CN112488745A - Intelligent charge control management method, device, equipment and storage medium - Google Patents
Intelligent charge control management method, device, equipment and storage medium Download PDFInfo
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Abstract
The application relates to an intelligent charge control management method, an intelligent charge control management device, computer equipment and a storage medium. The method comprises the following steps: acquiring integrated table code data from a metering automation system; based on preset association table information, establishing an association relation between the integrated archive data and the integrated table code data; the incidence relation table stores corresponding relations between different integrated archive data and different integrated table code data; and acquiring the power resource data through the distributed computing system by utilizing the incidence relation. By adopting the method, the power resource data can be acquired by utilizing the distributed computing system by establishing the incidence relation between the archive data stored in the power marketing system and the meter code data of the metering automation system, so that the data processing efficiency of the fee control service is improved.
Description
Technical Field
The present application relates to the field of power resource management technologies, and in particular, to an intelligent cost control management method, an intelligent cost control management apparatus, a computer device, and a storage medium.
Background
With the steady development of power production, the industry requirements of power operation are continuously transformed, and from the traditional power business operation, the method gradually faces to customers, takes customer service as drive, develops the control and competitive business, and injects new power into the power industry. Under the background of power market reform, emerging technologies such as cloud computing, big data, artificial intelligence and the internet of things have introduced into the power industry, so that the transformation of the power industry is promoted, and the efficiency of transacting business by customers is improved.
At present, with the construction of an electric power marketing system, a standard software process management is adopted, according to the business requirements of a power grid and a moderately advanced principle, the computing capacity and the storage capacity of the system are planned according to the scale and the quantity charge calculation rule of a power consumer of the power grid, meanwhile, the business construction requirements of remote charge control are more and more concerned by people, people can realize a remote electric charge management function through a remote charge control intelligent electric meter, however, the current marketing system cannot support the smooth development of the remote charge control business, and the processing efficiency of relevant data of the charge control business is too low.
Disclosure of Invention
In view of the foregoing, it is desirable to provide an intelligent fee control management method, apparatus, computer device and storage medium.
An intelligent cost control management method, the method comprising:
acquiring integrated archive data from a power marketing system;
acquiring integrated table code data from a metering automation system;
based on preset association table information, establishing an association relationship between the integrated archive data and the integrated table code data; the incidence relation table stores corresponding relations between different integrated archive data and different integrated table code data;
and acquiring the power resource data through the distributed computing system by utilizing the incidence relation.
In one embodiment, the acquiring integrated profile data from the electricity marketing system through the data extraction tool includes: acquiring initial archive data from a power database table in the power marketing system in a parallel mode through a data extraction tool; performing paradigm conversion processing on the initial archive data, and converting the data format of the initial archive data into a preset distributed data storage format; and carrying out data standardization processing on the initial archive data after the data format conversion and storing the initial archive data in a first storage format to obtain the integrated archive data.
In one embodiment, the power database table comprises a file change intermediate table; the method further comprises the following steps: acquiring updated archive data from the archive change intermediate table in a parallel mode through the data extraction tool; wherein the updated archive data is updated initial archive data stored in the power marketing system; and carrying out the normal form conversion processing and the data standardization processing on the updated archive data to obtain the integrated archive data.
In one embodiment, the acquiring integrated meter data from a metering automation system includes: acquiring initial table code data from the metering automation system in a multithreading mode according to a preset time interval; and converting the initial table code data according to a preset table code data structure by using a distributed processing method, and storing the initial table code data after the data structure conversion in a second storage format to be used as the integrated table code data.
In one embodiment, the integration table code data includes first integration table code data and second integration table code data; before the establishing of the association relationship between the integrated archive data and the integrated table code data based on the preset association table information, the method further comprises the following steps: performing data verification on the integrated table code data based on a preset data verification rule to obtain a data verification result identifier of the integrated table code data; if the data verification result identification is that verification is passed, determining the integrated table code data as the first integrated table code data, and establishing an association relationship between the integrated archive data and the first integrated table code data; and if the data verification result identification is that verification is not passed, determining that the integrated table code data are the second integrated table code data, and writing the second integrated table code data into an abnormal data table of the electric power marketing system.
In one embodiment, the obtaining, by the distributed computing system, the power resource data using the association relationship includes: extracting user number data corresponding to a target user from the integrated table code data; acquiring user meter reading data corresponding to the user number data from the integrated archive data by utilizing the incidence relation; and acquiring the power resource data through a distributed computing system by using the user meter reading data based on a preset power resource data acquisition model.
In one embodiment, the power resource data includes: electricity quantity data or electricity charge data; wherein the electricity quantity data comprises: at least one of reading electric quantity, changing electric quantity, free electric quantity, returning and supplementing electric quantity, total power distribution electric quantity, variable power loss electric quantity, line power loss electric quantity, fixed ratio electric quantity, quantitative electric quantity, common share electric quantity of a utility meter and total electric quantity; the electricity fee data includes: at least one of a stepped threshold electricity rate, an electricity rate, a fund electricity rate, an additional electricity rate, a basic electricity rate, a power factor adjustment electricity rate, and a total electricity rate.
An intelligent cost control management device, the device comprising:
the integrated archive acquisition module is used for acquiring integrated archive data from the electric power marketing system;
the integrated table code acquisition module is used for acquiring integrated table code data from the metering automation system;
the incidence relation establishing module is used for establishing incidence relation between the integrated archive data and the integrated table code data based on preset incidence table information; the incidence relation table stores corresponding relations between different integrated archive data and different integrated table code data;
and the resource data acquisition module is used for acquiring the power resource data through the distributed computing system by utilizing the incidence relation.
A computer device comprising a memory storing a computer program and a processor implementing the steps of the above method when executing the computer program.
A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the above-mentioned method.
The intelligent charge control management method, the intelligent charge control management device, the computer equipment and the storage medium acquire integrated archive data from the electric power marketing system; acquiring integrated table code data from a metering automation system; based on preset association table information, establishing an association relation between the integrated archive data and the integrated table code data; the incidence relation table stores corresponding relations between different integrated archive data and different integrated table code data; and acquiring the power resource data through the distributed computing system by utilizing the incidence relation. According to the method and the device, the incidence relation between the archive data stored in the electric power marketing system and the meter code data of the metering automation system is established, so that the electric power resource data are acquired by using the distributed computing system, and the data processing efficiency of the fee control service is improved.
Drawings
FIG. 1 is a diagram of an exemplary implementation of an intelligent cost management method;
FIG. 2 is a schematic flow chart diagram of a method for intelligent cost management in one embodiment;
FIG. 3 is a flow diagram illustrating the process of obtaining integrated profile data from the electricity marketing system, according to one embodiment;
FIG. 4 is a schematic diagram illustrating a process for obtaining power resource data via a distributed computing system using an association relationship, according to an embodiment;
FIG. 5 is a diagram of a hardware architecture of an intelligent cost control method in an application example;
FIG. 6 is a block diagram of an embodiment of an intelligent cost management device;
FIG. 7 is a diagram illustrating an internal structure of a computer device according to an embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
The intelligent charge control management method provided by the application can be applied to the application environment shown in fig. 1. The big data computing server 101 communicates with the power marketing system 102 and the metering automation system 103 through a network. Specifically, the big data computing server 101 can read archive data from the electricity marketing system 102 and store the archive data integrally to form integrated archive data, and can also read table code data from the metering automation system 103 and store the table code data integrally as integrated table code data. The big data computing server 101 may further obtain the power resource data by using a distributed computing function according to the obtained integrated archive data and the integrated table data. The big data computing server 101, the power marketing system 102, and the metering automation system 103 may be implemented by independent servers or a server cluster composed of a plurality of servers.
In one embodiment, as shown in fig. 2, an intelligent fee control management method is provided, which is described by taking the method as an example applied to the big data computing server 101 in fig. 1, and includes the following steps:
in step S201, the big data computing server 101 acquires the integrated archive data from the electricity marketing system 102.
The integrated archive data refers to the marketing archive data stored in the electric power marketing system 102 by the big data computing server 101, and is stored in the distributed storage in the big data computing server 101, and the integrated archive data may include the relevant data of the electric charge algorithm parameter table, the metering point transformer relation table and the shared electric quantity and electricity price relation table obtained from the electric power marketing system 102. Specifically, the big data computing server 101 may obtain marketing profile data from the electricity marketing system through a data extraction tool, such as Spark, and integrally store the marketing profile data to form integrated profile data.
In step S202, the big data computing server 101 acquires the integrated table data from the metering automation system 103.
The integrated meter data refers to data obtained by the big data computing server 101 performing data integration operation on the meter data obtained from the metering automation system 103 and storing the data in the big data computing server 101 in a distributed manner, and may include data such as power supply unit codes, metering point numbers, asset numbers, user numbers, meter reading dates, forward and reverse active electric quantities and forward and reverse reactive electric quantities. And acquiring integrated archive data, calling a downloading function by the big data computing server 101 in a Linux timing task mode for the integrated table code data, automatically obtaining a plurality of table code data from the metering automation system 103 at a specific time point, and performing integrated storage.
Step S203, based on the preset association table information, the big data computing server 101 establishes the association relationship between the integrated archive data and the integrated table code data; the incidence relation table stores corresponding relations between different integrated archive data and different integrated table code data.
The association table information is data table information used for storing the corresponding relation between different integrated archive data and different integrated table code data and is used for establishing the association relation between the integrated archive data and the integrated table code data. For example: the integrated table data stores user number data, the integrated file data can store account balance, and the big data computing server 101 can realize the association between the user number data and the account balance through the corresponding relationship between the user number data and the account balance established by the association data table.
In step S204, the big data computing server 101 obtains the power resource data through the distributed computing system by using the association relationship.
After the big data computing server 101 completes the association relationship between the integrated archive data and the integrated table code data in step S203, the distributed computing system of the big data computing server 101 may perform distributed computing on the integrated archive data and the integrated table code data through the association relationship to obtain power resource data such as the available power of the user and the power rate balance of the user.
In the intelligent charge control management method, a big data calculation server 101 acquires integrated archive data from an electric power marketing system 102; acquiring integrated table code data from the metering automation system 103; based on preset association table information, establishing an association relation between the integrated archive data and the integrated table code data; the incidence relation table stores corresponding relations between different integrated archive data and different integrated table code data; and acquiring the power resource data through the distributed computing system by utilizing the incidence relation. According to the method and the system, the incidence relation between the archive data stored in the electric power marketing system 102 and the table code data of the metering automation system 103 is established through the big data computing server 101, the electric power resource data are obtained through the distributed computing system, and therefore the data processing efficiency of the charge control service is improved.
In one embodiment, as shown in fig. 3, step S201 may include:
in step S301, the big data computing server 101 obtains initial archive data from the power database table in the power marketing system 102 in a parallel manner through a data extraction tool.
The initial archive data refers to raw data obtained in the big data computing server 101 without data processing, and the data may include: user profile, metering point information, transformer information, metering point transformer relationship, account balance, electricity price information, and the like. Specifically, the big data computing server 101 may extract data of a plurality of database tables in the power marketing system 102 as initial archive data simultaneously in a parallel manner by using Spark as a data extraction tool.
In step S302, the big data computing server 101 performs a paradigm shift process on the initial archive data, and converts the data format of the initial archive data into a preset distributed data storage format.
In order to facilitate further processing of the business, after the big data computing server 101 finishes extracting the initial archive data, the obtained initial archive data is converted into a data format convenient for distributed computing, for example, it may be converted into a form of a distributed elastic data set.
Step S303, the big data computing server 101 performs data normalization processing on the initial archive data after data format conversion and stores the initial archive data in the first storage format to obtain integrated archive data.
The first storage format refers to a storage format for data storage of the initial archive data, and may be, for example, a columnar storage format that facilitates distributed storage. Specifically, the data standardization processing process may be that the big data computing server 101 analyzes the rule by loading a standardization rule, and performs standardization operation on the archive data according to a corresponding rule condition, and performs operations such as conversion or filtering on data that does not meet the computing requirement, thereby completing the data standardization processing. And then, partitioning the user file data, generating a Partition rule corresponding to each marketing file table according to the configuration file, re-fragmenting the extracted marketing file table data according to the Partition rule, splitting according to the given rule during processing so that the data of each Partition can be dispersed to different paths to be stored as far as possible, so that the calculation performance and the incremental modification efficiency can be remarkably improved, and finally, storing the file data subjected to the data standardization processing process in a distributed storage system of the big data calculation server 101 in a first storage format to serve as integrated file data.
Further, the power database table in the power marketing system 102 may include a profile change intermediate table; the intelligent charge control management method may further include: the big data computing server 101 acquires updated archive data from the archive change intermediate table in a parallel mode through a data extraction tool; wherein, the updated archive data is the updated initial archive data stored in the electric power marketing system 102; and performing paradigm conversion processing and data standardization processing on the updated archive data to obtain integrated archive data.
If the initial archive data stored in the electricity marketing system 102 changes, the integrated archive data obtained by the big data computing server 101 needs to be updated synchronously. The intermediate profile change table is used for storing the initial profile data updated by the power marketing system 102, i.e., the updated profile data. Specifically, the big data calculation server 101 may extract the updated archive data from the archive change intermediate table in the electricity marketing system 102 by using a data extraction tool such as Spark, and perform a paradigm shift process and a data normalization process on the updated archive data to obtain integrated archive data to replace the originally stored integrated archive data.
In this embodiment, the big data computing server 101 extracts the initial archive data from the power marketing system 102 through the data extraction tool in parallel, so that the efficiency of data extraction can be improved, and the obtained initial archive data is subjected to the normal form conversion and the data standardization processing, so that the storage space occupied by the data in the distributed storage system can be reduced, the accuracy of data storage can be improved, and the operation efficiency can be further improved. In addition, the updated archive data is extracted from the archive change intermediate table, so that the corresponding update of the integrated archive data is realized, and the accuracy of the integrated archive data stored by the big data computing server 101 is further ensured.
In one embodiment, step S202 may include: the big data computing server 101 acquires initial table code data from the metering automation system 103 in a multithreading mode according to a preset time interval; and converting the initial table code data according to a preset table code data structure by using a distributed processing method, and storing the initial table code data after the data structure conversion in a second storage format to be used as integrated table code data.
The preset time interval may be selected by the user as needed, for example, may be 24 hours, and may be implemented by setting a timing task. Specifically, the user can set the manner in which the big data computing server 101 times the task, so that the big data computing server 101 can automatically acquire the initial table data from the metering automation system 103 at a set point of time, and because the data size of the table code data is too large, in order to improve the acquisition speed of the initial table code data, in this embodiment, the big data computing server 101 may read the initial table data from the metering automation system 103 in a multi-threaded manner, using the plurality of storage nodes of the distributed storage system of the big data computing server 101, and simultaneously reading initial table code data, converting the obtained initial table code data according to a preset table code data structure to form a structure of a distributed data set, and storing the distributed data set in a second storage format, wherein the second storage format can be a column type storage format to form integrated table code data.
In this embodiment, the big data computing server 101 may obtain the initial table code data from the metering automation system 103 in a multithreading manner according to a preset time interval, which is beneficial to improving the table code data obtaining efficiency, and may perform data structure conversion on the obtained initial table code data and store the initial table code data according to a specific storage format, so as to reduce the storage space occupied by data in the distributed storage system, improve the accuracy of data storage, and further improve the operation efficiency.
In one embodiment, the integration table code data may include first integration table code data and second integration table code data; before step S203, the intelligent fee control and management method may further include: the big data computing server 101 conducts data verification on the integrated table code data based on a preset data verification rule to obtain a data verification result identifier of the integrated table code data; if the data verification result identification is that verification is passed, determining the integrated table code data as first integrated table code data, and establishing an association relationship between the integrated archive data and the first integrated table code data; and if the data verification result identification indicates that the verification is not passed, determining that the integrated table code data are second integrated table code data, and writing the second integrated table code data into an abnormal data table of the power marketing system.
The first integrated table code data and the second integrated table code data respectively represent integrated table code data passing data verification and integrated table code data failing data verification, and due to the fact that the data volume of the table code data is large, the obtained integrated table code data is prone to data abnormity, the big data computing server 101 needs to verify the integrated table code data before the incidence relation between the integrated table code data and the integrated archive data is established, and the incidence relation between the integrated table code data and the integrated archive data is established after the verification is passed.
Specifically, the big data computing server 101 may complete a data verification process for the integrated table data based on a preset data verification rule, where the verification rule may be set by a user and stored in the big data computing server 101. The big data computing server 101 can obtain a data verification result of each integrated table code data by calling the verification rule, if the verification is passed, the big data computing server 101 can set the data as the first integrated table code data and simultaneously establish an association relationship between the integrated archive data and the first integrated table code data, and if the verification is not passed, the big data computing server 101 can set the data as the second integrated table code data, namely, the abnormal table code data, and write the abnormal table code data into the abnormal data table of the power marketing system 102.
In this embodiment, the big data computing server 101 performs data verification on the integrated table code data, and only establishes an association relationship between the integrated table code data passing the data verification and the integrated archive data, so as to further improve the accuracy of the power resource data obtained by the distributed computing system.
In one embodiment, as shown in fig. 4, step S204 may further include:
in step S401, the big data computing server 101 extracts the user number data corresponding to the target user from the integrated table data.
The user number data is stored in the integrated table code data and is used for identifying different power users. When it is necessary to acquire power resource data of a specific power consumer, for example, power consumption of a certain household, first, the big data computing server 101 needs to find out user number data corresponding to the power consumer from the integrated table data.
In step S402, the big data computing server 101 obtains user meter reading data corresponding to the user number data from the integrated archive data by using the association relationship.
The user meter reading data refers to meter reading data of the target user electric energy meter, the user meter reading data of the user can be obtained firstly due to the fact that the electricity consumption of the power user needs to be calculated, then the electricity consumption is calculated, and the user meter reading data is stored in the integrated archive data. Since the association relationship between the integrated archive data and the integrated meter code data is already established in step S203, the established association relationship can be queried through the user number data of the power consumer, so as to obtain the user meter reading data associated with the user number data of the power consumer from the integrated archive data.
Step S403, based on the preset power resource data obtaining model, the big data computing server 101 obtains the power resource data through the distributed computing system by using the user meter reading data.
The electric power resource data acquisition model may be pre-set in the big data calculation server 101 in the form of an algorithm, and may include an electric quantity calculation algorithm and an electric power fee calculation algorithm, and after the big data calculation server 101 obtains the user meter reading data, the electric power resource data such as the electric quantity or the electric power fee may be calculated correspondingly through a distributed calculation system of the big data calculation server 101.
And wherein the power data may further comprise: at least one of reading electric quantity, changing electric quantity, free electric quantity, back compensation electric quantity, total power distribution electric quantity, variable power loss electric quantity, line power loss electric quantity, fixed ratio electric quantity, quantitative electric quantity, common meter shared electric quantity and total electric quantity calculated based on the electric quantity. The electricity rate data may include at least one of a stepped threshold electricity rate, an electricity rate, a fund electricity rate, an additional electricity rate, a basic electricity rate, a power factor adjustment electricity rate, and a total electricity rate calculated based on the electricity rates.
In an application example, an intelligent fee control management method is provided, which can be applied to a hardware architecture of an electric power system as shown in fig. 5, and the archive data of a marketing system is extracted from a production library of the marketing system by a data extraction tool and put into a fee control computing system for a series of subsequent operations on metering data, including: and the intelligent management of fee control is realized by data initialization, data synchronization, integration and distribution of table code data, data verification, fee control calculation comparison and the like. The method specifically comprises the following steps:
(1) first, file initialization is required. Before the first time of executing the fee control calculation, or under the condition that the system needs to update the basic data of the marketing archive again in full, the related data of the electric charge algorithm parameter table, the metering point transformer relation table and the shared electric quantity and electricity price relation table are synchronized to the distributed storage of the fee control calculation from the marketing production library through batch data integration operation.
The file initialization comprises three functions of original data initialization, paradigm conversion and initial file normalization, and distributed parallel extraction, paradigm conversion and normalization of file basic data are realized through a big data technology.
For the initialization of original data, the method utilizes spark JDBC interface to obtain archive data from ORACLE. Due to the fact that the data volume of the file is too large, distributed processing is needed for the related base table to improve efficiency. Because the number of archive tables needing to be initialized is large, a plurality of library tables are extracted in a parallel mode to improve the efficiency. The initialized table and some operation parameters need to be configured properly according to the table field, the data volume and the cluster resource, so that the program can work efficiently according to the configuration information. And after the data extraction is finished, respectively storing the data to the part below the calculation path and the part below the updating path, wherein the calculation path and the updating path also need to be configured in the configuration information.
For the paradigm conversion, the marketing original data is converted into a distributed elastic data set by using a Spark JDBC interface, and after the conversion is finished, the Spark interface is called to compress the data and store the data in a columnar storage format to realize performance and storage benefit. Distributed columnar storage files may speed up queries, significantly reducing storage on disk.
For file normalization, the method analyzes rules by loading normalized rules, performs normalized operation on file data according to corresponding rule conditions, and performs operations such as conversion or filtering on data which do not meet calculation requirements to complete data normalization processing. And then, partitioning the user file data, generating a Partition rule corresponding to each marketing file table according to the configuration file, carrying out re-fragmentation processing on the extracted marketing file table data according to the Partition rule, splitting according to the given rule during processing so that the data of each Partition can be dispersed to different paths to be stored as far as possible, and remarkably improving the calculation performance and the efficiency of incremental modification.
(2) Marketing archive synchronization
If marketing profile data changes, incremental updates to the raw data are required.
Specifically, the distributed data extraction from the incremental intermediate table of the marketing system is realized by using Spark, and the table needing file change is extracted in parallel, so that the file change data extraction duration of the marketing system is greatly shortened, and the performance is improved. And then, acquiring corresponding Partition rules and paths according to Partition information in the marketing archive table and the configuration file which need to be changed, extracting original marketing archive data from the distributed file in parallel according to the corresponding rules and paths, and changing the original data according to the result of extracting the marketing archive change data after the extraction is successful. And after the marketing archive data are completely changed successfully, writing the changed marketing archive data back to the distributed file system, and modifying the path of deleting the changed original marketing archive so as to enable the latest archive data to take effect.
(3) Table data integration
Before the charge control system calculates the charge, the charge control system firstly completes the integration of the archive data and the table code data. Wherein the integration of the archival data is completed by (1) and (2), so that the integration of the archival data is also performed.
Specifically, Linux is used for completing calling of corresponding functions in jar packets, a data downloading method is called through timing tasks, data downloading runs on each machine of a cluster in a multithreading mode, hardware resources in the cluster are conveniently and fully utilized, each node immediately calls an uploading program to upload a table code file to a distributed file system after completing data downloading, a cost control system calls a table code processing function after completing uploading all nodes, the corresponding table code file in the distributed file is loaded, the table code file is converted into a corresponding data structure, each table code is processed in a redistribution mode, the table code is converted into a DataFrame, Schema information is added according to a table code description file, and finally the processed data is stored in the distributed file system in a distributed columnar storage format and used when a calculation service calculates electric quantity data.
And if the table code data has problems, performing table code increment data integration on the table code, calling a data acquisition data function, converting the table code data into a DataFrame, increasing and modifying the increment table code data through Transform of the DataFrame, and storing the processed DataFrame into the distributed file system in a partial format.
(4) Table code data distribution
In order to improve the processing speed of meter code data, the whole process time of cost control calculation is greatly shortened. After the table code data are integrated into each node, the cost control system realizes online real-time processing of the table code by using a distributed message system. And after the data of the table code is obtained, the table code is stored persistently and various electric quantities are calculated.
Specifically, the synchronous action of the table code data is triggered at each node through a distributed technology, a distributed message is sent to each node of the fee control system computing cluster, and after each node receives an instruction, data downloading and processing are carried out in a multithreading mode, so that hardware resources of each node are fully utilized, the processing capacity of each node is greatly improved, the processing time is reduced, and the processing performance is improved.
(5) Data quality verification
When the fee control calculation is carried out, part of work orders are possibly transmitted in the process, and the business data is not filed yet, so that the calculation amount fee inevitably causes wrong results due to the problem of the business data. The form code data is synchronized to the fee control system from the metering automation system in a form of form code file, the relationship between the copied form code and the archival data is not correlated and verified, the possibility of data problems is very high, and the possibility of data problems is very high. Therefore, it is very necessary to develop a special tool for verifying the table code data according to the characteristics of the table code file of the power system in the cost control system.
Specifically, when the table code of the whole full charge control user is integrated for table code verification, a batch processing task is called to load a check rule and data by using a JDBC interface, the data is processed and assembled into a data structure required by data caching, and then the rule and the data are transmitted to a functional module corresponding to the check rule for data check. Processing in a flow calculation mode when the incomplete cost control user table codes are integrated, loading rules and service data through a JDBC interface when flow calculation is started, organizing related contents into a data structure required by distributed calculation cache after the rules and the service data are loaded, and caching the data structure into a memory; in the running process of the system, a rule and service data updating mechanism is triggered through a distributed message mechanism, incremental cache modification and newly added data are carried out, after the updating mechanism is started, data in a memory are firstly processed, then caching is carried out again, and when calculation is needed, the rule and the data are applied to the calculation process.
(6) Verification of form code
And checking the table code after the data quality check is finished. And acquiring the added table code check rule, converting the rule (the check rule that the difference of the start and end codes is 0, the multiplying power is less than 1, the start and end codes are abnormal and the like) into a filtering expression of corresponding data in the big data application program, wherein the filtering expressions may be different for different data. And then identifying the data according to the expression, finding abnormal data, and finding data related to the abnormal metering points according to the abnormal metering point data. Filtering is carried out in the calculation process, and abnormal data and abnormal related data are not calculated.
(7) Inventory verification
And after the data quality verification is finished, performing inventory verification. The method comprises the steps of obtaining current electricity price related data from a database through a JDBC interface of Spark, broadcasting the electricity price related data by utilizing Spark broadcast variables and other methods, and distributing the electricity price related data to each node, so that multi-node distributed calculation of Spark is facilitated.
(8) Fee control calculation (core)
8.1 preparation of calculation fee
1. And acquiring current electricity price related data from a database, broadcasting the electricity price related data by using methods such as Spark broadcast variables and the like, and distributing the electricity price related data to each node, so that convenience is brought to multi-node distributed calculation of Spark.
2. The operation of acquiring the metering data is carried out by using a distributed related technology in a big data technology stack, the metering data synchronization action is triggered at each node through the distributed technology, and the data is distributed to each node of the cost control system computing cluster in a distributed mode, so that the hardware resources of each node are fully utilized, the processing capacity of each node is greatly improved, the processing time is reduced, and the processing performance is improved.
3. The operation of acquiring the user file data is carried out by using a distributed related technology in a big data technology stack, the synchronous action of the user file data is triggered at each node through the distributed technology, and the data is distributed to each node of the cost control system computing cluster in a distributed mode, so that the hardware resources of each node are fully utilized, the processing capacity of each node is greatly improved, the processing time is reduced, and the processing performance is improved.
4. The operation of obtaining the metering point transformer data is carried out by using a distributed related technology in a big data technology stack, the metering point transformer data synchronous action is triggered at each node through the distributed technology, the data is distributed to each node of a cost control system computing cluster in a distributed mode, and after each node receives an instruction, the relationship among the metering point transformers is constructed at the same time, so that the hardware resources of each node are fully utilized, the processing capacity of each node is greatly improved, the processing time is reduced, and the processing performance is improved.
5. The operation of acquiring the balance information of the charge control user and the log record data by using a distributed related technology in a big data technology stack is used, the synchronous action of the balance information of the charge control user and the log record data of the marketing system is triggered at each node by the distributed technology, the data is distributed to each node of the charge control system calculation cluster in a distributed mode, the hardware resources of each node are fully utilized, the processing capacity of each node is greatly improved, the processing time is reduced, and the processing performance is improved.
6. Before the computation of the measuring fee is started, the initialization data, the broadcast variables, the accumulator, the computation relationship mapping and the like used by the computation of the measuring fee are constructed on each node of the cluster and stored in the memory, so that the computation is conveniently distributed on each node in the measuring fee computation process.
8.2 Algorithm library initialization
1. And the algorithm in the algorithm library used for calculating the electric quantity is completely loaded on each node of the cluster and is stored in the memory, so that the electric quantity calculation can be conveniently distributed on each node in the later electric quantity calculation process.
2. Before the calculation of the amount fee is started, the electric quantity and the electric charge subprocess to be calculated in the calculation process of the amount fee is selected, and the calculation of the amount fee only calculates the selected electric quantity and the electric charge subprocess. And skipping the sub-process of the electricity quantity and the electricity charge which are not selected, and not calculating.
8.3 electric quantity calculation
1. Reading electric quantity, calculating and recording log
2. Change table electric quantity calculation and log recording
3. Free electricity quantity calculation and log recording
4. Calculating the back-compensation electric quantity and recording the log
5. Totally divide the electric quantity and record the log
6. Calculating and recording log of variable loss electric quantity
7. Line loss power calculation and log recording
8. Quantitative electric quantity calculation and log recording
9. Calculating specific electric quantity and recording log
10. Calculating and recording log of electric quantity of shared share of utility meter
11. Calculating the total electric quantity and recording the log
8.4 Electricity fee calculation
1. Step threshold calculation and log recording
2. Calculating and recording the electric charge of the electric power
3. Fund price calculation and log recording
4. Additional electricity price calculation and logging
5. Basic electricity charge calculation and logging
6. Power factor adjusted electricity charge calculation and logging
7. Aggregate electricity charge calculation and logging
(9) Amount comparison
1. Setting an alarm threshold value: the function provides foreground page, provides the "add" button of warning threshold value at the system page, and the administrator shows the bullet frame on the system page through clicking the "add" button, can input arrearage warning threshold value in the bullet frame, and the system will use this data as basis to formulate arrearage warning rule, and the warning threshold value can be set for through a plurality of dimensions, both can carry out unified setting according to user's area, user type, user reputation grade classification, also can appoint to concrete user and carry out the independent setting. The alarm threshold value can set balance and arrearage amount, so that the alarm can be given after the user arrearages, and the active early warning can be performed before the user arrearages. In addition, if the administrator does not set the alarm threshold, the system can perform arrearage analysis based on historical data of the electricity consumption of the user, predict the electricity charge amount of the next week of the user in advance, and use the predicted amount as the alarm threshold so as to realize the function of adding a new alarm threshold on a page.
2. And comparing the account balance of the remote fee control user with the daily amount and fee calculation result, thereby calculating the account balance information of the remote fee control user and obtaining the daily electric charge balance of the electric user, wherein the calculation result is used as the basis of the arrearage alarm.
3. And according to the alarm rule of the remote fee control system, combining the balance calculation result to obtain the electricity fee balance of each electricity consumer, and sending a payment notice to the electricity consumers with insufficient electricity fee balance through comparison and analysis.
4. When the payment amount of the power consumers is insufficient and reaches the power failure standard, the power failures of the power consumers are carried out, the function can send power failure notification to the power failure users in time, so that the power consumers can find the power failure in time and know the reason of the power failure, and the function can effectively relieve the pressure of customer service staff.
In the application example, the marketing archive data is extracted from the production library of the marketing system through the data extraction tool and is put into the fee control computing system, and the fee control intelligent management is realized through the initialization of the archive data, the synchronization of the archive data, the integrated distribution of the table code data, the data verification, the fee control computation, the comparison and the like, and the efficiency of data management is improved.
It should be understood that, although the steps in the flowcharts of the present application are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least some of the steps in the figures may include multiple steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of execution of the steps or stages is not necessarily sequential, but may be performed alternately or in alternation with other steps or at least some of the other steps or stages.
In one embodiment, as shown in fig. 6, there is provided an intelligent fee control management device, including: an integrated archive acquisition module 601, an integrated table code acquisition module 602, an association relationship establishment module 603, and a resource data acquisition module 604, wherein:
an integrated archive acquisition module 601, configured to acquire integrated archive data from the power marketing system;
an integrated table code acquiring module 602, configured to acquire integrated table code data from a metering automation system;
an association relationship establishing module 603, configured to establish an association relationship between the integrated archive data and the integrated table code data based on preset association table information; the incidence relation table stores corresponding relations between different integrated archive data and different integrated table code data;
and a resource data obtaining module 604, configured to obtain the power resource data through the distributed computing system by using the association relationship.
In one embodiment, the integrated archive acquisition module 601 is further configured to acquire initial archive data from an electric power database table in the electric power marketing system in a parallel manner through a data extraction tool; performing paradigm conversion processing on the initial archive data, and converting the data format of the initial archive data into a preset distributed data storage format; and carrying out data standardization processing on the initial archive data after the data format conversion and storing the initial archive data in a first storage format to obtain integrated archive data.
In one embodiment, the integrated archive acquisition module 601 is further configured to acquire update archive data from the archive change intermediate table in a parallel manner through a data extraction tool; the updated archive data is updated initial archive data stored in the electric power marketing system; and performing paradigm conversion processing and data standardization processing on the updated archive data to obtain integrated archive data.
In an embodiment, the integrated form code acquiring module 602 is further configured to acquire initial form code data from the metering automation system at preset time intervals in a multi-thread manner; and converting the initial table code data according to a preset table code data structure by using a distributed processing method, and storing the initial table code data after the data structure conversion in a second storage format to be used as integrated table code data.
In one embodiment, the integration table code data includes first integration table code data and second integration table code data; the intelligent charge control management device further comprises: the table code data checking module is used for carrying out data checking on the integrated table code data based on a preset data checking rule to obtain a data checking result identifier of the integrated table code data; if the data verification result identification is that verification is passed, determining the integrated table code data as first integrated table code data, and establishing an association relationship between the integrated archive data and the first integrated table code data; and if the data verification result identification indicates that the verification is not passed, determining that the integrated table code data are second integrated table code data, and writing the second integrated table code data into an abnormal data table of the power marketing system.
In an embodiment, the resource data obtaining module 604 is further configured to extract user number data corresponding to the target user from the integrated table code data; acquiring user meter reading data corresponding to the user number data from the integrated archive data by utilizing the incidence relation; and acquiring the power resource data through a distributed computing system by using the user meter reading data based on a preset power resource data acquisition model.
For specific limitations of the intelligent fee control management device, reference may be made to the above limitations of the intelligent fee control management method, which will not be described herein again. All or part of each module in the intelligent charge control management device can be realized by software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a computer device is provided, which may be a server, the internal structure of which may be as shown in fig. 7. 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 comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The database of the computer device is used for storing the integrated archive data and the integrated table 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 intelligent cost control management method.
Those skilled in the art will appreciate that the architecture shown in fig. 7 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, a computer device is further provided, which includes a memory and a processor, the memory stores a computer program, and the processor implements the steps of the above method embodiments when executing the computer program.
In an embodiment, a computer-readable storage medium is provided, on which a computer program is stored which, when being executed by a processor, carries out the steps of the above-mentioned method embodiments.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database or other medium used in the embodiments provided herein can include at least one of non-volatile and volatile memory. Non-volatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical storage, or the like. Volatile Memory can include Random Access Memory (RAM) or external cache Memory. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM), among others.
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. An intelligent charge control management method, characterized in that the method comprises:
acquiring integrated archive data from a power marketing system;
acquiring integrated table code data from a metering automation system;
based on preset association table information, establishing an association relationship between the integrated archive data and the integrated table code data; the incidence relation table stores corresponding relations between different integrated archive data and different integrated table code data;
and acquiring the power resource data through the distributed computing system by utilizing the incidence relation.
2. The method of claim 1, wherein the obtaining integrated profile data from the electricity marketing system via a data extraction tool comprises:
acquiring initial archive data from a power database table in the power marketing system in a parallel mode through a data extraction tool;
performing paradigm conversion processing on the initial archive data, and converting the data format of the initial archive data into a preset distributed data storage format;
and carrying out data standardization processing on the initial archive data after the data format conversion and storing the initial archive data in a first storage format to obtain the integrated archive data.
3. The method of claim 2, wherein the power database table comprises a profile change intermediate table; the method further comprises the following steps:
acquiring updated archive data from the archive change intermediate table in a parallel mode through the data extraction tool; wherein the updated archive data is updated initial archive data stored in the power marketing system;
and carrying out the normal form conversion processing and the data standardization processing on the updated archive data to obtain the integrated archive data.
4. The method of claim 1, wherein the obtaining integrated form data from a metering automation system comprises:
acquiring initial table code data from the metering automation system in a multithreading mode according to a preset time interval;
and converting the initial table code data according to a preset table code data structure by using a distributed processing method, and storing the initial table code data after the data structure conversion in a second storage format to be used as the integrated table code data.
5. The method of claim 1, wherein the integrated table code data comprises first integrated table code data and second integrated table code data;
before the establishing of the association relationship between the integrated archive data and the integrated table code data based on the preset association table information, the method further comprises the following steps:
performing data verification on the integrated table code data based on a preset data verification rule to obtain a data verification result identifier of the integrated table code data;
if the data verification result identification is that verification is passed, determining the integrated table code data as the first integrated table code data, and establishing an association relationship between the integrated archive data and the first integrated table code data;
and if the data verification result identification is that verification is not passed, determining that the integrated table code data are the second integrated table code data, and writing the second integrated table code data into an abnormal data table of the electric power marketing system.
6. The method according to any one of claims 1 to 5, wherein the obtaining power resource data through a distributed computing system by using the association relationship comprises:
extracting user number data corresponding to a target user from the integrated table code data;
acquiring user meter reading data corresponding to the user number data from the integrated archive data by utilizing the incidence relation;
and acquiring the power resource data through a distributed computing system by using the user meter reading data based on a preset power resource data acquisition model.
7. The method of claim 6, wherein the power resource data comprises: electricity quantity data or electricity charge data; wherein the electricity quantity data comprises: at least one of reading electric quantity, changing electric quantity, free electric quantity, returning and supplementing electric quantity, total power distribution electric quantity, variable power loss electric quantity, line power loss electric quantity, fixed ratio electric quantity, quantitative electric quantity, common share electric quantity of a utility meter and total electric quantity; the electricity fee data includes: at least one of a stepped threshold electricity rate, an electricity rate, a fund electricity rate, an additional electricity rate, a basic electricity rate, a power factor adjustment electricity rate, and a total electricity rate.
8. An intelligent cost control management device, the device comprising:
the integrated archive acquisition module is used for acquiring integrated archive data from the electric power marketing system;
the integrated table code acquisition module is used for acquiring integrated table code data from the metering automation system;
the incidence relation establishing module is used for establishing incidence relation between the integrated archive data and the integrated table code data based on preset incidence table information; the incidence relation table stores corresponding relations between different integrated archive data and different integrated table code data;
and the resource data acquisition module is used for acquiring the power resource data through the distributed computing system by utilizing the incidence relation.
9. A computer device comprising a memory and a processor, the memory storing a computer program, wherein the processor implements the steps of the method of any one of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 7.
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