CN116882804A - Intelligent power monitoring method and system - Google Patents
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- 238000012544 monitoring process Methods 0.000 title claims abstract description 69
- 238000000034 method Methods 0.000 title claims abstract description 46
- 230000005611 electricity Effects 0.000 claims abstract description 127
- 230000002159 abnormal effect Effects 0.000 claims abstract description 122
- 206010000117 Abnormal behaviour Diseases 0.000 claims abstract description 35
- 230000006399 behavior Effects 0.000 claims abstract description 20
- 230000005856 abnormality Effects 0.000 claims description 24
- 238000004590 computer program Methods 0.000 claims description 21
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- 238000012549 training Methods 0.000 claims description 7
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- 238000004364 calculation method Methods 0.000 description 4
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- 238000005265 energy consumption Methods 0.000 description 2
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Classifications
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/06—Electricity, gas or water supply
Abstract
The application relates to the field of power monitoring, in particular to an intelligent power monitoring method and system, wherein the intelligent power monitoring method comprises the following steps: acquiring historical electricity utilization data and dividing user attributes; according to the user attributes and the historical electricity consumption data, an intelligent monitoring model is constructed to identify abnormal data; removing abnormal data which are abnormal due to untimely updating in the identification result of the abnormal data according to preset user updating information; and acquiring power utilization real-time data, calculating comprehensive indexes of abnormal behaviors of the user, judging that the user is abnormal when the comprehensive indexes are more than a preset threshold value, and generating a power utilization abnormal list. According to the application, the purpose of automatically monitoring and identifying the abnormal electricity consumption phenomenon is realized by constructing the intelligent monitoring model, the abnormal electricity consumption data judgment of various factors is integrated into the comprehensive index judgment of the abnormal user behavior, and the efficiency and accuracy of the abnormal user electricity consumption behavior judgment are improved.
Description
Technical Field
The application relates to the field of power monitoring, in particular to an intelligent power monitoring method and system.
Background
Intelligent power monitoring is a method for real-time, comprehensive and accurate monitoring and management of power equipment and systems. The intelligent power monitoring aims to improve the power supply reliability, reduce the energy consumption cost, prolong the service life of equipment and improve the production efficiency and the service quality of enterprises. The intelligent power monitoring system mainly comprises a sensor module, a communication module, a computer module and the like. The sensor module is used for detecting parameter changes of the power equipment in real time, such as voltage, current, power factor and the like; the communication module is used for transmitting the data acquired by the sensor to the computer for analysis and processing; the computer module is mainly applied to the aspects of data analysis, fault diagnosis, power generation capacity, energy consumption assessment, prediction and the like.
In the prior art, the electricity consumption behavior of a user can be monitored through an intelligent electric power monitoring system, and through analyzing the electricity consumption mode of the user, the electricity consumption rule and behavior characteristics of each user can be known, and abnormal electricity consumption conditions can be found remotely.
However, parameters such as voltage, current, power factor angle and the like which influence electric energy metering are mainly used, and electricity stealing users mainly achieve the aims of under-voltage and under-current through various means, so that the electric quantity of the ammeter is reduced, and particularly in electricity utilization scenes of users in towns, abnormal electricity utilization data of a large number of different factors brings difficulty to abnormal electricity utilization analysis of an electric power monitoring system, and the efficiency is low.
Disclosure of Invention
In order to improve efficiency and accuracy of abnormal electricity behavior judgment of a user, the application provides an intelligent power monitoring method and system.
In a first aspect, the present application provides an intelligent power monitoring method, which adopts the following technical scheme:
an intelligent power monitoring method comprises the following steps: acquiring historical electricity utilization data and dividing user attributes; according to the user attributes and the historical electricity consumption data, an intelligent monitoring model is constructed to identify abnormal data; removing abnormal data due to untimely updating in the identification result of the abnormal data according to preset user updating information; and acquiring power utilization real-time data, calculating comprehensive indexes of abnormal behaviors of the user, judging that the user is abnormal when the comprehensive indexes are more than a preset threshold value, and generating a power utilization abnormal list.
By adopting the technical scheme, the automatic monitoring and the recognition of the abnormal electricity consumption phenomenon are realized by constructing the intelligent monitoring model, the abnormal electricity consumption data judgment of various factors is integrated into the comprehensive index judgment of the abnormal electricity consumption behavior of the user by calculating the comprehensive index of the abnormal electricity consumption behavior of the user, the efficiency and the accuracy of the abnormal electricity consumption behavior judgment of the user can be improved, the abnormal data can be rapidly recognized, measures can be timely taken, and the loss is avoided.
Preferably, in the calculating the comprehensive index of the abnormal behavior of the user, the comprehensive index of the abnormal behavior of the user = (the power consumption abnormal result, the power consumption weight, the power consumption period abnormal result, the power consumption period weight, the power consumption load weight)/the total weight is equal to the sum of the power consumption weight, the power consumption period weight and the power consumption load weight.
By adopting the technical scheme, the three indexes are comprehensively calculated and the comprehensive indexes of the abnormal behaviors of the user are weighted, so that the overall abnormal behavior condition of the user can be reflected more accurately. Different abnormal conditions may have different importance in practical application, and the degree of abnormal behavior of the user can be more reasonably judged after weighting. Meanwhile, the comprehensive calculation of a plurality of indexes can effectively avoid the overlarge influence of a single index on the judgment result, so that a more objective, comprehensive and reasonable judgment result is obtained.
Preferably, the abnormal result of the electricity consumption period is calculated by the difference time between the specific time period of electricity consumption behavior of the user and the historical electricity consumption time of the user, and the greater the value of the difference time is, the higher the abnormal degree is.
Through adopting above-mentioned technical scheme, when the average difference of power consumption and historical power consumption is great, probably have the abnormality, through comparing the difference size, can preliminary judge the degree that the user used the power consumption is unusual.
Preferably, the abnormal result of the electricity consumption is obtained by comparing the difference between the average value of the electricity consumption of the user and the average value of the historical electricity consumption, and the greater the difference is, the higher the degree of abnormality is.
Through adopting above-mentioned technical scheme, user's power consumption can fluctuate in certain scope, if the power consumption has obviously exceeded the average value of historical power consumption for a certain time, just probably has the abnormal situation, and what user's power consumption is unusual great can be assisted to the method of using the average value difference, is favorable to carrying out effectual monitoring and management to the electric power network.
Preferably, the abnormal result of the power consumption load is calculated according to the difference between the current power consumption load of the user and the average value of the historical power consumption loads of the user, and the greater the load difference is, the higher the abnormal degree is, and the greater the abnormal result of the power consumption load is.
By adopting the technical scheme, the abnormal electricity consumption condition of the user is monitored from the electricity consumption load dimension by knowing the abnormal degree of the electricity consumption load.
Preferably, the building of the intelligent monitoring model according to the user attribute and the historical electricity consumption data includes the following steps: setting a data sample with abnormal electricity consumption; performing data processing on the user attributes and the historical electricity consumption data; converting the user attribute into a numerical feature; performing model training by using the historical electricity consumption data and the data samples of the electricity consumption abnormality; and (5) optimizing the model and adjusting parameters.
By adopting the technical scheme, the purpose of constructing an intelligent monitoring model is achieved, and the intelligent monitoring model is used.
Preferably, the removing, according to the preset user update information, the abnormal data that is abnormal due to untimely update in the identification result of the abnormal data includes the following steps:
acquiring update information of a user; and removing error data in the abnormal data according to the updated information.
By adopting the technical scheme, if the updated information contains the information conforming to the abnormal data, the abnormal data are updated or deleted, the abnormal data are prevented from affecting the subsequent data processing result, and the quality and accuracy of the data are ensured. And the data is updated after the abnormal data is screened, so that the calculated amount of the data is reduced compared with the updating when the real-time data is initially acquired.
In a second aspect, the present application discloses an intelligent power monitoring system, which adopts the above intelligent power monitoring method, comprising: the database module is used for acquiring historical electricity utilization data and dividing user attributes; the model construction module is used for constructing an intelligent monitoring model according to the user attributes and the historical electricity consumption data so as to identify abnormal data; the data processing module is used for removing abnormal data which are abnormal due to untimely updating in the identification result of the abnormal data according to preset user updating information; and the abnormality judging module is used for acquiring the electricity utilization real-time data, calculating the comprehensive index of the abnormal behavior of the user, judging the abnormality of the user when the comprehensive index accords with a preset threshold value, and generating an electricity utilization abnormality list.
By adopting the technical scheme, the intelligent monitoring model is constructed through the database module and the model construction module, automatic monitoring and power consumption abnormality recognition are realized, the comprehensive index of the abnormal behavior of the user is calculated through the abnormality judgment model, the abnormal power consumption data judgment of various factors is integrated into the comprehensive index judgment of the abnormal behavior of the user, the efficiency and accuracy of the abnormal power consumption behavior judgment of the user can be improved, abnormal data can be rapidly recognized, measures can be timely taken, and loss is avoided.
In a third aspect, the present application discloses a terminal device, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor, where the processor adopts the above-mentioned intelligent power monitoring method when loading and executing the computer program.
By adopting the technical scheme, the computer program is generated by the intelligent power monitoring method and is stored in the memory to be loaded and executed by the processor, so that the terminal equipment is manufactured according to the memory and the processor, and the use of a user is facilitated.
In a fourth aspect, the present application discloses a computer readable storage medium, which adopts the following technical scheme: a computer readable storage medium having a computer program stored therein, which when loaded and executed by a processor, employs the intelligent power monitoring method described above.
By adopting the technical scheme, the intelligent power monitoring method generates a computer program, stores the computer program in a computer readable storage medium to be loaded and executed by a processor, and facilitates the reading and storage of the computer program through the computer readable storage medium.
Drawings
FIG. 1 is a flow chart of a method of steps S1-S5 in an intelligent power monitoring method of the present application.
Fig. 2 is a flow chart of a method of steps S10-S13 in an intelligent power monitoring method according to the present application.
Fig. 3 is a flowchart of a method of steps S20-S24 in an intelligent power monitoring method according to the present application.
Fig. 4 is a flowchart of a method of steps S30-S31 in an intelligent power monitoring method according to the present application.
Fig. 5 is a flowchart of a method of steps S40-S41 in an intelligent power monitoring method according to the present application.
Detailed Description
The application is described in further detail below with reference to fig. 1-5.
The embodiment of the application discloses an intelligent power monitoring method, referring to fig. 1, which comprises the following steps S1-S5:
s1, acquiring historical electricity utilization data and dividing user attributes. Referring to fig. 2, step S1 includes steps S10 to S13, specifically as follows:
s10, acquiring collected historical electricity utilization data.
A plurality of smart meters are provided in, for example, a residential building, a factory building, or a large commercial super-building, and output collected data through a connection network and store the data in a database. Historical electricity usage data may include power consumption, voltage, current, etc. per minute or hour.
And S11, carrying out data processing on the electricity consumption data.
And performing operations such as de-duplication, outlier processing, missing value filling and the like on the acquired data so as to ensure the quality and accuracy of the data.
And S12, storing the processed data in the cloud.
The processed electricity data is stored, and the processed data can be stored on a cloud database through cloud computing and cloud storage technology, for example, a AWS (Amazon Web Services) or Azure (Microsoft Azure) cloud platform is adopted. The cloud storage has the advantages that data interaction and sharing can be conveniently carried out, and meanwhile, the safety and reliability of data can be ensured.
And S13, establishing a data model for dividing the user attributes.
And establishing a data model, importing historical data into the data model, and dividing users into different categories according to user attributes, such as household users, business users, industrial users and the like.
And a user ID is set for each user and is used as a unique identifier, so that statistics and tracking in the analysis process are facilitated. Associating user information by user ID, the information of the user including: geographic location may be determined by its zip code, name of a city or county; the power types, such as coal-fired power, wind power generation and the like, can be divided according to the region where the user is located or the power supply region of a power supply company; the electricity consumption is calculated according to the period, such as month, quarter and year, so that the electricity consumption is compared and analyzed; and electricity consumption is acquired to acquire the consumption condition of the electricity consumption user so as to analyze and compare the consumption. Peak/valley power usage; the user's situation in terms of peak or valley electricity usage may help determine price policies and plan power supplies.
S2, constructing an intelligent monitoring model according to the user attribute and the historical electricity consumption data so as to identify abnormal data; referring to fig. 3, step S2 includes S20 to S24, specifically as follows:
and S20, setting a data sample with abnormal electricity consumption.
The data sample records various abnormal electricity consumption conditions, such as that a user uses 20 times of the average electricity consumption in a certain month. In a certain period of time, the power consumption of the user is 5 times higher than that of the user under normal conditions. The 20% of the electricity fee in the electricity bill of the user is found to be "unknown source", i.e., not recorded in the use of any electric appliance. The electricity fee expenditure of a certain user in a week is 50% higher than that of the user in normal conditions, but no emergency or special activity occurs. These data samples may indicate that there is a theft or power anomaly.
And S21, carrying out data processing on the user attribute and the historical electricity utilization data.
Specifically, the method comprises the following steps: missing value processing, outlier processing, data normalization and feature selection. For missing values, average, median, mode, etc. may be filled. For outlier handling: outliers may be processed using scaling or deleting outliers. In data normalization, min-max normalization or Z-score normalization is used to normalize features with different scales. For feature selection, features that most affect the output are selected by analyzing the correlation coefficients, variance thresholds, PCA and other methods.
S22, converting the user attribute into a numerical type characteristic.
User attributes are converted into numeric features so that they can be used for model training. For example, residential and commercial electricity users may be converted to 1 and 0, and other attributes, such as residence, etc., may be converted using one-hot coding.
S23, performing model training by using historical electricity consumption data and data samples with abnormal electricity consumption.
Prior to training, the segmentation data is a training set and a test set, for example, a model training may be performed using a machine learning algorithm such as linear regression, random forest, support vector machine, and the like. And predicting the electricity consumption abnormality by using the trained model to perform early warning, evaluating and analyzing the prediction result according to historical electricity consumption data and other related influence factors, comparing the difference between the prediction value and the actual value, finding out the reason for the difference, and optimizing the model and the algorithm.
And S24, optimizing the model and adjusting parameters.
And optimizing the model and adjusting parameters, selecting the most suitable model and adjusting parameters of the model to obtain the best performance.
And S3, removing abnormal data due to untimely updating in the identification result of the abnormal data according to preset user updating information. Referring to fig. 4, step S3 includes steps S30 to S31, specifically as follows:
s30, acquiring update information of the user.
The method comprises the steps of calling updated information of a user, wherein the updated information comprises a new address of the user, changing a power consumption mode, reporting abnormal power consumption conditions, installing or changing an ammeter, paying conditions, maintaining electric power facilities and the like. For example, if a user reports a fault in a household electricity meter, the updated information may include information about the specific condition of the fault in the electricity meter, the time of the fault, maintenance personnel, etc. This information can be retrieved and recorded by the system for subsequent handling of the fault of the electricity meter.
And S31, removing error data in the abnormal data according to the updated information.
If the updated information contains information conforming to the abnormal data, the abnormal data is updated or deleted, the abnormal data is prevented from affecting the subsequent data processing result, and the quality and the accuracy of the data are ensured.
And the data is updated after the abnormal data is screened, so that the calculated amount of the data is reduced compared with the updating when the real-time data is initially acquired.
And S4, acquiring the real-time electricity consumption data, calculating the comprehensive index of the abnormal behavior of the user, judging the abnormality of the user when the comprehensive index accords with a preset threshold value, and generating an electricity consumption abnormality list. Referring to fig. 5, step S4 includes steps S40 to S41, specifically as follows:
s40, setting a calculation formula of the comprehensive index of the abnormal behavior of the user;
the comprehensive index of abnormal behavior of the user is = Σ (multiple abnormal electricity utilization results are corresponding weights of multiple abnormal electricity utilization results)/total weight, and the multiple abnormal electricity utilization results comprise electricity consumption amount, electricity consumption period and electricity consumption load,
the comprehensive index of the abnormal behavior of the user= (the abnormal result of the electricity consumption is the electricity consumption weight+the abnormal result of the electricity consumption period is the electricity consumption period weight+the abnormal result of the electricity consumption load is the electricity consumption load weight)/the total weight, and the electricity consumption weight, the electricity consumption period weight and the electricity consumption load weight are all nonnegative numbers, and the sum of the electricity consumption weight, the electricity consumption period weight and the electricity consumption load weight is the total weight.
Specifically, for each index, normalization processing is performed so that the value of each index is between 0 and 1, each index is multiplied by a corresponding weight, and finally the values obtained by multiplying all the indexes by the weights are added up, so that the value of the comprehensive index of the abnormal behavior can be obtained.
The abnormal result of the electricity consumption is obtained by comparing the difference value between the average value of the electricity consumption of the user and the average value of the historical electricity consumption, and the greater the difference value is, the higher the degree of abnormality is. The abnormal result of the electricity consumption period is calculated by the difference time between the specific time period of electricity consumption behavior of the user and the historical electricity consumption time of the user, and the greater the value of the difference time is, the higher the degree of abnormality is. The abnormal result of the power consumption load is calculated according to the difference between the current power consumption load of the user and the average value of the historical power consumption load of the user, and the greater the load difference is, the higher the degree of abnormality is, and the greater the abnormal result of the power consumption load is.
The electricity consumption weight, the electricity consumption period weight and the electricity consumption load weight can be set according to the actual situation of a user, and the total weight needs to ensure that the sum of the three weights is 1. And obtaining the comprehensive index of the abnormal behavior of the user by weighted average of the three abnormal results, and judging whether the abnormal behavior exists in the user.
For example, assuming that the abnormal result of the electricity consumption of a certain user is 0.8, the abnormal result of the electricity consumption period is 0.6, the abnormal result of the electricity consumption load is 0.9, the electricity consumption weight is 0.3, the weight of the electricity consumption period is 0.5, and the weight of the electricity consumption load is 0.2, the comprehensive index of the abnormal behavior of the user is:
comprehensive index of user abnormal behavior= (0.8×0.3+0.6×0.5+0.9×0.2)/(0.3+0.5+0.2) =0.733; the weight of the abnormal electricity consumption result is 0.3, which accounts for 30% of the total weight; the weight of the abnormal result of the electricity consumption period is 0.5, which accounts for 50% of the total weight; the weight of the abnormal result of the electric load is 0.2, which accounts for 20 percent of the total weight. The value range of the comprehensive index of the abnormal behavior of the user is 0-1, and the larger the numerical value is, the larger the relative strength of the abnormal behavior is.
S41, judging an abnormal behavior result.
Importing the abnormal data into a calculation formula of the set comprehensive index of the abnormal behavior of the user, obtaining the comprehensive index of the abnormal behavior of the user, comparing the comprehensive index of the abnormal behavior of the user with a preset threshold index,
when the comprehensive index of the abnormal behavior of the user is greater than or equal to the threshold index, judging that the abnormal behavior of the user exists, recording the abnormal behavior of the power utilization, generating an abnormal power utilization list, and sending the abnormal power utilization list to the central console.
And when the comprehensive index of the abnormal behavior of the user is smaller than the threshold index, judging that the abnormal electric behavior of the user does not exist.
The implementation principle of the intelligent power monitoring method provided by the embodiment of the application is as follows: by constructing an intelligent monitoring model, automatic monitoring and recognition of abnormal electricity consumption are realized, abnormal electricity consumption data judgment of various factors is integrated into comprehensive index judgment of abnormal user behaviors by calculating comprehensive indexes of abnormal user behaviors, and the three indexes are comprehensively calculated and weighted to calculate the comprehensive indexes of the abnormal user behaviors, so that the overall abnormal behavior condition of the user can be reflected more accurately. The comprehensive calculation of the multiple indexes can also effectively avoid the overlarge influence of a single index on the judgment result, so that a more objective, comprehensive and reasonable judgment result is obtained, and the efficiency and accuracy of the abnormal electricity behavior judgment of the user are improved.
The embodiment of the application also discloses an intelligent power monitoring system, which comprises a database module, a data processing module and a data processing module, wherein the database module acquires historical power utilization data and divides user attributes; the model construction module is used for constructing an intelligent monitoring model according to the user attributes and the historical electricity consumption data so as to identify abnormal data; the data processing module is used for removing abnormal data which are abnormal due to untimely updating in the identification result of the abnormal data according to preset user updating information; and the abnormality judging module is used for acquiring the electricity utilization real-time data, calculating the comprehensive index of the abnormal behavior of the user, judging the abnormality of the user when the comprehensive index accords with a preset threshold value, and generating an electricity utilization abnormality list.
The implementation principle of the intelligent power monitoring system provided by the embodiment of the application is as follows: the intelligent monitoring model is constructed through the database module and the model construction module, automatic monitoring is realized, the abnormal electricity consumption phenomenon is identified, the comprehensive index of the abnormal user behavior is calculated through the abnormal judgment model, the abnormal electricity consumption data judgment of various factors is integrated into the comprehensive index judgment of the abnormal user behavior, the efficiency and the accuracy of the abnormal user electricity consumption behavior judgment can be improved, the abnormal data can be identified quickly, measures can be taken timely, and loss is avoided.
The embodiment of the application also discloses a terminal device which comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the intelligent power monitoring method of the embodiment is adopted when the processor executes the computer program.
The terminal device may be a computer device such as a desktop computer, a notebook computer, or a cloud server, and the terminal device includes, but is not limited to, a processor and a memory, for example, the terminal device may further include an input/output device, a network access device, a bus, and the like.
The processor may be a Central Processing Unit (CPU), or of course, according to actual use, other general purpose processors, digital Signal Processors (DSP), application Specific Integrated Circuits (ASIC), ready-made programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc., and the general purpose processor may be a microprocessor or any conventional processor, etc., which is not limited in this respect.
The memory may be an internal storage unit of the terminal device, for example, a hard disk or a memory of the terminal device, or an external storage device of the terminal device, for example, a plug-in hard disk, a Smart Memory Card (SMC), a secure digital card (SD), or a flash memory card (FC) provided on the terminal device, or the like, and may be a combination of the internal storage unit of the terminal device and the external storage device, where the memory is used to store a computer program and other programs and data required by the terminal device, and the memory may be used to temporarily store data that has been output or is to be output, which is not limited by the present application.
The intelligent power monitoring method of the embodiment is stored in the memory of the terminal device through the terminal device, and is loaded and executed on the processor of the terminal device, so that the user can use the intelligent power monitoring method conveniently.
The embodiment of the application also discloses a computer readable storage medium, and the computer readable storage medium stores a computer program, wherein the intelligent power monitoring method of the embodiment is adopted when the computer program is executed by a processor.
The computer program may be stored in a computer readable medium, where the computer program includes computer program code, where the computer program code may be in a source code form, an object code form, an executable file form, or some middleware form, etc., and the computer readable medium includes any entity or device capable of carrying the computer program code, a recording medium, a usb disk, a removable hard disk, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a Random Access Memory (RAM), an electrical carrier signal, a telecommunication signal, a software distribution medium, etc., where the computer readable medium includes, but is not limited to, the above components.
The intelligent power monitoring method of the embodiment is stored in the computer readable storage medium through the computer readable storage medium, and is loaded and executed on a processor so as to facilitate storage and application.
The foregoing description of the preferred embodiments of the application is not intended to limit the scope of the application in any way, including the abstract and drawings, in which case any feature disclosed in this specification (including abstract and drawings) may be replaced by alternative features serving the same, equivalent purpose, unless expressly stated otherwise. That is, each feature is one example only of a generic series of equivalent or similar features, unless expressly stated otherwise.
Claims (10)
1. An intelligent power monitoring method is characterized by comprising the following steps:
acquiring historical electricity utilization data and dividing user attributes;
according to the user attributes and the historical electricity consumption data, an intelligent monitoring model is constructed to identify abnormal data;
removing abnormal data due to untimely updating in the identification result of the abnormal data according to preset user updating information;
and acquiring power utilization real-time data, calculating comprehensive indexes of abnormal behaviors of the user, judging that the user is abnormal when the comprehensive indexes are more than a preset threshold value, and generating a power utilization abnormal list.
2. The intelligent power monitoring method according to claim 1, wherein, in the calculating the comprehensive index of the abnormal behavior of the user, the comprehensive index of the abnormal behavior of the user= (the power consumption abnormal result, the power consumption weight, the power consumption period abnormal result, the power consumption period weight, the power consumption load weight)/the total weight is equal to the sum of the power consumption weight, the power consumption period weight, the power consumption load weight and the power consumption load weight, which is a non-negative number.
3. The intelligent power monitoring method of claim 2, wherein,
the abnormal result of the electricity consumption period is calculated by the difference time between the specific time period of electricity consumption behavior of the user and the historical electricity consumption time of the user, and the greater the value of the difference time is, the higher the degree of abnormality is.
4. The intelligent power monitoring method of claim 2, wherein,
the abnormal electricity consumption result is obtained by comparing the difference value between the average value of the electricity consumption of the user and the average value of the historical electricity consumption, and the greater the difference value is, the higher the degree of abnormality is.
5. The intelligent power monitoring method according to claim 2, wherein the abnormal result of the power consumption load is calculated according to a difference between a current power consumption load of a user and an average value of historical power consumption loads of the user, and the greater the load difference is, the higher the degree of abnormality is, and the greater the abnormal result of the power consumption load is.
6. The intelligent power monitoring method according to claim 1, wherein the constructing an intelligent monitoring model according to the user attribute and the historical power consumption data comprises the following steps:
setting a data sample with abnormal electricity consumption;
performing data processing on the user attributes and the historical electricity consumption data;
converting the user attribute into a numerical feature;
performing model training by using the historical electricity consumption data and the data samples of the electricity consumption abnormality;
and (5) optimizing the model and adjusting parameters.
7. The intelligent power monitoring method according to any one of claims 1 to 6, wherein the step of removing, from the abnormal data that is generated due to untimely updating in the identification result of the abnormal data, according to the preset user update information, includes the steps of:
acquiring update information of a user;
and removing error data in the abnormal data according to the updated information.
8. An intelligent power monitoring system, characterized in that the intelligent power monitoring method according to any one of claims 1 to 7 is used, comprising:
the database module is used for acquiring historical electricity utilization data and dividing user attributes;
the model construction module is used for constructing an intelligent monitoring model according to the user attributes and the historical electricity consumption data so as to identify abnormal data;
the data processing module is used for removing abnormal data which are abnormal due to untimely updating in the identification result of the abnormal data according to preset user updating information;
and the abnormality judging module is used for acquiring the electricity utilization real-time data, calculating the comprehensive index of the abnormal behavior of the user, judging the abnormality of the user when the comprehensive index accords with a preset threshold value, and generating an electricity utilization abnormality list.
9. A terminal device comprising a memory, a processor and a computer program stored in the memory and capable of running on the processor, characterized in that the intelligent power monitoring method according to any of claims 1-7 is used when the computer program is loaded and executed by the processor.
10. A computer readable storage medium having a computer program stored therein, wherein the computer program, when loaded and executed by a processor, employs the intelligent power monitoring method of any of claims 1-7.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117290797A (en) * | 2023-11-24 | 2023-12-26 | 国网山东省电力公司济宁供电公司 | Building energy consumption prediction method, system, device and medium |
| CN117491792A (en) * | 2023-12-27 | 2024-02-02 | 四川中威能电力科技有限公司 | Power consumption abnormality detection method, system and storage medium based on intelligent ammeter |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117290797A (en) * | 2023-11-24 | 2023-12-26 | 国网山东省电力公司济宁供电公司 | Building energy consumption prediction method, system, device and medium |
| CN117290797B (en) * | 2023-11-24 | 2024-02-02 | 国网山东省电力公司济宁供电公司 | Building energy consumption prediction method, system, device and medium |
| CN117491792A (en) * | 2023-12-27 | 2024-02-02 | 四川中威能电力科技有限公司 | Power consumption abnormality detection method, system and storage medium based on intelligent ammeter |
| CN117491792B (en) * | 2023-12-27 | 2024-03-22 | 四川中威能电力科技有限公司 | Power consumption abnormality detection method, system and storage medium based on intelligent ammeter |
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