CN111179109A - Electricity consumption data processing method for detecting elderly people living alone - Google Patents
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Abstract
The embodiment of the application provides a power consumption data processing method for detecting elderly people living alone, which comprises the steps of obtaining power consumption data of elderly users; analyzing the electricity utilization data, and judging whether electricity utilization abnormity exists or not; if the electricity consumption is abnormal, judging whether the old user is at home or not by combining historical electricity consumption; and if the user is judged to be at home, taking a home-entry rescue measure. Through analyzing the electricity consumption data, the activity condition of the old user is monitored to a certain degree, so that measures can be taken as soon as possible when abnormal conditions occur.
Description
Technical Field
The invention belongs to the field of data analysis, and particularly relates to an electricity consumption data processing method for detecting solitary old people.
Background
With the development of time, social aging has become an inevitable trend, and the number of elderly people who are alone at home is gradually increased. Serious follow-up accidents can easily happen if the old people can not seek medical advice in time when the old people have sudden diseases. Although more advanced internet of things monitoring equipment is additionally arranged, the activity condition of the old can be monitored, the installation rate of the monitoring equipment cannot be guaranteed due to a large fund gap.
Disclosure of Invention
In order to solve the defects and shortcomings in the prior art, the invention provides the electricity utilization data processing method for detecting the elderly living alone, and the activity condition of the elderly user is monitored to a certain extent by analyzing the electricity utilization data, so that the measures can be taken as soon as possible in the case of abnormal conditions.
Specifically, the electricity consumption data processing method for detecting the elderly living alone provided by the embodiment of the application includes:
acquiring power consumption data of an old user;
analyzing the electricity utilization data, and judging whether electricity utilization abnormity exists or not;
if the electricity consumption is abnormal, judging whether the old user is at home or not by combining historical electricity consumption;
and if the user is judged to be at home, taking a home-entry rescue measure.
Optionally, the acquiring of the electricity consumption data of the elderly user includes:
acquiring user information in a power supply district, and screening out old users according to the user information;
and selecting the electricity consumption data of the old user from the electricity consumption data corresponding to the user information.
Optionally, the analyzing the power consumption data to determine whether there is a power consumption abnormality includes:
calculating the average value and the variance of each time point data of each user, and calculating the normal electricity consumption and the variance of each time point;
when a group of data is analyzed, mean value filtering is carried out on the data at the front moment and the data at the back moment with the data;
and (4) making a difference between the data obtained by solving and the normal electric quantity, and if the deviation exceeds a triple variance line, judging that the abnormal data exists and judging that the power utilization is abnormal.
Optionally, if there is power consumption abnormality, determining whether the old user is at home by combining historical power consumption includes:
solving the sum and variance of electric quantity data of the old in one day;
performing secondary classification on the daily average power consumption of each user by using a K-means clustering algorithm, and taking data with a higher central value as the standard that the old people are at home and taking data with a lower central value as the standard that the old people are not at home;
and acquiring a group of sample data, calculating the Euclidean distance between the sample data and the two standard values, if the distance is closer to the home standard, judging that the old is at home, otherwise, judging that the old is not at home.
Optionally, the method includes:
and acquiring the electricity utilization data of the old user through different platforms in the data center.
Optionally, the acquiring of the electricity consumption data of the elderly user through different platforms in the data center includes:
firstly, acquiring power consumption data in real time by an Hbase platform;
and compiling project-specific Topic through a Kafka platform, filtering and excavating original electricity consumption data of the required user, and storing the original electricity consumption data into a database in real time.
The technical scheme provided by the invention has the beneficial effects that:
through analyzing the electricity consumption data, the activity condition of the old user is monitored to a certain degree, so that measures can be taken as soon as possible when abnormal conditions occur.
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In order to more clearly illustrate the technical solution of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on the drawings without creative efforts.
Fig. 1 is a schematic flow chart of an electricity consumption data processing method for elderly people living alone detection according to an embodiment of the present application.
Detailed Description
To make the structure and advantages of the present invention clearer, the structure of the present invention will be further described with reference to the accompanying drawings.
Example one
In order to solve technical defects in the prior art, the embodiment of the application provides a user state judgment method, which can analyze the power consumption of users, particularly old users, and discover abnormal conditions of the old as early as possible according to the angle of abnormal power consumption, so that rescue measures can be taken as soon as possible when abnormality occurs.
Specifically, as shown in fig. 1, the method for processing power consumption data for detecting elderly people living alone according to the embodiment of the present application includes:
11. acquiring power consumption data of an old user;
12. analyzing the electricity utilization data, and judging whether electricity utilization abnormity exists or not;
13. if the electricity consumption is abnormal, judging whether the old user is at home or not by combining historical electricity consumption;
14. and if the user is judged to be at home, taking a home-entry rescue measure.
In implementation, the user state judgment method provided by the embodiment of the application can be divided into two parts of primary screening of abnormal power consumption and fine daily service, the abnormal power consumption mainly provides a judgment basis for the accident of the elderly living alone, and a more accurate result can be obtained after the secondary judgment of water and natural gas information of the government is integrated, so that community workers and volunteers can conveniently check the information; the realization cost is very low because no additional equipment is needed to be installed, and the popularization value is very high.
Optionally, the acquiring of the electricity consumption data of the elderly user in step 11 includes:
111. acquiring user information in a power supply district, and screening out old users according to the user information;
112. and selecting the electricity consumption data of the old user from the electricity consumption data corresponding to the user information.
In the implementation, since the user information includes information such as the age and the contact information of the user, the old user to be monitored can be obtained by directly traversing the registered user information.
Optionally, the determining whether there is an abnormal power consumption in step 12 includes:
121. calculating the average value and the variance of each time point data of each user, and calculating the normal electricity consumption and the variance of each time point;
122. when a group of data is analyzed, mean value filtering is carried out on the data at the front moment and the data at the back moment with the data;
123. and (4) making a difference between the data obtained by solving and the normal electric quantity, and if the deviation exceeds a triple variance line, judging that the abnormal data exists and judging that the power utilization is abnormal.
In the implementation, the power consumption is screened by means of average value, variance calculation and the like, so that the influence of accidental events on power consumption abnormal events can be effectively avoided by highlighting the old users with abnormal power consumption.
Optionally, the determining whether the elderly user is at home in step 13 includes:
131. solving the sum and variance of electric quantity data of the old in one day;
132. performing secondary classification on the daily average power consumption of each user by using a K-means clustering algorithm, and taking data with a higher central value as the standard that the old people are at home and taking data with a lower central value as the standard that the old people are not at home;
133. and acquiring a group of sample data, calculating the Euclidean distance between the sample data and the two standard values, if the distance is closer to the home standard, judging that the old is at home, otherwise, judging that the old is not at home.
In implementation, the k-means algorithm accepts an input k; the n data objects are then divided into k clusters so that the obtained clusters satisfy: the similarity of objects in the same cluster is higher; while the object similarity in different clusters is smaller. Cluster similarity is calculated using a "center object" (center of gravity) obtained from the mean of the objects in each cluster.
The operation of the k-means algorithm is illustrated as follows:
firstly, randomly selecting k objects from n data objects as initial clustering centers; for the other objects left, they are respectively assigned to the most similar clusters (represented by the cluster centers) according to their similarity (distance) to the cluster centers; then calculating the cluster center of each obtained new cluster (the mean value of all objects in the cluster); this process is repeated until the standard measure function begins to converge. The k clusters have the following characteristics: the clusters themselves are as compact as possible and the clusters are as separated as possible.
Optionally, the method includes:
and acquiring the electricity utilization data of the old user through different platforms in the data center.
In implementation, the method for acquiring the electricity utilization data of the old user through different platforms in the data center is divided into two steps, including:
firstly, acquiring power consumption data in real time by an Hbase platform;
secondly, compiling project-specific Topic through a Kafka platform, filtering and excavating original electricity consumption data of the required users, and storing the original electricity consumption data into a database in real time.
The data mining part is formed by interaction of different platforms in the Zhe electric cloud data center, firstly, user electricity utilization data are collected in real time by an Hbase platform, then a special Topic for a project of caring about the elderly living alone is compiled through a Kafka platform, and the special Topic is used for filtering and mining original electricity utilization data of a needed user and storing the original electricity utilization data into a Zhe electric cloud ODPS database in real time. The model realization part adopts a Kubeflow algorithm platform of Zhe electric cloud, and outputs a logic judgment result and a desensitization data volume result through two modes of a general screening algorithm model and an artificial intelligence algorithm model. The network output part caches the data by adopting a thundercloud RDS database on the electric power information inner network, then caches result information in an electric power information outer network application server by penetrating through an inner and outer network isolation device (a network gate) by SQL, and finally transmits the result information to a government platform by a special line of the electric power information outer network-government affairs outer network.
The system mainly stores user data into an Hbase database, reads relevant data through Topic real-time filtering of 'caring the solitary old' on a Kafka platform, caches the data to an ODPS database, writes results back to the ODPS database after analysis and calculation of a Kubeflow platform, caches the results to an RDS database, and penetrates through SQL. And caching the data to an information extranet application server, and finally transmitting the data to a government platform data receiving end through a power information extranet-government affair extranet special line.
The sequence numbers in the above embodiments are merely for description, and do not represent the sequence of the assembly or the use of the components.
The above description is only exemplary of the present invention and should not be taken as limiting the invention, as any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (6)
1. The power utilization data processing method for detecting the elderly living alone is characterized in that the user state judging method comprises the following steps:
acquiring power consumption data of an old user;
analyzing the electricity utilization data, and judging whether electricity utilization abnormity exists or not;
if the electricity consumption is abnormal, judging whether the old user is at home or not by combining historical electricity consumption;
and if the user is judged to be at home, taking a home-entry rescue measure.
2. The method for processing electricity consumption data for elderly solitary detection according to claim 1, wherein the acquiring electricity consumption data of elderly users comprises:
acquiring user information in a power supply district, and screening out old users according to the user information;
and selecting the electricity consumption data of the old user from the electricity consumption data corresponding to the user information.
3. The method for processing electricity consumption data for elderly people living alone detection according to claim 1, wherein the analyzing the electricity consumption data to determine whether there is abnormal electricity consumption comprises:
calculating the average value and the variance of each time point data of each user, and calculating the normal electricity consumption and the variance of each time point;
when a group of data is analyzed, mean value filtering is carried out on the data at the front moment and the data at the back moment with the data;
and (4) making a difference between the data obtained by solving and the normal electric quantity, and if the deviation exceeds a triple variance line, judging that the abnormal data exists and judging that the power utilization is abnormal.
4. The electricity consumption data processing method for elderly solitary detection according to claim 1, wherein the determining whether the elderly user is at home in combination with historical electricity consumption if there is an abnormal electricity consumption comprises:
solving the sum and variance of electric quantity data of the old in one day;
performing secondary classification on the daily average power consumption of each user by using a K-means clustering algorithm, and taking data with a higher central value as the standard that the old people are at home and taking data with a lower central value as the standard that the old people are not at home;
and acquiring a group of sample data, calculating the Euclidean distance between the sample data and the two standard values, if the distance is closer to the home standard, judging that the old is at home, otherwise, judging that the old is not at home.
5. The electricity consumption data processing method for elderly solitary detection according to claim 1, comprising:
and acquiring the electricity utilization data of the old user through different platforms in the data center.
6. The method for processing electricity consumption data for elderly people living alone detection according to claim 1, wherein the obtaining electricity consumption data of elderly users through different platforms in a data center comprises:
firstly, acquiring power consumption data in real time by an Hbase platform;
and compiling project-specific Topic through a Kafka platform, filtering and excavating original electricity consumption data of the required user, and storing the original electricity consumption data into a database in real time.
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| CN112396087A (en) * | 2020-09-28 | 2021-02-23 | 国网浙江省电力有限公司杭州供电公司 | Smart electric meter based method and device for analyzing electricity consumption data of elderly people living alone |
| CN112505402A (en) * | 2020-11-10 | 2021-03-16 | 杭州凯达电力建设有限公司自动化运维分公司 | Electric energy monitoring system for solitary old people based on database prediction |
| CN112505401A (en) * | 2020-11-10 | 2021-03-16 | 杭州凯达电力建设有限公司自动化运维分公司 | Distributed electric quantity monitoring and alarming system for activity analysis of solitary old people |
| CN112801154A (en) * | 2021-01-19 | 2021-05-14 | 城云科技（中国）有限公司 | Behavior analysis method and system for solitary old people |
| CN112882431A (en) * | 2021-01-12 | 2021-06-01 | 深圳美华电力工程设计有限公司 | Energy-saving power distribution room monitoring system and monitoring method |
| CN113159140A (en) * | 2021-04-01 | 2021-07-23 | 上海应用技术大学 | Daily water average value data processing method for solitary old people |
| CN113298577A (en) * | 2021-06-23 | 2021-08-24 | 福建亿力优能电力科技有限公司 | Abnormal state alarm monitoring method for solitary old people based on intelligent monitoring terminal |
| CN113780452A (en) * | 2021-09-16 | 2021-12-10 | 国网北京市电力公司 | Monitoring method and monitoring device for solitary group and electronic equipment |
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| CN112396087A (en) * | 2020-09-28 | 2021-02-23 | 国网浙江省电力有限公司杭州供电公司 | Smart electric meter based method and device for analyzing electricity consumption data of elderly people living alone |
| CN112396087B (en) * | 2020-09-28 | 2024-04-26 | 国网浙江省电力有限公司杭州供电公司 | Method and device for analyzing power consumption data of solitary old people based on intelligent ammeter |
| CN112505402A (en) * | 2020-11-10 | 2021-03-16 | 杭州凯达电力建设有限公司自动化运维分公司 | Electric energy monitoring system for solitary old people based on database prediction |
| CN112505401A (en) * | 2020-11-10 | 2021-03-16 | 杭州凯达电力建设有限公司自动化运维分公司 | Distributed electric quantity monitoring and alarming system for activity analysis of solitary old people |
| CN112505402B (en) * | 2020-11-10 | 2023-08-29 | 杭州凯达电力建设有限公司自动化运维分公司 | Electric energy monitoring system for solitary old people based on database prediction |
| CN112505401B (en) * | 2020-11-10 | 2023-08-29 | 杭州凯达电力建设有限公司自动化运维分公司 | Distributed electric quantity monitoring alarm system for activity analysis of solitary old people |
| CN112882431A (en) * | 2021-01-12 | 2021-06-01 | 深圳美华电力工程设计有限公司 | Energy-saving power distribution room monitoring system and monitoring method |
| CN112801154A (en) * | 2021-01-19 | 2021-05-14 | 城云科技（中国）有限公司 | Behavior analysis method and system for solitary old people |
| CN112801154B (en) * | 2021-01-19 | 2024-02-02 | 城云科技（中国）有限公司 | Behavior analysis method and system for orphan elderly people |
| CN113159140A (en) * | 2021-04-01 | 2021-07-23 | 上海应用技术大学 | Daily water average value data processing method for solitary old people |
| CN113298577A (en) * | 2021-06-23 | 2021-08-24 | 福建亿力优能电力科技有限公司 | Abnormal state alarm monitoring method for solitary old people based on intelligent monitoring terminal |
| CN113780452A (en) * | 2021-09-16 | 2021-12-10 | 国网北京市电力公司 | Monitoring method and monitoring device for solitary group and electronic equipment |
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