent electricity utilization safety monitoring method and a main control computer based on the Internet of things, which are used for solving the technical problem that the safe and effective adjustment of the electricity utilization condition of electric equipment is difficult to ensure in the prior 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.
Referring to fig. 1, a flowchart of an intelligent electricity safety monitoring method based on the internet of things according to an embodiment of the present invention is shown, where the method is applied to a main control computer, where the main control computer is a computer that monitors the electricity utilization condition of the electricity utilization equipment in a set area and adjusts the electricity utilization policy according to the electricity utilization condition of the electricity utilization equipment. In detail, the main control computer can be in wired connection with the electric equipment, and under the condition that some geographic environments are severe, the main control computer can also be in wireless connection with the electric equipment, for example, the main control computer can be connected through a specific Bluetooth frequency band, and also can be connected through a wifi mode, and the limitation is not made herein. The method shown in fig. 1 may include the following:
step S21, acquiring real-time current information and real-time voltage information of first electric equipment in a set time period, acquiring power utilization state information of the first electric equipment in the set time period according to the real-time current information and the real-time voltage information, and determining a power utilization state record table of the first electric equipment according to the power utilization state information, wherein the power utilization state record table comprises characteristic values of the power utilization state of the first electric equipment in the set time period.
Step S22, obtaining an influence factor between the feature values of the electricity consumption state record table to which the first flag information is not added, weighting the feature values to which the flag information is not added according to the influence factor to obtain a weighted feature value, and adding the second flag information to the weighted feature value.
Step S23, when the characteristic values to which the first flag information or the second flag information is not added still exist in the power consumption state record table, clustering the characteristic values to which the first flag information or the second flag information is not added to obtain a clustering characteristic value, and adding third flag information to the clustering characteristic value.
Step S24, when the target characteristic value to which the first flag information, the second flag information, or the third flag information is not added still exists in the power consumption state record table, determining fourth flag information of the target characteristic value according to a power flow distribution coefficient between the target characteristic value and the cluster characteristic value.
Step S25, determining risk weight levels of the first label information, the second label information, the third label information, and the fourth label information, respectively.
And step S26, determining a power consumption load predicted value based on the power consumption state recording table according to the risk weight grade, and generating a control strategy for adjusting the power consumption state of the first electric equipment according to the power consumption load predicted value.
In step S21, the set period may be a period before the current time, for example, one week before the current time as the end time. It is understood that the set time period can be adjusted according to actual conditions, and therefore, will not be further described herein.
Through the steps S21 to S26, on the premise that the power consumption state record table is determined, the flag information can be added to the characteristic value in the power consumption state record table for multiple times, so that the state of the first electric device in other information dimensions can be determined based on the limited power consumption information of the first electric device, and it is further ensured that the main control computer can determine the power consumption load predicted value according to the risk weight levels of different flag information, so that the main control computer can generate a control strategy for adjusting the power consumption state of the first electric device based on the power consumption load predicted value, ensure the power consumption safety of the first electric device, and prevent the power consumption risk of the first electric device.
In order to improve the stability and safety of the power utilization area where the first electrical device is located, when determining the control policy of the first electrical device, the power utilization of other electrical devices needs to be considered, and therefore, in step S26, the generating of the control policy for adjusting the power utilization state of the first electrical device according to the predicted power utilization load value may specifically include the following:
step S261 is to obtain a historical control strategy execution success rate of a second electrical device in the power consumption area of the first electrical device, where the historical control strategy execution success rate is a success rate of adjusting the power consumption load according to a plurality of historical control strategies issued by the main control computer when the second electrical device executes the plurality of historical control strategies.
Step S262, determining a target historical control strategy meeting preset conditions in the set time period according to the adjustment coefficient of each historical control strategy; the preset condition is that the harmonic occupancy of the second electrical equipment is lower than a preset ratio when the historical control strategy is executed.
Step S263, determining the execution success rate of the target historical control policy based on the execution success rate of the historical control policy.
And step S264, when the execution success rate reaches a preset value, correcting the predicted value of the electric load according to a target historical control strategy, and predicting the signal disturbance grade when the first electric equipment communicates with the main control computer according to the corrected predicted value of the electric load.
And step S265, generating a control strategy for adjusting the power consumption state of the first electrical device according to the signal disturbance level and the corrected power consumption load predicted value.
It is understood that based on steps S261-S265, the historical control strategy execution success rate of the second electrical device can be taken into account, and determining a target historical control strategy in which the harmonic occupancy of the second electrical device is lower than a preset ratio when the historical control strategy is executed, determining the control strategy for the first electrical device on the basis of the target historical control strategy enables minimizing the effects of harmonic pollution and, further, the power consumption load predicted value is corrected according to the target historical control strategy, the signal disturbance level when the first electric equipment is communicated with the main control computer is predicted according to the corrected power consumption load predicted value, and the accuracy and the reliability of the generated control strategy for adjusting the power consumption state of the first electric equipment can be ensured by taking the electromagnetic interference generated when the first electric equipment is communicated with the main control computer into consideration.
In a specific implementation, in order to avoid adding the tag information into the dead cycle, it is necessary to add a feature value of no tag information into the power consumption state record table according to multiple criteria, so as to ensure reliability of the added tag information on the premise of avoiding the added tag information from entering the dead cycle, and for this purpose, in step S24, the fourth tag information of the target feature value is determined according to a power flow distribution coefficient between the target feature value and the cluster feature value, which specifically includes the following:
step S241, performing binary processing on the feature vector where the target feature value is located to obtain a first feature vector.
Step S242, performing denoising processing on the first feature vector to obtain a noise feature value in the first feature vector, and performing partial deletion on the noise feature value according to the character string length of the target feature value to implement dimension reduction processing on the first feature vector to obtain a second feature vector.
Step S243, determining a stability coefficient of the target feature value according to a hamming distance between feature values in the second feature vector.
And step S244, determining a power flow distribution coefficient between the power flow distribution coefficient and the clustering characteristic value based on the stability coefficient.
Step S245, determining a target node of the power flow distribution coefficient in the power grid where the first electrical device is located.
And step S246, detecting an important coefficient of the target node in the power flow distribution coefficient.
Step S247, selecting a marking mode for performing information marking on the target characteristic value according to an important coefficient of the target node in the power flow distribution coefficient.
Step S248, adding the fourth marking information to the target feature value according to the marking manner.
Through steps S241 to S248, the second differentiation processing, the denoising processing, and the dimension reduction processing can be performed on the feature vector of the target feature value, so as to determine the stability coefficient of the target feature value, thus ensuring the stability of the determined power flow distribution coefficient, avoiding the reliability risk brought by directly adding the mark information to the target feature value, further, adding the fourth mark information to the target feature value according to the mark mode, and taking the important coefficient in the power flow distribution coefficient into consideration, thereby ensuring that the fourth mark information can accurately reflect the feature of the target feature value, and further ensuring that the main control computer can determine the predicted value of the power consumption load according to the risk weight levels of different mark information.
In a specific implementation, in order to ensure reliability of the power consumption state information of the first electrical device in a set time period, not only a safe power consumption state of the first electrical device but also stability of a power grid where the first electrical device is located need to be considered, for this reason, in step S21, the obtaining of the power consumption state information of the first electrical device in the set time period according to the real-time current information and the real-time voltage information may specifically include the following:
step S211, obtaining power information of the first electrical device in the set time period according to the real-time current information and the real-time voltage information.
Step S212, a power flow distribution record table of the power information and each power flow node are obtained.
Step S213, when determining that the power information contains a phase distribution sequence according to the power flow distribution record table, determining node power values between each power flow node under the non-phase distribution sequence of the power information and each power flow node under the phase distribution sequence of the power information according to power flow nodes and phase angles in a plurality of pre-stored historical power phase distribution sequences, and modifying the power flow nodes under the non-phase distribution sequence of the power information, which are similar to the power flow nodes under the phase distribution sequence, into the power flow nodes under the corresponding phase distribution sequence according to the node power values.
Step S214, when the current non-phase distribution sequence of the power information contains a plurality of power flow nodes, determining node power values among the power flow nodes in the current non-phase distribution sequence of the power information according to power flow points and phase angles in the phase distribution sequences of the plurality of historical powers, and injecting node power into the power flow nodes in the current non-phase distribution sequence according to the node power values among the power flow nodes.
Step S215, distributing a migration interface for each power flow node obtained by the node power injection according to the power flow nodes in the plurality of historical power phase distribution sequences and the phase angles thereof, and migrating each power flow node to the phase distribution sequence corresponding to the migration interface to obtain a target phase distribution sequence.
Step S216, determining power consumption status information of the first electrical device in the set time period according to the target phase distribution sequence.
It can be understood that, through steps S211 to S216, power flow analysis can be performed on the power grid where the first electrical device is located, so as to determine stability of the power grid where the first electrical device is located, and thus reliability of the power utilization state information of the first electrical device in the set time period is ensured according to the stability of the power grid where the first electrical device is located.
On the basis of the above, the embodiment of the present invention provides a host computer 200. Fig. 2 is a functional block diagram of a host computer 200 according to an embodiment of the present invention, where the host computer 200 includes:
the obtaining module 201 is configured to obtain real-time current information and real-time voltage information of a first electrical device in a set time period, obtain power consumption state information of the first electrical device in the set time period according to the real-time current information and the real-time voltage information, and determine a power consumption state record table of the first electrical device according to the power consumption state information, where the power consumption state record table includes a characteristic value of a power consumption state of the first electrical device in the set time period.
An adding module 202, configured to obtain an influence factor between feature values to which the first flag information is not added in the power consumption state record table, weight the feature values that are not marked according to the influence factor to obtain a weighted feature value, and add second flag information to the weighted feature value.
The clustering module 203 is configured to, when feature values to which the first tag information or the second tag information is not added still exist in the power consumption state record table, cluster the feature values to which the first tag information or the second tag information is not added to obtain a clustering feature value, and add third tag information to the clustering feature value.
A first determining module 204, configured to determine, when a target characteristic value to which the first flag information, the second flag information, or the third flag information is not added still exists in the power consumption state record table, fourth flag information of the target characteristic value according to a power flow distribution coefficient between the target characteristic value and the cluster characteristic value.
A second determining module 205, configured to determine risk weight levels of the first flag information, the second flag information, the third flag information, and the fourth flag information, respectively.
A generating module 206, configured to determine a predicted power consumption load value based on the power consumption state record table according to the risk weight level, and generate a control policy for adjusting the power consumption state of the first electrical device according to the predicted power consumption load value.
In an optional manner, the generating module 206 is specifically configured to:
acquiring a historical control strategy execution success rate of second electrical equipment in an electrical utilization area of the first electrical equipment, wherein the historical control strategy execution success rate is a success rate of adjusting electrical loads according to a plurality of historical control strategies when the second electrical equipment executes the plurality of historical control strategies issued by the main control computer;
determining a target historical control strategy meeting preset conditions in the set time period according to the adjustment coefficient of each historical control strategy; the preset condition is that the harmonic occupancy of the second electrical equipment is lower than a preset ratio when a historical control strategy is executed;
determining the execution success rate of the target historical control strategy on the basis of the execution success rate of the historical control strategy;
when the execution success rate reaches a preset value, correcting the power load predicted value according to a target historical control strategy, and predicting a signal disturbance level when the first electric equipment communicates with the main control computer according to the corrected power load predicted value;
and generating a control strategy for adjusting the power utilization state of the first electric equipment according to the signal disturbance grade and the corrected power utilization load predicted value.
In an optional manner, the first determining module 204 is specifically configured to:
carrying out binary processing on the characteristic vector of the target characteristic value to obtain a first characteristic vector;
denoising the first eigenvector to obtain a noise eigenvalue in the first eigenvector, and partially deleting the noise eigenvalue according to the character string length of the target eigenvalue to realize the dimension reduction processing of the first eigenvector to obtain a second eigenvector;
determining a stability coefficient of the target characteristic value according to the Hamming distance between the characteristic values in the second characteristic vector;
determining a power flow distribution coefficient between the power flow distribution coefficient and the clustering characteristic value based on the stability coefficient;
determining a target node of the power flow distribution coefficient in the power grid where the first electric equipment is located;
detecting an important coefficient of the target node in the power flow distribution coefficient;
selecting a marking mode for carrying out information marking on the target characteristic value according to an important coefficient of the target node in the power flow distribution coefficient;
and adding the fourth marking information to the target characteristic value according to the marking mode.
In an optional manner, the obtaining module 201 is specifically configured to:
obtaining power information of the first electric equipment in the set time period according to the real-time current information and the real-time voltage information;
obtaining a power flow distribution record table of the power information and each power flow node;
when the power information is determined to contain the phase distribution sequence according to the power flow distribution record table, determining node power values between the power flow nodes under the non-phase distribution sequence of the power information and the power flow nodes under the phase distribution sequence of the power information according to the power flow nodes and phase angles in the pre-stored phase distribution sequences of a plurality of historical powers, and modifying the power flow nodes under the non-phase distribution sequence of the power information, which are similar to the power flow nodes under the phase distribution sequence, into the power flow nodes under the corresponding phase distribution sequence according to the node power values;
when the current non-phase distribution sequence of the power information contains a plurality of power flow nodes, determining node power values among the power flow nodes in the current non-phase distribution sequence of the power information according to power flow points and phase angles of the power flow points in the phase distribution sequences of the historical powers, and injecting node power into the power flow nodes in the current non-phase distribution sequence according to the node power values among the power flow nodes;
allocating a migration interface for each power flow node obtained by injecting the node power according to the power flow nodes in the plurality of historical power phase distribution sequences and phase angles thereof, and migrating each power flow node to a phase distribution sequence corresponding to the migration interface to obtain a target phase distribution sequence;
and determining the power utilization state information of the first electric equipment in the set time period according to the target phase distribution sequence.
The main control computer 200 includes a processor and a memory, the obtaining module 201, the adding module 202, the clustering module 203, the first determining module 204, the second determining module 205, the generating module 206, and the like are stored in the memory as program units, and the processor executes the program units stored in the memory to implement corresponding functions.
The processor comprises a kernel, and the kernel calls the corresponding program unit from the memory. The kernel can be set to be one or more, and the electricity utilization safety of the first electric equipment is ensured and the electricity utilization risk of the first electric equipment is prevented by adjusting the kernel parameters.
The embodiment of the invention provides a storage medium, wherein a program is stored on the storage medium, and when the program is executed by a processor, the intelligent electricity safety monitoring method 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 electricity utilization safety monitoring method based on the Internet of things is executed when the program runs.
In the embodiment of the present invention, as shown in fig. 3, the host computer 200 includes at least one processor 301, and at least one memory 302 and a bus connected to the processor 301; wherein, the processor 301 and the memory 302 complete the communication with each other through the bus 303; the processor 301 is configured to call the program instructions in the memory 302 to execute the above-mentioned method for monitoring smart electricity safety based on internet of things. The host computer 200 herein may be a server, PC, PAD, cell phone, etc.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, host computers (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 host computer to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing host computer, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In a typical configuration, the host computer includes one or more processors (CPUs), memory, and a bus. The host computer 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 technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape storage or other magnetic storage host computers, or any other non-transmission medium that can be used to store information that can be accessed by the computing host computer. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
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 host computer that comprises 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 host computer. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in the process, method, article of manufacture, or host computer in which the element is comprised.
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 (10)
1. An intelligent electricity safety monitoring method based on the Internet of things is characterized by being applied to a main control computer, and at least comprising the following steps:
the method comprises the steps of obtaining real-time current information and real-time voltage information of first electric equipment in a set time period, obtaining power utilization state information of the first electric equipment in the set time period according to the real-time current information and the real-time voltage information, and determining a power utilization state record table of the first electric equipment according to the power utilization state information, wherein the power utilization state record table comprises a characteristic value of the power utilization state of the first electric equipment in the set time period;
acquiring influence factors among the characteristic values without adding the first mark information in the electricity consumption state recording table, weighting the characteristic values without being marked according to the influence factors to obtain weighted characteristic values, and adding second mark information to the weighted characteristic values;
when the characteristic value without the first mark information or the second mark information is still present in the electricity consumption state recording table, clustering the characteristic value without the first mark information or the second mark information to obtain a clustering characteristic value, and adding third mark information to the clustering characteristic value;
when a target characteristic value which is not added with the first mark information, the second mark information or the third mark information still exists in the electricity utilization state recording table, determining fourth mark information of the target characteristic value according to a power flow distribution coefficient between the target characteristic value and the clustering characteristic value;
respectively determining the risk weight levels of the first mark information, the second mark information, the third mark information and the fourth mark information;
and determining a power consumption load predicted value on the basis of the power consumption state recording table according to the risk weight grade, and generating a control strategy for adjusting the power consumption state of the first power consumption equipment according to the power consumption load predicted value.
2. The method according to claim 1, wherein generating a control strategy for adjusting the power consumption state of the first electrical device according to the power consumption load predicted value comprises:
acquiring a historical control strategy execution success rate of second electrical equipment in an electrical utilization area of the first electrical equipment, wherein the historical control strategy execution success rate is a success rate of adjusting electrical loads according to a plurality of historical control strategies when the second electrical equipment executes the plurality of historical control strategies issued by the main control computer;
determining a target historical control strategy meeting preset conditions in the set time period according to the adjustment coefficient of each historical control strategy; the preset condition is that the harmonic occupancy of the second electrical equipment is lower than a preset ratio when a historical control strategy is executed;
determining the execution success rate of the target historical control strategy on the basis of the execution success rate of the historical control strategy;
when the execution success rate reaches a preset value, correcting the power load predicted value according to a target historical control strategy, and predicting a signal disturbance level when the first electric equipment communicates with the main control computer according to the corrected power load predicted value;
and generating a control strategy for adjusting the power utilization state of the first electric equipment according to the signal disturbance grade and the corrected power utilization load predicted value.
3. The method according to claim 1 or 2, wherein the determining fourth label information of the target characteristic value according to the power flow distribution coefficient between the target characteristic value and the cluster characteristic value comprises:
carrying out binary processing on the characteristic vector of the target characteristic value to obtain a first characteristic vector;
denoising the first eigenvector to obtain a noise eigenvalue in the first eigenvector, and partially deleting the noise eigenvalue according to the character string length of the target eigenvalue to realize the dimension reduction processing of the first eigenvector to obtain a second eigenvector;
determining a stability coefficient of the target characteristic value according to the Hamming distance between the characteristic values in the second characteristic vector;
determining a power flow distribution coefficient between the power flow distribution coefficient and the clustering characteristic value based on the stability coefficient;
determining a target node of the power flow distribution coefficient in the power grid where the first electric equipment is located;
detecting an important coefficient of the target node in the power flow distribution coefficient;
selecting a marking mode for carrying out information marking on the target characteristic value according to an important coefficient of the target node in the power flow distribution coefficient;
and adding the fourth marking information to the target characteristic value according to the marking mode.
4. The method of claim 1, wherein obtaining the power consumption status information of the first electric device in the set time period according to the real-time current information and the real-time voltage information comprises:
obtaining power information of the first electric equipment in the set time period according to the real-time current information and the real-time voltage information;
obtaining a power flow distribution record table of the power information and each power flow node;
when the power information is determined to contain the phase distribution sequence according to the power flow distribution record table, determining node power values between the power flow nodes under the non-phase distribution sequence of the power information and the power flow nodes under the phase distribution sequence of the power information according to the power flow nodes and phase angles in the pre-stored phase distribution sequences of a plurality of historical powers, and modifying the power flow nodes under the non-phase distribution sequence of the power information, which are similar to the power flow nodes under the phase distribution sequence, into the power flow nodes under the corresponding phase distribution sequence according to the node power values;
when the current non-phase distribution sequence of the power information contains a plurality of power flow nodes, determining node power values among the power flow nodes in the current non-phase distribution sequence of the power information according to power flow points and phase angles of the power flow points in the phase distribution sequences of the historical powers, and injecting node power into the power flow nodes in the current non-phase distribution sequence according to the node power values among the power flow nodes;
allocating a migration interface for each power flow node obtained by injecting the node power according to the power flow nodes in the plurality of historical power phase distribution sequences and phase angles thereof, and migrating each power flow node to a phase distribution sequence corresponding to the migration interface to obtain a target phase distribution sequence;
and determining the power utilization state information of the first electric equipment in the set time period according to the target phase distribution sequence.
5. A master control computer, comprising at least:
the acquisition module is used for acquiring real-time current information and real-time voltage information of first electric equipment in a set time period, acquiring power utilization state information of the first electric equipment in the set time period according to the real-time current information and the real-time voltage information, and determining a power utilization state record table of the first electric equipment according to the power utilization state information, wherein the power utilization state record table comprises a characteristic value of the power utilization state of the first electric equipment in the set time period;
the adding module is used for acquiring influence factors among the characteristic values which are not added with the first mark information in the power consumption state recording table, weighting the characteristic values which are not marked according to the influence factors to obtain weighted characteristic values, and adding second mark information to the weighted characteristic values;
the clustering module is used for clustering the characteristic values without the first mark information or the second mark information to obtain a clustering characteristic value and adding third mark information for the clustering characteristic value when the characteristic values without the first mark information or the second mark information are still existed in the electricity utilization state record table and the short circuit risk coefficient between the characteristic values without the first mark information or the second mark information is not added;
a first determining module, configured to determine, when a target characteristic value to which the first flag information, the second flag information, or the third flag information is not added still exists in the power consumption state record table, fourth flag information of the target characteristic value according to a power flow distribution coefficient between the target characteristic value and the cluster characteristic value;
a second determining module, configured to determine risk weight levels of the first marker information, the second marker information, the third marker information, and the fourth marker information, respectively;
and the generating module is used for determining an electricity load predicted value on the basis of the electricity utilization state recording table according to the risk weight grade and generating a control strategy for adjusting the electricity utilization state of the first electric equipment according to the electricity load predicted value.
6. The master control computer of claim 5, wherein the generation module is specifically configured to:
acquiring a historical control strategy execution success rate of second electrical equipment in an electrical utilization area of the first electrical equipment, wherein the historical control strategy execution success rate is a success rate of adjusting electrical loads according to a plurality of historical control strategies when the second electrical equipment executes the plurality of historical control strategies issued by the main control computer;
determining a target historical control strategy meeting preset conditions in the set time period according to the adjustment coefficient of each historical control strategy; the preset condition is that the harmonic occupancy of the second electrical equipment is lower than a preset ratio when a historical control strategy is executed;
determining the execution success rate of the target historical control strategy on the basis of the execution success rate of the historical control strategy;
when the execution success rate reaches a preset value, correcting the power load predicted value according to a target historical control strategy, and predicting a signal disturbance level when the first electric equipment communicates with the main control computer according to the corrected power load predicted value;
and generating a control strategy for adjusting the power utilization state of the first electric equipment according to the signal disturbance grade and the corrected power utilization load predicted value.
7. The master control computer of claim 5 or 6, wherein the first determining module is specifically configured to:
carrying out binary processing on the characteristic vector of the target characteristic value to obtain a first characteristic vector;
denoising the first eigenvector to obtain a noise eigenvalue in the first eigenvector, and partially deleting the noise eigenvalue according to the character string length of the target eigenvalue to realize the dimension reduction processing of the first eigenvector to obtain a second eigenvector;
determining a stability coefficient of the target characteristic value according to the Hamming distance between the characteristic values in the second characteristic vector;
determining a power flow distribution coefficient between the power flow distribution coefficient and the clustering characteristic value based on the stability coefficient;
determining a target node of the power flow distribution coefficient in the power grid where the first electric equipment is located;
detecting an important coefficient of the target node in the power flow distribution coefficient;
selecting a marking mode for carrying out information marking on the target characteristic value according to an important coefficient of the target node in the power flow distribution coefficient;
and adding the fourth marking information to the target characteristic value according to the marking mode.
8. The master control computer of claim 5, wherein the obtaining module is specifically configured to:
obtaining power information of the first electric equipment in the set time period according to the real-time current information and the real-time voltage information;
obtaining a power flow distribution record table of the power information and each power flow node;
when the power information is determined to contain the phase distribution sequence according to the power flow distribution record table, determining node power values between the power flow nodes under the non-phase distribution sequence of the power information and the power flow nodes under the phase distribution sequence of the power information according to the power flow nodes and phase angles in the pre-stored phase distribution sequences of a plurality of historical powers, and modifying the power flow nodes under the non-phase distribution sequence of the power information, which are similar to the power flow nodes under the phase distribution sequence, into the power flow nodes under the corresponding phase distribution sequence according to the node power values;
when the current non-phase distribution sequence of the power information contains a plurality of power flow nodes, determining node power values among the power flow nodes in the current non-phase distribution sequence of the power information according to power flow points and phase angles of the power flow points in the phase distribution sequences of the historical powers, and injecting node power into the power flow nodes in the current non-phase distribution sequence according to the node power values among the power flow nodes;
allocating a migration interface for each power flow node obtained by injecting the node power according to the power flow nodes in the plurality of historical power phase distribution sequences and phase angles thereof, and migrating each power flow node to a phase distribution sequence corresponding to the migration interface to obtain a target phase distribution sequence;
and determining the power utilization state information of the first electric equipment in the set time period according to the target phase distribution sequence.
9. A master control computer is characterized by comprising a processor, a memory and a bus, wherein the memory and the bus are connected with the processor; wherein, the processor and the memory complete mutual communication through the bus; the processor is used for calling the program instructions in the memory to execute the method for monitoring intelligent electricity safety based on the internet of things as claimed in any one of the claims 1 to 4.
10. A storage medium having a program stored thereon, wherein the program, when executed by a processor, implements the method for monitoring smart electricity safety based on internet of things according to any one of claims 1 to 4.
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