CN117097768B - Intelligent ammeter secure communication transmission system and method based on big data - Google Patents
Intelligent ammeter secure communication transmission system and method based on big data Download PDFInfo
- Publication number
- CN117097768B CN117097768B CN202311349557.1A CN202311349557A CN117097768B CN 117097768 B CN117097768 B CN 117097768B CN 202311349557 A CN202311349557 A CN 202311349557A CN 117097768 B CN117097768 B CN 117097768B
- Authority
- CN
- China
- Prior art keywords
- data
- user
- electric power
- module
- user use
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000004891 communication Methods 0.000 title claims abstract description 47
- 230000005540 biological transmission Effects 0.000 title claims abstract description 23
- 238000000034 method Methods 0.000 title claims abstract description 20
- 238000012545 processing Methods 0.000 claims abstract description 34
- 238000007405 data analysis Methods 0.000 claims abstract description 18
- 230000005611 electricity Effects 0.000 claims description 63
- 238000012544 monitoring process Methods 0.000 claims description 15
- 238000004458 analytical method Methods 0.000 claims description 10
- 238000012502 risk assessment Methods 0.000 claims description 9
- 210000001503 joint Anatomy 0.000 claims description 6
- 239000011159 matrix material Substances 0.000 claims description 6
- 238000004364 calculation method Methods 0.000 claims description 3
- 230000002159 abnormal effect Effects 0.000 abstract description 7
- 230000009471 action Effects 0.000 description 3
- 230000008447 perception Effects 0.000 description 3
- 230000008569 process Effects 0.000 description 3
- 230000006870 function Effects 0.000 description 2
- 230000006872 improvement Effects 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000008439 repair process Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 230000003111 delayed effect Effects 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/06—Management of faults, events, alarms or notifications
- H04L41/0631—Management of faults, events, alarms or notifications using root cause analysis; using analysis of correlation between notifications, alarms or events based on decision criteria, e.g. hierarchy, tree or time analysis
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L47/00—Traffic control in data switching networks
- H04L47/10—Flow control; Congestion control
- H04L47/24—Traffic characterised by specific attributes, e.g. priority or QoS
- H04L47/2425—Traffic characterised by specific attributes, e.g. priority or QoS for supporting services specification, e.g. SLA
- H04L47/2433—Allocation of priorities to traffic types
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L63/00—Network architectures or network communication protocols for network security
- H04L63/20—Network architectures or network communication protocols for network security for managing network security; network security policies in general
Landscapes
- Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Computing Systems (AREA)
- Computer Security & Cryptography (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Medical Informatics (AREA)
- Computer Hardware Design (AREA)
- General Engineering & Computer Science (AREA)
- Emergency Alarm Devices (AREA)
Abstract
The invention discloses a secure communication transmission system and method for an intelligent electric energy meter based on big data, and belongs to the technical field of data communication transmission. The invention comprises the following steps: the system comprises an electric power Internet of things module, an electric power data analysis module, a big data processing and calling module, a safety communication module and a mark feedback module; the output end of the electric power Internet of things module is connected with the input end of the electric power data analysis module; the output end of the power data analysis module is connected with the input end of the big data processing and calling module; the output end of the big data processing calling module is connected with the input end of the safety communication module; the output end of the safety communication module is connected with the input end of the marking feedback module. The invention can present the priority of the data security risk on the basis of the data transmission of the intelligent electric energy meter, and timely find out the abnormal data communication or abnormal data of the electric energy meter in the data calling direction, thereby improving the normal security communication capability of the intelligent electric energy meter.
Description
Technical Field
The invention relates to the technical field of data communication transmission, in particular to an intelligent ammeter safety communication transmission system and method based on big data.
Background
The intelligent electric energy meter is used as terminal equipment for marketing business, electricity information and energy distribution of the intelligent power grid, has extremely wide coverage range, can meet the flexible access of the intelligent equipment besides meeting the basic metering function in the construction process of the current energy Internet, realizes the perception, collection and control of equipment data, and has the non-metering function of providing powerful data support in the aspects of distribution network operation and maintenance management, customer experience improvement, service country energy strategy transformation upgrading and the like.
However, with the higher requirements of the energy internet on information perception depth, breadth and density, the problem of the intelligent electric energy meter is gradually revealed, the user port power consumption has a peak-valley period, the peak-valley period is caused by data communication of the intelligent electric energy meter, most of the intelligent electric energy meters at present acquire updated data in a period of about 15 minutes, but in order to timely find out abnormal data communication or abnormal data of the electric energy meter, rapid repair or stop operation is realized, the acquisition period of part of the intelligent electric energy meter is continuously shortened, the data congestion problem at present is also caused, namely, a large amount of data is continuously in data flow in the peak period of the user power consumption, and is continuously updated, so that the whole electric energy meter communication system is data congestion and delayed, meanwhile, a false alarm phenomenon can occur, and the normal safety communication capability is seriously affected.
Disclosure of Invention
The invention aims to provide a secure communication transmission system and method for an intelligent ammeter based on big data, which are used for solving the problems in the background technology.
In order to solve the technical problems, the invention provides the following technical scheme: a secure communication transmission method of an intelligent electric energy meter based on big data comprises the following steps:
s1, constructing an electric power Internet of things cloud platform, wherein the electric power Internet of things cloud platform is respectively connected with a user use port and a third party electric power port, and the user use port provides user electricity data information to the electric power Internet of things cloud platform through an intelligent electric energy meter;
s2, the cloud platform of the electric power Internet of things calls stored historical user electricity utilization data information in an electricity utilization valley period, and different prediction security risk scores are formed after the historical electricity utilization data information is analyzed and processed;
s3, based on different predicted security risk scores, preferentially calling intelligent electric energy meter data corresponding to the user use port in the electricity consumption peak period, and mixing the data to historical user electricity consumption data information corresponding to the user use port to form a new predicted security risk score;
s4, setting a safety risk threshold, entering a first priority channel when the new predicted safety risk score exceeds the safety risk threshold, setting a time period, and preferentially calling intelligent ammeter data of a corresponding user use port according to the new predicted safety risk score in the first priority channel in the time period to reform the predicted safety risk score;
s5, the system sets a quantity threshold, if the quantity of the user using ports entering the first priority channel exceeds the quantity threshold, the corresponding user using ports are marked to the third-party power ports, and the third-party power ports are fed back to an administrator for early warning.
According to the technical scheme, the electric power internet of things cloud platform is internally provided with the database, and historical user electricity utilization data information of all user use ports in butt joint of the electric power internet of things cloud platform is stored in the database;
the system is provided with a power utilization data time interval section, and a power utilization peak period and a power utilization valley period are manually marked. For example, typically 5 pm: 00-12:00 belongs to the electricity consumption peak period, and 0 in the early morning: 00-6:00, belonging to electricity consumption valley period and the like.
According to the technical scheme, the method further comprises the following steps:
acquiring historical user electricity data information, wherein the user electricity data information comprises electric energy electricity data, voltage and current data at a user total incoming line, household appliance load data and internal temperature data of a connecting terminal;
taking the time period as a limit, acquiring difference data of corresponding data in each two adjacent user electricity data information, and forming a historical user electricity data information curve of a user use port;
acquiring historical user electricity data information curves of all user use ports, taking each group of curves as a sequence, taking the number of time periods on the curves as the sequence length, calculating the difference value between the maximum value and the minimum value in each group of curves, taking the curve with the minimum difference value as a standard sequence, marking the standard sequence as Q, and marking the length as n;
comparing any one of the residual sequences with a standard sequence, wherein any one of the residual sequences is marked as P, and the length is marked as m;
then there are:
、/>constructing an n-m matrix, setting a twisted path to pass through the n-m matrix, and marking any element of the twisted path as k, which is expressed as +.>The horizontal and vertical i and j respectively represent two points with aligned sequences;
constraints for constructing the twisted path are as follows:
which is a kind of=/>、/>=/>To ensure the sequence head-to-tail matching and the sequence matching;
the twisted path satisfies monotonicity and continuity, and adjacent points on any two paths、/>The transverse and longitudinal difference values of (2) should be equal to or greater than 0 and equal to or less than 1 at the same time;
for all twisted paths meeting the constraint conditions, performing iterative calculation of i from 1 to m and j from 1 to n:
wherein (1)>Refers to +.>Distance when i and j are aligned; />Refers to the sequence distance when i-1 matches j; />Indicating the sequence distance at which finger i matches j-1,refers to the sequence distance when i-1 matches j-1;
in the technical scheme, each user port has a curve of the user, the curve has a certain trend, in order to ensure the accuracy of data, and because the use habits of the users are different, the detection mode of each intelligent ammeter is also possibly different, so that each user port is only compared with the user port to form a trend curve of the user port, the user port selects a peaceful trend as a standard, all the curves are compared with the standard trend, the problem of numerical value is not existed, only whether the trend is abnormal or not is reflected in the data, namely, the trend approximation degree of the two curves is reflected, the shortest path is searched as the optimal approximation degree under the condition that the initial point to the final point are kept unchanged in a twisted path mode, and a security risk score is formed by comparing the optimal approximation degree;
the finally obtained D (m, n) is the dynamic time warping distance between the two groups of sequences P and Q;
selecting a minimum value of the dynamic time warping distance as a predicted security risk score of a user use port corresponding to the sequence P;
based on different predicted security risk scores, intelligent electric energy meter data of a user use port with high predicted security risk score is preferentially called in a power consumption peak period, and the data are mixed to historical user power consumption data information of a corresponding user use port, so that first user power consumption data information is removed, a new sequence is formed for comparison again, and a new predicted security risk score is generated.
According to the above technical solution, in step S4, further includes:
the system sets a quantity threshold value, the cloud platform of the electric power internet of things monitors data call information of each user use port at any time, a first priority channel is used as a monitoring object, after any user use port enters the first priority channel once, 1 is added to the corresponding user use port, when the quantity of any user use port exceeds the quantity threshold value, the corresponding user use port is marked to a third party electric power port, and the third party electric power port is fed back to an administrator for early warning.
An intelligent ammeter secure communication transmission system based on big data, the system comprising: the system comprises an electric power Internet of things module, an electric power data analysis module, a big data processing and calling module, a safety communication module and a mark feedback module;
the electric power Internet of things module comprises an electric power Internet of things cloud platform, a user use port and a third party electric power port, wherein the electric power Internet of things cloud platform is respectively connected with the user use port and the third party electric power port, and the user use port provides user electricity data information to the electric power Internet of things cloud platform through an intelligent electric energy meter; the power data analysis module is used for calling stored historical user power consumption data information in a power consumption valley period through the power internet of things cloud platform, and forming different predicted security risk scores after analyzing and processing the historical power consumption data information; the big data processing and calling module preferentially calls intelligent ammeter data corresponding to the user use port in the electricity consumption peak period based on different predicted security risk scores, and mixes the data into historical user electricity consumption data information corresponding to the user use port to form a new predicted security risk score; the safety communication module is used for setting a safety risk threshold, entering a first priority channel when the new predicted safety risk score exceeds the safety risk threshold, setting a time period, and preferentially calling intelligent ammeter data corresponding to a user use port according to the new predicted safety risk score in the first priority channel in the time period to reform the predicted safety risk score; the marking feedback module is used for setting a quantity threshold value, and if the quantity of the user using ports entering the first priority channel exceeds the quantity threshold value, marking the corresponding user using ports to the third-party power ports, and feeding back to an administrator for early warning;
the output end of the electric power Internet of things module is connected with the input end of the electric power data analysis module; the output end of the power data analysis module is connected with the input end of the big data processing and calling module; the output end of the big data processing calling module is connected with the input end of the safety communication module; the output end of the safety communication module is connected with the input end of the marking feedback module.
According to the technical scheme, the electric power internet of things module comprises a database and an interval setting unit;
historical user electricity utilization data information of all user use ports in butt joint of the electric power internet of things cloud platform is stored in the database; the interval setting unit is provided with a power utilization data time interval section and manually marks a power utilization peak period and a power utilization valley period;
the database is connected with the interval setting unit.
According to the technical scheme, the power data analysis module comprises a storage unit and a prediction unit;
the storage unit is used for calling stored historical user electricity utilization data information in an electricity utilization valley period through the electric power internet of things cloud platform; the prediction unit is used for analyzing and processing the historical electricity consumption data information to form different prediction security risk scores;
the output end of the storage unit is connected with the input end of the prediction unit.
According to the technical scheme, the big data processing and calling module comprises a priority analysis unit and a data processing unit;
the priority analysis unit preferentially calls intelligent ammeter data corresponding to the user use port in the electricity consumption peak period based on different predicted security risk scores; the data processing unit is used for mixing data to historical user electricity data information of a corresponding user use port, constructing a new sequence and forming a new predicted security risk score;
the output end of the priority analysis unit is connected with the input end of the data processing unit.
According to the technical scheme, the safety communication module comprises a monitoring unit and a safety risk analysis unit;
the monitoring unit is used for monitoring the first priority channel, setting a time period and preferentially calling intelligent ammeter data corresponding to the user using port according to the new predicted security risk score value in the first priority channel in the time period; the safety risk analysis unit reforms a predicted safety risk score based on the mixed data sequence;
the output end of the monitoring unit is connected with the input end of the security risk analysis unit.
According to the technical scheme, the marking feedback module comprises a threshold setting unit and a feedback unit;
the threshold setting unit is used for setting a quantity threshold, and if the quantity of the user using ports entering the first priority channel exceeds the quantity threshold, marking the corresponding user using ports to the third-party power ports; the feedback unit is used for receiving the marking information and feeding back to an administrator for early warning;
the output end of the threshold setting unit is connected with the input end of the feedback unit.
Compared with the prior art, the invention has the following beneficial effects: according to the invention, on the basis of data transmission of the intelligent electric energy meter, data peak-valley analysis can be realized, the priority of data security risk is presented, the information perception depth is improved in the data calling direction, and abnormal data communication or abnormal data of the electric energy meter is timely found, so that quick repair or stop operation is realized, and the normal security communication capability of the intelligent electric energy meter is improved.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings:
FIG. 1 is a flow chart of a secure communication transmission system and method for an intelligent ammeter based on big data.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, in a first embodiment: the utility model provides a safe communication transmission method of intelligent ammeter based on big data, which comprises the following steps:
the method comprises the steps of constructing an electric power Internet of things cloud platform, wherein the electric power Internet of things cloud platform is respectively connected with a user use port and a third party electric power port, and the user use port provides user electricity data information to the electric power Internet of things cloud platform through an intelligent electric energy meter; a database is arranged in the electric power internet of things cloud platform, and historical user electricity utilization data information of all user use ports of the electric power internet of things cloud platform in butt joint is stored in the database;
the system is provided with a power utilization data time interval section and manually marks the peak period and the valley period of the power utilization
The power internet of things cloud platform calls stored historical user power consumption data information in a power consumption valley period, and forms different predicted security risk scores after analyzing and processing the historical power consumption data information;
the user electricity consumption data information comprises electric energy electricity consumption data, voltage and current data at the user total incoming line, household appliance load data and internal temperature data of the connecting terminal;
taking electric energy data as an example, the first time period is 10, the second time period is 15, and the difference is 5;
taking the time period as a limit, acquiring difference data of corresponding data in each two adjacent user electricity data information, and forming a historical user electricity data information curve of a user use port;
acquiring historical user electricity data information curves of all user use ports, taking each group of curves as a sequence, taking the number of time periods on the curves as the sequence length, calculating the difference value between the maximum value and the minimum value in each group of curves, taking the curve with the minimum difference value as a standard sequence, marking the standard sequence as Q, and marking the length as n;
comparing any one of the residual sequences with a standard sequence, wherein any one of the residual sequences is marked as P, and the length is marked as m;
then there are:
、/>constructing an n-m matrix, setting a twisted path to pass through the n-m matrix, and marking any element of the twisted path as k, which is expressed as +.>The horizontal and vertical i and j respectively represent two points with aligned sequences;
constraints for constructing the twisted path are as follows:
which is a kind of=/>、/>=/>To ensure the sequence head-to-tail matching and the sequence matching;
the twisted path satisfies monotonicity and continuity, and adjacent points on any two paths、/>The transverse and longitudinal difference values of (2) should be equal to or greater than 0 and equal to or less than 1 at the same time;
for all twisted paths meeting the constraint conditions, performing iterative calculation of i from 1 to m and j from 1 to n:
wherein,refers to +.>Distance when i and j are aligned; />Refers to the sequence distance when i-1 matches j; />Indicating the sequence distance at which finger i matches j-1,refers to the sequence distance when i-1 matches j-1;
the finally obtained D (m, n) is the dynamic time warping distance between the two groups of sequences P and Q;
selecting a minimum value of the dynamic time warping distance as a predicted security risk score of a user use port corresponding to the sequence P;
based on different predicted security risk scores, intelligent electric energy meter data of a user use port with high predicted security risk score is preferentially called in a power consumption peak period, and the data are mixed to historical user power consumption data information of a corresponding user use port, so that first user power consumption data information is removed, a new sequence is formed for comparison again, and a new predicted security risk score is generated.
The system sets a quantity threshold value, the cloud platform of the electric power internet of things monitors data call information of each user use port at any time, a first priority channel is used as a monitoring object, after any user use port enters the first priority channel once, 1 is added to the corresponding user use port, when the quantity of any user use port exceeds the quantity threshold value, the corresponding user use port is marked to a third party electric power port, and the third party electric power port is fed back to an administrator for early warning.
In a second embodiment, a secure communication transmission system of an intelligent ammeter based on big data is provided, the system includes: the system comprises an electric power Internet of things module, an electric power data analysis module, a big data processing and calling module, a safety communication module and a mark feedback module;
the electric power Internet of things module comprises an electric power Internet of things cloud platform, a user use port and a third party electric power port, wherein the electric power Internet of things cloud platform is respectively connected with the user use port and the third party electric power port, and the user use port provides user electricity data information to the electric power Internet of things cloud platform through an intelligent electric energy meter; the power data analysis module is used for calling stored historical user power consumption data information in a power consumption valley period through the power internet of things cloud platform, and forming different predicted security risk scores after analyzing and processing the historical power consumption data information; the big data processing and calling module preferentially calls intelligent ammeter data corresponding to the user use port in the electricity consumption peak period based on different predicted security risk scores, and mixes the data into historical user electricity consumption data information corresponding to the user use port to form a new predicted security risk score; the safety communication module is used for setting a safety risk threshold, entering a first priority channel when the new predicted safety risk score exceeds the safety risk threshold, setting a time period, and preferentially calling intelligent ammeter data corresponding to a user use port according to the new predicted safety risk score in the first priority channel in the time period to reform the predicted safety risk score; the marking feedback module is used for setting a quantity threshold value, and if the quantity of the user using ports entering the first priority channel exceeds the quantity threshold value, marking the corresponding user using ports to the third-party power ports, and feeding back to an administrator for early warning;
the output end of the electric power Internet of things module is connected with the input end of the electric power data analysis module; the output end of the power data analysis module is connected with the input end of the big data processing and calling module; the output end of the big data processing calling module is connected with the input end of the safety communication module; the output end of the safety communication module is connected with the input end of the marking feedback module.
The electric power internet of things module comprises a database and an interval setting unit;
historical user electricity utilization data information of all user use ports in butt joint of the electric power internet of things cloud platform is stored in the database; the interval setting unit is provided with a power utilization data time interval section and manually marks a power utilization peak period and a power utilization valley period;
the database is connected with the interval setting unit.
The power data analysis module comprises a storage unit and a prediction unit;
the storage unit is used for calling stored historical user electricity utilization data information in an electricity utilization valley period through the electric power internet of things cloud platform; the prediction unit is used for analyzing and processing the historical electricity consumption data information to form different prediction security risk scores;
the output end of the storage unit is connected with the input end of the prediction unit.
The big data processing and calling module comprises a priority analysis unit and a data processing unit;
the priority analysis unit preferentially calls intelligent ammeter data corresponding to the user use port in the electricity consumption peak period based on different predicted security risk scores; the data processing unit is used for mixing data to historical user electricity data information of a corresponding user use port, constructing a new sequence and forming a new predicted security risk score;
the output end of the priority analysis unit is connected with the input end of the data processing unit.
The safety communication module comprises a monitoring unit and a safety risk analysis unit;
the monitoring unit is used for monitoring the first priority channel, setting a time period and preferentially calling intelligent ammeter data corresponding to the user using port according to the new predicted security risk score value in the first priority channel in the time period; the safety risk analysis unit reforms a predicted safety risk score based on the mixed data sequence;
the output end of the monitoring unit is connected with the input end of the security risk analysis unit.
The marking feedback module comprises a threshold setting unit and a feedback unit;
the threshold setting unit is used for setting a quantity threshold, and if the quantity of the user using ports entering the first priority channel exceeds the quantity threshold, marking the corresponding user using ports to the third-party power ports; the feedback unit is used for receiving the marking information and feeding back to an administrator for early warning;
the output end of the threshold setting unit is connected with the input end of the feedback unit.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus 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 apparatus.
Finally, it should be noted that: the foregoing description is only a preferred embodiment of the present invention, and the present invention is not limited thereto, but it is to be understood that modifications and equivalents of some of the technical features described in the foregoing embodiments may be made by those skilled in the art, although the present invention has been described in detail with reference to the foregoing embodiments. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (8)
1. A secure communication transmission method of an intelligent electric energy meter based on big data is characterized in that: the method comprises the following steps:
s1, constructing an electric power Internet of things cloud platform, wherein the electric power Internet of things cloud platform is respectively connected with a user use port and a third party electric power port, and the user use port provides user electricity data information to the electric power Internet of things cloud platform through an intelligent electric energy meter;
s2, the cloud platform of the electric power Internet of things calls stored historical user electricity utilization data information in an electricity utilization valley period, and different prediction security risk scores are formed after the historical electricity utilization data information is analyzed and processed;
s3, based on different predicted security risk scores, preferentially calling intelligent electric energy meter data corresponding to the user use port in the electricity consumption peak period, and mixing the data to historical user electricity consumption data information corresponding to the user use port to form a new predicted security risk score;
s4, setting a safety risk threshold, entering a first priority channel when the new predicted safety risk score exceeds the safety risk threshold, setting a time period, and preferentially calling intelligent ammeter data of a corresponding user use port according to the new predicted safety risk score in the first priority channel in the time period to reform the predicted safety risk score;
s5, the system sets a quantity threshold, if the quantity of the user using ports entering the first priority channel exceeds the quantity threshold, the corresponding user using ports are marked to the third-party power ports, and the third-party power ports are fed back to an administrator for early warning;
a database is arranged in the electric power internet of things cloud platform, and historical user electricity utilization data information of all user use ports of the electric power internet of things cloud platform in butt joint is stored in the database;
the system is provided with a power utilization data time interval section, and a power utilization peak period and a power utilization valley period are marked manually;
further comprises:
acquiring historical user electricity data information, wherein the user electricity data information comprises electric energy electricity data, voltage and current data at a user total incoming line, household appliance load data and internal temperature data of a connecting terminal;
taking the time period as a limit, acquiring difference data of corresponding data in each two adjacent user electricity data information, and forming a historical user electricity data information curve of a user use port;
acquiring historical user electricity data information curves of all user use ports, taking each group of curves as a sequence, taking the number of time periods on the curves as the sequence length, calculating the difference value between the maximum value and the minimum value in each group of curves, taking the curve with the minimum difference value as a standard sequence, marking the standard sequence as Q, and marking the length as n; comparing any one of the residual sequences with a standard sequence, wherein any one of the residual sequences is marked as P, and the length is marked as m;
then there are:
constructing an n-m matrix, and setting a twisted path to traverse the n-m matrix, wherein any element of the twisted path is denoted as k and expressed asThe horizontal and vertical i and j respectively represent two points with aligned sequences;
constraints for constructing the twisted path are as follows:
which is a kind of=/>、/>=/>To ensure the sequence head-to-tail matching and the sequence matching;
the twisted path satisfies monotonicity and continuity, and adjacent points on any two paths、/>The transverse and longitudinal difference values of (2) should be equal to or greater than 0 and equal to or less than 1 at the same time;
for all twisted paths meeting the constraint conditions, performing iterative calculation of i from 1 to m and j from 1 to n:
wherein,refers to +.>Distance when i and j are aligned; />Refers to the sequence distance when i-1 matches j; />Indicating the sequence distance of i matched with j-1,/I>Refers to the sequence distance when i-1 matches j-1;
the finally obtained D (m, n) is the dynamic time warping distance between the two groups of sequences P and Q;
selecting a minimum value of the dynamic time warping distance as a predicted security risk score of a user use port corresponding to the sequence P;
based on different predicted security risk scores, intelligent electric energy meter data of a user use port with high predicted security risk score is preferentially called in a power consumption peak period, and the data are mixed to historical user power consumption data information of a corresponding user use port, so that first user power consumption data information is removed, a new sequence is formed for comparison again, and a new predicted security risk score is generated.
2. The intelligent ammeter safe communication transmission method based on big data according to claim 1, wherein the method comprises the following steps: in step S4, further comprising:
the system sets a quantity threshold value, the cloud platform of the electric power internet of things monitors data call information of each user use port at any time, a first priority channel is used as a monitoring object, after any user use port enters the first priority channel once, 1 is added to the corresponding user use port, when the quantity of any user use port exceeds the quantity threshold value, the corresponding user use port is marked to a third party electric power port, and the third party electric power port is fed back to an administrator for early warning.
3. The intelligent ammeter safety communication transmission system based on big data applying the intelligent ammeter safety communication transmission method based on big data as set forth in claim 1, wherein: the system comprises: the system comprises an electric power Internet of things module, an electric power data analysis module, a big data processing and calling module, a safety communication module and a mark feedback module;
the electric power Internet of things module comprises an electric power Internet of things cloud platform, a user use port and a third party electric power port, wherein the electric power Internet of things cloud platform is respectively connected with the user use port and the third party electric power port, and the user use port provides user electricity data information to the electric power Internet of things cloud platform through an intelligent electric energy meter; the power data analysis module is used for calling stored historical user power consumption data information in a power consumption valley period through the power internet of things cloud platform, and forming different predicted security risk scores after analyzing and processing the historical power consumption data information; the big data processing and calling module preferentially calls intelligent ammeter data corresponding to the user use port in the electricity consumption peak period based on different predicted security risk scores, and mixes the data into historical user electricity consumption data information corresponding to the user use port to form a new predicted security risk score; the safety communication module is used for setting a safety risk threshold, entering a first priority channel when the new predicted safety risk score exceeds the safety risk threshold, setting a time period, and preferentially calling intelligent ammeter data corresponding to a user use port according to the new predicted safety risk score in the first priority channel in the time period to reform the predicted safety risk score; the marking feedback module is used for setting a quantity threshold value, and if the quantity of the user using ports entering the first priority channel exceeds the quantity threshold value, marking the corresponding user using ports to the third-party power ports, and feeding back to an administrator for early warning;
the output end of the electric power Internet of things module is connected with the input end of the electric power data analysis module; the output end of the power data analysis module is connected with the input end of the big data processing and calling module; the output end of the big data processing calling module is connected with the input end of the safety communication module; the output end of the safety communication module is connected with the input end of the marking feedback module.
4. The intelligent ammeter safety communication transmission system based on big data according to claim 3, wherein: the electric power internet of things module comprises a database and an interval setting unit;
historical user electricity utilization data information of all user use ports in butt joint of the electric power internet of things cloud platform is stored in the database; the interval setting unit is provided with a power utilization data time interval section and manually marks a power utilization peak period and a power utilization valley period;
the database is connected with the interval setting unit.
5. The intelligent ammeter safety communication transmission system based on big data according to claim 3, wherein: the power data analysis module comprises a storage unit and a prediction unit;
the storage unit is used for calling stored historical user electricity utilization data information in an electricity utilization valley period through the electric power internet of things cloud platform; the prediction unit is used for analyzing and processing the historical electricity consumption data information to form different prediction security risk scores;
the output end of the storage unit is connected with the input end of the prediction unit.
6. The intelligent ammeter safety communication transmission system based on big data according to claim 3, wherein: the big data processing and calling module comprises a priority analysis unit and a data processing unit;
the priority analysis unit preferentially calls intelligent ammeter data corresponding to the user use port in the electricity consumption peak period based on different predicted security risk scores; the data processing unit is used for mixing data to historical user electricity data information of a corresponding user use port, constructing a new sequence and forming a new predicted security risk score;
the output end of the priority analysis unit is connected with the input end of the data processing unit.
7. The intelligent ammeter safety communication transmission system based on big data according to claim 3, wherein: the safety communication module comprises a monitoring unit and a safety risk analysis unit;
the monitoring unit is used for monitoring the first priority channel, setting a time period and preferentially calling intelligent ammeter data corresponding to the user using port according to the new predicted security risk score value in the first priority channel in the time period; the safety risk analysis unit reforms a predicted safety risk score based on the mixed data sequence;
the output end of the monitoring unit is connected with the input end of the security risk analysis unit.
8. The intelligent ammeter safety communication transmission system based on big data according to claim 3, wherein: the marking feedback module comprises a threshold setting unit and a feedback unit;
the threshold setting unit is used for setting a quantity threshold, and if the quantity of the user using ports entering the first priority channel exceeds the quantity threshold, marking the corresponding user using ports to the third-party power ports; the feedback unit is used for receiving the marking information and feeding back to an administrator for early warning; the output end of the threshold setting unit is connected with the input end of the feedback unit.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311349557.1A CN117097768B (en) | 2023-10-18 | 2023-10-18 | Intelligent ammeter secure communication transmission system and method based on big data |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311349557.1A CN117097768B (en) | 2023-10-18 | 2023-10-18 | Intelligent ammeter secure communication transmission system and method based on big data |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN117097768A CN117097768A (en) | 2023-11-21 |
| CN117097768B true CN117097768B (en) | 2023-12-22 |
Family
ID=88770346
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311349557.1A Active CN117097768B (en) | 2023-10-18 | 2023-10-18 | Intelligent ammeter secure communication transmission system and method based on big data |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117097768B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117851960B (en) * | 2024-03-08 | 2024-05-17 | 傲世威科技(深圳)有限公司 | Temperature test data management method based on electronic cigarette atomizer |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105787606A (en) * | 2016-03-24 | 2016-07-20 | 国网辽宁省电力有限公司电力科学研究院 | Power dispatching online trend early warning system based on ultra short term load prediction |
| CN109447048A (en) * | 2018-12-25 | 2019-03-08 | 苏州闪驰数控系统集成有限公司 | A kind of artificial intelligence early warning system |
| CN113869571A (en) * | 2021-09-22 | 2021-12-31 | 国网天津市电力公司电力科学研究院 | Smart energy building comprehensive information physical fusion method based on heterogeneous data |
| CN114971173A (en) * | 2022-04-18 | 2022-08-30 | 国网安徽省电力有限公司电力科学研究院 | Transmission of electricity panorama wisdom management and control platform based on internet of things |
| CN115425764A (en) * | 2022-11-04 | 2022-12-02 | 广州泓盈信息科技有限公司 | Real-time monitoring method, system and storage medium for intelligent network risk of electric power system |
| CN115423554A (en) * | 2022-08-30 | 2022-12-02 | 鑫洋互联网科技(广州)有限公司 | Life optimization method based on Internet technology |
| CN116302809A (en) * | 2023-02-23 | 2023-06-23 | 中车工业研究院有限公司 | Edge end data analysis and calculation device |
| CN116824484A (en) * | 2023-05-30 | 2023-09-29 | 北京东方通网信科技有限公司 | Urban safety risk monitoring and early warning system based on big data |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20230162123A1 (en) * | 2018-02-17 | 2023-05-25 | Electro Industries/Gauge Tech | Devices, systems and methods for cost management and risk mitigation in power distribution systems |
| US11754997B2 (en) * | 2018-02-17 | 2023-09-12 | Ei Electronics Llc | Devices, systems and methods for predicting future consumption values of load(s) in power distribution systems |
-
2023
- 2023-10-18 CN CN202311349557.1A patent/CN117097768B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105787606A (en) * | 2016-03-24 | 2016-07-20 | 国网辽宁省电力有限公司电力科学研究院 | Power dispatching online trend early warning system based on ultra short term load prediction |
| CN109447048A (en) * | 2018-12-25 | 2019-03-08 | 苏州闪驰数控系统集成有限公司 | A kind of artificial intelligence early warning system |
| CN113869571A (en) * | 2021-09-22 | 2021-12-31 | 国网天津市电力公司电力科学研究院 | Smart energy building comprehensive information physical fusion method based on heterogeneous data |
| CN114971173A (en) * | 2022-04-18 | 2022-08-30 | 国网安徽省电力有限公司电力科学研究院 | Transmission of electricity panorama wisdom management and control platform based on internet of things |
| CN115423554A (en) * | 2022-08-30 | 2022-12-02 | 鑫洋互联网科技(广州)有限公司 | Life optimization method based on Internet technology |
| CN115425764A (en) * | 2022-11-04 | 2022-12-02 | 广州泓盈信息科技有限公司 | Real-time monitoring method, system and storage medium for intelligent network risk of electric power system |
| CN116302809A (en) * | 2023-02-23 | 2023-06-23 | 中车工业研究院有限公司 | Edge end data analysis and calculation device |
| CN116824484A (en) * | 2023-05-30 | 2023-09-29 | 北京东方通网信科技有限公司 | Urban safety risk monitoring and early warning system based on big data |
Non-Patent Citations (5)
| Title |
|---|
| Zijian Zhang ; Mianxiong Dong ; Liehuang Zhu ; Zhitao Guan ; Ruoyu Chen ; Rixin Xu ; Kaoru Ota."Achieving Privacy-Friendly Storage and Secure Statistics for Smart Meter Data on Outsourced Clouds".《IEEE Transactions on Cloud Computing》.2019,全文. * |
| 基于BP神经网络的智能电表数据分析预测;郑建柏;王芳;;仪器仪表与分析监测(第04期);全文 * |
| 大数据理念在电力信息网络管理中的应用;陈强;李纯阳;吴凯;;电力信息与通信技术(第11期);全文 * |
| 智能配电网电能计量设备的运行状态监测终端研究;刘春雨;罗群;顾强;张志龙;葛春萌;王子南;;中国测试(第07期);全文 * |
| 面向电力大数据日志分析平台的异常监测集成预测算法;倪震;李千目;郭雅娟;;南京理工大学学报(第05期);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN117097768A (en) | 2023-11-21 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN117097768B (en) | Intelligent ammeter secure communication transmission system and method based on big data | |
| Carpaneto et al. | Probabilistic characterisation of the aggregated residential load patterns | |
| CN109165763B (en) | Method and device for evaluating potential complaints of power grid customer service work order | |
| CN106651169A (en) | Fuzzy comprehensive evaluation-based distribution automation terminal state evaluation method and system | |
| CA2925251A1 (en) | System and methods for improved demand response management system (drms) | |
| CN115498640B (en) | Micro-grid energy control method and system based on virtual power plant | |
| CN108198408B (en) | Self-adaptive anti-electricity-stealing monitoring method and system based on electricity information acquisition system | |
| CN103024761A (en) | Establishing method for energy consumption model of base station, and energy consumption predicating method and device | |
| US20200366092A1 (en) | Method for analyzing correlation between different line loss actions | |
| CN110930052A (en) | Method, system, equipment and readable storage medium for predicting failure rate of power transformation equipment | |
| CN107525964A (en) | A kind of recognition methods of non-intrusion type load and device based on fusion decision-making | |
| CN112700144A (en) | Transformer substation operation state risk assessment method, device, equipment and storage medium | |
| CN115146977A (en) | Enterprise energy efficiency data management method and system based on Internet of things | |
| CN114629128A (en) | User low-voltage management method and system based on marketing and distribution data fusion | |
| CN116402187A (en) | Enterprise pollution discharge prediction method based on power big data | |
| CN116923168B (en) | Charging pile electric energy dispatching system and dispatching method based on transformer substation networking | |
| CN108763304B (en) | Blood-cause relationship-based power standing book data verification method and device | |
| CN107732902A (en) | Power distribution network economical operation monitoring and evaluation method | |
| CN116566057A (en) | User terminal fault detection system for virtual power plant | |
| CN112611997B (en) | Online verification method and system for hitching relation of platform area gateway table | |
| CN113792264B (en) | Method, device, equipment and medium for identifying abnormal electricity consumption user | |
| CN114202188A (en) | Information management system for user side electric energy demand prediction and distribution | |
| CN114707860A (en) | ECI-based user power utilization information management system | |
| CN106384297A (en) | Method and system for operating and maintaining photovoltaic power generation | |
| CN110110961A (en) | Power quality data monitoring method, device and terminal device |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |