CN117891884A - Power consumption monitoring management system based on internet of things technology - Google Patents
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Abstract
The invention is applicable to the technical field of electricity consumption monitoring, and provides an electricity consumption monitoring management system based on the technology of the Internet of things, which comprises the following components: the system comprises a data acquisition module, a storage management module, a prediction maintenance module, a region analysis module and a data recording module. And the prediction maintenance module analyzes and predicts the electricity consumption data by establishing a prediction model and combining the historical data and the real-time data. Therefore, the power utilization trend can be better known, the possible abnormal situation can be predicted, measures can be timely taken for maintenance and management, and the reliability and stability of the power utilization system are improved. The regional analysis module integrates the electricity consumption parameter data of different regions and different users, and compares and analyzes the electricity consumption parameter data to detect whether abnormal electricity consumption behaviors exist. By setting a threshold or a machine learning algorithm, data deviating from a normal electricity consumption mode can be found in time, and an abnormality report can be generated. This helps to reduce energy waste, prevent illegal power consumption, etc.
Description
Technical Field
The invention belongs to the technical field of electricity consumption monitoring, and particularly relates to an electricity consumption monitoring management system based on the internet of things technology.
Background
The electricity consumption monitoring technology refers to a technical means for monitoring and collecting data of an electric power system in real time through various sensors, meters and data acquisition systems. By utilizing the electricity consumption monitoring technology, the comprehensive monitoring and analysis of the electricity consumption system can be realized, the energy waste and illegal electricity consumption behaviors can be timely found and corrected, the energy utilization efficiency is improved, the energy consumption cost is reduced, and the sustainable energy development is promoted.
Currently, some residents steal power resources by means of electric meter operation, electric wire secret drawing, and the like, directly acquire power from power sources of other people, or by bypassing a metering device or operating the metering device to record, and conceal actual power consumption. Traditional systems often only focus on the overall electricity usage, and lack fine-grained analysis and detection capabilities for different areas and different users. Thus, abnormal electricity utilization behaviors of individual users or specific areas cannot be accurately found, and comprehensive management and maintenance of the electricity utilization system are limited. Meanwhile, the detection of abnormal electricity utilization behavior is usually based on manual experience and rule setting, and the capability of intellectualization and automation is lacking. This makes the detection process cumbersome, time consuming, and prone to false or false alarms.
Disclosure of Invention
The invention aims to provide an electricity consumption monitoring management system based on the internet of things technology, and aims to solve the technical problems in the prior art determined in the background technology.
The invention is realized in such a way that the electricity consumption monitoring and management system based on the technology of the Internet of things comprises:
the data acquisition module is used for monitoring the electricity consumption parameter data of each area in real time; and centralizing the data through the data channel;
the storage management module is used for receiving the received electricity consumption parameter data, carrying out classification pretreatment on the data, establishing a historical database and storing the treated data;
the prediction maintenance module is used for analyzing the historical data through a prediction model, predicting the data trend by combining the collected electricity consumption parameter data, and adding an alarm mark to the region data when abnormal data appear in the prediction result;
the regional analysis module integrates the power consumption parameter data of different regions and different users, compares the data of different regions and different users in the regions respectively, and detects whether abnormal power consumption behaviors exist or not;
and the data recording module is used for storing the analyzed data and the processing result of the data in the history database.
As a further aspect of the present invention, the data acquisition module includes:
the interface connection unit is used for receiving the signal of the sensor and converting the signal into a digital signal;
the data acquisition control unit is used for controlling the frequency and time interval of data acquisition;
the data transmission unit is used for establishing a data channel and transmitting the acquired data in the data channel through a network.
As a further aspect of the present invention, the storage management module includes:
the data receiving and classifying unit is used for receiving the original electricity parameter data transmitted by the data transmission unit and classifying the original electricity parameter data according to different areas and different users in each area as standards;
the data preprocessing unit is used for preprocessing the received original electricity consumption parameter data, including data cleaning, de-duplication and format conversion, and maintaining the accuracy and consistency of the data;
the database establishing unit is used for establishing a historical database and storing the preprocessed electricity consumption parameter data into the historical database.
As a further aspect of the present invention, the prediction maintenance module includes:
the model building unit is used for building a prediction model for predicting future electricity utilization trend;
the model training unit is used for extracting historical electricity consumption parameter data from the historical database, extracting characteristic data according to the prediction model, and training the prediction model through the characteristic data;
the data prediction unit is used for substituting the preprocessed electricity parameter data of each region into a prediction model to obtain future electricity data prediction of each region;
the abnormality judging unit is used for analyzing the acquired future electricity utilization data by combining the historical data in the historical database, judging whether the abnormal data exist, and adding an alarm mark to the abnormal data when the abnormal data exist.
As a further aspect of the present invention, the area analysis module includes:
the data integration unit is used for integrating the electricity consumption parameter data of different areas and different users and classifying the data;
the abnormal detection unit is used for comparing and analyzing all the electricity utilization data in the classification cluster and detecting whether abnormal electricity utilization behaviors exist or not;
behavior recognition unit: modeling and identifying power consumption behaviors of different areas and users, and predicting possible illegal power consumption behaviors;
and the alarm notification unit is used for generating an alarm and adding an alarm mark when the abnormal electricity utilization behavior is found.
As a further aspect of the present invention, the area analysis module further includes:
and the data visualization unit is used for visualizing the analyzed data, generating a processing report and storing the processing report in the history database.
As a further aspect of the present invention, the data recording module includes:
the data receiving unit is used for receiving the collected data and the processed data from the prediction maintenance module and the area analysis module;
the analysis result recording unit is used for classifying the collected data and the processed data of the prediction maintenance module and the area analysis module according to different areas and different users, and uniformly storing the data and the data stored in the historical database.
As a further aspect of the present invention, the system further includes:
the electricity consumption detection module is used for analyzing the collected electricity consumption parameter data, setting a threshold interval by combining the historical data in the historical database, and comparing the collected electricity consumption parameter data with the threshold interval;
the warning notification module is used for presetting warning signals and sending warning information to the power supply department when the acquired electricity consumption parameter data exceeds or is lower than a threshold value interval.
As a further aspect of the present invention, the electricity usage detection module includes:
the parameter analysis unit is used for analyzing the collected electricity consumption parameter data, including real-time monitoring and analysis of parameters such as power, current, voltage and the like, and calculation and evaluation of indexes such as electricity consumption, energy consumption and the like;
a threshold setting unit for setting a threshold interval of the electricity consumption parameter data in combination with the history data in the history database;
the parameter comparison unit is used for comparing the acquired electricity consumption parameter data with a preset threshold interval and judging whether the electricity consumption parameter data exceeds or is lower than a threshold;
and the abnormality labeling unit is used for adding an alarm label to the electricity consumption parameter data when the data exceeds or is lower than a threshold value interval.
As a further aspect of the present invention, the alert notification module includes:
the alarm judging unit is used for identifying all the existing alarm labels and the data information of the existing alarm labels;
and the alarm notification unit is used for generating an alarm notification for the data with the alarm mark and synchronizing the alarm notification to the power supply department.
The beneficial effects of the invention are as follows:
the system transmits the collected electricity consumption parameter data to the storage management module in a centralized way through the data channel. Therefore, centralized management and unified storage of data can be realized, subsequent data processing, analysis and inquiry are facilitated, and the complicated process of traditional manual arrangement and storage is avoided.
And the prediction maintenance module analyzes and predicts the electricity consumption data by establishing a prediction model and combining the historical data and the real-time data. Therefore, the power utilization trend can be better known, the possible abnormal situation can be predicted, measures can be timely taken for maintenance and management, and the reliability and stability of the power utilization system are improved.
The regional analysis module integrates the electricity consumption parameter data of different regions and different users, and compares and analyzes the electricity consumption parameter data to detect whether abnormal electricity consumption behaviors exist. By setting a threshold or a machine learning algorithm, data deviating from a normal electricity consumption mode can be found in time, and an abnormality report can be generated. This helps to reduce energy waste, prevent illegal power consumption, etc.
Drawings
Fig. 1 is a flowchart of an electricity monitoring and management system based on the internet of things technology according to an embodiment of the present invention;
FIG. 2 is a block diagram of a data acquisition module according to an embodiment of the present invention;
FIG. 3 is a block diagram illustrating a memory management module according to an embodiment of the present invention;
FIG. 4 is a block diagram of a predictive maintenance module according to an embodiment of the invention;
FIG. 5 is a block diagram of a region analysis module according to an embodiment of the present invention;
FIG. 6 is a block diagram illustrating a data recording module according to an embodiment of the present invention;
FIG. 7 is a block diagram of a power utilization detection module according to an embodiment of the present invention;
fig. 8 is a block diagram of an alert notification module according to an embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
It will be understood that the terms "first," "second," and the like, as used herein, may be used to describe various elements, but these elements are not limited by these terms unless otherwise specified. These terms are only used to distinguish one element from another element. For example, a first xx script may be referred to as a second xx script, and similarly, a second xx script may be referred to as a first xx script, without departing from the scope of the present application.
Fig. 1 is a flowchart of an electricity consumption monitoring and managing system based on the internet of things technology according to an embodiment of the present invention, as shown in fig. 1, the electricity consumption monitoring and managing system based on the internet of things technology includes:
the data acquisition module 100 is used for monitoring the electricity consumption parameter data of each area in real time; and centralizing the data through the data channel;
in the module, a sensor is adopted for data acquisition, and the sensor can be various types of electricity consumption parameter sensors, such as a current sensor, a voltage sensor, a power sensor and the like. The interfacing unit is responsible for communicating with the sensors, receiving the raw signals they collect and converting them into digitized data for subsequent processing.
The system can flexibly set the frequency and time interval of data acquisition so as to adapt to the monitoring requirements of different areas and devices. This can effectively manage power consumption and reduce the burden of data transmission.
Meanwhile, the module can adopt various communication technologies, such as Ethernet, wireless network and the like, and can carry out data communication with a server or a cloud platform of the monitoring system. The collected electricity consumption parameter data can be transmitted to the storage management module in real time through the data transmission unit for subsequent processing and analysis.
The data acquisition control unit controls the acquisition frequency and the time interval, establishes a data channel through the data transmission unit and transmits data to the subsequent processing and storage module.
The storage management module 200 is used for receiving the received electricity consumption parameter data, classifying and preprocessing the data, establishing a historical database and storing the processed data;
in this module, the original data is transmitted to the data receiving and classifying unit for processing through the data channel. In the unit, the raw electricity parameter data is classified according to different areas and different user criteria for each area. This may facilitate subsequent data management and analysis, enabling the data to be categorized according to specific criteria.
The preprocessing process comprises operations such as data cleaning, deduplication, format conversion and the like, and aims to maintain the accuracy and consistency of data. The data cleaning can remove abnormal values and noise, and ensure the reliability of the data. The deduplication operation may exclude duplicate data records, avoiding redundant storage. The format conversion can unify the data acquired by different sensors into the same format, so that the subsequent data analysis and inquiry are convenient.
The history database is a core component of the storage management module, which can store a large amount of electricity consumption parameter data and provide an efficient data access interface. In the database creation unit, a database management system suitable for the electrical monitoring system, such as a relational database or a time series database, may be selected. By storing the data in the database, subsequent operations such as data analysis, trend prediction, anomaly detection, and the like can be conveniently performed.
In summary, the storage management module ensures that the storage management module can effectively receive, pre-process and store the electricity consumption parameter data transmitted from the data acquisition module, and provides a high-quality data base for the prediction maintenance module and the area analysis module.
The prediction maintenance module 300 is used for analyzing the historical data through a prediction model, predicting the data trend by combining the collected electricity consumption parameter data, and adding an alarm mark to the region data when abnormal data appears in the prediction result;
in the present module, the prediction model includes a time series model, a regression model, and a neural network model. At this stage, it is necessary to select the most suitable prediction model according to the characteristics of actual conditions and history data, and perform initialization and configuration of the model.
In the model training process, the historical data needs to be cleaned and processed, useful features are extracted, data standardization and other operations are performed, and therefore the trained model can accurately reflect the regularity of the historical data. Through the prediction model, the system can predict the electricity consumption condition of each region in a period of time in the future, and provides reference basis for electricity planning and resource allocation. When abnormal data exists, the abnormal data is recorded and alarm labels are added for subsequent manual auditing and processing. The abnormality judgment unit can judge abnormal data by setting threshold values, rules, deviation of model prediction and the like, so that the power consumption management system can timely find and process abnormal conditions.
The area analysis module 400 integrates the electricity consumption parameter data of different areas and different users, respectively compares the data of different areas and different users in the areas, and detects whether abnormal electricity consumption behaviors exist;
in the step, through the data channel, the electricity consumption parameter data received from the storage management module is transmitted to the module, and the data of different areas and users are integrated into corresponding data sets, so that the subsequent comparison and analysis are convenient. The data may also be classified according to need, for example by region, user type or time, etc., for more flexibility in subsequent analysis and identification.
By comparing the historical data with the real-time data, the module can find the data deviating from the normal electricity utilization mode. Common abnormal electricity usage behavior includes excessive energy consumption, equipment failure, illegal electricity usage, and the like. The module can judge according to a preset threshold value or based on a machine learning algorithm and generate an abnormal report.
The module can analyze historical data and real-time data, extract features and build a behavioral model. By comparing the actual data with the behavior model, the behavior recognition unit can determine whether there is an illegal behavior, such as unauthorized device access, illegal power consumption behavior, or the like. When suspicious behavior is found, the module will generate an alert and add a corresponding annotation.
Once abnormal behavior is found, the module will trigger an alert mechanism and send an alert notification to the relevant personnel. The notification mode can comprise short messages, mails, mobile phone application program pushing and the like so as to take measures in time to treat abnormal conditions.
In summary, the area analysis module is responsible for integrating the electricity consumption parameter data of different areas and different users, performing anomaly detection and behavior recognition, generating an alarm and notifying. The system comprises a data integration unit, an abnormality detection unit, a behavior recognition unit and an alarm notification unit, and the safety and the efficiency of the electricity consumption monitoring system can be improved through the cooperative work of the sub-modules.
The data recording module 500 is configured to store the analyzed data and the processing result of the data in a history database.
In the module, data interaction is carried out through the data channel, the prediction maintenance module and the area analysis module, real-time electricity consumption parameter data and processed analysis results are received, and timeliness and integrity of the data are ensured. And the received data can be classified and arranged and stored in a history database for subsequent inquiry and analysis.
Through the data recording module, the system can realize unified management and storage of collected data and processing results, and provides convenient data query and analysis functions for system users. Meanwhile, the data in the historical database also provides important basis and support for predictive analysis and anomaly detection of the system.
Fig. 2 is a block diagram of a data acquisition module according to an embodiment of the present invention, as shown in fig. 2, where the data acquisition module includes:
an interface connection unit 110 for receiving signals of the sensor and converting them into digital signals;
a data acquisition control unit 120 for controlling the frequency and time interval of data acquisition;
and the data transmission unit 130 is used for establishing a data channel and transmitting the acquired data in the data channel through a network.
Fig. 3 is a block diagram of a storage management module according to an embodiment of the present invention, where, as shown in fig. 3, the storage management module includes:
the data receiving and classifying unit 210 is configured to receive the original electrical parameter data transmitted by the data transmission unit, and classify the original electrical parameter data according to different areas and different users in each area as standards;
the data preprocessing unit 220 is configured to preprocess the received original electrical parameter data, including data cleaning, deduplication, and format conversion, and maintain accuracy and consistency of the data;
the database creation unit 230 is configured to create a history database and store the preprocessed electricity parameter data into the history database.
Fig. 4 is a block diagram of a prediction maintenance module according to an embodiment of the present invention, as shown in fig. 4, where the prediction maintenance module includes:
a model building unit 310 for building a prediction model for predicting a future electricity usage trend;
the model training unit 320 is configured to extract historical electrical parameter data from the historical database, extract feature data according to the prediction model, and train the prediction model through the feature data;
the data prediction unit 330 is configured to respectively substitute the preprocessed electricity parameter data of each region into a prediction model, and obtain future electricity data prediction of each region;
the anomaly determination unit 340 is configured to analyze the obtained future electricity consumption data in combination with the historical data in the historical database, determine whether there is anomaly data, and add an alarm tag to the anomaly data when there is anomaly data.
Fig. 5 is a block diagram of a region analysis module according to an embodiment of the present invention, as shown in fig. 5, where the region analysis module includes:
the data integration unit 410 is used for integrating the electricity consumption parameter data of different users and classifying the dataClassification ofStep (a)The method comprises the following steps:
s1, setting K electricity consumption values;
s2, calculating the electricity consumption parameter data distances of different users, wherein the calculation formula is as follows:
wherein, 1, 2..n represents the type of electricity parameter data, x represents the value of the initially monitored electricity parameter, y represents the value of the finally monitored electricity parameter;
s3, comparing and classifying a plurality of d (x, y) with the set K electricity consumption values to obtain K clusters;
s4, calculating the average value of the K clusters, replacing corresponding electricity consumption values, and repeating the steps S2 to S3 until the newly calculated average value is equal to the last average value, so as to obtain final K classification clusters;
the anomaly detection unit 420 is configured to compare and analyze all the electricity consumption data in the classification cluster, detect whether there is an abnormal electricity consumption behavior, and calculate the formula as follows:
wherein Z is an abnormal score, x is the electricity consumption, mu is the average electricity consumption in the corresponding classification cluster, and sigma is the standard deviation of the corresponding classification cluster;
setting a threshold score, and judging that the electricity consumption of the user is abnormal when the abnormality score exceeds the threshold score;
behavior recognition unit 430: modeling and identifying power consumption behaviors of different areas and users, and predicting possible illegal power consumption behaviors;
the alarm notification unit 440 is configured to generate an alarm and add an alarm tag when abnormal electricity behavior is found.
As shown in fig. 5, as a further aspect of the present invention, the area analysis module further includes:
and the data visualization unit 450 is used for visualizing the analyzed data, generating a processing report and storing the processing report in the history database.
Fig. 6 is a block diagram of a data recording module according to an embodiment of the present invention, and as shown in fig. 6, the data recording module includes:
a data receiving unit 510, configured to receive collected data and processed data from the prediction maintenance module and the area analysis module;
the analysis result recording unit 520 is configured to classify the collected data and the processed data of the prediction maintenance module and the area analysis module according to different areas and different users, and store the collected data and the processed data and the data stored in the history database in a unified manner.
As shown in fig. 1, as a further aspect of the present invention, the system further includes:
the electricity consumption detection module 600 is configured to analyze the collected electricity consumption parameter data, set a threshold interval in combination with the historical data in the historical database, and compare the collected electricity consumption parameter data with the threshold interval;
the warning notification module 700 is configured to preset a warning signal, and send warning information to the power supply department when the collected electricity consumption parameter data exceeds or falls below a threshold interval.
Fig. 7 is a block diagram of a power consumption detection module according to an embodiment of the present invention, as shown in fig. 7, where the power consumption detection module includes:
the parameter analysis unit 610 is configured to analyze the collected electricity consumption parameter data, including real-time monitoring and analysis of parameters such as power, current, voltage, etc., and calculation and evaluation of indexes such as electricity load, energy consumption, etc.;
a threshold setting unit 620, configured to set a threshold interval of the electricity consumption parameter data in combination with the history data in the history database;
the parameter comparison unit 630 is configured to compare the collected electricity consumption parameter data with a preset threshold interval, and determine whether the electricity consumption parameter data exceeds or is lower than a threshold;
and an anomaly labeling unit 640, configured to add an alarm label to the electrical parameter data when the electrical parameter data exceeds or falls below a threshold interval.
Fig. 8 is a block diagram of an alert notification module according to an embodiment of the present invention, where, as shown in fig. 8, the alert notification module includes:
an alarm judging unit 710 for identifying all existing alarm tags and data information of the existing alarm tags;
an alarm notification unit 720 for generating an alarm notification for the data with the alarm tag, and synchronizing the alarm notification to the power supply department.
It should be understood that, although the steps in the flowcharts of the embodiments of the present invention are shown in order as indicated by the arrows, these steps are not necessarily performed in order as indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in various embodiments may include multiple sub-steps or stages that are not necessarily performed at the same time, but may be performed at different times, nor do the order in which the sub-steps or stages are performed necessarily performed in sequence, but may be performed alternately or alternately with at least a portion of the sub-steps or stages of other steps or other steps.
Those skilled in the art will appreciate that all or part of the processes in the methods of the above embodiments may be implemented by a computer program for instructing relevant hardware, where the program may be stored in a non-volatile computer readable storage medium, and where the program, when executed, may include processes in the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the various embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), memory bus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
The technical features of the above-described embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above-described embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The foregoing examples illustrate only a few embodiments of the invention and are described in detail herein without thereby limiting the scope of the invention. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the invention, which are all within the scope of the invention. Accordingly, the scope of protection of the present invention is to be determined by the appended claims.
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, and alternatives falling within the spirit and principles of the invention.
Claims (10)
1. Power consumption monitoring management system based on internet of things technology, which is characterized in that the system comprises:
the data acquisition module is used for monitoring the electricity consumption parameter data of each area in real time;
the storage management module is used for receiving the received electricity consumption parameter data, carrying out classification pretreatment on the data, establishing a historical database and storing the treated data;
the prediction maintenance module is used for analyzing the historical data through a prediction model, predicting the data trend by combining the collected electricity consumption parameter data, and adding an alarm mark to the region data when abnormal data appear in the prediction result;
the regional analysis module integrates the power consumption parameter data of different regions and different users, compares the data of different regions and different users in the regions respectively, and detects whether abnormal power consumption behaviors exist or not;
and the data recording module is used for storing the analyzed data and the processing result in the historical database.
2. The system of claim 1, wherein the data acquisition module comprises:
the interface connection unit is used for receiving the signal of the sensor and converting the signal into a digital signal;
the data acquisition control unit is used for controlling the frequency and time interval of data acquisition;
the data transmission unit is used for establishing a data channel and transmitting the acquired data in the data channel through a network.
3. The system of claim 2, wherein the storage management module comprises:
the data receiving and classifying unit is used for receiving the original electricity parameter data transmitted by the data transmission unit and classifying the original electricity parameter data according to different areas and different users in each area as standards;
the data preprocessing unit is used for preprocessing the received original electricity consumption parameter data and comprises data cleaning, de-duplication and format conversion;
the database establishing unit is used for establishing a historical database and storing the preprocessed electricity consumption parameter data into the historical database.
4. The system of claim 3, wherein the predictive maintenance module comprises:
the model building unit is used for building a prediction model for predicting future electricity utilization trend;
the model training unit is used for extracting historical electricity consumption parameter data from the historical database, extracting characteristic data according to the prediction model, and training the prediction model through the characteristic data;
the data prediction unit is used for substituting the preprocessed electricity parameter data of each region into a prediction model to obtain future electricity data prediction of each region;
the abnormality judging unit is used for analyzing the acquired future electricity utilization data by combining the historical data in the historical database, judging whether the abnormal data exist, and adding an alarm mark to the abnormal data when the abnormal data exist.
5. The system of claim 4, wherein the region analysis module comprises:
the data integration unit is used for integrating the electricity consumption parameter data of different users and classifying the dataClassification ofStep (a)The method comprises the following steps:
s1, setting K electricity consumption values;
s2, calculating the electricity consumption parameter data distances of different users, wherein the calculation formula is as follows:
wherein, 1, 2..n represents the type of electricity parameter data, x represents the value of the initially monitored electricity parameter, y represents the value of the finally monitored electricity parameter;
s3, comparing and classifying a plurality of d (x, y) with the set K electricity consumption values to obtain K clusters;
s4, calculating the average value of the K clusters, replacing corresponding electricity consumption values, and repeating the steps S2 to S3 until the newly calculated average value is equal to the last average value, so as to obtain final K classification clusters;
the abnormal detection unit is used for comparing and analyzing all the electricity utilization data in the classification cluster, detecting whether abnormal electricity utilization behaviors exist or not, and the calculation formula is as follows:
wherein Z is an abnormal score, x is the electricity consumption, mu is the average electricity consumption in the corresponding classification cluster, and sigma is the standard deviation of the corresponding classification cluster;
setting a threshold score, and judging that the electricity consumption of the user is abnormal when the abnormality score exceeds the threshold score;
behavior recognition unit: modeling and identifying power consumption behaviors of different areas and users, and predicting possible illegal power consumption behaviors;
and the alarm notification unit is used for generating an alarm and adding an alarm mark when the abnormal electricity utilization behavior is found.
6. The system of claim 5, wherein the region analysis module further comprises:
and the data visualization unit is used for visualizing the analyzed data, generating a processing report and storing the processing report in the history database.
7. The system of claim 5, wherein the data logging module comprises:
the data receiving unit is used for receiving the collected data and the processed data from the prediction maintenance module and the area analysis module;
the analysis result recording unit is used for classifying the collected data and the processed data of the prediction maintenance module and the area analysis module according to different areas and different users, and uniformly storing the data and the data stored in the historical database.
8. The system of claim 1, wherein the system further comprises:
the electricity consumption detection module is used for analyzing the collected electricity consumption parameter data, setting a threshold interval by combining the historical data in the historical database, and comparing the collected electricity consumption parameter data with the threshold interval;
the warning notification module is used for presetting warning signals and sending warning information to the power supply department when the acquired electricity consumption parameter data exceeds or is lower than a threshold value interval.
9. The system of claim 8, wherein the power usage detection module comprises:
the parameter analysis unit is used for analyzing the collected electricity consumption parameter data, including real-time monitoring and analysis of parameters such as power, current, voltage and the like, and calculation and evaluation of electricity consumption load and energy consumption;
a threshold setting unit for setting a threshold interval of the electricity consumption parameter data in combination with the history data in the history database;
the parameter comparison unit is used for comparing the acquired electricity consumption parameter data with a preset threshold interval and judging whether the electricity consumption parameter data exceeds or is lower than a threshold;
and the abnormality labeling unit is used for adding an alarm label to the electricity consumption parameter data when the data exceeds or is lower than a threshold value interval.
10. The system of claim 9, wherein the alert notification module comprises:
the alarm judging unit is used for identifying all the existing alarm labels and the data information of the existing alarm labels;
and the alarm notification unit is used for generating an alarm notification for the data with the alarm mark and synchronizing the alarm notification to the power supply department.
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