CN112184489A - Power consumer grouping management system and method - Google Patents
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Abstract
The invention provides a power consumer grouping management system, which comprises a user data acquisition module, a user data processing module and a user operation log data processing module, wherein the user data acquisition module is used for acquiring payment data, power consumption data and user operation log data of users; the user data analysis module is used for respectively carrying out data analysis on payment data, electricity utilization data and user operation log data of a plurality of users according to a preset rule and classifying the users according to a preset category; grading a plurality of users in each category according to a first preset rule to obtain a grading result corresponding to the category; and the user marking module is used for acquiring the grading result, marking the user according to the grading result and a preset marking rule and acquiring a marking result. The invention combines the requirements of customers, and can provide customized personalized power supply service for the customers by taking the customer value as a reference basis.
Description
Technical Field
The invention relates to the technical field of power systems, in particular to a power consumer grouping management system and a power consumer grouping management method.
Background
The big data can exert the powerful decision-making assistance capability, the insight and mining capability and the business process optimization capability by effective analysis and processing, so that the enterprises can be continuously improved and advanced. With the mutual integration of intelligence and management, the application of big data already relates to all trades, and the operation modes of all trades are also being changed subtly. The strategy provided by aspects of customer behavior analysis, market information acquisition, information mining and the like generates deep marketing for modern enterprise operation.
The overall situation of the power supply and demand development of power enterprises in the world and China is transversely observed, and the power supply enterprises develop towards a large trend of complete open competition. The electric power system in China is reformed to accelerate the progress of electric power marketization development, the competitive pressure of modern power supply enterprises is increased, the supply and demand relationship of electric power is continuously changed, the power supply enterprises are forced to actively and yiningly compete for the coming society, the operation and service strategy is gradually changed from the past products to the client behaviors as the guide, and the analysis and research work of the client value is listed as the key point of the enterprise marketing service work. By systematically identifying the customers, knowing the customers and keeping the customers, according to the characteristics of the customers and combining the requirements of the customers, no matter peak load shifting, capacity increasing and expanding, electricity charge service or emergency repair service, customized personalized power supply service is provided for the customers by taking the value of the customers as a reference basis.
With the continuous deep application of the power marketing system, massive power consumption behavior information is accumulated, and a service strategy valuable to power supply enterprises is mined according to the manner of judging the value of a client from the massive data information.
Disclosure of Invention
The invention aims to provide a power consumer grouping management system and a power consumer grouping management method, which solve the technical problems that when the existing method is used for providing services for power consumers, service positioning is inaccurate, and personalized services cannot be accurately provided.
In one aspect of the present invention, a power consumer grouping management system is provided, including:
the system comprises a user data acquisition module, a user data analysis module and a user data analysis module, wherein the user data acquisition module is used for acquiring payment data, power utilization data and user operation log data of a user from a power utilization data storage module or intelligent terminals of a plurality of users and outputting the payment data, the power utilization data and the user operation log data of the user to the user data analysis module;
the user data analysis module is used for respectively carrying out data analysis on payment data, electricity utilization data and user operation log data of a plurality of users according to a preset rule and classifying the users according to a preset category; grading a plurality of users in each category according to a first preset rule to obtain a grading result corresponding to the category; the preset categories comprise a high-value user category, a power failure sensitive user category and an active user category;
the user marking module is used for acquiring the grading result, marking the user according to the grading result and a preset marking rule and acquiring a marking result; the marking results at least comprise active users, power failure sensitive users, high-value users and defaulting high-risk users.
Preferably, the method further comprises the following steps: the user service module is used for acquiring the marking result, matching the marking result with a corresponding service strategy according to a second preset rule, calling the service strategy data, responding to a service request of a user intelligent terminal, and outputting the service strategy data to the user intelligent terminal or responding to a service suggestion request of a service providing intelligent terminal, and outputting the service strategy data to the service providing intelligent terminal;
and the electricity utilization data storage module is used for storing data input or output by the user data acquisition module, the user data analysis module, the user classification module and the user service module.
Preferably, the user payment data collected by the user data collection module at least comprises payment limit data, payment time data, payment frequency data, default amount data and default time data; the user electricity consumption data collected by the user data collecting module at least comprises electricity consumption load data, peak-valley time period data and power factor data; the user operation log data collected by the user data collecting module at least comprises operation record data of business handling of the user on the electricity utilization service system.
Preferably, the user data analysis module includes:
the user classification module is used for acquiring power consumption data and payment data of a plurality of users, calling power consumption parameters and payment parameters according to preset standards, and taking the power consumption parameters or the payment parameters as forward count values; calling an arrearage parameter according to a preset standard, and taking the arrearage parameter as a negative count value; calculating a value judgment index according to the positive counting value and the negative counting value, and listing the users with the value judgment index larger than a first preset judgment threshold value as a high-value user category;
the method is also used for acquiring operation log data of a plurality of users, calling the complaint frequency p and the power failure duration h before complaint, and calculating the power failure sensitive index of the user; the users with the power failure sensitive indexes larger than a second preset judgment threshold value are classified as power failure sensitive users;
the method comprises the steps of obtaining operation log data of a plurality of users and calling interactive operation data; and calculating the total interactive operation score of the user according to the preset interactive operation item score, and listing the user with the total interactive operation score larger than a third preset judgment threshold as an active user category.
Preferably, the user data analysis module includes:
and the user category positioning module is used for acquiring the user category data output by the user classification module, calling all the category item data of any user when the user is identified to be classified into a plurality of category items, and selecting the category item with the highest occurrence frequency as the final category of the user according to a preset screening rule.
Preferably, the user data analysis module includes:
the ranking module is used for ranking the users in the high-value user category according to a plurality of third preset rules from high to low in sequence according to the value judgment indexes; the system is also used for distinguishing the users in the power failure sensitive user category according to a plurality of preset judging values, and sequentially distinguishing the users into potential high-sensitive users, potential secondary high-sensitive users, potential common users and potential low-sensitive users; and the method is used for distinguishing the users in the active user category according to a plurality of fourth preset rules and sequencing the users in sequence from high to low according to the total score of the interactive operation.
The invention also provides a power consumer grouping management method which is realized by the power consumer grouping management system and comprises the following steps:
step S1, collecting payment data, electricity utilization data and user operation log data of the users from an electricity utilization data storage module or intelligent terminals of a plurality of users;
step S2, performing data analysis on payment data, electricity consumption data and user operation log data of a plurality of users according to preset rules, and classifying the users according to preset categories; grading a plurality of users in each category according to a first preset rule to obtain a grading result corresponding to the category; the preset categories comprise a high-value user category, a power failure sensitive user category and an active user category;
step S3, marking the user according to the grade division result and a preset marking rule to obtain a marking result; the marking results at least comprise active users, power failure sensitive users, high-value users and defaulting high-risk users;
step S4, matching the marking result with the corresponding service strategy according to a second preset rule and calling the service strategy data, responding to the service request of the user intelligent terminal and outputting the service strategy data to the user intelligent terminal or responding to the service suggestion request of the service providing intelligent terminal and outputting the service strategy data to the service providing intelligent terminal.
Preferably, the step S1 includes: collecting payment limit data, payment time data, payment frequency data, default amount data and default time data and storing the payment limit data, the payment time data, the default amount data and the default time data as user payment data; collecting power consumption load data, peak-valley period data and power factor data and storing the data as user power consumption data; and collecting operation record data of the user transacting business on the power utilization service system and storing the operation record data as user operation log data.
The step S2 includes:
acquiring power consumption data and payment data of a plurality of users, calling power consumption parameters and payment parameters according to preset standards, and taking the power consumption parameters or the payment parameters as forward count values; calling an arrearage parameter according to a preset standard, and taking the arrearage parameter as a negative count value; calculating a value judgment index according to the positive counting value and the negative counting value, and listing the users with the value judgment index larger than a first preset judgment threshold value as a high-value user category; obtaining operation log data of a plurality of users, calling complaint frequency p and power failure duration h before complaint, and calculating power failure sensitive indexes of the users; the users with the power failure sensitive indexes larger than a second preset judgment threshold value are classified as power failure sensitive users; obtaining operation log data of a plurality of users and calling interactive operation data; calculating the total interactive operation score of the user according to the preset interactive operation item score, and listing the user with the total interactive operation score larger than a third preset judgment threshold as an active user category;
when any user is identified to be classified into a plurality of category items, calling all category item data of the user and selecting the category item with the largest occurrence number as the final category of the user according to a preset screening rule.
Preferably, the users in the high-value user category are distinguished according to a plurality of third preset rules, and the users are sequentially sorted from high to low according to the value judgment indexes; distinguishing users in the power failure sensitive user category according to a plurality of preset judgment values, and sequentially distinguishing the users into potential high-sensitive users, potential secondary high-sensitive users, potential common users and potential low-sensitive users; and distinguishing the users in the active user category according to a plurality of fourth preset rules, and sequencing the users in sequence from high to low according to the total score of the interactive operation.
In summary, the embodiment of the invention has the following beneficial effects:
the power consumer grouping management system and the method provided by the invention have the advantages that data acquisition is utilized to complete data acquisition and processing, different analyses are completed according to different client group target training to complete client grouping, and finally different marketing applications are completed by utilizing client grouping results; the common nature of the data and the rule of data discovery are found out from massive data sets, and the data characteristics are summarized, so that a solid foundation is provided for customer grouping management; by combining the requirements of customers, no matter peak load shifting, capacity increasing and expanding, electricity charge service or emergency repair service, customized personalized power supply service can be provided for the customers by taking the customer value as a reference basis.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art 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 within the scope of the present invention for those skilled in the art to obtain other drawings based on the drawings without inventive exercise.
Fig. 1 is a schematic structural diagram of a power consumer clustering management system according to an embodiment of the present invention.
Fig. 2 is a main flow diagram of a power consumer group management system according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail with reference to the accompanying drawings.
Fig. 1 is a schematic diagram of an embodiment of a power consumer clustering management system according to the present invention. In this embodiment, the method includes:
the system comprises a user data acquisition module, a user data analysis module and a user data analysis module, wherein the user data acquisition module is used for acquiring payment data, power utilization data and user operation log data of a user from a power utilization data storage module or intelligent terminals of a plurality of users and outputting the payment data, the power utilization data and the user operation log data of the user to the user data analysis module; it can be understood that the data acquisition and processing for the bottom layer data are the basic parts of the system, and mainly comprise three parts of data of user payment data, user electricity consumption data and user operation logs.
In specific implementation, the user payment data collected by the user data collection module at least comprises payment limit data, payment time data, payment frequency data, default amount data and default time data; this data is used to characterize the risk and rating labels of the user. The user electricity consumption data collected by the user data collecting module at least comprises electricity consumption load data, peak-valley time period data and power factor data; the data is used to characterize the various electricity usage labels of the user. The user operation log data collected by the user data collecting module at least comprises operation record data of business handling of the user on the electricity utilization service system; data such as consultations, queries, complaints, recommendations, etc. that are utilized to characterize the user service preference tags; the user behavior data refers to behavior data of user browsing, clicking and the like.
The user data analysis module is used for respectively carrying out data analysis on payment data, electricity utilization data and user operation log data of a plurality of users according to a preset rule and classifying the users according to a preset category; grading a plurality of users in each category according to a first preset rule to obtain a grading result corresponding to the category; the preset categories comprise a high-value user category, a power failure sensitive user category and an active user category; it can be understood that the method is used for processing and processing data, and provides services for customer service subgroups by using statistical analysis and a machine learning algorithm model.
In a specific embodiment, the user data analysis module includes: the user classification module is used for acquiring power consumption data and payment data of a plurality of users, calling a power consumption parameter and a payment parameter according to a preset standard, and taking the power consumption parameter or the payment parameter as a forward count value; calling an arrearage parameter according to a preset standard, and taking the arrearage parameter as a negative count value; calculating a value judgment index according to the positive counting value and the negative counting value, and listing the users with the value judgment index larger than a first preset judgment threshold value as a high-value user category; the value indexes of the user can be measured by indexes such as monthly average power consumption, monthly average growth amount, total payment amount, monthly average payment amount, total arrearage times and the like; specifically, the indexes of the electricity consumption class and the payment indexes are used as positive counts, the indexes of the arrearage class are used as negative counts, the score index obtained by adding the positive counts and the negative counts is used as a final value index, and the larger the score is, the higher the value of the user is represented.
The user classification module is further used for acquiring operation log data of a plurality of users, calling the complaint frequency p and the power failure duration h before complaint, and calculating the power failure sensitive index of the user; the users with the power failure sensitive indexes larger than a second preset judgment threshold value are classified as power failure sensitive users; it can be understood that the complaint frequency p of the user, the power failure time length h before complaint and the value of p/h are calculated to be used as the power-on sensitivity index of the user, and the larger the p/h is, the more sensitive the user is to the power failure.
The user classification module is also used for acquiring operation log data of a plurality of users and calling interactive operation data; calculating the total interactive operation score of the user according to the preset interactive operation item score, and listing the user with the total interactive operation score larger than a third preset judgment threshold as an active user category; it can be understood that by collecting the interactive behaviors of the user on the service system, such as lottery drawing, payment and inquiry notice, and specifying the interactive scores of each operation, such active behaviors are accumulated according to scores, and the more scores represent that the user is more active.
Specifically, the user data analysis module further includes a user category positioning module, configured to obtain user category data output by the user classification module, and when it is recognized that any user is classified into multiple category items, call all category item data of the user and select a category item with the largest occurrence number as a final category of the user according to a preset screening rule, it can be understood that a KNN algorithm is used to train and identify a final type of the user, and a general flow of an algorithm model stage is described by taking a defaulting high-risk user as an example: dividing the defaulting risk grade of the user into a high risk grade and a low risk grade by utilizing a KNN algorithm; calculating the distance between the sample to be marked and all samples in the data set, taking K samples with the shortest distance, voting, namely selecting the category with the largest occurrence frequency as the category of the sample to be marked; therefore, by collecting relevant characteristic data of the electricity consumption of the user, such as attributes of user arrearage records, payment records, liability records and the like, and by data preprocessing and characteristic conversion, the division of the user arrearage risk level is completed, so that the high-risk arrearage user is identified.
More specifically, the user data analysis module further includes a ranking module, configured to rank the users in the high-value user category according to a third preset rule, and rank the users in order from high to low according to the value determination index. The system is also used for distinguishing the users in the power failure sensitive user category according to a plurality of preset judging values, and sequentially distinguishing the users into potential high-sensitive users, potential secondary high-sensitive users, potential common users and potential low-sensitive users; it is understood that the users are divided into a potentially highly sensitive customer group, a potentially less sensitive customer group, a potentially common customer group, and a potentially less sensitive customer group by a number of predefined thresholds. And the method is used for distinguishing the users in the active user category according to a plurality of fourth preset rules and sequencing the users in sequence from high to low according to the total score of the interactive operation.
The user marking module is used for acquiring the grading result, marking the user according to the grading result and a preset marking rule and acquiring a marking result; the marking results at least comprise active users, power failure sensitive users, high-value users and defaulting high-risk users; it will be appreciated that the classification of various attributes of users, using services, enables customer clustering. The customer clustering is embodied in that according to various data of users, a trained machine learning model is used for marking corresponding labels for the users, for example, a simple example, data such as payment records, arrearage records, power consumption records and company credit conditions of the users are used, a classification algorithm such as a decision tree is used for generating the power credit rating of each user, and high credit and low credit labels are marked correspondingly.
The user service module is used for acquiring the marking result, matching the marking result with a corresponding service strategy according to a second preset rule, calling the service strategy data, responding to a service request of a user intelligent terminal, and outputting the service strategy data to the user intelligent terminal or responding to a service suggestion request of a service providing intelligent terminal, and outputting the service strategy data to the service providing intelligent terminal; it can be understood that the method is applied to customer grouping, operation can be optimized, service is improved, and accurate marketing is achieved. For example, according to diversified bill inquiry and payment services, the client with high risk level of electric charge recovery is informed in advance and urged to charge; the method mainly ensures the power supply of high-value customers, reasonably configures the power supply of important customers, and requires the important customers to have emergency power supplies.
And the electricity utilization data storage module is used for storing data input or output by the user data acquisition module, the user data analysis module, the user classification module and the user service module.
As shown in fig. 2, an embodiment of the present invention further provides a power consumer clustering management method, implemented by the power consumer clustering management system, including the following steps:
step S1, collecting payment data, electricity utilization data and user operation log data of the users from an electricity utilization data storage module or intelligent terminals of a plurality of users;
in the specific embodiment, collected payment limit data, payment time data, payment frequency data, default amount data and default time data are stored as user payment data; collecting power consumption load data, peak-valley period data and power factor data and storing the data as user power consumption data; and collecting operation record data of the user transacting business on the power utilization service system and storing the operation record data as user operation log data.
Step S2, performing data analysis on payment data, electricity consumption data and user operation log data of a plurality of users according to preset rules, and classifying the users according to preset categories; grading a plurality of users in each category according to a first preset rule to obtain a grading result corresponding to the category; the preset categories comprise a high-value user category, a power failure sensitive user category and an active user category;
in a specific embodiment, power consumption data and payment data of a plurality of users are acquired, power consumption parameters and payment parameters are called according to preset standards, and the power consumption parameters or the payment parameters are used as forward count values; calling an arrearage parameter according to a preset standard, and taking the arrearage parameter as a negative count value; calculating a value judgment index according to the positive counting value and the negative counting value, and listing the users with the value judgment index larger than a first preset judgment threshold value as a high-value user category; obtaining operation log data of a plurality of users, calling complaint frequency p and power failure duration h before complaint, and calculating power failure sensitive indexes of the users; the users with the power failure sensitive indexes larger than a second preset judgment threshold value are classified as power failure sensitive users; obtaining operation log data of a plurality of users and calling interactive operation data; calculating the total interactive operation score of the user according to the preset interactive operation item score, and listing the user with the total interactive operation score larger than a third preset judgment threshold as an active user category;
when any user is identified to be classified into a plurality of category items, calling all category item data of the user and selecting the category item with the largest occurrence number as a final category of the user according to a preset screening rule;
distinguishing users in the high-value user category according to a plurality of third preset rules, and sequencing the users in sequence from high to low according to value judgment indexes; distinguishing users in the power failure sensitive user category according to a plurality of preset judgment values, and sequentially distinguishing the users into potential high-sensitive users, potential secondary high-sensitive users, potential common users and potential low-sensitive users; and distinguishing the users in the active user category according to a plurality of fourth preset rules, and sequencing the users in sequence from high to low according to the total score of the interactive operation.
Step S3, marking the user according to the grade division result and a preset marking rule to obtain a marking result; the marking results at least comprise active users, power failure sensitive users, high-value users and defaulting high-risk users.
Step S4, matching the marking result with the corresponding service strategy according to a second preset rule and calling the service strategy data, responding to the service request of the user intelligent terminal and outputting the service strategy data to the user intelligent terminal or responding to the service suggestion request of the service providing intelligent terminal and outputting the service strategy data to the service providing intelligent terminal.
In summary, the embodiment of the invention has the following beneficial effects:
the power consumer grouping management system and the method provided by the invention have the advantages that data acquisition is utilized to complete data acquisition and processing, different analyses are completed according to different client group target training to complete client grouping, and finally different marketing applications are completed by utilizing client grouping results; the common nature of the data and the rule of data discovery are found out from massive data sets, and the data characteristics are summarized, so that a solid foundation is provided for customer grouping management; by combining the requirements of customers, no matter peak load shifting, capacity increasing and expanding, electricity charge service or emergency repair service, customized personalized power supply service can be provided for the customers by taking the customer value as a reference basis.
While the invention has been described in connection with what is presently considered to be the most practical and preferred embodiment, it is to be understood that the invention is not to be limited to the disclosed embodiment, but on the contrary, is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims.
Claims (10)
1. An electric power consumer clustering management system, comprising:
the system comprises a user data acquisition module, a user data analysis module and a user data analysis module, wherein the user data acquisition module is used for acquiring payment data, power utilization data and user operation log data of a user from a power utilization data storage module or intelligent terminals of a plurality of users and outputting the payment data, the power utilization data and the user operation log data of the user to the user data analysis module;
the user data analysis module is used for respectively carrying out data analysis on payment data, electricity utilization data and user operation log data of a plurality of users according to a preset rule and classifying the users according to a preset category; grading a plurality of users in each category according to a first preset rule to obtain a grading result corresponding to the category; the preset categories comprise a high-value user category, a power failure sensitive user category and an active user category;
the user marking module is used for acquiring the grading result, marking the user according to the grading result and a preset marking rule and acquiring a marking result; the marking results at least comprise active users, power failure sensitive users, high-value users and defaulting high-risk users.
2. The system of claim 1, further comprising:
the user service module is used for acquiring the marking result, matching the marking result with a corresponding service strategy according to a second preset rule, calling the service strategy data, responding to a service request of a user intelligent terminal, and outputting the service strategy data to the user intelligent terminal or responding to a service suggestion request of a service providing intelligent terminal, and outputting the service strategy data to the service providing intelligent terminal;
and the electricity utilization data storage module is used for storing data input or output by the user data acquisition module, the user data analysis module, the user classification module and the user service module.
3. The system of claim 2, wherein the user payment data collected by the user data collection module at least comprises payment amount data, payment time data, payment frequency data, default amount data and default time data; the user electricity consumption data collected by the user data collecting module at least comprises electricity consumption load data, peak-valley time period data and power factor data; the user operation log data collected by the user data collecting module at least comprises operation record data of business handling of the user on the electricity utilization service system.
4. The system of claim 3, wherein the user data analysis module comprises:
the user classification module is used for acquiring power consumption data and payment data of a plurality of users, calling power consumption parameters and payment parameters according to preset standards, and taking the power consumption parameters or the payment parameters as forward count values; calling an arrearage parameter according to a preset standard, and taking the arrearage parameter as a negative count value; calculating a value judgment index according to the positive counting value and the negative counting value, and listing the users with the value judgment index larger than a first preset judgment threshold value as a high-value user category;
the method is also used for acquiring operation log data of a plurality of users, calling the complaint frequency p and the power failure duration h before complaint, and calculating the power failure sensitive index of the user; the users with the power failure sensitive indexes larger than a second preset judgment threshold value are classified as power failure sensitive users;
the method comprises the steps of obtaining operation log data of a plurality of users and calling interactive operation data; and calculating the total interactive operation score of the user according to the preset interactive operation item score, and listing the user with the total interactive operation score larger than a third preset judgment threshold as an active user category.
5. The system of claim 4, wherein the user data analysis module comprises:
and the user category positioning module is used for acquiring the user category data output by the user classification module, calling all the category item data of any user when the user is identified to be classified into a plurality of category items, and selecting the category item with the highest occurrence frequency as the final category of the user according to a preset screening rule.
6. The system of claim 5, wherein the user data analysis module comprises:
the ranking module is used for distinguishing the users in the high-value user category according to a plurality of third preset rules and sequencing the users in sequence from high to low according to value judgment indexes; the system is also used for distinguishing the users in the power failure sensitive user category according to a plurality of preset judging values and sequentially distinguishing the users into potential high-sensitive users, potential secondary high-sensitive users, potential common users and potential low-sensitive users; and the method is used for distinguishing the users in the active user category according to a plurality of fourth preset rules and sequencing the users in sequence from high to low according to the total score of the interactive operation.
7. A power consumer group management method implemented by means of a system according to any one of claims 1 to 6, characterized in that it comprises the following steps:
step S1, collecting payment data, electricity utilization data and user operation log data of the users from an electricity utilization data storage module or intelligent terminals of a plurality of users;
step S2, performing data analysis on payment data, electricity consumption data and user operation log data of a plurality of users according to preset rules, and classifying the users according to preset categories; grading a plurality of users in each category according to a first preset rule to obtain a grading result corresponding to the category; the preset categories comprise a high-value user category, a power failure sensitive user category and an active user category;
step S3, marking the user according to the grade division result and a preset marking rule to obtain a marking result; the marking results at least comprise active users, power failure sensitive users, high-value users and defaulting high-risk users;
step S4, matching the marking result with the corresponding service strategy according to a second preset rule and calling the service strategy data, responding to the service request of the user intelligent terminal and outputting the service strategy data to the user intelligent terminal or responding to the service suggestion request of the service providing intelligent terminal and outputting the service strategy data to the service providing intelligent terminal.
8. The method of claim 7, wherein the step S1 includes: collecting payment limit data, payment time data, payment frequency data, default amount data and default time data and storing the payment limit data, the payment time data, the default amount data and the default time data as user payment data; collecting power consumption load data, peak-valley period data and power factor data and storing the data as user power consumption data; and collecting operation record data of the user transacting business on the power utilization service system and storing the operation record data as user operation log data.
9. The method of claim 8, wherein the step S2 includes:
acquiring power consumption data and payment data of a plurality of users, calling power consumption parameters and payment parameters according to preset standards, and taking the power consumption parameters or the payment parameters as forward count values; calling an arrearage parameter according to a preset standard, and taking the arrearage parameter as a negative count value; calculating a value judgment index according to the positive counting value and the negative counting value, and listing the users with the value judgment index larger than a first preset judgment threshold value as a high-value user category; obtaining operation log data of a plurality of users, calling complaint frequency p and power failure duration h before complaint, and calculating power failure sensitive indexes of the users; the users with the power failure sensitive indexes larger than a second preset judgment threshold value are classified as power failure sensitive users; obtaining operation log data of a plurality of users and calling interactive operation data; calculating the total interactive operation score of the user according to the preset interactive operation item score, and listing the user with the total interactive operation score larger than a third preset judgment threshold as an active user category;
when any user is identified to be classified into a plurality of category items, calling all category item data of the user and selecting the category item with the largest occurrence number as the final category of the user according to a preset screening rule.
10. The method of claim 9, wherein the step S2 includes:
distinguishing users in the high-value user category according to a plurality of third preset rules, and sequencing the users in sequence from high to low according to value judgment indexes; distinguishing users in the power failure sensitive user category according to a plurality of preset judgment values, and sequentially distinguishing the users into potential high-sensitive users, potential secondary high-sensitive users, potential common users and potential low-sensitive users; and distinguishing the users in the active user category according to a plurality of fourth preset rules, and sequencing the users in sequence from high to low according to the total score of the interactive operation.
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