CN112906736A - Community safety accurate management and control method and system based on household electricity consumption - Google Patents
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Abstract
The invention relates to a community safety accurate control method and a community safety accurate control system based on household electricity consumption, wherein the method comprises the following steps: s1: the electricity utilization information acquisition system acquires electricity utilization data of all residents in the whole community, and integrates the electricity utilization data to obtain basic data; s2: carrying out data preprocessing on the basic data; s3: selecting model variables required by modeling; s4: carrying out data standardization on the model variable values, and then carrying out clustering analysis to obtain the group classification of the residents; s5: excavating the electricity utilization characteristics and rules of each classified household, and establishing a safety control model, wherein the safety control model comprises a frequent night returning personnel model, a group renting house model, a daily renting short renting model, a suspected idle house model, a suspected reimbursement model and a suspected idle house living model; s6: and the community security department analyzes and judges community residents according to the security control model. The method fully excavates the social value of the power data and realizes safe and accurate management and control of communities.
Description
Technical Field
The invention belongs to the technical field of community safety control, and relates to a community safety accurate control method and a community safety accurate control system, in particular to a community safety accurate control method and a community safety accurate control system based on household power consumption.
Background
With the coming of the information era, the traditional social security prevention and control system is difficult to adapt to the requirements of the ever-changing social security situation, and the establishment of the social security prevention and control system based on the information era is urgently needed, so that the working efficiency of public security organs and the integral driving capability of the social security are comprehensively improved. In recent years, the construction of social security prevention and control systems is in a mature stage, and the community security management is gradually developed towards intellectualization and remote.
At present, in order to strengthen the comprehensive establishment of a three-dimensional and information-based social security and prevention system in various places, the ministry of public security proposes to fully utilize Internet of things sensing facilities and equipment, accelerate the application of information-based scientific and technological means such as intelligent sensing and intelligent acquisition, and emphasize the application of energy data mining to security analysis and judgment. The working process shows that the electricity consumption data volume is large, potential safety hazards exist in other government departments when user data are shared, and the practical application is not deeply combined with public security services, so that a safe and effective electricity consumption behavior studying and judging system based on electricity consumption is not provided for a resident to assist the public security departments in carrying out safe and accurate community management and control. This is a disadvantage of the prior art.
Therefore, aiming at the above defects in the prior art, it is very necessary to provide a method and a system for accurately managing and controlling community safety based on electricity consumption of residents to solve the problems in the prior art.
Disclosure of Invention
The invention aims to provide a community safety accurate control method and system based on household power consumption, aiming at the problems that the electric power data volume is large and a household power consumption behavior research and judgment system is lacked to assist the community to control security accurately, so as to solve the technical problems, mine the social value of the electric power data and strengthen the community safety accurate control.
In order to achieve the purpose, the invention provides the following technical scheme:
a community safety accurate management and control method based on household electricity consumption comprises the following steps:
s1: acquiring power consumption data of all residents in the whole community by using a power consumption information acquisition system of a power grid, and acquiring basic data by integrating the power consumption data;
s2: carrying out data preprocessing on the basic data, clearing abnormal values, correcting error data and ensuring the integrity and consistency of the data;
s3: selecting model variables required by modeling;
s4: carrying out data standardization on the model variable values, carrying out cluster analysis after the data standardization, combining the data objects with larger similarity together, dispersing residents with different characteristics, and carrying out resident group classification;
s5: excavating the electricity utilization characteristics and rules of each classified household, and establishing a safety control model, wherein the safety control model comprises a frequent night returning personnel model, a group renting house model, a daily renting short renting model, a suspected idle house model, a suspected reimbursement model and a suspected idle house living model;
s6: and the community security department analyzes and judges community residents according to the security control model, and accurately controls the security of the community residents.
Preferably, the data preprocessing in the step S2 includes data cleaning, data integration, data transformation and data reduction, the data cleaning includes performing interpolation, smoothing noise data, and identifying or deleting outliers on incomplete data, the data integration combines and uniformly stores data in a plurality of data sources, the data exchange process converts the data into a form suitable for data mining through smooth aggregation, data generalization and data normalization, and the huge data amount is subjected to data reduction to obtain a reduced representation of the data set; the basic data is processed through data preprocessing, so that the data mining quality is effectively improved, and the time required by actual mining is reduced.
Preferably, the model variables in step S3 include household electricity meter profile information, peak power consumption rate, valley power consumption rate, average power consumption rate, daytime electricity consumption, nighttime electricity consumption, household electricity consumption peak time, daily electricity consumption, monthly electricity consumption, and annual electricity consumption.
Preferably, in step S4, the data normalization process is as follows:
firstly, the variable value of the model is standardized, the dimension is eliminated, the influence of each attribute on the distance is balanced, and the standardized formula is as follows: wherein xiThe variable is represented by a number of variables,
denotes mean value, δ denotes standard deviation, x'iRepresenting normalized data;
then, converting the discrete data, deriving a new variable, and converting the discrete variable into a continuous variable; and finally, filtering the boundary values of the various standardized variables of the cluster before cluster analysis, eliminating interference, and removing boundary points outside 3 times of standard deviation.
Preferably, the clustering analysis in the step S4 uses a K-means algorithm and is improved;
firstly, inputting the clustering number N and a data set;
the second step calculates the distance d (x) between the data points according to the Euclidean distance calculation formulai,yi)；
Third step calculate the mean distance of the data set
The average distance calculation formula is as follows:
and according to the distance value d (x) between the data pointsi,yi) And average distance
Calculating the density of the data points to obtain a density set M;
taking the data point with the maximum density in the density set M as a first initial clustering center, taking the data point with the second maximum density as a second clustering center, and so on to obtain all clustering centers;
fifthly, calculating a distance value between each data point and a cluster center by using an Euclidean distance formula, and classifying according to the distance value to form clusters;
sixthly, calculating the mean value of the clustering samples;
and seventhly, calculating and judging whether the standard error function is converged, wherein a calculation formula of the standard error function is as follows:
where N is the number of clusters, xmRepresents a variable, niIndicates the number of class i samples, μiMeans representing the mean of the class i samples; finishing the algorithm when the standard error function is converged, otherwise, returning to the fifth step for classification;
community residents with the same electricity utilization behavior characteristics are gathered together through an improved K-means cluster analysis algorithm, community residents with different characteristics are scattered, the residents can be classified, and the algorithm is simple and high in convergence speed.
Preferably, in step S5, the step of mining the electricity consumption characteristics and rules of each classified household is as follows: firstly, drawing a power utilization curve of each type of household, observing the change condition of the power utilization curve within a period of time, and analyzing each power utilization device of the household through mutation information in the power utilization curve; then, sequencing all the electric equipment of the type of household according to time, constructing an electric behavior sequence of the type of household, and combining a time sequence incidence relation analysis algorithm to obtain electric behavior characteristics and rules of the type of household; finally, a safety control model is constructed according to the electricity utilization behavior characteristics of various residents; and the power utilization characteristics and rules of each classified household are mined and analyzed, so that different models can be conveniently constructed subsequently according to the power utilization characteristics and rules.
Preferably, in step S6, after the community security department analyzes and judges the community residents according to the security management and control model, the community security department classifies and records the results of the analysis and judgment, and periodically visits the residents with potential safety hazards, thereby implementing accurate management and control.
The invention also provides a community safety accurate control system based on the electricity consumption of residents, which comprises an electricity consumption data acquisition unit, wherein data obtained by the electricity consumption data acquisition unit enters a data preprocessing unit and enters an analysis modeling unit after passing through the data preprocessing unit, the analysis modeling unit comprises a data standardization subunit, a cluster analysis subunit and a model generation subunit, the analysis modeling unit is connected with a model verification unit, the model verification unit judges whether the electricity consumption data of the community residents meet various model conditions or not, and a verification result is written in a community control information table; the power utilization data of residents in the power grid system are acquired through the power utilization data acquisition unit, the data preprocessing unit performs data cleaning, data integration, data transformation and data reduction on the power utilization data, the quality of data required by data mining is improved, clustering analysis is performed on community residents through the analysis modeling unit, different models are built by combining power utilization characteristics of various residents, and finally the community residents are verified and analyzed through the model verification unit, the results are recorded, and safety management and control are performed on the community conveniently.
Preferably, the model generation subunit is connected with the clustering analysis subunit, analyzes different types of residents, and respectively generates a frequent night returning person model, a group renting house model, a daily renting short renting model, a suspected idle house model, a suspected reimbursement model and a suspected empty house living model according to the electricity utilization characteristics of the various types of residents; and analyzing the electricity consumption data of community residents to form various models for the community security department to study and judge.
The community security protection system has the advantages that electricity consumption data of community residents are collected, the electricity consumption data are preprocessed, then models are built by using a data mining and data analysis method, and a community security protection department conducts research and judgment analysis according to the provided models, so that residents with safety risks such as frequent night returning residents, group renting residents, suspected sales residents and the like are controlled in an enhanced mode, and classified management of the residents in the community and accurate control of the safety of the residents are achieved; by utilizing the community safety accurate control method and system based on the electricity consumption of the residents, the accurate strike of public security can be realized, the social value of the electric power data is released, and the government social improvement requirement is met. In addition, the invention has reliable design principle, simple structure and very wide application prospect.
Therefore, compared with the prior art, the invention has prominent substantive features and remarkable progress, and the beneficial effects of the implementation are also obvious.
Drawings
In order to more clearly illustrate the embodiments or technical solutions in the prior art of the present invention, the drawings used in the description of the embodiments or prior art will be briefly described below, and it is obvious for those skilled in the art that other drawings can be obtained based on these drawings without creative efforts.
Fig. 1 is a schematic flow chart of a community security accurate management and control method based on household power consumption provided by the present invention.
FIG. 2 is a flow chart of cluster analysis.
Fig. 3 is a structural block diagram of a community security precise control system based on household electricity consumption provided by the present invention.
The system comprises an electricity consumption data acquisition unit, a data preprocessing unit, an analysis modeling unit, a data standardization subunit, a clustering analysis subunit, a model generation subunit, a 4 model verification unit and a community management and control information table, wherein the electricity consumption data acquisition unit is 1, the data preprocessing unit is 2, the analysis modeling unit is 3, the data standardization subunit is 3.1, the cluster analysis subunit is 3.2, the model generation subunit is 3.3, the model verification unit is 4, and the community management and control information.
Detailed Description
The present invention will be described in detail below with reference to the accompanying drawings by way of specific examples, which are illustrative of the present invention and are not limited to the following embodiments.
Example 1:
as shown in fig. 1, the method for accurately controlling community security based on electricity consumption of residents provided by the present invention includes the following steps:
s1: acquiring power consumption data of all residents in the whole community by using a power consumption information acquisition system of a power grid, and acquiring basic data by integrating the power consumption data;
s2: carrying out data preprocessing on the basic data, clearing abnormal values, correcting error data and ensuring the integrity and consistency of the data;
the data preprocessing comprises data cleaning, data integration, data transformation and data reduction, the data cleaning comprises the steps of compensating incomplete data, smoothing noise data, and identifying or deleting outliers, the data integration combines and uniformly stores data in a plurality of data sources, the data exchange process converts the data into a form suitable for data mining through smooth aggregation, data generalization and data normalization, and the huge data amount is subjected to data reduction to obtain a reduction representation of the data set; the basic data is processed through data preprocessing, so that the data mining quality is effectively improved, and the time required by actual mining is reduced.
S3: selecting model variables required by modeling;
the model variables comprise household electric meter archive information, peak power consumption rate, valley power consumption rate, average power consumption rate, power consumption in daytime, power consumption in night, household power consumption peak time, daily power consumption, monthly power consumption and annual power consumption.
S4: carrying out data standardization on the model variable values, carrying out cluster analysis after the data standardization, combining the data objects with larger similarity together, dispersing residents with different characteristics, and carrying out resident group classification;
firstly, the variable value of the model is standardized, the dimension is eliminated, the influence of each attribute on the distance is balanced, and the standardized formula is as follows: wherein xiThe variable is represented by a number of variables,
denotes mean value, δ denotes standard deviation, x'iRepresenting normalized data;
then, converting the discrete data, deriving a new variable, and converting the discrete variable into a continuous variable; and finally, filtering the boundary values of the various standardized variables of the cluster before cluster analysis, eliminating interference, and removing boundary points outside 3 times of standard deviation.
After the data processing is completed, cluster analysis is performed, as shown in fig. 2, the cluster analysis adopts a K-means algorithm and is improved:
firstly, inputting the clustering number N and a data set;
the second step calculates the distance d (x) between the data points according to the Euclidean distance calculation formulai,yi)；
Third step calculate the mean distance of the data set
The average distance calculation formula is as follows:
and according to the distance value d (x) between the data pointsi,yi) And average distance
Calculating the density of the data points to obtain a density set M;
taking the data point with the maximum density in the density set M as a first initial clustering center, taking the data point with the second maximum density as a second clustering center, and so on to obtain all clustering centers;
fifthly, calculating a distance value between each data point and a cluster center by using an Euclidean distance formula, and classifying according to the distance value to form clusters;
sixthly, calculating the mean value of the clustering samples;
and seventhly, calculating and judging whether the standard error function is converged, wherein a calculation formula of the standard error function is as follows:
where N is the number of clusters, xmRepresents a variable, niIndicates the number of class i samples, μiMeans representing the mean of the class i samples; finishing the algorithm when the standard error function is converged, otherwise, returning to the fifth step for classification;
community residents with the same electricity utilization behavior characteristics are gathered together through an improved K-means cluster analysis algorithm, community residents with different characteristics are scattered, the residents can be classified, and the algorithm is simple and high in convergence speed.
S5: excavating the electricity utilization characteristics and rules of each classified household, and establishing a safety control model, wherein the safety control model comprises a frequent night returning personnel model, a group renting house model, a daily renting short renting model, a suspected idle house model, a suspected reimbursement model and a suspected idle house living model;
the steps of mining the electricity utilization characteristics and rules of each classified household are as follows: firstly, drawing a power utilization curve of each type of household, observing the change condition of the power utilization curve within a period of time, and analyzing each power utilization device of the household through mutation information in the power utilization curve; then, sequencing all the electric equipment of the type of household according to time, constructing an electric behavior sequence of the type of household, and combining a time sequence incidence relation analysis algorithm to obtain electric behavior characteristics and rules of the type of household; finally, a safety control model is constructed according to the electricity utilization behavior characteristics of various residents; and the power utilization characteristics and rules of each classified household are mined and analyzed, so that different models can be conveniently constructed subsequently according to the power utilization characteristics and rules.
S6: and the community security department analyzes and judges community residents according to the safety control model, classifies and records the analysis and judgment results, and regularly visits residents with potential safety hazards to realize accurate control on the safety of the community residents.
Example 2:
as shown in fig. 3, the invention further provides a community safety accurate management and control system based on household electricity consumption, which includes an electricity consumption data acquisition unit 1, data obtained by the electricity consumption data acquisition unit 1 enters a data preprocessing unit 2, and enters an analysis modeling unit 3 after passing through the data preprocessing unit 2, the analysis modeling unit 3 includes a data standardization subunit 3.1, a cluster analysis subunit 3.2 and a model generation subunit 3.3, the analysis modeling unit 3 is connected with a model verification unit 4, the model verification unit 4 judges whether the electricity consumption data of community households meet various model conditions, and writes a verification result into a community management and control information table 5; the power utilization data of residents in a power grid system are acquired through the power utilization data acquisition unit 1, the data preprocessing unit 2 performs data cleaning, data integration, data transformation and data reduction on the power utilization data, the quality of data required by data mining is improved, clustering analysis is performed on community residents through the analysis modeling unit 3, different models are built by combining the power utilization characteristics of various residents, and finally, the community residents are verified and analyzed through the model verification unit 4, the results are recorded, and safety management and control are performed on the community conveniently.
In this embodiment, the model generation subunit 3.3 is connected to the cluster analysis subunit 3.2, and analyzes different types of residents, and generates a frequent night returning member model, a group renting house model, a daily renting short renting model, a suspected idle house model, a suspected distribution model and a suspected empty house living model according to the electricity consumption characteristics of the various types of residents; and analyzing the electricity consumption data of community residents to form various models for the community security department to study and judge.
The above disclosure is only for the preferred embodiments of the present invention, but the present invention is not limited thereto, and any non-inventive changes that can be made by those skilled in the art and several modifications and amendments made without departing from the principle of the present invention shall fall within the protection scope of the present invention.
Claims (9)
1. A community safety accurate management and control method based on household electricity consumption is characterized by comprising the following steps:
s1: acquiring power consumption data of all residents in the whole community by using a power consumption information acquisition system of a power grid, and acquiring basic data by integrating the power consumption data;
s2: carrying out data preprocessing on the basic data, clearing abnormal values, correcting error data and ensuring the integrity and consistency of the data;
s3: selecting model variables required by modeling;
s4: carrying out data standardization on the model variable values, carrying out cluster analysis after the data standardization, combining the data objects with larger similarity together, dispersing residents with different characteristics, and carrying out resident group classification;
s5: excavating the electricity utilization characteristics and rules of each classified household, and establishing a safety control model, wherein the safety control model comprises a frequent night returning personnel model, a group renting house model, a daily renting short renting model, a suspected idle house model, a suspected reimbursement model and a suspected idle house living model;
s6: and the community security department analyzes and judges community residents according to the security control model, and accurately controls the security of the community residents.
2. The method for community safety precise control based on electricity consumption of residents according to claim 1, wherein the data preprocessing in the step S2 includes data cleaning, data integration, data transformation and data reduction, the data cleaning includes compensating incomplete data, smoothing noise data, and identifying or deleting outliers, the data integration combines and uniformly stores data in a plurality of data sources, the data exchange process converts data into a form suitable for data mining through smooth aggregation, data generalization and data normalization, and the huge data reduction obtains a reduced representation of a data set.
3. The method as claimed in claim 2, wherein the model variables in step S3 include household electricity meter profile information, peak power consumption rate, valley power consumption rate, average power consumption rate, daytime power consumption, nighttime power consumption, household peak power consumption, daily power consumption, monthly power consumption, and annual power consumption.
4. The method as claimed in claim 3, wherein in the step S4, the data standardization process is as follows:
firstly, the variable value of the model is standardized, the dimension is eliminated, the influence of each attribute on the distance is balanced, and the standardized formula is as follows: wherein xiThe variable is represented by a number of variables,
denotes mean value, δ denotes standard deviation, x'iRepresenting normalized data;
then, converting the discrete data, deriving a new variable, and converting the discrete variable into a continuous variable; and finally, filtering the boundary values of the various standardized variables of the cluster before cluster analysis, eliminating interference, and removing boundary points outside 3 times of standard deviation.
5. The method as claimed in claim 4, wherein the clustering analysis in step S4 is improved by using a K-means algorithm;
firstly, inputting the clustering number N and a data set;
the second step calculates the distance d (x) between the data points according to the Euclidean distance calculation formulai,yi)；
Third step calculate the mean distance of the data set
The average distance calculation formula is as follows:
and according to the distance value d (x) between the data pointsi,yi) And average distance
Calculating the density of the data points to obtain a density set M;
taking the data point with the maximum density in the density set M as a first initial clustering center, taking the data point with the second maximum density as a second clustering center, and so on to obtain all clustering centers;
fifthly, calculating a distance value between each data point and a cluster center by using an Euclidean distance formula, and classifying according to the distance value to form clusters;
sixthly, calculating the mean value of the clustering samples;
and seventhly, calculating and judging whether the standard error function is converged, wherein a calculation formula of the standard error function is as follows:
where N is the number of clusters, xmRepresents a variable, niIndicates the number of class i samples, μiMeans representing the mean of the class i samples; and ending the algorithm when the standard error function converges, and returning to the fifth step for classification if the standard error function converges.
6. The method as claimed in claim 5, wherein the step of mining the electricity consumption characteristics and rules of each classified household in the step S5 is as follows: firstly, drawing a power utilization curve of each type of household, observing the change condition of the power utilization curve within a period of time, and analyzing each power utilization device of the household through mutation information in the power utilization curve; then, sequencing all the electric equipment of the type of household according to time, constructing an electric behavior sequence of the type of household, and combining a time sequence incidence relation analysis algorithm to obtain electric behavior characteristics and rules of the type of household; and finally, constructing a safety control model according to the electricity utilization behavior characteristics of various residents.
7. The method as claimed in claim 6, wherein in step S6, after the community security department analyzes and judges the community residents according to the security management and control model, the community security department classifies and records the results of the analysis and judgment, periodically visits the residents with potential safety hazards, and implements precise management and control.
8. The utility model provides an accurate management and control system of community safety based on resident family power consumption, its characterized in that, includes the power consumption data acquisition unit, the data that the power consumption data acquisition unit obtained get into data preprocessing unit, get into the analysis modeling unit behind the data preprocessing unit, the analysis modeling unit includes data standardization subunit, cluster analysis subunit and model generation subunit, and the analysis modeling unit connects model verification unit, whether model verification unit judges the power consumption data of community resident family and satisfies all kinds of model conditions, writes into community management and control information table with the verification result.
9. The system according to claim 8, wherein the model generation subunit is connected to the cluster analysis subunit, analyzes different types of residents, and generates a frequent night returning member model, a group renting house model, a daily short renting model, a suspected idle house model, a suspected reimbursement model and a suspected empty house living model according to the electricity consumption characteristics of the various types of residents.
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