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CN112463760B - Information processing method, apparatus, computer device, and storage medium - Google Patents

Information processing method, apparatus, computer device, and storage medium Download PDF

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Publication number
CN112463760B
CN112463760B CN202011169835.1A CN202011169835A CN112463760B CN 112463760 B CN112463760 B CN 112463760B CN 202011169835 A CN202011169835 A CN 202011169835A CN 112463760 B CN112463760 B CN 112463760B
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information
user
data
source data
storage list
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CN112463760A (en
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钟文瑜
汤良杰
陈华仙
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Shenzhen Power Supply Bureau Co Ltd
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Shenzhen Power Supply Bureau Co Ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/21—Design, administration or maintenance of databases
    • G06F16/211—Schema design and management
    • G06F16/212—Schema design and management with details for data modelling support
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/22—Indexing; Data structures therefor; Storage structures
    • G06F16/2282—Tablespace storage structures; Management thereof
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/23—Updating
    • G06F16/2365—Ensuring data consistency and integrity
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/24—Querying
    • G06F16/245—Query processing
    • G06F16/2457—Query processing with adaptation to user needs
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/26—Visual data mining; Browsing structured data
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Energy or water supply
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S10/00—Systems supporting electrical power generation, transmission or distribution
    • Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications

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  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Databases & Information Systems (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
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  • Business, Economics & Management (AREA)
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  • Human Resources & Organizations (AREA)
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  • Tourism & Hospitality (AREA)
  • General Business, Economics & Management (AREA)
  • Software Systems (AREA)
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  • Computer Security & Cryptography (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

The application relates to an information processing method, a device, a computer device and a storage medium, wherein the computer device adopts a big data analysis technology to extract user information with multiple dimensions from multiple service systems based on the association relation between the dimensions of the user information and the service systems, so as to obtain source data of each service system; the business system comprises at least two systems of a power marketing system, an electric quantity metering system, a power distribution network system, a power production system, a power customer service system and an external information system; then, verifying the source data to obtain target source data meeting preset conditions; updating a preset cubic data model based on target source data; the cubic data model comprises a data storage list of user information with multiple dimensions and association relations among the dimensions of the user information. By adopting the method, the query efficiency of the user information can be improved.

Description

Information processing method, apparatus, computer device, and storage medium
Technical Field
The present application relates to the field of power technologies, and in particular, to an information processing method, an information processing apparatus, a computer device, and a storage medium.
Background
With the continuous development of power reform, the enhanced user experience service mode with the 'user as the center' becomes an important development direction of the future power industry.
In the conventional method, information of users in an electric power enterprise is dispersed in a plurality of business systems, such as a marketing system, a production management system, and the like. The power enterprise can analyze the electricity behavior trend and the service demand trend of the clients based on the plurality of dimension information, and accurate marketing to the clients is achieved. When acquiring multiple dimensions of a user, a worker of an electric power enterprise needs to respectively query information of different dimensions of the user from multiple business systems.
But the customer information acquisition efficiency is low by adopting the method.
Disclosure of Invention
In view of the foregoing, it is desirable to provide an information processing method, apparatus, computer device, and storage medium.
An information processing method, the method comprising:
based on the association relation between the dimension of the user information and the service systems, extracting the user information of a plurality of dimensions from a plurality of service systems by adopting a big data analysis technology, and obtaining the source data of each service system; the business system comprises at least two systems of a power marketing system, an electric quantity metering system, a power distribution network system, a power production system, a power customer service system and an external information system;
verifying the source data to obtain target source data meeting preset conditions;
updating a preset cubic data model based on target source data; the cubic data model comprises a data storage list of user information with multiple dimensions and association relations among the dimensions of the user information.
In one embodiment, the dimensions of the user information include a plurality of dimensions of user basic information, user classification information, power supply information, power consumption amount information, power load information, power consumption amount information, power consumption peak-to-valley ratio information, power charge information, power price information, user demand information, user service records, user management status information, user association relationship information, and user tag information.
In one embodiment, the extracting the user information of multiple dimensions in multiple service systems includes:
extracting user basic information, user classification information, electricity consumption capacity information, user demand information, user service records, user association relation information and user label information from the electric power marketing system;
Extracting user basic information, electricity load information, electricity peak-valley ratio information and electricity charge information from an electricity metering system;
Extracting power supply information from a power distribution network system;
extracting power supply information in a power generation system;
Extracting power supply information and user association relation information in a power customer service system;
And extracting electricity price information and user operation condition information from the external information system.
In one embodiment, the verifying the source data to obtain the target source data meeting the preset condition includes:
Performing character verification on the source data to obtain verification source data;
Comparing the preset parameters of the verification source data with the preset parameters of the history source data to obtain a comparison result; the preset parameters comprise at least one of a data model corresponding to the source data, key fields in the source data and data record quantity; verifying that the data sources of the source data and the historical source data are the same;
And according to the comparison result, carrying out data cleaning on the source data to obtain target source data.
In one embodiment, updating the preset cubic data model based on the target source data includes:
acquiring the dimension value of the user information in the target source data based on the dimension of the user information;
respectively comparing the dimension value with a corresponding preset threshold value, and judging whether the dimension value is legal or not;
If the dimension value is legal, writing the dimension value into a model frame of the cubic data model to obtain a temporary data storage list;
Based on the temporary data storage list, the opponent data model is updated.
In one embodiment, the updating the opposite party data model based on the temporary data storage list includes:
Acquiring a current data storage list matched with the temporary data storage list in the cubic data model;
Performing difference analysis on the temporary data storage list and the current data storage list to obtain an analysis result;
if the analysis result meets the preset updating condition, updating the current data storage list into a temporary data storage list to obtain an updated cube data model.
In one embodiment, the method further comprises:
based on the cubic data model, user information in multiple dimensions is visually displayed.
In one embodiment, the method further comprises:
Constructing a user portrait based on the cubic data model; the user portrait includes user features obtained based on a plurality of dimensional data in the cubic data model;
and adding the user portrait in the visually displayed page.
An information processing apparatus, said apparatus comprising:
The extraction module is used for extracting the user information with multiple dimensions from the multiple service systems by adopting a big data analysis technology based on the association relation between the dimensions of the user information and the service systems, and obtaining the source data of each service system; the business system comprises at least two systems of a power marketing system, a device metering system, a power distribution network system, a power production system, a power customer service system and an external information system;
The verification module is used for verifying the source data to obtain target source data meeting preset conditions;
The updating module is used for updating the preset cubic data model based on the target source data; the cubic data model comprises a data storage list of user information with multiple dimensions and association relations among the dimensions of the user information.
A computer device comprising a memory storing a computer program and a processor implementing the steps of the above-described information processing method when the processor executes the computer program.
A computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the information processing method described above.
According to the information processing method, the information processing device, the computer equipment and the storage medium, the computer equipment adopts a big data analysis technology to extract user information with multiple dimensions from multiple service systems based on the association relation between the dimensions of the user information and the service systems, and source data of each service system is obtained; the business system comprises at least two systems of a power marketing system, an electric quantity metering system, a power distribution network system, a power production system, a power customer service system and an external information system; then, verifying the source data to obtain target source data meeting preset conditions; updating a preset cubic data model based on target source data; the cubic data model comprises a data storage list of user information with multiple dimensions and association relations among the dimensions of the user information. The computer equipment extracts the source data in the plurality of service systems, so that the user information of a plurality of dimensions contained in the plurality of service systems can be integrated; further, the computer equipment checks the source data, so that the computer equipment can update the opposite-side data model by adopting the target source data which passes the check, and the accuracy of user information in the cubic data model is improved; by updating the cubic data model, the computer equipment integrates the user information with multiple dimensions in the cubic data model, and establishes the association between the user information with multiple dimensions, so that a worker can conveniently inquire the user information with multiple dimensions in the cubic data model without respectively inquiring in multiple business systems, the inquiring efficiency of the user information is improved, the user information obtained by the worker is more complete, and higher-quality service is provided for the user.
Drawings
FIG. 1 is a diagram of an application environment for a method of information processing in one embodiment;
FIG. 2 is a flow chart of a method of processing information in one embodiment;
FIG. 3 is a flow chart of a method of processing information in one embodiment;
FIG. 4 is a flow chart of a method of processing information according to another embodiment;
FIG. 5 is a block diagram showing the structure of an information processing apparatus in one embodiment;
FIG. 6 is a block diagram showing the structure of an information processing apparatus in one embodiment;
FIG. 7 is a block diagram showing the structure of an information processing apparatus in one embodiment;
FIG. 8 is a block diagram showing the structure of an information processing apparatus in one embodiment;
Fig. 9 is an internal structural diagram of a computer device in one embodiment.
Detailed Description
The present application will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present application more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.
The information processing method provided by the application can be applied to an application environment shown in fig. 1. Wherein the computer device 100 is communicatively coupled to a server 200 of a plurality of business systems. The computer device 100 and the server 200 of the service system may be implemented as a stand-alone server or as a server cluster composed of a plurality of servers.
In one embodiment, as shown in fig. 2, there is provided an information processing method, which is described by taking an example that the method is applied to the computer device in fig. 1, including:
s101, extracting user information of multiple dimensions from multiple service systems by adopting a big data analysis technology based on the association relation between the dimensions of the user information and the service systems, and obtaining source data of each service system; the business system comprises at least two systems of a power marketing system, an electric quantity metering system, a power distribution network system, a power production system, a power customer service system and an external information system;
The business system can be at least two of an electric power marketing system, an equipment metering system, an electric power distribution network system, an electric power production system, an electric power customer service system and an external information system. The above-described power marketing system may be a system for specifying marketing strategies for users of a power enterprise, and may include user marketing strategy related information. The electric quantity metering system can be used for automatically metering the electric quantity used by the user and can comprise electric quantity information of the user and the like. The power distribution network system can comprise operation information, power supply information and the like of each power equipment in the power network. The power generation system may include a power generation condition of a power supply network. The power customer service system may provide a service interface to a user, and may further include regional meshing information in the power system, and the like. The external information system may be a public internet or other enterprise private network associated with the power enterprise, which is not limited herein.
After integrating the user information in the multiple service systems, the computer equipment can obtain the user information in multiple dimensions. The plurality of dimensions may include user basic information, user marketing information, user service information, user power information, and the like. The user information may be information of an individual user or information of an enterprise user, and is not limited herein.
The association relationship between the dimension of the user information and the service system can be included in the computer equipment, and the association relationship can include the user information of which dimension is included in each service system, wherein each service system can be associated with the user information of a plurality of dimensions, and the user information of each dimension can also be associated with a plurality of service systems.
The computer equipment can adopt big data analysis technology, and simultaneously extract the user information of the dimension associated with each service system in a multi-point concurrency mode. The computer device may extract user information from each service system according to a preset extraction period, or may capture updated user information from the service system in real time, which is not limited herein. The extraction periods corresponding to different service systems can be the same or different; the extraction periods corresponding to the user information with different dimensions in the same service system can be the same or different.
The computer equipment extracts user information from the service system to obtain source data of the service system. Further, the computer device may synchronize the source data to the data caching layer, and store the source data. The computer device may store the source data in the form of a source data table pair or may store the source data in the form of a source data file, which is not limited herein.
S102, verifying the source data to obtain target source data meeting preset conditions.
On the basis of obtaining the source data, the computer device may check the source data to determine whether there is a problem in the source data extraction process. If the source data extraction process is normal, the computer device may consider that the source data satisfies a preset condition, and determine the source data as target source data. If the source data extraction process is abnormal, the computer device may consider that the source data does not satisfy the preset condition. If the source data does not meet the preset condition, the computer equipment can identify problem data in the source data extraction process and form a problem list based on the problem data, so that staff can conveniently check extraction faults between the service system and the computer equipment.
Specifically, the computer device may use a preset verification algorithm to verify the source data, for example, the computer device may perform CRC calculation on the received source data and the source data in the service system, and compare the obtained CRC check code to determine whether the metadata received by the computer device is the same as the source data in the service system; or the computer equipment can also compare part of bytes in the received source data with corresponding bytes in the source data in the service system to determine whether the extraction process is abnormal; the verification method is not limited herein.
S103, updating a preset cubic data model based on target source data; the cubic data model comprises a data storage list of user information with multiple dimensions and association relations among the dimensions of the user information.
The cubic data model may include a data storage list of user information of multiple dimensions, and may further include an association relationship between dimensions of the user information. For example, the data storage list of the user information with multiple dimensions may include a user electricity consumption list, a user operation information list and a user electricity fee information list, where each data storage list includes a number of the user, and each data storage list in the cubic data model may be associated by a number of the user.
The computer device may update the opposite party data model according to the verified target source data, such that the updated cubic data model includes user information of multiple dimensions.
Specifically, the computer device may extract the dimension value of the user information in the target source data, and then perform a storage operation to populate the dimension value in, for example, a cubic data model.
According to the information processing method, the computer equipment adopts the big data analysis technology to extract the user information with multiple dimensions from the multiple service systems based on the association relation between the dimensions of the user information and the service systems, so as to obtain the source data of each service system; the business system comprises at least two systems of a power marketing system, an electric quantity metering system, a power distribution network system, a power production system, a power customer service system and an external information system; then, verifying the source data to obtain target source data meeting preset conditions; updating a preset cubic data model based on target source data; the cubic data model comprises a data storage list of user information with multiple dimensions and association relations among the dimensions of the user information. The computer equipment extracts the source data in the plurality of service systems, so that the user information of a plurality of dimensions contained in the plurality of service systems can be integrated; further, the computer equipment checks the source data, so that the computer equipment can update the opposite-side data model by adopting the target source data which passes the check, and the accuracy of user information in the cubic data model is improved; by updating the cubic data model, the computer equipment integrates the user information with multiple dimensions in the cubic data model, and establishes the association between the user information with multiple dimensions, so that a worker can conveniently inquire the user information with multiple dimensions in the cubic data model without respectively inquiring in multiple business systems, the inquiring efficiency of the user information is improved, the user information obtained by the worker is more complete, and higher-quality service is provided for the user.
In one embodiment, the dimensions of the user information include a plurality of dimensions of user basic information, user classification information, power supply information, power consumption amount information, power load information, power consumption amount information, peak-to-valley ratio information, power rate information, power price information, user appeal information, user service records, user management status information, user association relationship information, and user tag information on the basis of the above embodiments.
The user basic information can include user basic information, metering point information of the user, contact information of the user and the like. The user basic information can comprise user numbers, user names, electricity consumption addresses, industries, administrative areas, payment modes, invoice acquisition modes and the like. The metering point information may include the number of the metering point, the name of the metering point, the electricity price of the metering point, a fixed ratio deduction sign, a month fixed electric quantity, an electric quantity allocation formula and other charging related information. The user contact information may include information such as a main contact, a newspaper dress service contact, a communication contact, a power cut and power transmission contact, and an electric contact of the user.
The user classification information may include classification results of different dimensions. The computer equipment can divide the users into private industry users, private business and other users, private mixed users, public industry users, public business and other users and public resident users according to the types of the users and the electricity prices of the users. The computer device may also divide the users into categories of large complex users, industrial park users, office building users, charging pile users, 5G users, government agency users, hospital users, school users, agency users, hotel users, public facility users, transportation users, port users, gateway users, cell users, resident users, etc., based on the user's electricity usage attributes.
The power supply information can be used for representing the power supply stability condition of a user. The power supply information can comprise power supply stability conditions, voltage sensitive equipment use conditions, emergency power supply use conditions, fire emergency mechanisms, power supply line information and the like of the user for nearly three years. The power supply stability conditions for the last three years may include a planned power outage and a number of fault power outages per month, respectively, and customer care conditions. The use condition of the voltage sensitive device can comprise the content of the name, the installation position, the required voltage level, the power supply line and the like of the voltage sensitive device owned by the user. The emergency power use case may include emergency equipment information of the user, such as power, number, supply area, etc. of the emergency power equipment. The fire emergency mechanism can comprise related contents of a fire detection alarm system, a fire control linkage control system, a combustible gas detection alarm system and an electric fire alarm system of a user. The power supply line condition may include information of a power supply line, line attribute, annual maximum load current, annual maximum load rate, line length, the number of overhauls/power conversion in the last 3 years, a power supply area, and the like of a user.
The above-mentioned electricity consumption information may include the total capacity of the user's transformers and detailed information of the individual transformers. Wherein the customer transformer total capacity includes a sum of customer transformer capacities, a running capacity sum, a pause capacity sum, a running capacity sum rank (e.g., a city rank, a regional rank, an industry rank). The detailed information of the single transformer may include information such as the number of each transformer, the name of the transformer, the installation address, the operation status, and the capacity of the transformer owned by the user.
The electricity load information may include a user's annual load condition, monthly load condition, and daily load condition. The annual load condition may include the current annual maximum power load, and may further include an annual maximum load of approximately five years, an annual synchronous load change, an annual load predictive value sum, and a load predictive accuracy. The monthly load condition can comprise the maximum load of the present month, and also can comprise the same ratio change of the monthly power consumption load, the ring ratio change of the monthly power consumption load, the predicted value of the monthly load and the prediction accuracy rate of the monthly load. The daily load condition can comprise the daily maximum electricity load, the daily load homonymous change condition, the predicted value and the predicted accuracy of the daily electricity load.
The electricity consumption information can collect electricity consumption data of a user in a mode of entering layer by layer from year to month to day to time. The electricity consumption information may include annual electricity consumption information of the user for about 5 years, and the annual electricity consumption information may include annual electricity consumption, annual electricity consumption comparably change, annual electricity consumption variance, annual electricity consumption predicted value, annual electricity consumption prediction accuracy, annual electricity consumption peak value, annual electricity consumption valley value, and annual electricity consumption level. The electricity consumption information may further include month-to-month electricity consumption information, and the month-to-month electricity consumption information may include a month electricity value, a month electricity homonymous change, a month electricity annular ratio change, a month electricity variance ranking, a month electricity prediction value, a month electricity prediction accuracy, a month electricity peak electricity, a month electricity valley electricity, a month flat electricity and a variance. The electricity consumption information may further include daily electricity consumption information, where the daily electricity consumption information may include daily electricity consumption value, daily electricity consumption comparably changing, daily electricity consumption predicted value, daily electricity consumption predicted accuracy, daily electricity consumption peak electricity consumption, daily electricity consumption valley electricity consumption, daily electricity consumption flat electricity consumption. In addition, the electricity consumption information may further include a time-dependent change curve of electricity consumption, for example, a line graph showing 24 hours of electricity consumption in hours.
The peak-to-valley ratio information may be a peak-to-valley ratio of the user, including a peak-to-valley ratio of annual power, a peak-to-valley ratio of monthly power, and a peak-to-valley ratio of daily power. The annual peak-to-valley ratio weight of the annual electric quantity can comprise peak-to-valley electric quantity and duty ratio information of approximately 5 years, and the annual peak-to-valley electric quantity and duty ratio information can comprise annual peak-to-peak electric quantity and duty ratio, annual average electric quantity and duty ratio, annual valley electric quantity and duty ratio, and peak-to-valley electric quantity duty ratio average value of all cities, peak-to-valley electric quantity duty ratio average value of areas and peak-to-valley electric quantity duty ratio average value of the same industry. The peak-to-valley specific weight of the lunar electric quantity comprises lunar peak-to-valley electric quantity and duty ratio information, such as lunar peak electric quantity and duty ratio, lunar average electric quantity and duty ratio, lunar valley electric quantity and duty ratio, and lunar peak-to-valley electric quantity duty ratio average value of all cities, lunar peak-to-valley electric quantity duty ratio average value of areas and lunar peak-to-valley electric quantity duty ratio average value of the same industry. The daily peak-to-valley ratio weight includes daily peak-to-valley power and duty cycle information such as daily peak power and duty cycle, daily average power and duty cycle, daily valley power and duty cycle, and daily peak-to-valley power duty cycle average value of all cities, regional daily peak-to-average valley power duty cycle average value and daily peak-to-average valley power duty cycle average value of the same industry.
The above-described electric charge information may include annual electric charge data and monthly electric charge data of the user. The annual electricity rate data includes an annual electricity rate sum of approximately 5 years, and the annual electricity rate data includes annual total electricity rates, annual electricity rate congruent changes, annual electricity rate ranks (all-city ranks, regional ranks, industry ranks, same electricity use category ranks). The monthly electricity charge data comprises monthly electricity charge, monthly electricity charge homonymy change, monthly electricity charge cyclic ratio change and monthly electricity charge ranking (whole market ranking, regional ranking, industry ranking and same electricity use category ranking).
The electricity rate information includes the annual average electricity rate data and the monthly average electricity rate data of the user. The annual average electricity price data comprises an average electricity price of nearly 5 years, and also comprises annual average electricity value, annual average electricity price comparably change and annual average electricity price ranking (whole market ranking, regional ranking, industry ranking, electricity utilization category ranking and electricity utilization scheme ranking). The monthly average power price data comprises monthly average power value, monthly average power price homonymy change, monthly average power price cyclic ratio change, and monthly average power price ranking (whole market ranking, regional ranking, industry ranking, power class ranking and power scheme ranking).
The customer appeal information may include the handling of customer service handling work orders and customer problem work orders for the last three years. The business handling work order may include work order number, business category, application date, applicant, contact phone, application business specific content, acceptance person, acceptance date, work order status, processor, processing date, processing result, etc. The customer question work order may include the contents of a question number, a contact phone, a business category, a question description, a receiver time, a status, a power supply unit, a business process opinion, a process time limit, and the like.
The user service records may include interactions of the power enterprises with the users, and the interactions may include a last year customer visit record and a last three month customer interaction record. The client visit record in the last year can comprise information such as visit date, visit person, client feedback content and the like; the three month-recent customer interaction records may include questions that the customer reflects on the business system, including content reflecting the date of the questions, description of the questions, results of the processing, status of the questions, person processing, date of the processing, and the like.
The user operation status information may include a user tax identifier, a user operation range, a user asset status, whether the user has bad credit or illegal records, etc.
The user association information may include information for other users related to the user. For non-resident users, the user association relationship information can comprise other user information with the same value-added tax number, the same settlement account number, the same bank account number and the same user name as the current user; for resident users, the user association relationship information can comprise other user information of the same identity card number, the same telephone number and the same bank account number as the current user.
The user tag information may include tag information owned by the user, and the tag information may include a name of the tag, a tag description, a characteristic to which the tag belongs, and a tag update frequency. For example, the tag name may be "world 500 strong user" or the like.
For the user information with multiple dimensions, the computer equipment can respectively extract the user information from different service systems, specifically, the computer equipment can extract user basic information, user classification information, electricity consumption amount information, user demand information, user service records, user association relation information and user label information in the power marketing system; extracting user basic information, electricity load information, electricity peak-valley ratio information and electricity charge information from an equipment metering system; extracting power supply information from a power distribution network system; extracting power supply information in a power generation system; extracting power supply information and user association relation information in a power customer service system; and extracting electricity price information and user operation condition information from the external information system.
According to the information processing method, the computer equipment extracts the user basic information, the user classification information, the power supply information, the electricity consumption capacity information, the electricity consumption load information, the electricity consumption quantity information, the electricity consumption peak-valley ratio information, the electricity charge information, the electricity price information, the user demand information, the user service record, the user management condition information, the user association relation information, the user label information and other user information of multiple dimensions in each service system respectively, so that the integration of the user information of multiple dimensions is realized, the user information obtained in the computer equipment is more complete, and the method is more beneficial to an electric enterprise to specify an accurate marketing strategy for the user.
Fig. 3 is a flow chart of an information processing method in another embodiment, which relates to an implementation manner of verifying source data by a computer device, and on the basis of the above embodiment, as shown in fig. 3, the step S102 includes:
s201, performing character verification on the source data to obtain verification source data.
Specifically, the computer device may perform parity check, CRC check, or xor check on the source data, which is not limited herein. If the source data passes the character check, the source data may be determined to be check source data.
The computer device may perform verification on the source data in the same service system, or may perform verification on a plurality of data tables included in the source data, which is not limited herein.
S202, comparing preset parameters of the verification source data with preset parameters of the history source data to obtain a comparison result; the preset parameters comprise at least one of a data model corresponding to the source data, key fields in the source data and data record quantity; the check source data is the same as the data source of the history source data.
For verification source data passing through character verification, the computer equipment can perform secondary verification according to preset parameters. The computer device may compare the check source data with the historical source data using preset parameters. The data sources of the verification source data and the historical source data are the same, that is to say, the verification source data and the historical source data correspond to the same type of data in the same service data. The history source data may be the source data acquired in the previous extraction cycle, or may be the source data extracted in the previous extraction cycles, which is not limited herein.
The preset parameters may include at least one of a data model corresponding to the source data, a key field in the source data, and a data record amount. If the value in the source data is obtained through analysis of the data model, for example, the electric quantity value, the computer device may compare the verification source data with the historical source data to obtain whether the data model of the data is the same model, and the computer device may compare the version number or model name of the data model corresponding to the verification source data with the version number or model name of the data model corresponding to the historical source data to determine whether the data model is the same data model. If the comparison result is that the verification source data is the same as the data model corresponding to the historical source data, the computer equipment can consider that the verification source data can be updated into the three-dimensional data model. If the comparison result is that the data models corresponding to the verification source data and the historical source data are different, the computer equipment needs to process the verification source data according to the data models and then update the verification source data into the three-dimensional data model.
When the preset parameter is a key field, the computer device may extract field values of the key field from the verification source data and the history source data according to preset positions of the key field, and then compare the extracted field values to determine whether the field values of the key field in different source data are the same. If the comparison result is that the field values are the same, the computer equipment can consider that the extraction of the check source data is normal, and if the comparison result is that the field values are different, the computer equipment can consider that the extraction process of the check source data is abnormal.
In addition, the computer equipment can also compare the data record quantity of the check source data and the historical source data, and determine whether the problems of data omission and the like exist in the transmission process of the check source data.
S203, according to the comparison result, data cleaning is carried out on the source data to obtain target source data.
Further, the computer device may retain source data with normal extraction process according to the comparison result, and filter source data with abnormal extraction process, so as to complete data cleaning of the source data, and obtain target source data.
According to the information processing method, the computer equipment performs verification of different dimensions on the source data, so that the obtained user information is more accurate.
Fig. 4 is a flow chart of an information processing method in another embodiment, which relates to a manner of updating a stereoscopic data model by a computer device, and on the basis of the above embodiment, as shown in fig. 4, the step S103 includes:
s301, acquiring the dimension value of the user information in the target source data based on the dimension of the user information.
After the computer equipment obtains the target source data, the dimension value of the user information of the dimension can be extracted from the target source data according to the dimension of the user information in the three-dimensional data model. For example, the dimensions of the target source data including the user information include the user annual energy and the user monthly energy, and the computer device may extract each energy value in the user annual energy and each energy value in the user monthly energy in the target source data.
S302, comparing the dimension values with corresponding preset thresholds respectively, and judging whether the dimension values are legal or not.
Further, the computer device may compare the extracted dimension value with a preset threshold corresponding to the dimension, and determine whether the dimension value is legal. Specifically, for user information in different dimensions, the computer device may consider that the dimension value is legal when the dimension value is greater than a preset threshold, may consider that the dimension value is legal when the dimension value is less than the preset threshold, or may be legal when the dimension value is within a preset threshold range, which is not limited herein.
For example, if the preset threshold of the monthly power of the user is a, and the monthly power of the user M extracted by the computer device is greater than a, the computer device may consider that the dimension value is illegal.
And S303, if the dimension value is legal, writing the dimension value into a model frame of the cubic data model to obtain a temporary data storage list.
If the dimension value is legal, the computer equipment can search a storage position corresponding to the dimension value in a model frame of the cubic data model, and then write the dimension value into the model frame of the cubic data model to obtain a temporary data storage list.
S304, updating the opponent data model based on the temporary data storage list.
Further, the computer device may update the opponent data model based on the temporary data storage list. The computer device may employ the temporary data storage list to replace a corresponding data storage list in the cubic data model to obtain an updated cubic data model; or the computer device may update the opponent data model after checking the temporary data storage list.
Alternatively, the computer device may obtain a current data storage list matching the temporary data storage list in the cubic data model; performing difference analysis on the temporary data storage list and the current data storage list to obtain an analysis result; if the analysis result meets the preset updating condition, updating the current data storage list into a temporary data storage list to obtain an updated cube data model.
The above-mentioned differential analysis may include a structural analysis of the data storage list, to determine whether the structure of the current data storage table is identical to the structure of the temporary data storage list. Or the computer equipment can also determine whether the dimension values of the same storage position have larger difference, if the difference analysis result is that the dimension values have larger difference, the computer equipment can consider that the dimension values can be abnormal when written into the structural framework of the cubic data model.
If the analysis result indicates that the writing process of the dimension value by the computer equipment is normal, the temporary data storage list can be determined to meet the preset updating condition, the current data storage list is updated to be the temporary data storage list, and the updated cube data model is obtained.
According to the information processing method, when the computer equipment updates the cubic data model, the data storage list is checked, so that the accuracy of user information in the cubic data model is further ensured.
In one embodiment, in the process of processing the user information with multiple dimensions, the computer device can perform text processing on the source data, the target source data, the temporary data storage list, the comparison result, the analysis result and other data to form corresponding data files, so that staff can export the data files in the system for analysis.
In one embodiment, on the basis of the foregoing embodiment, since the same user may correspond to multiple sets of information in the service system, for example, the same user corresponds to multiple system accounts in the service system. When integrating information of multiple dimensions, the computer equipment can integrate information of the same user in the process of acquiring source data or in the process of updating a cube data model, so that standard and unified service can be provided for the user. Specifically, the computer devices may be integrated according to the number of the user, or may be integrated according to the name of the user, which is not limited herein.
In one embodiment, based on the above embodiment, the computer device may visually present user information of multiple dimensions based on the cubic data model after updating the cubic data model. The computer device may display user information in multiple dimensions in the application program, or may display user information in multiple dimensions on a web page, which is not limited herein. The computer device can perform hierarchical presentation on user information in multiple dimensions and multi-level classification information in multiple dimensions.
In one embodiment, the computer device may also construct a user portrait based on the cubic data model and add the user portrait to the visually presented page. Wherein the user portrait includes user features obtained based on a plurality of dimensional data in the cubic data model. For example, the computer device may determine that the user is a high-credit customer or a potential arrearage customer according to the electricity fee payment condition of the user, and may also determine the label of the user as a user feature.
According to the information processing method, the computer equipment visually displays the user information with multiple dimensions, so that staff can quickly find out the information of the user, and the acquisition efficiency of the user information is improved.
It should be understood that, although the steps in the flowcharts of fig. 2-4 are shown in order as indicated by the arrows, these steps are not necessarily performed in order as indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in fig. 2-4 may include multiple steps or stages that are not necessarily performed at the same time, but may be performed at different times, nor does the order in which the steps or stages are performed necessarily performed in sequence, but may be performed alternately or alternately with at least a portion of the steps or stages in other steps or other steps.
In one embodiment, as shown in fig. 5, there is provided an information processing apparatus including:
The extraction module 10 is configured to extract user information of multiple dimensions from multiple service systems by using a big data analysis technique based on an association relationship between the dimensions of the user information and the service systems, so as to obtain source data of each service system; the business system comprises at least two systems of a power marketing system, a device metering system, a power distribution network system, a power production system, a power customer service system and an external information system;
The verification module 20 is used for verifying the source data to obtain target source data meeting preset conditions;
An updating module 30, configured to update a preset cubic data model based on target source data; the cubic data model comprises a data storage list of user information with multiple dimensions and association relations among the dimensions of the user information.
The information processing device provided above may execute the above embodiment of the information processing method, and its implementation principle and technical effects are similar, and will not be described herein.
In one embodiment, the dimensions of the user information include a plurality of dimensions of user basic information, user classification information, power supply information, power consumption amount information, power load information, power consumption amount information, peak-to-valley ratio information, power rate information, power price information, user appeal information, user service records, user management status information, user association relationship information, and user tag information on the basis of the above embodiments.
In one embodiment, based on the above embodiment, the extraction module 10 is specifically configured to: extracting user basic information, user classification information, electricity consumption capacity information, user demand information, user service records, user association relation information and user label information from the electric power marketing system; extracting user basic information, electricity load information, electricity peak-valley ratio information and electricity charge information from an electricity metering system; extracting power supply information from a power distribution network system; extracting power supply information in a power generation system; extracting power supply information and user association relation information in a power customer service system; and extracting electricity price information and user operation condition information from the external information system.
In one embodiment, based on the above embodiment, as shown in fig. 6, the verification module 20 includes:
A verification unit 201, configured to perform character verification on the source data to obtain verification source data;
a comparison unit 202, configured to compare the preset parameter of the check source data with the preset parameter of the history source data to obtain a comparison result; the preset parameters comprise at least one of a data model corresponding to the source data, key fields in the source data and data record quantity; verifying that the data sources of the source data and the historical source data are the same;
And the cleaning unit 203 is configured to perform data cleaning on the source data according to the comparison result to obtain target source data.
In one embodiment, based on the above embodiment, as shown in fig. 7, the update module 30 includes:
An acquiring unit 301, configured to acquire a dimension value of user information in target source data based on a dimension of the user information;
a judging unit 302, configured to compare the dimension values with corresponding preset thresholds, respectively, and judge whether the dimension values are legal;
A writing unit 303, configured to write the dimension value into a model frame of the cubic data model to obtain a temporary data storage list when the dimension value is legal;
an updating unit 304, configured to update the opposite party data model based on the temporary data storage list.
In one embodiment, on the basis of the above embodiment, the updating unit 303 is specifically configured to: acquiring a current data storage list matched with the temporary data storage list in the cubic data model; performing difference analysis on the temporary data storage list and the current data storage list to obtain an analysis result; if the analysis result meets the preset updating condition, updating the current data storage list into a temporary data storage list to obtain an updated cube data model.
In one embodiment, on the basis of the foregoing embodiment, as shown in fig. 8, the foregoing apparatus further includes a display module 40, configured to: based on the cubic data model, user information in multiple dimensions is visually displayed.
In one embodiment, the display module 40 is further configured to: constructing a user portrait based on the cubic data model; the user portrait includes user features obtained based on a plurality of dimensional data in the cubic data model; and adding the user portrait in the visually displayed page.
The information processing device provided above may execute the above embodiment of the information processing method, and its implementation principle and technical effects are similar, and will not be described herein.
The specific limitation on the information processing apparatus may be referred to the limitation on the information processing method hereinabove, and will not be described herein. Each of the modules in the above-described information processing apparatus may be implemented in whole or in part by software, hardware, and a combination thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
In one embodiment, a computer device is provided, which may be a server, and the internal structure of which may be as shown in fig. 9. The computer device includes a processor, a memory, and a network interface connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the computer device is for storing information processing data. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement an information processing method.
It will be appreciated by persons skilled in the art that the architecture shown in fig. 9 is merely a block diagram of some of the architecture relevant to the present inventive arrangements and is not limiting as to the computer device to which the present inventive arrangements are applicable, and that a particular computer device may include more or fewer components than shown, or may combine some of the components, or have a different arrangement of components.
In one embodiment, a computer device is provided comprising a memory and a processor, the memory having stored therein a computer program, the processor when executing the computer program performing the steps of:
based on the association relation between the dimension of the user information and the service systems, extracting the user information of a plurality of dimensions from a plurality of service systems by adopting a big data analysis technology, and obtaining the source data of each service system; the business system comprises at least two systems of a power marketing system, an electric quantity metering system, a power distribution network system, a power production system, a power customer service system and an external information system;
verifying the source data to obtain target source data meeting preset conditions;
updating a preset cubic data model based on target source data; the cubic data model comprises a data storage list of user information with multiple dimensions and association relations among the dimensions of the user information.
In one embodiment, the dimensions of the user information include a plurality of dimensions of user basic information, user classification information, power supply information, electricity usage capacity information, electricity load information, electricity usage amount information, electricity usage peak to valley ratio information, electricity rate information, electricity price information, user appeal information, user service records, user business information, user association relationship information, and user label information.
In one embodiment, the processor when executing the computer program further performs the steps of: extracting user basic information, user classification information, electricity consumption capacity information, user demand information, user service records, user association relation information and user label information from the electric power marketing system; extracting user basic information, electricity load information, electricity peak-valley ratio information and electricity charge information from an electricity metering system; extracting power supply information from a power distribution network system; extracting power supply information in a power generation system; extracting power supply information and user association relation information in a power customer service system; and extracting electricity price information and user operation condition information from the external information system.
In one embodiment, the processor when executing the computer program further performs the steps of: performing character verification on the source data to obtain verification source data; comparing the preset parameters of the verification source data with the preset parameters of the history source data to obtain a comparison result; the preset parameters comprise at least one of a data model corresponding to the source data, key fields in the source data and data record quantity; verifying that the data sources of the source data and the historical source data are the same; and according to the comparison result, carrying out data cleaning on the source data to obtain target source data.
In one embodiment, the processor when executing the computer program further performs the steps of: acquiring the dimension value of the user information in the target source data based on the dimension of the user information; respectively comparing the dimension value with a corresponding preset threshold value, and judging whether the dimension value is legal or not; if the dimension value is legal, writing the dimension value into a model frame of the cubic data model to obtain a temporary data storage list; based on the temporary data storage list, the opponent data model is updated.
In one embodiment, the processor when executing the computer program further performs the steps of: acquiring a current data storage list matched with the temporary data storage list in the cubic data model; performing difference analysis on the temporary data storage list and the current data storage list to obtain an analysis result; if the analysis result meets the preset updating condition, updating the current data storage list into a temporary data storage list to obtain an updated cube data model.
In one embodiment, the processor when executing the computer program further performs the steps of: based on the cubic data model, user information in multiple dimensions is visually displayed.
In one embodiment, the processor when executing the computer program further performs the steps of: constructing a user portrait based on the cubic data model; the user portrait includes user features obtained based on a plurality of dimensional data in the cubic data model; and adding the user portrait in the visually displayed page.
The computer device provided in this embodiment has similar implementation principles and technical effects to those of the above method embodiment, and will not be described herein.
In one embodiment, a computer readable storage medium is provided having a computer program stored thereon, which when executed by a processor, performs the steps of:
based on the association relation between the dimension of the user information and the service systems, extracting the user information of a plurality of dimensions from a plurality of service systems by adopting a big data analysis technology, and obtaining the source data of each service system; the business system comprises at least two systems of a power marketing system, an electric quantity metering system, a power distribution network system, a power production system, a power customer service system and an external information system;
verifying the source data to obtain target source data meeting preset conditions;
updating a preset cubic data model based on target source data; the cubic data model comprises a data storage list of user information with multiple dimensions and association relations among the dimensions of the user information.
In one embodiment, the dimensions of the user information include a plurality of dimensions of user basic information, user classification information, power supply information, electricity usage capacity information, electricity load information, electricity usage amount information, electricity usage peak to valley ratio information, electricity rate information, electricity price information, user appeal information, user service records, user business information, user association relationship information, and user label information.
In one embodiment, the computer program when executed by the processor further performs the steps of: extracting user basic information, user classification information, electricity consumption capacity information, user demand information, user service records, user association relation information and user label information from the electric power marketing system; extracting user basic information, electricity load information, electricity peak-valley ratio information and electricity charge information from an electricity metering system; extracting power supply information from a power distribution network system; extracting power supply information in a power generation system; extracting power supply information and user association relation information in a power customer service system; and extracting electricity price information and user operation condition information from the external information system.
In one embodiment, the computer program when executed by the processor further performs the steps of: performing character verification on the source data to obtain verification source data; comparing the preset parameters of the verification source data with the preset parameters of the history source data to obtain a comparison result; the preset parameters comprise at least one of a data model corresponding to the source data, key fields in the source data and data record quantity; verifying that the data sources of the source data and the historical source data are the same; and according to the comparison result, carrying out data cleaning on the source data to obtain target source data.
In one embodiment, the computer program when executed by the processor further performs the steps of: acquiring the dimension value of the user information in the target source data based on the dimension of the user information; respectively comparing the dimension value with a corresponding preset threshold value, and judging whether the dimension value is legal or not; if the dimension value is legal, writing the dimension value into a model frame of the cubic data model to obtain a temporary data storage list; based on the temporary data storage list, the opponent data model is updated.
In one embodiment, the computer program when executed by the processor further performs the steps of: acquiring a current data storage list matched with the temporary data storage list in the cubic data model; performing difference analysis on the temporary data storage list and the current data storage list to obtain an analysis result; if the analysis result meets the preset updating condition, updating the current data storage list into a temporary data storage list to obtain an updated cube data model.
In one embodiment, the computer program when executed by the processor further performs the steps of: based on the cubic data model, user information in multiple dimensions is visually displayed.
In one embodiment, the computer program when executed by the processor further performs the steps of: constructing a user portrait based on the cubic data model; the user portrait includes user features obtained based on a plurality of dimensional data in the cubic data model; and adding the user portrait in the visually displayed page.
The computer storage medium provided in this embodiment has similar implementation principles and technical effects to those of the above method embodiments, and will not be described herein.
Those skilled in the art will appreciate that implementing all or part of the above-described methods in accordance with the embodiments may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, or the like. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory. By way of illustration, and not limitation, RAM can be in various forms such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), etc.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The foregoing examples illustrate only a few embodiments of the application, which are described in detail and are not to be construed as limiting the scope of the application. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the application, which are all within the scope of the application. Accordingly, the scope of protection of the present application is to be determined by the appended claims.

Claims (10)

1. An information processing method, characterized in that the method comprises:
Based on the association relation between the dimension of the user information and the service systems, extracting the user information of a plurality of dimensions from a plurality of service systems by adopting a big data analysis technology, and obtaining the source data of each service system; the business system comprises at least two systems of an electric power marketing system, an electric quantity metering system, an electric power distribution network system, an electric power production system, an electric power customer service system and an external information system;
verifying the source data to obtain target source data meeting preset conditions;
Updating a preset cubic data model based on the target source data; the cube data model comprises a data storage list of user information with multiple dimensions and association relations among the dimensions of the user information;
the verifying the source data to obtain target source data meeting preset conditions includes:
performing character verification on the source data to obtain verification source data, wherein the character verification mode comprises any one of parity check, exclusive or check and cyclic redundancy CRC (cyclic redundancy check);
Comparing the preset parameters of the verification source data with the preset parameters of the historical source data to obtain a comparison result; the preset parameters comprise at least one of a data model corresponding to source data, key fields in the source data and data record quantity; the verification source data and the historical source data have the same data source;
According to the comparison result, carrying out data cleaning on the source data to obtain the target source data;
The updating the preset cubic data model based on the target source data comprises the following steps:
Acquiring the dimension value of the user information in the target source data based on the dimension of the user information;
Comparing the dimension value with a corresponding preset threshold value respectively, and judging whether the dimension value is legal or not;
if the dimension value is legal, writing the dimension value into a model frame of the cubic data model to obtain a temporary data storage list;
updating the cubic data model based on the temporary data storage list;
The updating the cubic data model based on the temporary data storage list comprises the following steps:
Acquiring a current data storage list matched with the temporary data storage list in the cubic data model;
performing difference analysis on the temporary data storage list and the current data storage list to obtain an analysis result;
and if the analysis result meets a preset updating condition, updating the current data storage list into the temporary data storage list to obtain an updated cube data model.
2. The method of claim 1, wherein the dimensions of the user information include a plurality of dimensions of user basic information, user classification information, power supply information, power consumption amount information, power load information, power consumption amount information, peak-to-valley ratio information, power rate information, user appeal information, user service records, user business information, user association information, and user label information.
3. The method of claim 2, wherein extracting user information in multiple dimensions in multiple business systems comprises:
Extracting the user basic information, the user classification information, the electricity consumption information, the user demand information, the user service record, the user association relation information and the user label information from the electric power marketing system;
extracting the user basic information, the electricity load information, the electricity peak-valley ratio information and the electricity charge information from the electric quantity metering system;
extracting the power supply information from the power distribution network system;
extracting the power supply information in the power generation system;
Extracting the power supply information and the user association relation information from the power customer service system;
And extracting the electricity price information and the user operating condition information from the external information system.
4. A method according to any one of claims 1-3, wherein the method further comprises:
And based on the cubic data model, visually displaying the user information in multiple dimensions.
5. A method according to any one of claims 1-3, wherein the method further comprises:
based on the cubic data model, constructing a user portrait, and adding the user portrait in a visually displayed page; wherein the user portrait includes user features obtained based on a plurality of dimensional data in the cubic data model.
6. A method according to any one of claims 1-3, wherein the method further comprises:
and in the process of processing the user information with multiple dimensions, carrying out text processing on the source data, the target source data, the temporary data storage list, the comparison result and the analysis result data to form corresponding data files.
7. An information processing apparatus, characterized in that the apparatus comprises:
The extraction module is used for extracting the user information with multiple dimensions from the multiple service systems by adopting a big data analysis technology based on the association relation between the dimensions of the user information and the service systems, and obtaining the source data of each service system; the business system comprises at least two systems of an electric power marketing system, an equipment metering system, an electric power distribution network system, an electric power production system, an electric power customer service system and an external information system;
the verification module is used for verifying the source data to obtain target source data meeting preset conditions;
The updating module is used for updating a preset cubic data model based on the target source data; the cube data model comprises a data storage list of user information with multiple dimensions and association relations among the dimensions of the user information;
the verification module is specifically configured to:
performing character verification on the source data to obtain verification source data, wherein the character verification mode comprises any one of parity check, exclusive or check and cyclic redundancy CRC (cyclic redundancy check);
Comparing the preset parameters of the verification source data with the preset parameters of the historical source data to obtain a comparison result; the preset parameters comprise at least one of a data model corresponding to source data, key fields in the source data and data record quantity; the verification source data and the historical source data have the same data source;
According to the comparison result, carrying out data cleaning on the source data to obtain the target source data;
The updating module comprises:
The acquisition unit is used for acquiring the dimension value of the user information in the target source data based on the dimension of the user information;
the judging unit is used for comparing the dimension values with corresponding preset thresholds respectively and judging whether the dimension values are legal or not;
The writing unit is used for writing the dimension value into a model frame of the cubic data model if the dimension value is legal, so as to obtain a temporary data storage list;
an updating unit, configured to update the cubic data model based on the temporary data storage list;
the updating unit is specifically configured to:
Acquiring a current data storage list matched with the temporary data storage list in the cubic data model;
performing difference analysis on the temporary data storage list and the current data storage list to obtain an analysis result;
and if the analysis result meets a preset updating condition, updating the current data storage list into the temporary data storage list to obtain an updated cube data model.
8. The apparatus of claim 7, wherein the apparatus further comprises:
and the display module is used for visually displaying the user information in multiple dimensions based on the cubic data model.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any of claims 1 to 6 when the computer program is executed.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 6.
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