CN113779407A - Family intelligent energy-consumption package recommendation method and device - Google Patents
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Abstract
The invention discloses a family intelligent energy consumption package recommendation method and device, 1) extracting multi-source energy consumption data of gas, electric power and tap water; 2) establishing a comprehensive energy label for the home user according to basic information, energy consumption data, an energy consumption trend, a payment habit and a credit rating of the home user, dividing the comprehensive energy label into a payment label for the home user, an energy label and an energy consumption trend label for the home user, and marking the home user one by one; 3) classifying the family users according to the comprehensive energy tags of the family users; 4) calculating the matching degree of the family users and various energy-consumption packages according to the classification results of the family users, and selecting the recommended packages of the users; 5) and determining a family user package recommendation cycle according to the energy consumption trend label of the family user. The invention starts from the comprehensive energy consumption information and credit rating of the family users, classifies the family users and pushes energy consumption packages, considers the package recommendation period and meets the actual requirements of the family users.
Description
Technical Field
The invention relates to the technical field of intelligent energy consumption and consumption package, in particular to a family intelligent energy consumption package recommendation method and device.
Background
The resident family user has characteristics such as the cardinal number is big, the energy consumption mode is many, consumption level is various, and gas, electric power, water are all practical at present and really pay, and the mode is single, needs a set of wisdom to use can package recommendation method urgently, increases the application scene for national grid provincial wisdom energy service platform and provides wisdom to use can package for the family user.
Disclosure of Invention
The invention aims to provide a family intelligent energy package recommendation method and device to solve the problems in the background technology.
In order to achieve the purpose, the invention provides a family intelligent energy-use package recommendation method, which comprises the following steps:
step 1, extracting multi-source energy consumption data of gas, electric power and tap water of a family user;
step 2, establishing a comprehensive energy label for the home user according to basic information, energy consumption data, an energy consumption trend, a payment habit and a credit rating of the home user, dividing the comprehensive energy label into a payment label for the home user, an energy label and an energy consumption trend label for the home user, and marking the home user one by one;
step 3, classifying the family users according to the comprehensive energy tags of the family users;
step 4, calculating the matching degree of the family users and various energy-consumption packages according to the classification results of the family users, and selecting the user to recommend the packages;
and 5, determining a family user package recommendation cycle according to the family user energy consumption trend label.
Further, step 1 specifically includes:
extracting power energy consumption and payment data
Extracting power basic data of a home user, power data of the power home user every 1 minute, payment data of the power home user and power credit evaluation data.
Extraction of gas consumption and payment data
And extracting basic information of the gas family user, monthly gas consumption data, payment data and gas credit data.
Extracting running water payment data
Basic information of a tap water family user, monthly tap water consumption data, payment data and tap water credit data are extracted.
Further, step 2 specifically includes:
family user payment label
And establishing a family user payment label according to the charge-out time of the monthly gas bill, the electric power bill and the tap water bill, the family user payment time and the payment urging record information. And tags each home user. The family user payment label is divided into a front label and a negative label.
The front label has complete information for the family user, the family user can pay in time and the family user with high adaptability; the negative label has energy stealing risk family users, information loss family users, multiple/long-term arrearage family users and low-cooperation family users.
② energy label
The energy consumption label is divided into 3 parts, namely a gas label, an electric power label and a tap water label. And labels each home user.
The gas labels are divided into high-gas consumption family users and low-gas consumption family users.
The electric power label is constructed as follows because a household user has an HPLC high-precision 15-minute collection electric meter: the household users with large electric quantity, the household users with normal electric quantity, the household users with low electric quantity, the household users with zero electric quantity, the household users with high electric consumption in the morning, the household users with nine nights and the night, the household users with electric consumption in the daytime, the household users with high load in the noon and the evening, the household users with high load in the evening, and the household users with low difference in load in the working days and the rest days.
Tap water labels are divided into high-consumption tap water home users and low-consumption tap water home users.
Energy consumption trend label for family user
According to various energy consumption conditions of the family user, the energy consumption trend label of the family user is constructed as follows: a power-increasing type home user, a power-stabilizing type home user, a power-reducing type home user, a gas-increasing type home user, a gas-stabilizing type home user, a gas-reducing type home user, a tap water-increasing type home user, a tap water-stabilizing type home user, and a tap water-reducing type home user.
Further, step 3 specifically includes:
and classifying the users through a k-means algorithm according to the home user comprehensive energy label of the users.
Further, step 4 specifically includes:
and calculating the Mahalanobis distance between each type of user and the packages, and selecting the 3 packages with the closest distance as alternative recommended packages for the type of user. Then, the mahalanobis distance between each user and the 3 packages in the category is calculated, and the closest package is taken as the recommended package of the user.
Further, step 5 specifically includes:
according to the energy consumption trend label of the home user, determining the energy consumption type of the home user (growth type home user, stable type home user and weak type home user), and determining the package recommendation cycle of the home user.
When the home user has the growth type tag, the recommendation cycle is 3 months. When the user does not contain an extension tag, the recommended period is 1 year.
The invention also provides a family intelligent energy-consumption package recommending device, which comprises: the system comprises a data extraction module, a tag matching module, a user classification module and a package matching module;
the data extraction module is used for extracting electric power consumption and payment data, gas consumption and payment data and tap water payment data of a family user;
the label matching module is used for establishing a comprehensive energy label for the home user according to basic information, energy consumption data, an energy consumption trend, a payment habit and a credit rating of the home user, dividing the comprehensive energy label into a payment label for the home user, an energy consumption label and an energy consumption trend label for the home user, and marking the home user one by one;
the user classification module is used for classifying the family users according to the family user comprehensive energy labels of the family users;
the package matching module is used for calculating the Mahalanobis distance between various users and packages, selecting 3 packages with the closest distance as the alternative recommended packages of the users, then calculating the Mahalanobis distance between each user and the 3 alternative recommended packages of the category where the user is located, and taking the closest packages as the recommended packages of the user.
The invention has the beneficial effects that:
the intelligent energy consumption package recommendation method covering resident gas, electric power and tap water is established, the payment condition of a family user is analyzed, and the electric charge returning degree is determined; and finally, determining a package recommendation cycle according to the consumption condition of each item of data of the family user. The method is beneficial to the application of the national power grid provincial level intelligent energy service platform, and provides preferential and applicable packages for home users.
Drawings
FIG. 1 is a block diagram of a family intelligent energy-use package recommendation method according to the present invention;
FIG. 2 is a flow chart of a family user best matching package recommendation determination of the present invention;
fig. 3 is a schematic structural diagram of the family intelligent energy package recommending device of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention are clearly and completely described below, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all embodiments. All other embodiments obtained by persons of ordinary skill in the art based on the embodiments of the present invention without any creative efforts shall fall within the protection scope of the present invention.
The method relies on a national network provincial intelligent energy service platform, classifies the family users by analyzing credit information and energy consumption data of the family users, calculates the matching degree of packages, and determines the recommendation period according to the energy consumption trend condition of the family users. An aspect of an embodiment of the present invention provides a family intelligent energy package recommendation method, a flowchart of which is shown in fig. 1, and the method includes the following steps:
step 1, extracting multi-source energy consumption data such as gas, electric power, tap water and the like. In the aspect of electric power, basic information of a home user is extracted, and electric quantity data, payment data and electric power credit data are acquired by the home user in high-precision HPLC (high performance liquid chromatography) for 1 minute; in the aspect of gas, extracting basic information of a gas family user, monthly gas consumption data, payment data and gas credit data; and in the aspect of tap water, basic information of a tap water family user, monthly tap water consumption data, payment data and tap water credit data are extracted. The data are uploaded to national grid provincial intelligent energy service platforms by electric power companies, gas companies and tap water companies, and are analyzed and managed by the national grid provincial intelligent energy service platforms in a unified mode.
And 2, establishing a comprehensive energy label for the home user according to the basic information, the energy consumption data, the energy consumption trend, the payment habit, the credit rating and the like of the home user, dividing the comprehensive energy label into a payment label for the home user, an energy label and an energy consumption trend label for the home user, and marking the home user one by one.
Family user payment label
And establishing a family user payment label according to information such as the charge-out time of the monthly gas bill, the electric power bill and the tap water bill, the family user payment time, the payment urging record and the like. And tags each home user. The family user payment label is divided into a positive label and a negative label.
The front label has complete information for the family user, the family user can pay in time and the family user with high adaptability; the negative label has energy stealing risk family users, information loss family users, multiple/long-term arrearage family users and low-cooperation family users.
The specific judgment rules are as follows:
| label (R) | Label situation | Decision rule |
| Household user with complete information | Front label | Degree of information integrity>90% of the family users |
| Payment in time household user | Front label | Paying family user within specified time, no arrearage record and the like |
| High-adaptability home user | Front label | Return visit successful customer/manual marking by customer manager |
| Energy stealing risk family user | Negative label | Energy-using abnormal home user/manual tagging by energy vendors |
| Information-missing home user | Negative label | Degree of information integrity<60% of the family users |
| Multi/long term arrearage home subscriber | Negative label | There are 3 or more urging/owing records for the family user, etc |
| Low cooperation home user | Negative label | Refusal of call/multiple refusal of participation in energy vendor activities, etc |
② energy label
The energy consumption label is divided into 3 parts, namely a gas label, an electric power label and a tap water label. And labels each home user.
The gas labels are divided into high-consumption family users and low-consumption family users.
Tap water labels are divided into high-consumption tap water home users and low-consumption tap water home users.
The electric power label is constructed as follows because a household user has an HPLC high-precision 1-minute collection electric meter: the household users with large electric quantity, the household users with normal electric quantity, the household users with low electric quantity, the household users with zero electric quantity, the household users with high electric consumption in the morning, the household users with nine nights and the night, the household users with electric consumption in the daytime, the household users with high load in the noon and the evening, the household users with high load in the evening, and the household users with low difference in load in the working days and the rest days.
The specific judgment rules are as follows:
energy consumption trend label for family user
According to various energy consumption conditions of the family user, the energy consumption trend label of the family user is constructed as follows: a power-increasing type home user, a power-stabilizing type home user, a power-reducing type home user, a gas-increasing type home user, a gas-stabilizing type home user, a gas-reducing type home user, a tap water-increasing type home user, a tap water-stabilizing type home user, and a tap water-reducing type home user.
The specific judgment rules are as follows:
the flow chart of the family user best matching package recommendation determination is shown in fig. 2, and 2000 family user data are extracted in total at this time and marked for the user.
And step 3: and classifying the users through a k-means algorithm according to the home user comprehensive energy label of the users. Clustering 2000 families through a k-means algorithm, and classifying the users into 7 classes.
Wherein the total class is 7 classes, active users 2000. Category 1 includes 311 users, category 2 includes 258 users, category 3 includes 275 users, category 4 includes 310 users, category 5 includes 302 users, category 6 includes 262 users, and category 7 includes 282 users. The following table shows the user categories to which the 1-20 users belong after clustering.
And 4, step 4: and calculating the matching degree of the family users and various energy-consumption packages according to the classification results of the family users, and selecting the user to recommend the packages.
And calculating the Mahalanobis distance between each type of user and the packages, and selecting the 3 packages with the closest distance as alternative recommended packages for the type of user. And then calculating the mahalanobis distance between each user and the 3 packages in the category, and taking the latest package as the recommended package of the user. The following table shows the mahalanobis distance from each cluster category to each package. If the cluster type 3 is closest to package A, package D and package F, package A, package D and package F are taken as the type 3 user alternative recommended packages.
The following table shows the mahalanobis distance from user 1 to package a, package D, and package F.
| Set meal A | Set meal D | Set meal F | |
| User 1 | 2.012 | 2.531 | 2.123 |
As shown in the table above, user 1 is closest to package A, so package A is recommended to user 1.
And 5: and determining a family user package recommendation cycle according to the energy consumption trend label of the family user. If the family user 1 has three labels of a power-stable family user, a gas-stable family user and a tap water-stable family user, the package recommended period is 1 year.
In another aspect of the embodiments of the present invention, there is provided a family intelligent energy package recommendation device, as shown in fig. 3, including: a data extraction module 31, a label matching module 32, a user classification module 33 and a package matching module 34;
the data extraction module 31 is configured to extract electric power consumption and payment data, gas consumption and payment data, and tap water payment data of the home subscriber;
the tag matching module 32 is used for establishing a comprehensive energy tag for the home user according to basic information, energy consumption data, an energy consumption trend, a payment habit and a credit rating of the home user, dividing the comprehensive energy tag into a payment tag for the home user, an energy consumption tag and an energy consumption trend tag for the home user, and marking the home user one by one;
the user classification module 33 is configured to classify the home users according to home user integrated energy tags of the home users;
the package matching module 34 is configured to calculate mahalanobis distances between various users and packages, select 3 packages with the closest distance as candidate recommended packages for the users, then calculate mahalanobis distances between each user and the 3 candidate recommended packages of the category where the user is located, and use the closest packages as recommended packages for the user.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (6)
1. A family intelligent energy-consumption package recommendation method is characterized by comprising the following steps: the method comprises the following steps:
step 1, extracting multi-source energy consumption data of gas, electric power and tap water of a family user;
step 2, establishing a comprehensive energy label for the home user according to basic information, energy consumption data, an energy consumption trend, a payment habit and a credit rating of the home user, dividing the comprehensive energy label into a payment label for the home user, an energy label and an energy consumption trend label for the home user, and marking the home user one by one;
step 3, classifying the family users according to the comprehensive energy tags of the family users;
step 4, calculating the matching degree of the family users and various energy-consumption packages according to the classification results of the family users, and selecting the recommended packages of the users;
and 5, determining a family user package recommendation cycle according to the family user energy consumption trend label.
2. The method as claimed in claim 1, wherein the method comprises: the step 1 specifically comprises the following steps:
extracting power energy consumption and payment data
Extracting power basic data of a home user, power data of the power home user every 1 minute, payment data of the power home user and power credit evaluation data;
extraction of gas consumption and payment data
Extracting basic information, monthly gas consumption data, payment data and gas credit data of a gas family user;
extracting running water payment data
Basic information of a tap water family user, monthly tap water consumption data, payment data and tap water credit data are extracted.
3. The method as claimed in claim 1, wherein the method comprises: the step 2 specifically comprises the following steps:
family user payment label
According to the charge-out time of the monthly gas bill, the electric power bill, the tap water bill, the payment time of the family user and the payment urging record information, establishing a payment label of the family user, and marking a label for each family user, wherein the payment label of the family user is divided into a positive label and a negative label;
the front label has complete information for the family user, the family user can pay in time and the family user with high adaptability; the negative label has energy stealing risk family users, information loss family users, multiple/long-term arrearage family users and low-cooperation family users;
② energy label
Energy tags are divided into 3 parts, namely a gas tag, an electric power tag and a tap water tag, and each household user is labeled;
the gas labels are divided into high-gas consumption family users and low-gas consumption family users;
the tap water labels are divided into tap water high-consumption family users and tap water low-consumption family users;
the electric power label is constructed as follows because a household user has an HPLC high-precision 1-minute collection electric meter: the household users with high electric quantity, the household users with normal electric quantity, the household users with low electric quantity, the household users with zero electric quantity, the household users with high electric consumption in the morning, the household users with high electric consumption in the evening, the household users with continuous household users at night, the household users with electric consumption in the daytime, the household users with high load in the noon and the evening, the household users with high load in the evening, and the household users with low difference in load between working days and rest days;
energy consumption trend label for family user
According to various energy consumption conditions of the family user, the energy consumption trend label of the family user is constructed as follows: a power-increasing type home user, a power-stabilizing type home user, a power-reducing type home user, a gas-increasing type home user, a gas-stabilizing type home user, a gas-reducing type home user, a tap water-increasing type home user, a tap water-stabilizing type home user, and a tap water-reducing type home user.
4. The method as claimed in claim 1, wherein the method comprises: the step 3 specifically comprises the following steps:
and classifying the users through a k-means algorithm according to the home user comprehensive energy label of the users.
5. The method as claimed in claim 1, wherein the method comprises: the step 4 specifically comprises the following steps:
the Mahalanobis distance between various users and packages is calculated, the 3 packages with the closest distance are selected as the alternative recommended packages of the users, then the Mahalanobis distance between each user and the 3 packages of the category where the user is located is calculated, and the closest package is used as the recommended package of the user.
6. The utility model provides a family wisdom is with ability package recommendation device which characterized in that includes: the system comprises a data extraction module, a tag matching module, a user classification module and a package matching module;
the data extraction module is used for extracting electric power consumption and payment data, gas consumption and payment data and tap water payment data of a family user;
the label matching module is used for establishing a comprehensive energy label for the home user according to basic information, energy consumption data, an energy consumption trend, a payment habit and a credit rating of the home user, dividing the comprehensive energy label into a payment label for the home user, an energy consumption label and an energy consumption trend label for the home user, and marking the home user one by one;
the user classification module is used for classifying the family users according to the family user comprehensive energy labels of the family users;
the package matching module is used for calculating the Mahalanobis distance between various users and packages, selecting 3 packages with the closest distance as the alternative recommended packages of the users, then calculating the Mahalanobis distance between each user and the 3 alternative recommended packages of the category where the user is located, and taking the closest packages as the recommended packages of the user.
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